将Unet ++和门结构结合的PyTorch 代码

以下是将Unet和门结构结合的PyTorch代码：

import torch
import torch.nn as nn
import torch.nn.functional as F

class ConvBlock(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(ConvBlock, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1)
        self.bn1 = nn.BatchNorm2d(out_channels)
        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1)
        self.bn2 = nn.BatchNorm2d(out_channels)
    
    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = F.relu(x)
        
        x = self.conv2(x)
        x = self.bn2(x)
        x = F.relu(x)
        
        return x

class UNet(nn.Module):
    def __init__(self, in_channels=3, out_channels=1):
        super(UNet, self).__init__()
        
        self.down1 = ConvBlock(in_channels, 64)
        self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
        
        self.down2 = ConvBlock(64, 128)
        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
        
        self.down3 = ConvBlock(128, 256)
        self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2)
        
        self.down4 = ConvBlock(256, 512)
        self.pool4 = nn.MaxPool2d(kernel_size=2, stride=2)
        
        self.bottom = ConvBlock(512, 1024)
        
        self.up4 = nn.ConvTranspose2d(1024, 512, kernel_size=2, stride=2)
        self.upconv4 = ConvBlock(1024, 512)
        
        self.up3 = nn.ConvTranspose2d(512, 256, kernel_size=2, stride=2)
        self.upconv3 = ConvBlock(512, 256)
        
        self.up2 = nn.ConvTranspose2d(256, 128, kernel_size=2, stride=2)
        self.upconv2 = ConvBlock(256, 128)
        
        self.up1 = nn.ConvTranspose2d(128, 64, kernel_size=2, stride=2)
        self.upconv1 = ConvBlock(128, 64)
        
        self.outconv = nn.Conv2d(64, out_channels, kernel_size=1)
        
        # 定义门结构的两个分支
        self.branch1 = nn.Sequential(
            nn.Conv2d(1024, 1024, kernel_size=3, padding=1),
            nn.BatchNorm2d(1024),
            nn.ReLU(inplace=True),
            nn.Conv2d(1024, 1, kernel_size=1)
        )
        
        self.branch2 = nn.Sequential(
            nn.Conv2d(1024, 1024, kernel_size=3, padding=1),
            nn.BatchNorm2d(1024),
            nn.ReLU(inplace=True),
            nn.Conv2d(1024, 1, kernel_size=1)
        )
        
    def forward(self, x):
        # 下采样
        x1 = self.down1(x)
        x2 = self.pool1(x1)
        
        x3 = self.down2(x2)
        x4 = self.pool2(x3)
        
        x5 = self.down3(x4)
        x6 = self.pool3(x5)
        
        x7 = self.down4(x6)
        x8 = self.pool4(x7)
        
        # 中间部分
        x9 = self.bottom(x8)
        
        # 上采样
        x10 = self.up4(x9)
        x10 = torch.cat([x10, x7], dim=1)
        x11 = self.upconv4(x10)
        
        x12 = self.up3(x11)
        x12 = torch.cat([x12, x5], dim=1)
        x13 = self.upconv3(x12)
        
        x14 = self.up2(x13)
        x14 = torch.cat([x14, x3], dim=1)
        x15 = self.upconv2(x14)
        
        x16 = self.up1(x15)
        x16 = torch.cat([x16, x1], dim=1)
        x17 = self.upconv1(x16)
        
        # 输出结果
        x18 = self.outconv(x17)
        
        # 计算门控信号
        branch1 = self.branch1(x9)
        branch2 = self.branch2(x9)
        gate = F.sigmoid(branch2)
        x18 = gate*x18 + (1-gate)*branch1
        
        return x18

上述代码定义了一个Unet模型和一个门结构模块，并将它们结合起来。具体来说，Unet模型由一系列卷积块和反卷积块组成，使用了下采样和上采样操作实现了图像分割。门结构模块由两个分支组成，它们分别计算了输入特征图的重要程度，用sigmoid函数将它们结合起来得到一个门控信号，然后将这个信号应用于Unet模型输出的结果和门结构模块的输出结果来获得最终的输出。