o h @sDddlmmZddlmZddlmZdgZGdddeZ dS)N)Tensor)ModuleChannelShufflecsZeZdZUdZdgZeed<deddffdd Zdedefdd Z de fd d Z Z S) ra'Divides and rearranges the channels in a tensor. This operation divides the channels in a tensor of shape :math:`(N, C, *)` into g groups as :math:`(N, \frac{C}{g}, g, *)` and shuffles them, while retaining the original tensor shape in the final output. Args: groups (int): number of groups to divide channels in. Examples:: >>> channel_shuffle = nn.ChannelShuffle(2) >>> input = torch.arange(1, 17, dtype=torch.float32).view(1, 4, 2, 2) >>> input tensor([[[[ 1., 2.], [ 3., 4.]], [[ 5., 6.], [ 7., 8.]], [[ 9., 10.], [11., 12.]], [[13., 14.], [15., 16.]]]]) >>> output = channel_shuffle(input) >>> output tensor([[[[ 1., 2.], [ 3., 4.]], [[ 9., 10.], [11., 12.]], [[ 5., 6.], [ 7., 8.]], [[13., 14.], [15., 16.]]]]) groupsreturnNcst||_dSN)super__init__r)selfr __class__s/var/www/html/construction_image-detection-poc/venv/lib/python3.10/site-packages/torch/nn/modules/channelshuffle.pyr 0s  zChannelShuffle.__init__inputcCst||jSr)Fchannel_shuffler)r rrrrforward4szChannelShuffle.forwardcCs d|jS)Nzgroups=)r)r rrr extra_repr7s zChannelShuffle.extra_repr) __name__ __module__ __qualname____doc__ __constants__int__annotations__r rrstrr __classcell__rrr rr s ") torch.nn.functionalnn functionalrtorchrmoduler__all__rrrrrs