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Source code for torch.nn.modules.pixelshuffle

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

from torch import Tensor


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

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