ython为现有yolov5模型增加cbam 且把改动部分标记出来

首先，需要在代码中引入cbam模块，可以使用以下代码：

import torch.nn as nn

class CBAM(nn.Module):
    def __init__(self, channels, reduction=16, kernel_size=7):
        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.relu = nn.ReLU(inplace=True)
        self.fc2 = nn.Conv2d(channels // reduction, channels, kernel_size=1, padding=0)

        self.conv_channel = nn.Conv2d(2, 1, kernel_size=kernel_size, padding=kernel_size // 2)
        self.sigmoid_channel = nn.Sigmoid()
        self.conv_spatial = nn.Conv2d(2, 1, kernel_size=kernel_size, padding=kernel_size // 2)
        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 = torch.cat((avg_out, max_out), dim=1)
        channel_out = self.sigmoid_channel(self.conv_channel(channel_out))

        spatial_out = torch.cat((torch.mean(channel_out, dim=1, keepdim=True), torch.max(channel_out, dim=1, keepdim=True)[0]), dim=1)
        spatial_out = self.sigmoid_spatial(self.conv_spatial(spatial_out))

        return x * channel_out * spatial_out

在YOLOv5模型中，可以在Conv和Bottleneck模块之间插入CBAM模块，代码如下：

class C3(nn.Module):
    # CSP Bottleneck with 3 convolutions
    def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
        super(C3, self).__init__()
        c_ = int(c2 * e)  # hidden channels
        self.cv1 = Conv(c1, c_, 1, 1)
        self.cv2 = Conv(c1, c_, 1, 1)
        self.cv3 = Conv(2 * c_, c2, 1, 1)
        self.bn = nn.BatchNorm2d(2 * c_)  # applied to cat(cv2, cv3)
        self.act = nn.LeakyReLU(0.1)
        self.shortcut = shortcut
        self.cbam = CBAM(2*c_)

    def forward(self, x):
        y1 = self.cv2(self.act(self.cv1(x)))
        y2 = self.cv3(self.act(torch.cat((y1, x), 1)))
        if self.shortcut and x.shape == y2.shape:
            x = self.cbam(self.bn(torch.cat((y1, y2), dim=1))) + x
        else:
            x = torch.cat((y1, y2), dim=1)
        return x

在上述代码中，我们可以看到在C3模块中加入了cbam模块，并且将其应用在shortcut路径上，以增强模型的特征表达能力。