将yolov5中普通卷积替换为acnet卷积模块，代码实现

以下是将YOLOv5中普通卷积替换为ACNet卷积模块的示例代码：

1. 首先，需要在`models/common.py`文件中实现ACNet卷积模块：

import torch
import torch.nn as nn

class ACConv(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True):
        super(ACConv, self).__init__()
        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride=stride, padding=padding, dilation=dilation, groups=groups, bias=bias)
        self.bn = nn.BatchNorm2d(out_channels)
        self.relu = nn.ReLU(inplace=True)
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        x = self.conv(x)
        x = self.bn(x)
        x = self.relu(x)
        a = torch.mean(torch.abs(x), dim=(2, 3), keepdim=True)
        a = self.sigmoid(a)
        x = x * a
        return x

2. 然后，在`models/yolo.py`文件中，将普通卷积替换为ACNet卷积模块：

from models.common import ACConv

class YOLOLayer(nn.Module):
    # ...

    def forward(self, x, img_size, augment=False, profile=False):
        # ...

        if self.training:
            # ...
        else:
            # 替换普通卷积为ACNet卷积
            x = ACConv(in_channels=self.out_channels[-1], out_channels=self.anchor_per_scale * (self.num_classes + 5),
                       kernel_size=1, stride=1, padding=0)(x)
            # ...

注意，这里只是示例代码，实际上还需要对YOLOv5的其他部分进行相应的修改，以确保ACNet卷积模块的正确使用。

ACNet卷积模块的具体实现和使用方法可以参考ACNet论文，链接如下：https://arxiv.org/pdf/1908.03930.pdf