怎么在EfficientNetV2网络中添加EMA模块

在EfficientNetV2网络中添加EMA模块的具体步骤如下：

1. 首先，打开yolov5s_EMA.yaml文件，并找到你想要添加EMA模块的位置。

2. 在Neck端添加EMA模块的示例代码如下：

import torch
import torch.nn as nn

class EMA(nn.Module):
    def __init__(self, in_channels, reduction_ratio=16):
        super(EMA, 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

# 在Neck端添加EMA模块
class Neck(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(Neck, self).__init__()
        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1)
        self.ema = EMA(out_channels)

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

3. 在common.py文件中添加C3_EMA模块的示例代码如下：

import torch
import torch.nn as nn

class C3_EMA(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(C3_EMA, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1)
        self.conv3 = nn.Conv2d(out_channels, out_channels, kernel_size=1, stride=1, padding=0)
        self.ema = EMA(out_channels)

    def forward(self, x):
        x = self.conv1(x)
        x = self.conv2(x)
        x = self.conv3(x)
        x = self.ema(x)
        return x

以上是在EfficientNetV2网络中添加EMA模块的示例代码。你可以根据自己的需求进行修改和调整。