yolov7添加gam模块

要在YOLOv7中添加GAM模块，需要进行以下步骤：

1. 在YOLOv7的代码中添加GAM模块的定义，可以参考其他模块的定义方式进行添加。
2. 在YOLOv7的网络结构中添加GAM模块，可以参考其他模块的添加方式进行添加。
3. 在YOLOv7的训练过程中，将GAM模块加入到loss计算中，以便进行反向传播。

具体实现方式可以参考以下代码：

# 定义GAM模块
class GAM(nn.Module):
    def __init__(self, in_channels, reduction=16):
        super(GAM, self).__init__()
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.fc = nn.Sequential(
            nn.Linear(in_channels, in_channels // reduction, bias=False),
            nn.ReLU(inplace=True),
            nn.Linear(in_channels // reduction, in_channels, bias=False),
            nn.Sigmoid()
        )

    def forward(self, x):
        b, c, _, _ = x.size()
        y = self.avg_pool(x).view(b, c)
        y = self.fc(y).view(b, c, 1, 1)
        return x * y

# 在YOLOv7的网络结构中添加GAM模块
class YOLOv7(nn.Module):
    def __init__(self):
        super(YOLOv7, self).__init__()
        self.conv1 = nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=1)
        self.bn1 = nn.BatchNorm2d(32)
        self.relu1 = nn.LeakyReLU(0.1)
        self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)

        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1)
        self.bn2 = nn.BatchNorm2d(64)
        self.relu2 = nn.LeakyReLU(0.1)
        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)

        self.conv3 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1)
        self.bn3 = nn.BatchNorm2d(128)
        self.relu3 = nn.LeakyReLU(0.1)

        self.conv4 = nn.Conv2d(128, 64, kernel_size=1, stride=1, padding=0)
        self.bn4 = nn.BatchNorm2d(64)
        self.relu4 = nn.LeakyReLU(0.1)

        self.conv5 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1)
        self.bn5 = nn.BatchNorm2d(128)
        self.relu5 = nn.LeakyReLU(0.1)
        self.pool5 = nn.MaxPool2d(kernel_size=2, stride=2)

        self.conv6 = nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1)
        self.bn6 = nn.BatchNorm2d(256)
        self.relu6 = nn.LeakyReLU(0.1)

        self.conv7 = nn.Conv2d(256, 128, kernel_size=1, stride=1, padding=0)
        self.bn7 = nn.BatchNorm2d(128)
        self.relu7 = nn.LeakyReLU(0.1)

        self.conv8 = nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1)
        self.bn8 = nn.BatchNorm2d(256)
        self.relu8 = nn.LeakyReLU(0.1)
        self.pool8 = nn.MaxPool2d(kernel_size=2, stride=2)

        self.conv9 = nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1)
        self.bn9 = nn.BatchNorm2d(512)
        self.relu9 = nn.LeakyReLU(0.1)

        self.conv10 = nn.Conv2d(512, 256, kernel_size=1, stride=1, padding=0)
        self.bn10 = nn.BatchNorm2d(256)
        self.relu10 = nn.LeakyReLU(0.1)

        self.conv11 = nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1)
        self.bn11 = nn.BatchNorm2d(512)
        self.relu11 = nn.LeakyReLU(0.1)

        self.conv12 = nn.Conv2d(512, 256, kernel_size=1, stride=1, padding=0)
        self.bn12 = nn.BatchNorm2d(256)
        self.relu12 = nn.LeakyReLU(0.1)

        self.conv13 = nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1)
        self.bn13 = nn.BatchNorm2d(512)
        self.relu13 = nn.LeakyReLU(0.1)

        self.gam13 = GAM(512)

        self.conv14 = nn.Conv2d(512, 1024, kernel_size=3, stride=1, padding=1)
        self.bn14 = nn.BatchNorm2d(1024)
        self.relu14 = nn.LeakyReLU(0.1)

        self.conv15 = nn.Conv2d(1024, 512, kernel_size=1, stride=1, padding=0)
        self.bn15 = nn.BatchNorm2d(512)
        self.relu15 = nn.LeakyReLU(0.1)

        self.conv16 = nn.Conv2d(512, 1024, kernel_size=3, stride=1, padding=1)
        self.bn16 = nn.BatchNorm2d(1024)
        self.relu16 = nn.LeakyReLU(0.1)

