yolov11加入cbam

### 集成CBAM模块到YOLOv11

#### 修改`block.py`

为了在YOLOv11中集成CBAM模块，需编辑位于.../ultralytics/nn/modules/block.py的文件。在此文件头部添加必要的导入语句以及CBAM类声明[^3]。

from .cbam import CBAM  # 假设 cbam.py 文件在同一目录下并实现了 CBAM 类

接着，在该文件适当的位置定义CBAM模块：

class ConvBlock(nn.Module):
    """标准卷积块"""
    
    def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): 
        super().__init__()
        
        self.conv = nn.Conv2d(c1, c2, (k, k), stride=(s, s), padding=p, groups=g, bias=False)
        self.bn = nn.BatchNorm2d(c2)
        self.act = nn.SiLU() if act is True else (act if isinstance(act, nn.Module) else nn.Identity())
        self.cbam = CBAM(c2)

    def forward(self, x):
        y = self.act(self.bn(self.conv(x)))
        z = self.cbam(y)
        return z

此部分代码展示了如何通过继承自`torch.nn.Module`创建一个新的卷积层，并在其后立即应用CBAM机制来增强特征提取能力。

#### 定义CBAM模块

假设存在一个单独实现CBAM功能的Python脚本（如`cbam.py`），其中应包含如下所示的核心逻辑[^2]:

import torch
import torch.nn as nn


class ChannelAttention(nn.Module):
    def __init__(self, channel, reduction=16):
        super(ChannelAttention, self).__init__()
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.fc = nn.Sequential(
            nn.Linear(channel, channel // reduction),
            nn.ReLU(inplace=True),
            nn.Linear(channel // reduction, channel),
            nn.Sigmoid()
        )

    def forward(self, x):
        b, c, _, _ = x.size()
        avg_out = self.fc(self.avg_pool(x).view(b, c)).view(b, c, 1, 1)
        out = avg_out.expand_as(x)
        return out


class SpatialAttention(nn.Module):
    def __init__(self, kernel_size=7):
        super(SpatialAttention, self).__init__()

        assert kernel_size in (3, 7), 'kernel size must be 3 or 7'
        padding = 3 if kernel_size == 7 else 1
        
        self.conv1 = nn.Conv2d(2, 1, kernel_size, padding=padding, bias=False)
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        avg_out = torch.mean(x, dim=1, keepdim=True)
        max_out, _ = torch.max(x, dim=1, keepdim=True)
        scale = torch.cat([avg_out, max_out], dim=1)
        scale = self.conv1(scale)
        scale = self.sigmoid(scale)
        return scale * x


class CBAM(nn.Module):
    def __init__(self, channels, reduction_ratio=16, no_spatial=False):
        super(CBAM, self).__init__()
        self.channel_att = ChannelAttention(channels, reduction_ratio)
        self.spatial_att = None if no_spatial else SpatialAttention()

    def forward(self, x):
        ca_x = self.channel_att(x)
        sa_x = x if not self.spatial_att else self.spatial_att(ca_x)
        return sa_x

这段代码具体描述了CBAM的工作原理——先执行通道级注意操作再做空间级注意处理；最后两者相乘形成完整的注意力图谱用于调整输入张量中的重要性权重分布情况。