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9e8f27cc79
Options to address the "undocumented python objects": 1. Reference the functions in the .rst via the torch.nn.modules namespace. Note that this changes the generated doc filenames / locations for most of these functions! 2. [Not an option] Monkeypatch `__module__` for these objects (broke several tests in CI due to `inspect.findsource` failing after this change) 3. Update the .rst files to also document the torch.nn.modules forms of these functions, duplicating docs. #### [this is the docs page added](https://docs-preview.pytorch.org/pytorch/pytorch/158491/nn.aliases.html) This PR takes option 3 by adding an rst page nn.aliases that documents the aliases in nested namespaces, removing all the torch.nn.modules.* entries from the coverage skiplist except - NLLLoss2d (deprecated) - Container (deprecated) - CrossMapLRN2d (what is this?) - NonDynamicallyQuantizableLinear This mostly required adding docstrings to `forward`, `extra_repr` and `reset_parameters`. Since forward arguments are already part of the module docstrings I just added a very basic docstring. Pull Request resolved: https://github.com/pytorch/pytorch/pull/158491 Approved by: https://github.com/janeyx99
63 lines
1.6 KiB
Python
63 lines
1.6 KiB
Python
import torch.nn.functional as F
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from torch import Tensor
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from .module import Module
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__all__ = ["ChannelShuffle"]
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class ChannelShuffle(Module):
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r"""Divides and rearranges the channels in a tensor.
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This operation divides the channels in a tensor of shape :math:`(N, C, *)`
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into g groups as :math:`(N, \frac{C}{g}, g, *)` and shuffles them,
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while retaining the original tensor shape in the final output.
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Args:
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groups (int): number of groups to divide channels in.
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Examples::
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>>> channel_shuffle = nn.ChannelShuffle(2)
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>>> input = torch.arange(1, 17, dtype=torch.float32).view(1, 4, 2, 2)
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>>> input
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tensor([[[[ 1., 2.],
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[ 3., 4.]],
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[[ 5., 6.],
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[ 7., 8.]],
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[[ 9., 10.],
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[11., 12.]],
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[[13., 14.],
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[15., 16.]]]])
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>>> output = channel_shuffle(input)
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>>> output
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tensor([[[[ 1., 2.],
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[ 3., 4.]],
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[[ 9., 10.],
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[11., 12.]],
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[[ 5., 6.],
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[ 7., 8.]],
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[[13., 14.],
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[15., 16.]]]])
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"""
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__constants__ = ["groups"]
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groups: int
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def __init__(self, groups: int) -> None:
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super().__init__()
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self.groups = groups
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def forward(self, input: Tensor) -> Tensor:
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"""
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Runs the forward pass.
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"""
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return F.channel_shuffle(input, self.groups)
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def extra_repr(self) -> str:
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"""
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Return the extra representation of the module.
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"""
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return f"groups={self.groups}"
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