yolov8添加注意力机制CBAM

您好！对于在YOLOv8中添加注意力机制CBAM，可以按照以下步骤进行：

1. 导入必要的模块和库：

   import torch
   import torch.nn as nn

2. 定义CBAM模块：

   class CBAM(nn.Module):
       def __init__(self, channels, reduction=16):
           super(CBAM, self).__init__()
           self.channels = channels
           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.relu = nn.ReLU(inplace=True)
           self.fc2 = nn.Conv2d(channels // reduction, channels, kernel_size=1, padding=0)
           
           self.sigmoid_channel = nn.Sigmoid()
           
           self.conv_after_concat = nn.Conv2d(2, 1, kernel_size=3, stride=1, padding=1)
           self.sigmoid_spatial = nn.Sigmoid()
           
       def forward(self, x):
           avg_out = self.fc2(self.relu(self.fc1(self.avg_pool(x))))
           max_out = self.fc2(self.relu(self.fc1(self.max_pool(x))))
           channel_out = self.sigmoid_channel(avg_out + max_out)
           
           spatial_out = torch.cat([avg_out, max_out], dim=1)
           spatial_out = self.conv_after_concat(spatial_out)
           spatial_out = self.sigmoid_spatial(spatial_out)
           
           return x * channel_out * spatial_out

3. 在YOLOv8的特征提取部分使用CBAM模块：

   class YOLOv8(nn.Module):
       def __init__(self):
           super(YOLOv8, self).__init__()
           # ...
           self.cbam = CBAM(channels=XX)
           # ...
           
       def forward(self, x):
           # ...
           x = self.cbam(x)
           # ...
           return x

请注意，上述代码中的`XX`需要替换为适合您的网络的通道数。这是一个简单的示例，您还可以根据需要进行更改和调整。

希望能对您有所帮助！如果您有任何疑问，请随时提问。