如何将ACNet加入yolo5，代码实现

以下是一个使用ACNet替换YOLOv5中卷积层的示例代码，仅供参考：

import torch.nn as nn
import torch.nn.functional as F
from models.common import Conv

class ACNetConv(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, padding=None, dilation=1, groups=1, bias=False, padding_mode='zeros', eps=1e-5, momentum=0.1):
        super(ACNetConv, self).__init__()
        if padding is None:
            padding = (kernel_size - 1) // 2 * dilation
        self.conv1 = Conv(in_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, groups=groups, bias=bias, padding_mode=padding_mode)
        self.conv2 = Conv(in_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, groups=groups, bias=bias, padding_mode=padding_mode)
        self.bn = nn.BatchNorm2d(out_channels, eps=eps, momentum=momentum)
        self.act = nn.ReLU(inplace=True)

    def forward(self, x):
        out1 = self.conv1(x)
        out2 = self.conv2(x)
        out = torch.max(out1, out2)
        out = self.bn(out)
        out = self.act(out)
        return out

class ACNetBlock(nn.Module):
    def __init__(self, in_channels, out_channels, depthwise=False, bottleneck=0.0, shortcut=True, **kwargs):
        super(ACNetBlock, self).__init__()
        self.depthwise = depthwise
        self.shortcut = shortcut
        if not depthwise:
            hidden_channels = int(out_channels * bottleneck)
            self.conv1 = ACNetConv(in_channels, hidden_channels, **kwargs)
            self.conv2 = ACNetConv(hidden_channels, out_channels, kernel_size=1, **kwargs)
        else:
            hidden_channels = int(in_channels * bottleneck)
            self.conv1 = Conv(in_channels, hidden_channels, kernel_size=1, **kwargs)
            self.conv2 = Conv(hidden_channels, hidden_channels, kernel_size=3, stride=1, padding=1, dilation=1, groups=hidden_channels, bias=False, padding_mode='zeros')
            self.conv3 = ACNetConv(hidden_channels, out_channels, kernel_size=1, **kwargs)
            self.bn = nn.BatchNorm2d(hidden_channels, eps=1e-5, momentum=0.1)
            self.act = nn.ReLU(inplace=True)

    def forward(self, x):
        if not self.depthwise:
            out = self.conv1(x)
            out = self.conv2(out)
        else:
            out = self.conv1(x)
            out = self.bn(out)
            out = self.act(out)
            out = self.conv2(out)
            out = self.conv3(out)
        if self.shortcut:
            out += x
        return out

class ACNetYolo(nn.Module):
    def __init__(self, cfg):
        super(ACNetYolo, self).__init__()
        self.cfg = cfg
        self.in_channels = cfg['in_channels']
        self.out_channels = cfg['out_channels']
        self.depthwise = cfg['depthwise']
        self.bottleneck = cfg['bottleneck']
        self.num_blocks = cfg['num_blocks']
        self.stride = cfg['stride']
        self.conv1 = ACNetConv(self.in_channels, self.out_channels, kernel_size=3, stride=1, padding=1)
        self.layers = self._make_layer()
        self.num_classes = cfg['num_classes']
        self.anchor_per_scale = cfg['anchor_per_scale']
        self.conv_output = self._make_conv_output()

    def _make_layer(self):
        layers = []
        for i in range(self.num_blocks):
            layers.append(ACNetBlock(self.out_channels, self.out_channels, depthwise=self.depthwise, bottleneck=self.bottleneck))
        return nn.Sequential(*layers)

    def _make_conv_output(self):
        output_filters = self.anchor_per_scale * (self.num_classes + 5)
        return Conv(self.out_channels, output_filters, kernel_size=1)

    def forward(self, x):
        x = self.conv1(x)
        x = self.layers(x)
        x = self.conv_output(x)
        return x

这个示例代码使用了ACNet替换了YOLOv5中的卷积层，同时保留了YOLOv5的其它结构，包括残差块和检测头部。需要注意的是，为了达到最好的效果，可能需要进一步调整参数和训练策略。