构建一个包含残差结构的VGG网络,使用pytorch实现

下面是在PyTorch中实现带有残差结构的VGG网络的代码示例：

import torch
import torch.nn as nn
import torch.nn.functional as F

class ResidualVGG(nn.Module):
    def __init__(self, in_channels, out_channels, stride=1):
        super(ResidualVGG, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, out_channels, 3, stride=stride, padding=1)
        self.bn1 = nn.BatchNorm2d(out_channels)
        self.conv2 = nn.Conv2d(out_channels, out_channels, 3, stride=1, padding=1)
        self.bn2 = nn.BatchNorm2d(out_channels)
        self.stride = stride

    def forward(self, x):
        residual = x
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.bn2(self.conv2(out))
        if self.stride != 1 or x.shape[1] != out.shape[1]:
            residual = F.avg_pool2d(x, 2, stride=2)
        out += residual
        out = F.relu(out)
        return out

class VGGNet(nn.Module):
    def __init__(self, num_classes=1000):
        super(VGGNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, 3, stride=1, padding=1)
        self.bn1 = nn.BatchNorm2d(64)
        self.layer1 = self._make_layer(64, 128, 2)
        self.layer2 = self._make_layer(128, 256, 2)
        self.layer3 = self._make_layer(256, 512, 2)
        self.layer4 = self._make_layer(512, 512, 2)
        self.fc = nn.Linear(512, num_classes)

    def _make_layer(self, in_channels, out_channels, num_blocks, stride=1):
        layers = []
        for i in range(num_blocks):
            if i == 0:
                layers.append(ResidualVGG(in_channels, out_channels, stride))
            else:
                layers.append(ResidualVGG(out_channels, out_channels, stride=1))
        return nn.Sequential(*layers)

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.layer1(out)
        out = self.layer2(out)
        out = self.layer3(out)
        out = self.layer4(out)
        out = F.avg_pool2d(out, 4)
        out = out.view(out.size