SCConv应用在yolov8中的代码

以下是将SCConv应用于YOLOv8模型的示例代码：

import torch
import torch.nn as nn

class SCConv(nn.Module):
    def __init__(self, in_channels, reduction_ratio=16):
        super(SCConv, self).__init__()
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.fc = nn.Sequential(
            nn.Linear(in_channels, in_channels // reduction_ratio),
            nn.ReLU(inplace=True),
            nn.Linear(in_channels // reduction_ratio, in_channels),
            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 YOLOv8(nn.Module):
    def __init__(self):
        super(YOLOv8, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1)
        self.scconv1 = SCConv(64)
        # ... add other layers and SCConv blocks

    def forward(self, x):
        x = self.conv1(x)
        x = self.scconv1(x)
        # ... apply other layers and SCConv blocks
        return x

# 创建YOLOv8模型实例
model = YOLOv8()
# 输入数据
input_data = torch.randn(1, 3, 416, 416)
# 前向传播
output = model(input_data)

在上面的代码中，SCConv 是一个自定义的模块，它接受输入特征图，通过全局平均池化和全连接层生成通道注意力权重，然后将其与输入特征图相乘得到最终的输出。YOLOv8 是一个包含多个卷积层和SCConv模块的模型。在前向传播过程中，输入数据通过卷积层和SCConv模块逐层传递，最终输出预测结果