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
import torch.nn.functional as F

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.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(out_channels)
        
        self.shortcut = nn.Sequential()
        if stride != 1 or in_channels != out_channels:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(out_channels)
            )
        
    def forward(self, x):
        residual = x
        
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.bn2(self.conv2(out))
        
        out += self.shortcut(x)
        out = F.relu(out)
        
        return out

class ResNet(nn.Module):
    def __init__(self, num_classes=10):
        super(ResNet, self).__init__()
        
        self.conv = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn = nn.BatchNorm2d(64)
        self.layer1 = self.make_layer(64, 64, blocks=2, stride=1)
        self.layer2 = self.make_layer(64, 128, blocks=2, stride=2)
        self.layer3 = self.make_layer(128, 256, blocks=2, stride=2)
        self.layer4 = self.make_layer(256, 512, blocks=2, stride=2)
        self.avg_pool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(512, num_classes)
        
    def make_layer(self, in_channels, out_channels, blocks, stride):
        layers = []
        
        layers.append(ResidualBlock(in_channels, out_channels, stride))
        
        for _ in range(1, blocks):
            layers.append(ResidualBlock(out_channels, out_channels))
        
        return nn.Sequential(*layers)
    
    def forward(self, x):
        out = F.relu(self.bn(self.conv(x)))
        out = self.layer1(out)
        out = self.layer2(out)
        out = self.layer3(out)
        out = self.layer4(out)
        out = self.avg_pool(out)
        out = out.view(out.size(0), -1)
        out = self.fc(out)
        
        return out

这段代码定义了一个ResNet模型，其中包含了残差块（ResidualBlock）和整个网络结构（ResNet）。你可以根据自己的需求进行调整和使用。