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import torch.nn.functional as F |
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from torch import Tensor |
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from .module import Module |
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__all__ = ["ChannelShuffle"] |
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class ChannelShuffle(Module): |
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r"""Divides and rearranges the channels in a tensor. |
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This operation divides the channels in a tensor of shape :math:`(N, C, *)` |
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into g groups as :math:`(N, \frac{C}{g}, g, *)` and shuffles them, |
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while retaining the original tensor shape in the final output. |
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Args: |
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groups (int): number of groups to divide channels in. |
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Examples:: |
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>>> channel_shuffle = nn.ChannelShuffle(2) |
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>>> input = torch.arange(1, 17, dtype=torch.float32).view(1, 4, 2, 2) |
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>>> input |
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tensor([[[[ 1., 2.], |
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[ 3., 4.]], |
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[[ 5., 6.], |
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[ 7., 8.]], |
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[[ 9., 10.], |
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[11., 12.]], |
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[[13., 14.], |
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[15., 16.]]]]) |
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>>> output = channel_shuffle(input) |
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>>> output |
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tensor([[[[ 1., 2.], |
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[ 3., 4.]], |
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[[ 9., 10.], |
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[11., 12.]], |
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[[ 5., 6.], |
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[ 7., 8.]], |
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[[13., 14.], |
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[15., 16.]]]]) |
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""" |
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__constants__ = ["groups"] |
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groups: int |
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def __init__(self, groups: int) -> None: |
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super().__init__() |
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self.groups = groups |
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def forward(self, input: Tensor) -> Tensor: |
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return F.channel_shuffle(input, self.groups) |
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def extra_repr(self) -> str: |
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return f"groups={self.groups}" |
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