| #!/usr/bin/env python3 | |
| # -*- coding:utf-8 -*- | |
| ############################################################# | |
| # File: pixelshuffle.py | |
| # Created Date: Friday July 1st 2022 | |
| # Author: Chen Xuanhong | |
| # Email: chenxuanhongzju@outlook.com | |
| # Last Modified: Friday, 1st July 2022 10:18:39 am | |
| # Modified By: Chen Xuanhong | |
| # Copyright (c) 2022 Shanghai Jiao Tong University | |
| ############################################################# | |
| import torch.nn as nn | |
| def pixelshuffle_block( | |
| in_channels, out_channels, upscale_factor=2, kernel_size=3, bias=False | |
| ): | |
| """ | |
| Upsample features according to `upscale_factor`. | |
| """ | |
| padding = kernel_size // 2 | |
| conv = nn.Conv2d( | |
| in_channels, | |
| out_channels * (upscale_factor**2), | |
| kernel_size, | |
| padding=1, | |
| bias=bias, | |
| ) | |
| pixel_shuffle = nn.PixelShuffle(upscale_factor) | |
| return nn.Sequential(*[conv, pixel_shuffle]) | |