yolov7添加SEnet

要为 YOLOv7 添加 SENet，可以按照以下步骤进行操作：

1. 在 YOLOv7 模型中添加 SENet 模块。
2. 修改网络的前向传播函数，以在 YOLOv7 中使用 SENet 模块。

以下是一个简单的示例代码，可以用于在 YOLOv7 中添加 SENet：

import torch.nn as nn
import torch.nn.functional as F

class SEBlock(nn.Module):
    def __init__(self, in_channels, reduction=16):
        super(SEBlock, 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.expand_as(x)

class YOLOv7(nn.Module):
    def __init__(self):
        super(YOLOv7, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1)
        self.bn1 = nn.BatchNorm2d(64)
        self.se1 = SEBlock(64)
        self.conv2 = nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1)
        self.bn2 = nn.BatchNorm2d(128)
        self.se2 = SEBlock(128)
        self.conv3 = nn.Conv2d(128, 256, kernel_size=3, stride=2, padding=1)
        self.bn3 = nn.BatchNorm2d(256)
        self.se3 = SEBlock(256)
        self.conv4 = nn.Conv2d(256, 512, kernel_size=3, stride=2, padding=1)
        self.bn4 = nn.BatchNorm2d(512)
        self.se4 = SEBlock(512)
        self.conv5 = nn.Conv2d(512, 1024, kernel_size=3, stride=2, padding=1)
        self.bn5 = nn.BatchNorm2d(1024)
        self.se5 = SEBlock(1024)
        self.conv6 = nn.Conv2d(1024, 1024, kernel_size=3, stride=1, padding=1)
        self.bn6 = nn.BatchNorm2d(1024)
        self.se6 = SEBlock(1024)
        
        self.yolo_head = nn.Sequential(
            nn.Conv2d(1024, 1024, kernel_size=3, stride=1, padding=1),
            nn.Conv2d(1024, 3 * (5 + 20), kernel_size=1, stride=1, padding=0)
        )
    
    def forward(self, x):
        x = F.relu(self.bn1(self.conv1(x)))
        x = self.se1(x)
        x = F.relu(self.bn2(self.conv2(x)))
        x = self.se2(x)
        x = F.relu(self.bn3(self.conv3(x)))
        x = self.se3(x)
        x = F.relu(self.bn4(self.conv4(x)))
        x = self.se4(x)
        x = F.relu(self.bn5(self.conv5(x)))
        x = self.se5(x)
        x = F.relu(self.bn6(self.conv6(x)))
        x = self.se6(x)
        x = self.yolo_head(x)
        return x

在上面的代码中，我们首先定义了一个 SENet 模块，然后在 YOLOv7 模型中添加了这个模块。我们还修改了前向传播函数，以在 YOLOv7 中使用 SENet 模块。请注意，这只是一个简单的示例，具体实现可能需要根据具体的需求进行修改。