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Enable CPU usage in RelatednessPytorch #61

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Enable CPU usage in RelatednessPytorch #61

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19 changes: 10 additions & 9 deletions senteval/tools/relatedness.py
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Original file line number Diff line number Diff line change
Expand Up @@ -19,15 +19,16 @@

from scipy.stats import pearsonr

device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')

class RelatednessPytorch(object):
# Can be used for SICK-Relatedness, and STS14
def __init__(self, train, valid, test, devscores, config):
# fix seed
np.random.seed(config['seed'])
torch.manual_seed(config['seed'])
assert torch.cuda.is_available(), 'torch.cuda required for Relatedness'
torch.cuda.manual_seed(config['seed'])
if torch.cuda.is_available():
torch.cuda.manual_seed(config['seed'])

self.train = train
self.valid = valid
Expand Down Expand Up @@ -58,12 +59,12 @@ def __init__(self, train, valid, test, devscores, config):

def prepare_data(self, trainX, trainy, devX, devy, testX, testy):
# Transform probs to log-probs for KL-divergence
trainX = torch.from_numpy(trainX).float().cuda()
trainy = torch.from_numpy(trainy).float().cuda()
devX = torch.from_numpy(devX).float().cuda()
devy = torch.from_numpy(devy).float().cuda()
testX = torch.from_numpy(testX).float().cuda()
testY = torch.from_numpy(testy).float().cuda()
trainX = torch.from_numpy(trainX).float().to(device)
trainy = torch.from_numpy(trainy).float().to(device)
devX = torch.from_numpy(devX).float().to(device)
devy = torch.from_numpy(devy).float().to(device)
testX = torch.from_numpy(testX).float().to(device)
testY = torch.from_numpy(testy).float().to(device)

return trainX, trainy, devX, devy, testX, testy

Expand Down Expand Up @@ -107,7 +108,7 @@ def trainepoch(self, X, y, nepoches=1):
all_costs = []
for i in range(0, len(X), self.batch_size):
# forward
idx = torch.from_numpy(permutation[i:i + self.batch_size]).long().cuda()
idx = torch.from_numpy(permutation[i:i + self.batch_size]).long().to(device)
Xbatch = X[idx]
ybatch = y[idx]
output = self.model(Xbatch)
Expand Down