yolov5 7.0添加CBAM注意力机制

要在Yolov5 7.0中添加CBAM（Channel Attention Module）注意力机制，您可以按照以下步骤进行操作：

1. 导入所需的库和模块：

import torch
from torch import nn

2. 定义CBAM模块：

class CBAM(nn.Module):
    def __init__(self, channels, reduction):
        super(CBAM, self).__init__()
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.max_pool = nn.AdaptiveMaxPool2d(1)
        
        self.fc1 = nn.Conv2d(channels, channels // reduction, kernel_size=1, padding=0)
        self.relu1 = nn.ReLU(inplace=True)
        self.fc2 = nn.Conv2d(channels // reduction, channels, kernel_size=1, padding=0)
        
        self.conv = nn.Conv2d(channels, 1, kernel_size=7, padding=3)
        
        self.sigmoid = nn.Sigmoid()
        
    def forward(self, x):
        avg_out = self.fc2(self.relu1(self.fc1(self.avg_pool(x))))
        max_out = self.fc2(self.relu1(self.fc1(self.max_pool(x))))
        
        attn = self.sigmoid(avg_out + max_out)
        out = x * attn
        
        spatial_attn = self.sigmoid(self.conv(out))
        out = out * spatial_attn
        
        return out

3. 修改Yolov5模型的定义，将CBAM模块添加到适当的位置：

from models.common import Conv

class YOLOv5(nn.Module):
    def __init__(self, ...):
        super(YOLOv5, self).__init__()
        
        # 在需要的位置添加CBAM模块
        self.cbam1 = CBAM(channels=64, reduction=16)
        self.cbam2 = CBAM(channels=128, reduction=16)
        self.cbam3 = CBAM(channels=256, reduction=16)
        self.cbam4 = CBAM(channels=512, reduction=16)
        self.cbam5 = CBAM(channels=1024, reduction=16)
        
        ...

    def forward(self, x):
        ...

        x3 = self.m2(x2)
        x3 = self.cbam3(x3)  # 添加CBAM模块

        x4 = self.m3(x3)
        x4 = self.cbam4(x4)  # 添加CBAM模块

        x5 = self.m4(x4)
        x5 = self.cbam5(x5)  # 添加CBAM模块

        ...

        return ...

通过以上步骤，您就可以将CBAM注意力机制添加到Yolov5 7.0中了。请注意，这只是一个示例代码，具体的实现可能需要根据您的需求进行调整和修改。