残差网络结合注意力机制的代码实现

残差网络（Residual Network, ResNet）结合注意力机制可以增强深度神经网络的学习能力，特别是在处理视觉信息时，比如图像分类、目标检测等领域。以下是一个简化的PyTorch代码示例，展示了如何在一个基本的ResNet架构中集成自注意力层：

import torch.nn as nn

# 定义基础的ResNet块
class BasicBlock(nn.Module):
    def __init__(self, in_channels, out_channels, attention=False):
        super(BasicBlock, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1)
        self.bn1 = nn.BatchNorm2d(out_channels)
        self.relu = nn.ReLU(inplace=True)
        
        if attention:
            # 添加注意力模块
            self.attention = nn.Sequential(
                nn.Linear(out_channels, out_channels // 8),
                nn.ReLU(),
                nn.Linear(out_channels // 8, out_channels),
                nn.Softmax(dim=-1)
            )
        else:
            self.attention = None
            
        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1)
        self.bn2 = nn.BatchNorm2d(out_channels)

    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)

        if self.attention is not None:
            # 使用注意力权重对特征图加权求和
            attention_weights = self.attention(out.mean(dim=[2, 3]))[:, :, None, None]
            out = out * attention_weights + residual

        return out

# 构建包含注意力的ResNet模型
class AttentionResNet(nn.Module):
    def __init__(self, num_blocks, attention_layers=1, ...):
        super(AttentionResNet, self).__init__()
        self.layer1 = ...  # 初始化基本的ResNet层序列
        self.attention_layers = attention_layers
        for i in range(attention_layers):
            self.layer1.add_module(f"attention_{i}", BasicBlock(..., attention=True))

        ...

    def forward(self, x):
        x = self.layer1(x)
        ...
        return final_output

在这个例子中，`attention_layers`参数控制了有多少层ResNet会包含注意力模块。注意这只是一个简化版本，实际应用中你需要根据具体的项目需求调整网络结构和超参数。