unet++ pytorch代码

以下是一个简单的UNet++模型的PyTorch代码实现：

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 = F.relu(self.bn1(self.conv1(x)))
        x = F.relu(self.bn2(self.conv2(x)))
        return x

class ConvTransBlock(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(ConvTransBlock, self).__init__()
        self.convtrans = nn.ConvTranspose2d(in_channels, out_channels, kernel_size=2, stride=2)
    
    def forward(self, x):
        x = self.convtrans(x)
        return x

class UNetPP(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(UNetPP, self).__init__()
        self.conv1 = ConvBlock(in_channels, 64)
        self.conv2 = ConvBlock(64, 128)
        self.conv3 = ConvBlock(128, 256)
        self.conv4 = ConvBlock(256, 512)
        self.conv5 = ConvBlock(512, 1024)
        self.conv6 = ConvBlock(1024, 2048)
        
        self.upconv1 = ConvTransBlock(2048, 1024)
        self.upconv2 = ConvTransBlock(1024, 512)
        self.upconv3 = ConvTransBlock(512, 256)
        self.upconv4 = ConvTransBlock(256, 128)
        self.upconv5 = ConvTransBlock(128, 64)
        
        self.conv7 = ConvBlock(3072, 1024)
        self.conv8 = ConvBlock(1536, 512)
        self.conv9 = ConvBlock(768, 256)
        self.conv10 = ConvBlock(384, 128)
        self.conv11 = ConvBlock(192, 64)
        
        self.final_conv = nn.Conv2d(64, out_channels, kernel_size=1)
        
    def forward(self, x):
        x1 = self.conv1(x)
        x2 = F.max_pool2d(x1, kernel_size=2, stride=2)
        x2 = self.conv2(x2)
        x3 = F.max_pool2d(x2, kernel_size=2, stride=2)
        x3 = self.conv3(x3)
        x4 = F.max_pool2d(x3, kernel_size=2, stride=2)
        x4 = self.conv4(x4)
        x5 = F.max_pool2d(x4, kernel_size=2, stride=2)
        x5 = self.conv5(x5)
        x6 = F.max_pool2d(x5, kernel_size=2, stride=2)
        x6 = self.conv6(x6)
        
        x7 = self.upconv1(x6)
        x7 = torch.cat([x7, x5], dim=1)
        x7 = self.conv7(x7)
        
        x8 = self.upconv2(x7)
        x8 = torch.cat([x8, x4], dim=1)
        x8 = self.conv8(x8)
        
        x9 = self.upconv3(x8)
        x9 = torch.cat([x9, x3], dim=1)
        x9 = self.conv9(x9)
        
        x10 = self.upconv4(x9)
        x10 = torch.cat([x10, x2], dim=1)
        x10 = self.conv10(x10)
        
        x11 = self.upconv5(x10)
        x11 = torch.cat([x11, x1], dim=1)
        x11 = self.conv11(x11)
        
        output = self.final_conv(x11)
        return output

这个模型实现了一个6层的UNet++结构，每个卷积块的通道数为64/128/256/512/1024/2048。在这个实现中，每个卷积块的结构都是两个3x3的卷积层，中间使用了BatchNorm层和ReLU激活函数。上采样层使用的是2x2的转置卷积。在UNet++结构中，每个上采样层都会将其对应的下采样层的输出与上采样后的特征图进行级联，然后再进行卷积操作。最终的输出使用了一个1x1的卷积层将特征图转换为目标类别的概率图。