pytorch实现残差块

以下是使用PyTorch实现残差块的代码，其中包括了两个卷积层和一个跳跃连接：

import torch.nn as nn

class ResidualBlock(nn.Module):
    def __init__(self, in_channels, out_channels, stride=1):
        super(ResidualBlock, 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.stride = stride

    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.stride != 1 or identity.shape[1] != out.shape[1]:
            identity = nn.Conv2d(identity.shape[1], out.shape[1], kernel_size=1, stride=self.stride, bias=False)(identity)
            identity = nn.BatchNorm2d(out.shape[1])(identity)

        out += identity
        out = self.relu(out)

        return out