File size: 3,677 Bytes
d1ceb73 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 |
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}'
|