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I do not understand how to evaluate this expression:

x.view(*(x.shape[:-2]),-1).mean(-1)`,

if x.shape == (N, C, H, W).

What does the asterisk * stand for? And what is mean(-1) ?

Davide Fiocco
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Mohammed Awney
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  • this pytorch expression? The title for this needs to improve – Charlie Parker Jul 12 '21 at 22:15
  • for others that were lost due to the bad naming of this question here is the link for other contexts of * https://stackoverflow.com/questions/53370003/pytorch-mapping-operators-to-functions – Charlie Parker Jul 12 '21 at 22:16

1 Answers1

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What is *?

For .view() pytorch expects the new shape to be provided by individual int arguments (represented in the doc as *shape). The asterisk (*) can be used in python to unpack a list into its individual elements, thus passing to view the correct form of input arguments it expects.
So, in your case, x.shape is (N, C, H, W), if you were to pass x.shape[:-2] without the asterisk, you would get x.view((N, C), -1) - which is not what view() expects. Unpacking (N, C) using the asterisk results with view receiving view(N, C, -1) arguments as it expects. The resulting shape is (N, C, H*W) (a 3D tensor instead of 4).

What is mean(-1)?

Simply look at the documentation of .mean(): the first argument is a dim argument. That is x.mean(-1) applies mean along the last dimension. In your case, since keepdim=False by default, your output will be a (N, C) sized tensor where each element correspond to the mean value along both spatial dimensions.
This is equivalent to

x.mean(-1).mean(-1)
Davide Fiocco
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Shai
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  • what does it mena here though `x = y * z` if y and z are tensors? (e.g. what sizes do they need to be) – Charlie Parker Jul 12 '21 at 22:15
  • @CharlieParker this is a simple element-wise multiplication they should have the same size, or a broadcastable one – Shai Jul 13 '21 at 04:02
  • yea, I think I was looking for `@` (to make matters worse there seems to be a difference between `.mm` vs `.matmul`...) – Charlie Parker Jul 13 '21 at 21:56