pytorch中残差网络代码

在PyTorch中实现残差网络（ResNet）的代码如下：

import torch
import torch.nn as nn

# 定义基本的残差块
class ResidualBlock(nn.Module):
    def __init__(self, in_channels, out_channels, stride=1):
        super(ResidualBlock, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(out_channels)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(out_channels)
        self.downsample = nn.Sequential(
            nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, bias=False),
            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

# 定义ResNet模型
class ResNet(nn.Module):
    def __init__(self, block, layers, num_classes=10):
        super(ResNet, self).__init__()
        self.in_channels = 64
        
        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        
        self.layer1 = self.make_layer(block, 64, layers[0])
        self.layer2 = self.make_layer(block, 128, layers[1], stride=2)
        self.layer3 = self.make_layer(block, 256, layers[2], stride=2)
        self.layer4 = self.make_layer(block, 512, layers[3], stride=2)
        
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(512 * block.expansion, num_classes)
    
    def make_layer(self, block, out_channels, blocks, stride=1):
        layers = []
        layers.append(block(self.in_channels, out_channels, stride))
        self.in_channels = out_channels * block.expansion
        
        for _ in range(1, blocks):
            layers.append(block(self.in_channels, out_channels))
        
        return nn.Sequential(*layers)
    
    def forward(self, x):
        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)
        out = self.maxpool(out)
        
        out = self.layer1(out)
        out = self.layer2(out)
        out = self.layer3(out)
        out = self.layer4(out)
        
        out = self.avgpool(out)
        out = torch.flatten(out, 1)
        out = self.fc(out)
        
        return out

# 创建ResNet模型
def ResNet18(num_classes=10):
    return ResNet(ResidualBlock, [2, 2, 2, 2], num_classes)

def ResNet34(num_classes=10):
    return ResNet(ResidualBlock, [3, 4, 6, 3], num_classes)

# 使用示例
model = ResNet18()

上述代码定义了一个基本的残差块（ResidualBlock）以及一个完整的ResNet模型（ResNet）。你可以根据需要选择不同的ResNet版本（如ResNet18或ResNet34）来创建模型。在示例中，创建了一个ResNet18模型的实例（`model = ResNet18()`）。