Predict on batch

[LKchemposer]

Is there a way to perform batch prediction to leverage GPU? I believe it is a functionality in the JS version, but I am not sure how to do that in this python version.

[de-code]

It wouldn't be difficult to add a predict on batch function.
Internally (except for TF Lite I believe), it is using a batch already.
I think were it becomes a bit tricky is to follow it through with the post processing.

e.g. currently with a single image it looks like this:

result = bodypix_model.predict_single(image_array)

# simple mask
mask = result.get_mask(threshold=0.75)

# colored mask (separate colour for each body part)
colored_mask = result.get_colored_part_mask(mask)

For a batch it would then be something like:

batch_result = bodypix_model.predict_batch(image_array_batch)

# simple mask
mask_batch = batch_result.get_mask_batch(threshold=0.75)

# colored mask (separate colour for each body part)
colored_mask_batch = batch_result.get_colored_part_mask_batch(mask_batch)

I also wonder whether there would be any noticable speed improvement.

What have you observed when using the JS version?
And what is your use-case?

[LKchemposer]

Thank you for the reply.

My current use case is measuring BodyPix's performance over a dataset of 10000+ images, but given the high image resolution (1080p), it's taking ~1.3-1.5 sec/image on Colab's default CPU. Any improvement on inference time is useful (without having to downsample).

I have not tried with the JS version yet, but it seems that there is "a large performance difference" according to tensorflow/tfjs#2197

[de-code]

Would you be happy to see whether you want to submit a PR to add batch support?