UNet经典模型实战

UNet是一种经典的语义分割模型，其结构简单而有效。以下是一个使用PyTorch实现UNet模型的示例代码：

import torch
import torch.nn as nn

class DoubleConv(nn.Module):
    def __init__(self, in_channels, out_channels):
        super().__init__()
        self.conv = nn.Sequential(
            nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True),
            nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True)
        )
        
    def forward(self, x):
        return self.conv(x)

class UNet(nn.Module):
    def __init__(self, in_channels=3, out_channels=1, features=[64, 128, 256, 512]):
        super().__init__()
        self.ups = nn.ModuleList()
        self.downs = nn.ModuleList()
        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
        
        # Down part of UNet
        for feature in features:
            self.downs.append(DoubleConv(in_channels, feature))
            in_channels = feature
        
        # Up part of UNet
        for feature in reversed(features):
            self.ups.append(nn.ConvTranspose2d(feature*2, feature, kernel_size=2, stride=2))
            self.ups.append(DoubleConv(feature*2, feature))
        
        self.bottleneck = DoubleConv(features[-1], features[-1]*2)
        self.final_conv = nn.Conv2d(features[0], out_channels, kernel_size=1)
        
    def forward(self, x):
        skip_connections = []
        
        # Down part of UNet
        for down in self.downs:
            x = down(x)
            skip_connections.append(x)
            x = self.pool(x)
        
        x = self.bottleneck(x)
        skip_connections = skip_connections[::-1]
        
        # Up part of UNet
        for idx in range(0, len(self.ups), 2):
            x = self.ups[idx](x)
            skip_connection = skip_connections[idx//2]
            
            if x.shape != skip_connection.shape:
                x = nn.functional.interpolate(x, size=skip_connection.shape[2:], mode='bilinear', align_corners=True)
            
            concat_skip = torch.cat((skip_connection, x), dim=1)
            x = self.ups[idx+1](concat_skip)
            
        return self.final_conv(x)

上述代码中，DoubleConv是UNet中使用的基本模块，包含两个3x3卷积层和一个ReLU激活函数。UNet类中，首先定义了一个Down part，其中包含多个DoubleConv模块和一个MaxPool2d层，用于从输入图像中提取特征。然后定义了一个Bottleneck模块，用于从提取的特征中进一步提取信息。最后定义了一个Up part，其中包含多个ConvTranspose2d层和多个DoubleConv模块，用于将特征图上采样并与Down part中的对应特征图拼接，最终输出分割结果。

使用该模型进行训练时，可以使用像Dice Loss或Cross-Entropy Loss等常见的分割任务损失函数，同时可以使用像Adam或SGD等常见的优化器进行模型优化。