# PixelShuffle¶

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

Rearranges elements in a tensor of shape $(*, C \times r^2, H, W)$ to a tensor of shape $(*, C, H \times r, W \times r)$, where r is an upscale factor.

This is useful for implementing efficient sub-pixel convolution with a stride of $1/r$.

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: $(*, C_{in}, H_{in}, W_{in})$, where * is zero or more batch dimensions

• Output: $(*, C_{out}, H_{out}, W_{out})$, where

$C_{out} = C_{in} \div \text{upscale\_factor}^2$
$H_{out} = H_{in} \times \text{upscale\_factor}$
$W_{out} = W_{in} \times \text{upscale\_factor}$

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])