yolov7添加SE

你好！很高兴回答您的问题。要在YOLOv7中添加SE（Squeeze-and-Excitation）模块，您需要进行以下步骤：

1. 导入所需的库和模块：

import torch
import torch.nn as nn

2. 创建SE模块的类：

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

3. 在YOLOv7网络的合适位置添加SE模块：

class Conv(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size=1, stride=1):
        super(Conv, self).__init__()
        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding=kernel_size // 2, bias=False)
        self.bn = nn.BatchNorm2d(out_channels)
        self.relu = nn.LeakyReLU(0.1)

    def forward(self, x):
        return self.relu(self.bn(self.conv(x)))


class Upsample(nn.Module):
    def __init__(self, in_channels):
        super(Upsample, self).__init__()
        self.conv = nn.ConvTranspose2d(in_channels, in_channels, 2, stride=2)

    def forward(self, x):
        return self.conv(x)


class YOLOv7(nn.Module):
    def __init__(self):
        super(YOLOv7, self).__init__()
        # ...
        # 定义YOLOv7的网络结构

        self.se1 = SELayer(channel=256)  # 添加SE模块
        self.se2 = SELayer(channel=512)  # 添加SE模块
        # ...

    def forward(self, x):
        # ...
        # 前向传播过程
        x = self.se1(x)  # 使用SE模块
        # ...
        return x

这样就在YOLOv7中添加了SE模块。请注意，这只是一种示例方法，您需要根据您的具体需求进行调整和修改。希望能帮到您！如果还有其他问题，请随时提问。