        self.conv17 = nn.Conv2d(1024, 512, kernel_size=1, stride=1, padding=0)
        self.bn17 = nn.BatchNorm2d(512)
        self.relu17 = nn.LeakyReLU(0.1)

        self.conv18 = nn.Conv2d(512, 1024, kernel_size=3, stride=1, padding=1)
        self.bn18 = nn.BatchNorm2d(1024)
        self.relu18 = nn.LeakyReLU(0.1)

        self.gam18 = GAM(1024)

        self.conv19 = nn.Conv2d(1024, 1024, kernel_size=3, stride=1, padding=1)
        self.bn19 = nn.BatchNorm2d(1024)
        self.relu19 = nn.LeakyReLU(0.1)

        self.conv20 = nn.Conv2d(1024, 1024, kernel_size=3, stride=1, padding=1)
        self.bn20 = nn.BatchNorm2d(1024)
        self.relu20 = nn.LeakyReLU(0.1)

        self.conv21 = nn.Conv2d(3072, 1024, kernel_size=3, stride=1, padding=1)
        self.bn21 = nn.BatchNorm2d(1024)
        self.relu21 = nn.LeakyReLU(0.1)

        self.conv22 = nn.Conv2d(1024, 75, kernel_size=1, stride=1, padding=0)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu1(x)
        x = self.pool1(x)

        x = self.conv2(x)
        x = self.bn2(x)
        x = self.relu2(x)
        x = self.pool2(x)

        x = self.conv3(x)
        x = self.bn3(x)
        x = self.relu3(x)

        x = self.conv4(x)
        x = self.bn4(x)
        x = self.relu4(x)

        x = self.conv5(x)
        x = self.bn5(x)
        x = self.relu5(x)
        x = self.pool5(x)

        x = self.conv6(x)
        x = self.bn6(x)
        x = self.relu6(x)

        x = self.conv7(x)
        x = self.bn7(x)
        x = self.relu7(x)

        x = self.conv8(x)
        x = self.bn8(x)
        x = self.relu8(x)
        x = self.pool8(x)

        x = self.conv9(x)
        x = self.bn9(x)
        x = self.relu9(x)

        x = self.conv10(x)
        x = self.bn10(x)
        x = self.relu10(x)

        x = self.conv11(x)
        x = self.bn11(x)
        x = self.relu11(x)

        x = self.conv12(x)
        x = self.bn12(x)
        x = self.relu12(x)

        x = self.conv13(x)
        x = self.bn13(x)
        x = self.relu13(x)
        x = self.gam13(x)

        x = self.conv14(x)
        x = self.bn14(x)
        x = self.relu14(x)

        x = self.conv15(x)
        x = self.bn15(x)
        x = self.relu15(x)

        x = self.conv16(x)
        x = self.bn16(x)
        x = self.relu16(x)

        x = self.conv17(x)
        x = self.bn17(x)
        x = self.relu17(x)

        x = self.conv18(x)
        x = self.bn18(x)
        x = self.relu18(x)
        x = self.gam18(x)

        x = self.conv19(x)
        x = self.bn19(x)
        x = self.relu19(x)

        x = self.conv20(x)
        x = self.bn20(x)
        x = self.relu20(x)

        # 将三个不同尺度的特征图进行拼接
        y = F.interpolate(x, scale_factor=2, mode='nearest')
        y = torch.cat([y, self.bn17(self.conv17(self.bn16(self.conv16(self.bn15(self.conv15(y))))))], 1)

        y = F.interpolate(y, scale_factor=2, mode='nearest')
        y = torch.cat([y, self.bn11(self.conv11(self.bn10(self.conv10(self.bn9(self.conv9(y))))))], 1)

        y = F.interpolate(y, scale_factor=2, mode='nearest')
        y = torch.cat([y, self.bn4(self.conv4(self.bn3(self.conv3(y))))], 1)

        y = self.conv21(y)
        y = self.bn21(y)
        y = self.relu21(y)

        y = self.conv22(y)

        return y

# 在YOLOv7的训练过程中，将GAM模块加入到loss计算中
class YOLOv7Loss(nn.Module):
    def __init__(self):
        super(YOLOv7Loss, self).__init__()

    def forward(self, output, target):
        # 计算loss
        return loss