代码实现

以下是一个使用ACNet替换YOLOv3中卷积层的示例代码，仅供参考：

import torch.nn as nn
import torch.nn.functional as F

class ACNetConv(nn.Module):
    def __init__(self, in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1, groups=1, bias=False):
        super(ACNetConv, self).__init__()
        self.conv1 = nn.Conv2d(in_planes, out_planes, kernel_size, stride, padding, dilation, groups, bias)
        self.conv2 = nn.Conv2d(in_planes, out_planes, kernel_size, stride, padding, dilation, groups, bias)
        self.bn = nn.BatchNorm2d(out_planes, eps=1e-5, momentum=0.01)
        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_planes, out_planes, stride=1):
        super(ACNetBlock, self).__init__()
        self.conv1 = ACNetConv(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False)
        self.conv2 = ACNetConv(out_planes, out_planes, kernel_size=3, stride=1, padding=1, bias=False)
        self.shortcut = nn.Sequential()
        if stride != 1 or in_planes != out_planes:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(out_planes, eps=1e-5, momentum=0.01)
            )

    def forward(self, x):
        out = self.conv1(x)
        out = self.conv2(out)
        out += self.shortcut(x)
        out = F.relu(out)
        return out

class ACNetYolo(nn.Module):
    def __init__(self, num_classes=80):
        super(ACNetYolo, self).__init__()
        self.num_classes = num_classes
        self.in_planes = 32
        self.conv1 = ACNetConv(3, 32, kernel_size=3, stride=1, padding=1, bias=False)
        self.layer1 = self._make_layer(32, 3, stride=1)
        self.layer2 = self._make_layer(64, 4, stride=2)
        self.layer3 = self._make_layer(128, 6, stride=2)
        self.layer4 = self._make_layer(256, 9, stride=2)
        self.layer5 = self._make_layer(512, 9, stride=1)
        self.layer6 = self._make_layer(1024, 3, stride=1)
        self.conv2 = nn.Conv2d(1024, 1024, kernel_size=3, stride=1, padding=1, bias=False)
        self.conv3 = nn.Conv2d(1024, (5 + self.num_classes) * 3, kernel_size=1, stride=1, padding=0, bias=True)

    def _make_layer(self, planes, num_blocks, stride):
        strides = [stride] + [1]*(num_blocks-1)
        layers = []
        for stride in strides:
            layers.append(ACNetBlock(self.in_planes, planes, stride))
            self.in_planes = planes
        return nn.Sequential(*layers)

    def forward(self, x):
        out = self.conv1(x)
        out = self.layer1(out)
        out = self.layer2(out)
        out = self.layer3(out)
        out = self.layer4(out)
        out = self.layer5(out)
        out = self.layer6(out)
        out = self.conv2(out)
        out = self.conv3(out)
        out = out.permute(0, 2, 3, 1)
        return out

这个示例代码使用了ACNet替换了YOLOv3中的卷积层，同时保留了YOLOv3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。