U *-eN@s>ddlmZddlmZddlmZdgZGdddeZdS) )Module) functional)TensorChannelShufflecsVeZdZUdZdgZeed<eddfdd Zeeddd Z e d d d Z Z S) ramDivide 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]], ]] groupsN)rreturncst||_dSN)super__init__r)selfr __class__`/var/www/html/Darija-Ai-Train/env/lib/python3.8/site-packages/torch/nn/modules/channelshuffle.pyr .s zChannelShuffle.__init__)inputr cCst||jSr )FZchannel_shuffler)r rrrrforward2szChannelShuffle.forward)r cCs d|jS)Nzgroups=)r)r rrr extra_repr5szChannelShuffle.extra_repr) __name__ __module__ __qualname____doc__Z __constants__int__annotations__r rrstrr __classcell__rrrrrs "N) modulerrrZtorchr__all__rrrrrs