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| from .module import Module | |
| from .. import functional as F | |
| from torch import Tensor | |
| __all__ = ['PixelShuffle', 'PixelUnshuffle'] | |
| class PixelShuffle(Module): | |
| r"""Rearrange elements in a tensor according to an upscaling factor. | |
| 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().__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 f'upscale_factor={self.upscale_factor}' | |
| class PixelUnshuffle(Module): | |
| r"""Reverse the PixelShuffle operation. | |
| 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().__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 f'downscale_factor={self.downscale_factor}' | |