SRResnet代码复现
时间: 2023-12-08 21:05:53 浏览: 25
以下是SRResnet代码的复现,使用PyTorch框架:
```python
import torch
import torch.nn as nn
class ResidualBlock(nn.Module):
def __init__(self, channels):
super(ResidualBlock, self).__init__()
self.conv1 = nn.Conv2d(channels, channels, kernel_size=3, stride=1, padding=1, bias=True)
self.conv2 = nn.Conv2d(channels, channels, kernel_size=3, stride=1, padding=1, bias=True)
self.relu = nn.ReLU(inplace=True)
def forward(self, x):
residual = x
out = self.relu(self.conv1(x))
out = self.conv2(out)
out += residual
return out
class SRResnet(nn.Module):
def __init__(self, scale_factor, num_channels=3, num_residual_blocks=16):
super(SRResnet, self).__init__()
self.conv_input = nn.Conv2d(num_channels, 64, kernel_size=9, stride=1, padding=4, bias=True)
self.relu = nn.ReLU(inplace=True)
self.residual_blocks = nn.Sequential()
for i in range(num_residual_blocks):
self.residual_blocks.add_module('residual_block' + str(i+1), ResidualBlock(64))
self.conv_mid = nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1, bias=True)
self.upscale = nn.Sequential(
nn.Conv2d(64, 256, kernel_size=3, stride=1, padding=1, bias=True),
nn.PixelShuffle(upscale_factor),
nn.ReLU(inplace=True),
nn.Conv2d(64, num_channels, kernel_size=9, stride=1, padding=4, bias=True)
)
def forward(self, x):
x = self.relu(self.conv_input(x))
residual = x
x = self.residual_blocks(x)
x = self.conv_mid(x)
x += residual
x = self.upscale(x)
return x
```
其中,ResidualBlock类实现了残差块,SRResnet类实现了整个网络。具体细节可以参考论文 "Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network" 中的描述。