FM / modules

/usr/local/lib64/python3.6/site-packages/torch/nn/modules/

/usr/local/lib64/python3.6/site-packages/torch/nn/modules

Name	Size	Mode	Actions
__pycache__/	-	0755	rm
activation.py	42422	0644	edit dl rm
adaptive.py	11168	0644	edit dl rm
batchnorm.py	36456	0644	edit dl rm
channelshuffle.py	1295	0644	edit dl rm
container.py	26310	0644	edit dl rm
conv.py	68856	0644	edit dl rm
distance.py	2909	0644	edit dl rm
dropout.py	8955	0644	edit dl rm
flatten.py	5320	0644	edit dl rm
fold.py	12552	0644	edit dl rm
instancenorm.py	18425	0644	edit dl rm
lazy.py	11542	0644	edit dl rm
linear.py	10341	0644	edit dl rm
loss.py	88112	0644	edit dl rm
module.py	78396	0644	edit dl rm
normalization.py	10750	0644	edit dl rm
padding.py	19421	0644	edit dl rm
pixelshuffle.py	3573	0644	edit dl rm
pooling.py	52314	0644	edit dl rm
rnn.py	53968	0644	edit dl rm
sparse.py	23015	0644	edit dl rm
transformer.py	23524	0644	edit dl rm
upsampling.py	10084	0644	edit dl rm
utils.py	2251	0644	edit dl rm
_functions.py	8635	0644	edit dl rm
__init__.py	5149	0644	edit dl rm

Edit: /usr/local/lib64/python3.6/site-packages/torch/nn/modules/channelshuffle.py (1295B)

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

from torch import Tensor

class ChannelShuffle(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::

>>> 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(ChannelShuffle, self).__init__()
        self.groups = groups

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

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

from .module import Module from .. import functional as F from torch import Tensor class ChannelShuffle(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:: >>> 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(ChannelShuffle, self).__init__() self.groups = groups def forward(self, input: Tensor) -> Tensor: return F.channel_shuffle(input, self.groups) def extra_repr(self) -> str: return 'groups={}'.format(self.groups)