from .module import Module
from .. import functional as F
[docs]class PixelShuffle(Module):
r"""Rearranges elements in a Tensor of shape :math:`(*, r^2C, H, W)` to a
tensor of shape :math:`(C, rH, rW)`.
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, C * \text{upscale_factor}^2, H, W)`
- Output: :math:`(N, C, H * \text{upscale_factor}, W * \text{upscale_factor})`
Examples::
>>> ps = nn.PixelShuffle(3)
>>> input = torch.tensor(1, 9, 4, 4)
>>> output = ps(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
"""
def __init__(self, upscale_factor):
super(PixelShuffle, self).__init__()
self.upscale_factor = upscale_factor
def forward(self, input):
return F.pixel_shuffle(input, self.upscale_factor)
def extra_repr(self):
return 'upscale_factor={}'.format(self.upscale_factor)