cpu occupacy is very high about graphcmr HOT 6 CLOSED

It seems that high cpu occupacy lies in the dataloader, because if I use a constant input_batch instead of dataloader, it won't occupy so much cpu.

input_batch.json contains one pre-stored batch from dataloader.

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Maxing out the CPU in general is a good thing, because it means that the input pipeline is efficient. You can reduce the num_workers option to use less processes if this is an issue on your system. But consequently this will reduce the training speed.

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Thanks for your advice. I am carefully looking for the cpu exhaustive operation. I found that the crop() in imutils.py is very cpu exhaustive. In fact, if I did't use crop in base_dataset.py, the cpu occupacy will drop to 1/4 as before! That's very strange. I am checking more on that. I don't know what about on your machine.

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I located the problem in the imutils.py.
In func transfrom():

    if invert:
        t = np.linalg.inv(t)

This inverse operation is very cpu exhaustive. Could this operation be avoided? Or move this operation to be computed on gpu?

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I found a simple solution, which is magically effective.

    if invert:
        # t = np.linalg.inv(t)
        t_torch = torch.from_numpy(t)
        t_torch = torch.inverse(t_torch)
        t = t_torch.numpy()

If anyone have the same problem, maybe can have a try.

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I will look into it. Ii'm surprised that numpy is so slow in this simple task of inverting a 4x4 matrix.

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