PixelShuffle — PyTorch 2.7 documentation (original) (raw)

class torch.nn.PixelShuffle(upscale_factor)[source][source]¶

Rearrange elements in a tensor according to an upscaling factor.

Rearranges elements in a tensor of shape (∗,C×r2,H,W)(*, C \times r^2, H, W)to a tensor of shape (∗,C,H×r,W×r)(*, 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 1/r1/r.

See the paper:Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Networkby Shi et al. (2016) for more details.

Parameters

upscale_factor (int) – factor to increase spatial resolution by

Shape:

• Input: (∗,Cin,Hin,Win)(*, C_{in}, H_{in}, W_{in}), where * is zero or more batch dimensions
• Output: (∗,Cout,Hout,Wout)(*, C_{out}, H_{out}, W_{out}), where

Cout=Cin÷upscale_factor2C_{out} = C_{in} \div \text{upscale\_factor}^2

Hout=Hin×upscale_factorH_{out} = H_{in} \times \text{upscale\_factor}

Wout=Win×upscale_factorW_{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])