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from .module import Module
from .. import functional as F
from torch import Tensor
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)`.
This is useful for implementing efficient sub-pixel convolution
with a stride of :math:`1/r`.
Look at 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:`(N, L, H_{in}, W_{in})` where :math:`L=C \times \text{upscale\_factor}^2`
- Output: :math:`(N, C, H_{out}, W_{out})` where
:math:`H_{out} = H_{in} \times \text{upscale\_factor}`
and :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)
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