Pytorch实现ResNet网络之Residual Block残差块

import torch.nn as nn

class ResidualBlock(nn.Module):
    def __init__(self, in_channels, out_channels, stride=1):
        super().__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.shortcut = nn.Sequential()
        if in_channels != out_channels or stride != 1:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(out_channels))
        
    def forward(self, x):
        residual = x
        
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        
        x = self.conv2(x)
        x = self.bn2(x)
        
        shortcut = self.shortcut(residual)
        x += shortcut
        x = self.relu(x)
        
        return x