用pytorch,使用VGG块与残差结构搭建一个网络模型

为了使用 PyTorch 搭建 VGG 块和残差结构的网络模型，您可以执行以下步骤：

1. 导入所需的 PyTorch 库。
2. 定义 VGG 块，其中包含多个卷积层和最大池化层。
3. 定义残差结构，其中包含跳过连接和残差块。
4. 定义整个网络模型，将 VGG 块和残差结构组合起来。
5. 定义损失函数和优化器。
6. 训练网络模型。

代码示例：

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

class VGGBlock(nn.Module):
    def __init__(self, in_channels, middle_channels, out_channels, act_func=nn.ReLU(True)):
        super(VGGBlock, self).__init__()
        self.act_func = act_func
        self.conv1 = nn.Conv2d(in_channels, middle_channels, 3, padding=1)
        self.bn1 = nn.BatchNorm2d(middle_channels)
        self.conv2 = nn.Conv2d(middle_channels, out_channels, 3, padding=1)
        self.bn2 = nn.BatchNorm2d(out_channels)

    def forward(self, x):
        out = self.conv1(x)
        out = self.bn1(out)
        out = self.act_func(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.act_func(out)

        return out

class ResidualBlock(nn.Module):
    def __init__(self, channels, act_func=nn.ReLU(True)):
        super(ResidualBlock, self).__init__()
        self.act_func = act_func
        self.conv1 = nn.Conv2d(channels, channels, 3, padding=1)
        self.bn1 = nn.BatchNorm2d(channels)
        self.conv2 = nn.Conv2d(channels, channels, 3, padding=1)
        self.bn2 = nn.BatchNorm2d(channels)

    def forward(self, x):
        residual = x