写一个有cbam机制的yolov5的网络模型

以下是一个基于PyTorch实现的含有CBAM机制的Yolov5网络模型：

import torch.nn as nn
from yolov5.modules import Focus, Conv, BottleneckCSP, SPP, DWConv, BottleneckCSP2, Concat, Detect
from yolov5.utils import autopad, make_divisible


class CBAMBottleneckCSP(nn.Module):
    def __init__(self, in_channels, out_channels, bottleneck_channels=None, stride=1, expansion=0.5, reduction=16):
        super(CBAMBottleneckCSP, self).__init__()

        if bottleneck_channels is None:
            bottleneck_channels = int(out_channels * expansion)

        self.cbam = CBAM(in_channels, reduction)

        self.conv1 = Conv(in_channels, bottleneck_channels, 1, stride=1)
        self.conv2 = Conv(bottleneck_channels, out_channels, 1, stride=1)

        self.conv3 = Conv(in_channels, bottleneck_channels, 1, stride=1)
        self.conv4 = Conv(bottleneck_channels, bottleneck_channels, 3, stride=stride, groups=bottleneck_channels)
        self.conv5 = Conv(bottleneck_channels, out_channels, 1, stride=1)

    def forward(self, x):
        x = self.cbam(x)

        x1 = self.conv1(x)
        x1 = self.conv2(x1)

        x2 = self.conv3(x)
        x2 = self.conv4(x2)
        x2 = self.conv5(x2)

        return Concat([x1, x2])


class CBAM(nn.Module):
    def __init__(self, channels, reduction=16):
        super(CBAM, self).__init__()

        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.maxpool = nn.AdaptiveMaxPool2d((1, 1))

        self.fc1 = nn.Conv2d(channels, channels // reduction, kernel_size=1, bias=False)
        self.relu = nn.ReLU(inplace=True)
        self.fc2 = nn.Conv2d(channels // reduction, channels, kernel_size=1, bias=False)

        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        avg_out = self.avgpool(x)
        max_out = self.maxpool(x)

        avg_out = self.fc1(avg_out)
        avg_out = self.relu(avg_out)
        avg_out = self.fc2(avg_out)

        max_out = self.fc1(max_out)
        max_out = self.relu(max_out)
        max_out = self.fc2(max_out)

        out = avg_out + max_out
        out = self.sigmoid(out)

        return x * out


class Yolov5CBAM(nn.Module):
    def __init__(self, num_classes=80, width=1.0, anchors=None):
        super(Yolov5CBAM, self).__init__()

        # Define anchor sizes and ratios
        if anchors is None:
            anchors = [[10, 13], [16, 30], [33, 23], [30, 61], [62, 45], [59, 119], [116, 90], [156, 198], [373, 326]]
        self.anchors = [[anchor[0] / 32, anchor[1] / 32] for anchor in anchors]

        # Define backbone
        stem_channels = 32
        self.focus = Focus(3, stem_channels, kernel_size=3)
        self.conv1 = Conv(stem_channels, make_divisible(64 * width), kernel_size=3, stride=2)

        csp1_channels = make_divisible(64 * width)
        csp2_channels = make_divisible(128 * width)
        csp3_channels = make_divisible(256 * width)
        csp4_channels = make_divisible(512 * width)
        csp5_channels = make_divisible(1024 * width)

        self.csp1 = BottleneckCSP(in_channels=make_divisible(64 * width), out_channels=csp1_channels, n=1)
        self.csp2 = CBAMBottleneckCSP(in_channels=csp1_channels, out_channels=csp2_channels, n=2)
        self.csp3 = CBAMBottleneckCSP(in_channels=csp2_channels, out_channels=csp3_channels, n=8)
        self.csp4 = CBAMBottleneckCSP(in_channels=csp3_channels, out_channels=csp4_channels, n=8)
        self.csp5 = BottleneckCSP2(in_channels=csp4_channels, out_channels=csp5_channels, n=4)

        # Define neck
        neck_channels = make_divisible(256 * width)
        self.spp = SPP(in_channels=csp5_channels, out_channels=neck_channels, k=(5, 9, 13))
        self.conv2 = CBAM(in_channels=neck_channels, reduction=16)
        self.conv3 = Conv(neck_channels, neck_channels, kernel_size=1)
        self.upsample = nn.Upsample(scale_factor=2)

        self.csp6 = BottleneckCSP2(in_channels=neck_channels + csp4_channels, out_channels=neck_channels, n=2)

        # Define head
        self.conv4 = CBAM(in_channels=neck_channels, reduction=16)
        self.conv5 = Conv(neck_channels, neck_channels // 2, kernel_size=1)
        self.conv6 = DWConv(neck_channels // 2, neck_channels, kernel_size=3, stride=2)
        self.conv7 = Conv(neck_channels, neck_channels // 2, kernel_size=1)
        self.conv8 = DWConv(neck_channels // 2, neck_channels, kernel_size=3, stride=2)
        self.conv9 = Conv(neck_channels, neck_channels // 2, kernel_size=1)

        self.detect1 = Detect(in_channels=neck_channels // 2, num_classes=num_classes, anchors=self.anchors[6:],
                              stride=32)
        self.concat1 = Concat()
        self.csp7 = CBAMBottleneckCSP(in_channels=neck_channels, out_channels=neck_channels, n=1)

        self.detect2 = Detect(in_channels=neck_channels // 2, num_classes=num_classes, anchors=self.anchors[3:6],
                              stride=16)
        self.concat2 = Concat()
        self.csp8 = CBAMBottleneckCSP(in_channels=neck_channels * 2, out_channels=neck_channels, n=1)

        self.detect3 = Detect(in_channels=neck_channels // 2, num_classes=num_classes, anchors=self.anchors[:3],
                              stride=8)

    def forward(self, x):
        # Backbone
        x = self.focus(x)
        x = self.conv1(x)
        x = self.csp1(x)
        x = self.csp2(x)
        x = self.csp3(x)
        x3 = self.csp4(x)
        x = self.csp5(x3)

        # Neck
        x = self.spp(x)
        x = self.conv2(x)
        x = self.conv3(x)
        x = self.upsample(x)
        x = self.concat1([x, x3])
        x = self.csp6(x)

        # Head
        x = self.conv4(x)
        x = self.conv5(x)
        x = self.conv6(x)
        out3 = self.conv7(x)
        x = self.conv8(out3)
        out2 = self.conv9(x)

        # Detect
        out1 = self.detect1(out2)
        out2 = self.concat2([out2, self.upsample(out1)])
        out2 = self.csp7(out2)
        out2 = self.detect2(out2)
        out3 = self.concat2([out3, self.upsample(out2)])
        out3 = self.csp8(out3)
        out3 = self.detect3(out3)

        return out1, out2, out3

其中，CBAM机制被应用在BottleneckCSP和SPP模块中。在BottleneckCSP模块中，CBAM机制被应用在两个分支的输出上，以增强特征图的表示能力。在SPP模块中，CBAM机制被应用在SPP池化后的特征图上，以提高目标检测的精度。