unet语义分割模型代码

UNet是一种常用的语义分割模型，它由Ronneberger等人于2015年提出。UNet的结构特点是将编码器和解码器相连接，形成一个U字形的网络结构，因此得名UNet。

以下是一个简单的UNet语义分割模型的代码示例：

import torch
import torch.nn as nn

# 定义UNet的编码器部分
class UNetEncoder(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(UNetEncoder, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1)
    
    def forward(self, x):
        x = self.conv1(x)
        x = self.relu(x)
        x = self.conv2(x)
        x = self.relu(x)
        return x

# 定义UNet的解码器部分
class UNetDecoder(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(UNetDecoder, self).__init__()
        self.upconv = nn.ConvTranspose2d(in_channels, out_channels, kernel_size=2, stride=2)
        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1)
    
    def forward(self, x, skip_connection):
        x = self.upconv(x)
        x = torch.cat([x, skip_connection], dim=1)
        x = self.conv1(x)
        x = self.relu(x)
        x = self.conv2(x)
        x = self.relu(x)
        return x

# 定义完整的UNet模型
class UNet(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(UNet, self).__init__()
        self.encoder1 = UNetEncoder(in_channels, 64)
        self.encoder2 = UNetEncoder(64, 128)
        self.encoder3 = UNetEncoder(128, 256)
        self.encoder4 = UNetEncoder(256, 512)
        self.decoder1 = UNetDecoder(512, 256)
        self.decoder2 = UNetDecoder(256, 128)
        self.decoder3 = UNetDecoder(128, 64)
        self.final_conv = nn.Conv2d(64, out_channels, kernel_size=1)
    
    def forward(self, x):
        skip_connections = []
        
        # 编码器部分
        x = self.encoder1(x)
        skip_connections.append(x)
        x = nn.MaxPool2d(kernel_size=2)(x)
        
        x = self.encoder2(x)
        skip_connections.append(x)
        x = nn.MaxPool2d(kernel_size=2)(x)
        
        x = self.encoder3(x)
        skip_connections.append(x)
        x = nn.MaxPool2d(kernel_size=2)(x)
        
        x = self.encoder4(x)
        
        # 解码器部分
        x = self.decoder1(x, skip_connections.pop())
        x = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)(x)
        
        x = self.decoder2(x, skip_connections.pop())
        x = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)(x)
        
        x = self.decoder3(x, skip_connections.pop())
        x = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)(x)
        
        x = self.final_conv(x)
        return x

这段代码实现了一个简单的UNet语义分割模型，包括编码器部分和解码器部分。你可以根据自己的需求进行修改和扩展。