unet加残差网络代码

以下是使用PyTorch实现的Unet加残差网络的代码示例：

import torch
import torch.nn as nn

class ResBlock(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(ResBlock, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1)
        self.bn1 = nn.BatchNorm2d(out_channels)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1)
        self.bn2 = nn.BatchNorm2d(out_channels)
        
        self.downsample = None
        if in_channels != out_channels:
            self.downsample = nn.Sequential(
                nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1),
                nn.BatchNorm2d(out_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)
        
        if self.downsample is not None:
            identity = self.downsample(x)
        
        out += identity
        out = self.relu(out)
        
        return out

class UnetResNet(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(UnetResNet, self).__init__()
        self.in_channels = in_channels
        self.out_channels = out_channels
        
        self.conv1 = nn.Conv2d(in_channels, 64, kernel_size=3, padding=1)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)

        self.res_block1 = ResBlock(64, 64)
        self.res_block2 = ResBlock(64, 128)
        self.res_block3 = ResBlock(128, 256)
        self.res_block4 = ResBlock(256, 512)
        
        self.maxpool = nn.MaxPool2d(kernel_size=2, stride=2)
        
        self.upconv1 = nn.ConvTranspose2d(512, 256, kernel_size=2, stride=2)
        self.conv2 = nn.Conv2d(512, 256, kernel_size=3, padding=1)
        self.bn2 = nn.BatchNorm2d(256)
        
        self.upconv2 = nn.ConvTranspose2d(256, 128, kernel_size=2, stride=2)
        self.conv3 = nn.Conv2d(256, 128, kernel_size=3, padding=1)
        self.bn3 = nn.BatchNorm2d(128)
        
        self.upconv3 = nn.ConvTranspose2d(128, 64, kernel_size=2, stride=2)
        self.conv4 = nn.Conv2d(128, 64, kernel_size=3, padding=1)
        self.bn4 = nn.BatchNorm2d(64)
        
        self.conv5 = nn.Conv2d(64, out_channels, kernel_size=1)
        
    def forward(self, x):
        x1 = self.conv1(x)
        x1 = self.bn1(x1)
        x1 = self.relu(x1)
        
        x2 = self.res_block1(x1)
        x2 = self.maxpool(x2)
        
        x3 = self.res_block2(x2)
        x3 = self.maxpool(x3)
        
        x4 = self.res_block3(x3)
        x4 = self.maxpool(x4)
        
        x5 = self.res_block4(x4)
        
        x6 = self.upconv1(x5)
        x6 = torch.cat([x6, x4], dim=1)
        x6 = self.conv2(x6)
        x6 = self.bn2(x6)
        x6 = self.relu(x6)
        
        x7 = self.upconv2(x6)
        x7 = torch.cat([x7, x3], dim=1)
        x7 = self.conv3(x7)
        x7 = self.bn3(x7)
        x7 = self.relu(x7)
        
        x8 = self.upconv3(x7)
        x8 = torch.cat([x8, x2], dim=1)
        x8 = self.conv4(x8)
        x8 = self.bn4(x8)
        x8 = self.relu(x8)
        
        x9 = self.conv5(x8)
        
        return x9

以上代码实现了一个带有残差块的Unet模型，可以通过调整网络层数、卷积核大小等参数进行修改。