使用pytorch,帮我使用VGG块与残差结构组合搭建一个网络模型

好的，我来帮助您使用 PyTorch 搭建一个网络模型，结合 VGG 块和残差结构。

首先，我们需要定义 VGG 块，并使用多个这样的块组合成更深的网络。下面是一个 VGG 块的定义：

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(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

接下来，我们需要定义残差块，下面是一个残差块的定义：

class ResidualBlock(nn.Module):
    def __init__(self, channels, act_func=nn.ReLU(inplace=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
        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)

        out += residual
        return out

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