使用用于残差块的pytoch类的前向函数中的for循环来创建卷积层堆栈

import torch
import torch.nn as nn

class ResidualBlock(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(ResidualBlock, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1)
        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1)
        self.relu = nn.ReLU(inplace=True)
        
    def forward(self, x):
        identity = x
        out = self.conv1(x)
        out = self.relu(out)
        out = self.conv2(out)
        out += identity
        out = self.relu(out)
        return out

class ResNet(nn.Module):
    def __init__(self, num_blocks, num_channels):
        super(ResNet, self).__init__()
        self.conv = nn.Conv2d(3, num_channels, kernel_size=3, padding=1)
        self.relu = nn.ReLU(inplace=True)
        self.blocks = nn.ModuleList([ResidualBlock(num_channels, num_channels) for _ in range(num_blocks)])
        
    def forward(self, x):
        out = self.conv(x)
        out = self.relu(out)
        for block in self.blocks:
            out = block(out)
        return out

# 创建一个ResNet模型实例
resnet = ResNet(num_blocks=3, num_channels=64)

# 输入数据
input_data = torch.randn(1, 3, 32, 32)

# 前向传播
output = resnet(input_data)