卷积层后加瓶颈残差模块代码实现

以下是卷积层后加瓶颈残差模块的代码实现（使用PyTorch框架）：

import torch
import torch.nn as nn

class ResidualBlock(nn.Module):
    def __init__(self, in_channels, out_channels, stride=1, downsample=None):
        super(ResidualBlock, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, bias=False)
        self.bn1 = nn.BatchNorm2d(out_channels)
        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(out_channels)
        self.conv3 = nn.Conv2d(out_channels, out_channels * 4, kernel_size=1, stride=1, bias=False)
        self.bn3 = nn.BatchNorm2d(out_channels * 4)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)

        out = self.conv3(out)
        out = self.bn3(out)

        if self.downsample is not None:
            residual = self.downsample(x)

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

        return out

该模块包含了3个卷积层和3个归一化层，使用ReLU作为激活函数。其中第一个卷积层的卷积核大小为1x1，目的是减小通道数；第二个卷积层的卷积核大小为3x3，实现卷积特征提取；第三个卷积层的卷积核大小仍为1x1，但通道数是第一个卷积层的4倍，目的是增加通道数。该模块的in_channels和out_channels分别为输入和输出的通道数，stride为步长，downsample为下采样模块，用于对residual分支进行下采样，使其与out分支的特征图大小相同，从而能够进行相加操作。在forward函数中，首先将输入x保存为residual，然后依次进行卷积、归一化和ReLU激活操作，最后进行residual分支的下采样、相加操作和ReLU激活操作。最后返回out输出特征图。