MLPY/Lib/site-packages/torch/nn/modules/channelshuffle.py

from .module import Module
from .. import functional as F

from torch import Tensor

__all__ = ['ChannelShuffle']

class ChannelShuffle(Module):
    r"""Divides and rearranges the channels in a tensor.

    This operation divides the channels in a tensor of shape :math:`(*, C , H, W)`
    into g groups and rearranges them as :math:`(*, \frac{C}{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: int

    def __init__(self, groups: int) -> None:
        super().__init__()
        self.groups = groups

    def forward(self, input: Tensor) -> Tensor:
        return F.channel_shuffle(input, self.groups)

    def extra_repr(self) -> str:
        return f'groups={self.groups}'