114 lines
3.6 KiB
Python
114 lines
3.6 KiB
Python
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from .module import Module
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from .. import functional as F
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from torch import Tensor
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__all__ = ['PixelShuffle', 'PixelUnshuffle']
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class PixelShuffle(Module):
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r"""Rearrange elements in a tensor according to an upscaling factor.
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Rearranges elements in a tensor of shape :math:`(*, C \times r^2, H, W)`
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to a tensor of shape :math:`(*, C, H \times r, W \times r)`, where r is an upscale factor.
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This is useful for implementing efficient sub-pixel convolution
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with a stride of :math:`1/r`.
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See the paper:
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`Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network`_
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by Shi et. al (2016) for more details.
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Args:
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upscale_factor (int): factor to increase spatial resolution by
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Shape:
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- Input: :math:`(*, C_{in}, H_{in}, W_{in})`, where * is zero or more batch dimensions
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- Output: :math:`(*, C_{out}, H_{out}, W_{out})`, where
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.. math::
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C_{out} = C_{in} \div \text{upscale\_factor}^2
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.. math::
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H_{out} = H_{in} \times \text{upscale\_factor}
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.. math::
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W_{out} = W_{in} \times \text{upscale\_factor}
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Examples::
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>>> pixel_shuffle = nn.PixelShuffle(3)
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>>> input = torch.randn(1, 9, 4, 4)
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>>> output = pixel_shuffle(input)
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>>> print(output.size())
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torch.Size([1, 1, 12, 12])
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.. _Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network:
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https://arxiv.org/abs/1609.05158
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"""
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__constants__ = ['upscale_factor']
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upscale_factor: int
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def __init__(self, upscale_factor: int) -> None:
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super().__init__()
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self.upscale_factor = upscale_factor
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def forward(self, input: Tensor) -> Tensor:
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return F.pixel_shuffle(input, self.upscale_factor)
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def extra_repr(self) -> str:
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return f'upscale_factor={self.upscale_factor}'
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class PixelUnshuffle(Module):
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r"""Reverse the PixelShuffle operation.
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Reverses the :class:`~torch.nn.PixelShuffle` operation by rearranging elements
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in a tensor of shape :math:`(*, C, H \times r, W \times r)` to a tensor of shape
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:math:`(*, C \times r^2, H, W)`, where r is a downscale factor.
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See the paper:
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`Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network`_
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by Shi et. al (2016) for more details.
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Args:
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downscale_factor (int): factor to decrease spatial resolution by
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Shape:
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- Input: :math:`(*, C_{in}, H_{in}, W_{in})`, where * is zero or more batch dimensions
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- Output: :math:`(*, C_{out}, H_{out}, W_{out})`, where
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.. math::
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C_{out} = C_{in} \times \text{downscale\_factor}^2
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.. math::
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H_{out} = H_{in} \div \text{downscale\_factor}
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.. math::
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W_{out} = W_{in} \div \text{downscale\_factor}
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Examples::
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>>> pixel_unshuffle = nn.PixelUnshuffle(3)
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>>> input = torch.randn(1, 1, 12, 12)
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>>> output = pixel_unshuffle(input)
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>>> print(output.size())
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torch.Size([1, 9, 4, 4])
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.. _Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network:
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https://arxiv.org/abs/1609.05158
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"""
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__constants__ = ['downscale_factor']
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downscale_factor: int
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def __init__(self, downscale_factor: int) -> None:
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super().__init__()
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self.downscale_factor = downscale_factor
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def forward(self, input: Tensor) -> Tensor:
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return F.pixel_unshuffle(input, self.downscale_factor)
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def extra_repr(self) -> str:
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return f'downscale_factor={self.downscale_factor}'
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