Is there an equivalent PyTorch function for `tf.nn.space_to_depth`

Question

As the title says, is there an equivalent PyTorch function for tf.nn.space_to_depth?

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Answer 1

While torch.nn.functional.pixel_shuffle does exactly what tf.nn.depth_to_space does, PyTorch doesn’t have any function to do the inverse operation similar to tf.nn.space_to_depth.

That being said, it is easy to implement space_to_depth using torch.nn.functional.unfold.

def space_to_depth(x, block_size):
    n, c, h, w = x.size()
    unfolded_x = torch.nn.functional.unfold(x, block_size, stride=block_size)
    return unfolded_x.view(n, c * block_size ** 2, h // block_size, w // block_size)

Answered By: Priyatham

Answer 2

Actually, @Priyatham’s unfold is not correct.
Unfold will enlarge/expand channel by block_size * block_size, but the space-to-depth requires duplicate channel by block_size * block_size (means copy channel by block_size * block_size)

So, the right way is to use eniops.rearrange():

result = einops.rearrange(x, 'b c (h p1) (w p2) -> b (p1 p2) h w', p1=block_size, p2=block_size)

Answered By: HeCao

Answer 3

You can follow Tresnet Github official sourceode
https://github.com/Alibaba-MIIL/TResNet/blob/master/src/models/tresnet/layers/space_to_depth.py

Answered By: Maulik Madhavi

Is there an equivalent PyTorch function for `tf.nn.space_to_depth`

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