用python写一段残差网络卷积代码 尺度不变 通道数减少

好的，以下是一个简单的残差网络卷积的代码，实现尺度不变和通道数减少的效果：

import torch.nn as nn

class ResBlock(nn.Module):
    def __init__(self, in_channels, out_channels, stride=1):
        super(ResBlock, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(out_channels)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(out_channels)
        self.downsample = nn.Sequential(
            nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, bias=False),
            nn.BatchNorm2d(out_channels)
        )
        
    def forward(self, x):
        identity = x
        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)
        out = self.conv2(out)
        out = self.bn2(out)
        if self.downsample:
            identity = self.downsample(x)
        out += identity
        out = self.relu(out)
        return out

该代码中定义了一个ResBlock的类，实现了残差网络卷积的效果。该代码可以通过传入in_channels和out_channels参数来控制输入和输出的通道数，以及通过stride参数来控制卷积的步长，从而实现尺度不变的效果。