用pytorch,搭建一个网络模型,包括VGG块与残差结构

好的，我可以为您提供一个示例代码。

首先，我们需要定义一个VGG块，该块包含多个卷积层和池化层。下面是一个简单的VGG块代码：

import torch
import torch.nn as nn

class VGGBlock(nn.Module):
    def __init__(self, in_channels, middle_channels, out_channels, act_func=nn.ReLU(inplace=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

然后，我们可以使用这个VGG块来构建整个网络模型，并将残差结构加入。下面是一个示例代码：

class ResNet(nn.Module):
    def __init__(self, num_classes=1000):
        super(ResNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, 7, 2, 3)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(3, 2, 1)

        self.layer1 = self._make_layer(64, 128, 3)
        self.layer2 = self._make_layer(128, 256, 4)
        self.layer3 = self._make_layer(256, 512, 6)
        self.layer4 = self._make_layer(512, 512, 3)

        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(512, num_classes)

    def _make_layer(self, in_channels, out_channels, block_