使用Pytorch实现SRResNet模型,完成图像超分辨率请给出模型部分代码,初始输入通道数为1,最后输出通道数为1
时间: 2023-06-08 08:06:48 浏览: 73
好的,以下是使用Pytorch实现SRResNet模型的部分代码:
```
import torch.nn as nn
import torch.nn.functional as F
# 定义ResNet块
class ResidualBlock(nn.Module):
def __init__(self, in_channels):
super(ResidualBlock, self).__init__()
self.conv1 = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(in_channels)
self.relu = nn.ReLU(inplace=True)
self.conv2 = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(in_channels)
def forward(self, x):
identity = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out += identity
out = self.relu(out)
return out
# 定义SRResNet模型
class SRResNet(nn.Module):
def __init__(self, in_channels, out_channels, num_residual_blocks):
super(SRResNet, self).__init__()
self.conv1 = nn.Conv2d(in_channels, 64, kernel_size=9, stride=1, padding=4, bias=False)
self.relu = nn.ReLU(inplace=True)
# 添加若干个ResNet块
self.res_blocks = nn.Sequential(*[ResidualBlock(64) for _ in range(num_residual_blocks)])
self.conv2 = nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(64)
# 上采样部分,可以使用不同的方法,如PixelShuffle或Deconvolution
self.conv3 = nn.Conv2d(64, 256, kernel_size=3, stride=1, padding=1, bias=False)
self.pixel_shuffle = nn.PixelShuffle(2)
# 使用两个卷积层将通道数降为目标输出通道数
self.conv4 = nn.Conv2d(64, 32, kernel_size=3, stride=1, padding=1, bias=False)
self.conv5 = nn.Conv2d(32, out_channels, kernel_size=9, stride=1, padding=4, bias=False)
def forward(self, x):
out = self.conv1(x)
out = self.relu(out)
out_res = self.res_blocks(out)
out = self.conv2(out_res)
out = self.bn2(out)
out += out_res
out = self.relu(out)
out = self.pixel_shuffle(self.conv3(out))
out = self.conv4(out)
out = self.conv5(out)
return out
```
这里实现了一个基于ResNet块的SRResNet模型,根据输入和输出的通道数和所需要的ResNet块的数量来初始化。其中包括卷积层、BN层、ReLu激活函数以及像素上采样的PixelShuffle等等。
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