PixelShuffle¶
- class torch.nn.PixelShuffle(upscale_factor)[source]¶
Rearranges elements in a tensor of shape to a tensor of shape , where r is an upscale factor.
This is useful for implementing efficient sub-pixel convolution with a stride of .
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.
- Parameters:
upscale_factor (int) – factor to increase spatial resolution by
- Shape:
Input: , where * is zero or more batch dimensions
Output: , where
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])