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Logon1028 OP t1_j0mbuxc wrote

Reply to comment by elbiot in Efficient Max Pooling Implementation by Logon1028

Yes, but that unravel_indices has to be applied to EVERY SINGLE ELEMENT of the last axis independently. i.e.

        for depth in range(strided_result.shape[0]):
            for x in range(strided_result.shape[1]):
                for y in range(strided_result.shape[2]):
                    local_stride_index = np.unravel_index(argmax_arr[depth][x][y], strided_result[depth][x][y].shape)

unravel_indices only takes a 1d array as input. In order to apply it to only the last axis of the 4D array you have to use a 4 loop. unravel_indices has no axis parameter.

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Logon1028 OP t1_j0mcyle wrote

What I ended up doing is using np.indices (multiplied by the stride) to apply a mask to the x and y argmax arrays using an elementwise multiplication. Then I used elementwise division and modulus to calculate the input indexes myself. The only for loop I have in the forward pass now is a simple one for the depth of the input. The backward pass still uses a triple for loop, but I can live with that.

The model I showed in the previous comment now trains in just under 4 minutes. So now I have a roughly 3x performance increase from my original implementation. And I think that is where I am going to leave it.

Thank you for your help. Even though I didn't use all your suggestions directly, it definitely guided me in the right direction. My current implementation is FAR more efficient than any examples I could find online unfortunately.

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