57 lines
1.5 KiB
Python
57 lines
1.5 KiB
Python
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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