Source code for torch.nn.modules.pixelshuffle

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


[docs]class PixelShuffle(Module): r"""Rearranges elements in a Tensor of shape :math:`(*, C * r^2, H, W]` to a tensor of shape :math:`(C, H * r, W * 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, C * {upscale\_factor}^2, H, W)` - Output: :math:`(N, C, H * {upscale\_factor}, W * {upscale\_factor})` Examples:: >>> ps = nn.PixelShuffle(3) >>> input = autograd.Variable(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 __repr__(self): return self.__class__.__name__ + ' (upscale_factor=' + str(self.upscale_factor) + ')'