[docs]classChannelShuffle(Module):r"""Divide the channels in a tensor of shape :math:`(*, C , H, W)` into g groups and rearrange them as :math:`(*, C \frac g, g, H, W)`, while keeping the original tensor shape. Args: groups (int): number of groups to divide channels in. Examples:: >>> # xdoctest: +IGNORE_WANT("FIXME: incorrect want") >>> channel_shuffle = nn.ChannelShuffle(2) >>> input = torch.randn(1, 4, 2, 2) >>> print(input) [[[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]], [[13, 14], [15, 16]], ]] >>> output = channel_shuffle(input) >>> print(output) [[[[1, 2], [3, 4]], [[9, 10], [11, 12]], [[5, 6], [7, 8]], [[13, 14], [15, 16]], ]] """__constants__=['groups']groups:intdef__init__(self,groups:int)->None:super().__init__()self.groups=groupsdefforward(self,input:Tensor)->Tensor:returnF.channel_shuffle(input,self.groups)defextra_repr(self)->str:returnf'groups={self.groups}'
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