SRResnet代码复现
时间: 2023-12-08 13:06:09 浏览: 49
以下是SRResnet的代码复现,使用Python和PyTorch实现:
首先,导入所需的库:
```python
import torch
import torch.nn as nn
```
接下来,定义SRResnet的主要模型类:
```python
class SRResnet(nn.Module):
def __init__(self, num_channels=3, num_blocks=16, upscale_factor=4):
super(SRResnet, self).__init__()
# Initial convolutional layer
self.init_conv = nn.Sequential(
nn.Conv2d(num_channels, 64, kernel_size=9, padding=4),
nn.PReLU()
)
# Residual blocks
self.res_blocks = nn.Sequential(*[ResidualBlock() for _ in range(num_blocks)])
# Middle convolutional layer
self.mid_conv = nn.Sequential(
nn.Conv2d(64, 64, kernel_size=3, padding=1),
nn.BatchNorm2d(64)
)
# Upscaling layers
self.upscale = nn.Sequential(
nn.Conv2d(64, 256, kernel_size=3, padding=1),
nn.PixelShuffle(upscale_factor),
nn.PReLU()
)
# Output layer
self.output = nn.Conv2d(64, num_channels, kernel_size=9, padding=4)
def forward(self, x):
# Initial convolutional layer
x = self.init_conv(x)
# Residual blocks
residual = x
x = self.res_blocks(x)
x += residual
# Middle convolutional layer
x = self.mid_conv(x)
# Upscaling layers
x = self.upscale(x)
# Output layer
x = self.output(x)
return x
```
在SRResnet中,使用了残差块(Residual Block)和像素洗牌(PixelShuffle)层,因此需要先定义这两个类:
```python
class ResidualBlock(nn.Module):
def __init__(self):
super(ResidualBlock, self).__init__()
self.conv1 = nn.Sequential(
nn.Conv2d(64, 64, kernel_size=3, padding=1),
nn.BatchNorm2d(64),
nn.PReLU()
)
self.conv2 = nn.Sequential(
nn.Conv2d(64, 64, kernel_size=3, padding=1),
nn.BatchNorm2d(64)
)
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.conv2(out)
out += residual
return out
class PixelShuffle(nn.Module):
def __init__(self, upscale_factor):
super(PixelShuffle, self).__init__()
self.upscale_factor = upscale_factor
def forward(self, x):
batch_size, channels, height, width = x.size()
channels //= self.upscale_factor ** 2
out_height = height * self.upscale_factor
out_width = width * self.upscale_factor
out = x.view(batch_size, channels, self.upscale_factor, self.upscale_factor, height, width)
out = out.permute(0, 1, 4, 2, 5, 3).contiguous()
out = out.view(batch_size, channels, out_height, out_width)
return out
```
最后,定义一个函数来实例化SRResnet模型:
```python
def get_srresnet(num_channels=3, num_blocks=16, upscale_factor=4):
srresnet = SRResnet(num_channels, num_blocks, upscale_factor)
return srresnet
```
现在,我们已经成功地复现了SRResnet的代码。
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