mobilenetv3与yolov5结合代码

将MobileNetV3和YOLOv5结合起来需要进行以下步骤:

1. 首先，需要下载YOLOv5的代码和预训练权重，以及MobileNetV3的代码和预训练权重。

2. 接着，需要在YOLOv5中修改模型结构，以便将MobileNetV3作为YOLOv5的特征提取器。可以在YOLOv5的models/yolov5s.py文件中进行修改，将原来的卷积层替换为MobileNetV3的卷积层。

3. 在训练时，需要将YOLOv5的训练数据集进行转换，以适应MobileNetV3的输入尺寸。可以使用YOLOv5的datasets.py文件中的resize方法进行转换。

4. 最后，在训练时，需要将YOLOv5的训练代码中的特征提取器替换为MobileNetV3，并按照MobileNetV3的训练方式进行训练。

下面是一个简单的参考代码，仅供参考：

import torch
import torch.nn as nn
import torchvision.models as models
from models.common import Conv, DWConv

class MobileNetV3(nn.Module):
    def __init__(self, width_mult=1.0):
        super(MobileNetV3, self).__init__()

        self.inplanes = 16
        self.cfgs = [
            # k, exp, c,  se,     nl,  s,
            [3,  16,  16,  False, 'relu', 1],
            [3,  64,  24,  False, 'relu', 2],
            [3,  72,  24,  False, 'relu', 1],
            [5,  72,  40,  True,  'relu', 2],
            [5,  120, 40,  True,  'relu', 1],
            [5,  120, 40,  True,  'relu', 1],
            [3,  240, 80,  False, 'hswish', 2],
            [3,  200, 80,  False, 'hswish', 1],
            [3,  184, 80,  False, 'hswish', 1],
            [3,  184, 80,  False, 'hswish', 1],
            [3,  480, 112, True,  'hswish', 1],
            [3,  672, 112, True,  'hswish', 1],
            [5,  672, 160, True,  'hswish', 2],
            [5,  960, 160, True,  'hswish', 1],
            [5,  960, 160, True,  'hswish', 1],
        ]

        # head
        self.conv1 = Conv(3, self.inplanes, kernel_size=3, stride=2, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(self.inplanes)
        self.hswish = Hswish(inplace=True)

        # body
        self.features = nn.ModuleList([])
        for k, exp, c, se, nl, s in self.cfgs:
            outplanes = int(c * width_mult)
            self.features.append(InvertedResidual(self.inplanes, outplanes, k, s, exp, se, nl))
            self.inplanes = outplanes

        # tail
        self.conv2 = Conv(self.inplanes, 960, kernel_size=1, stride=1, padding=0, bias=False)
        self.bn2 = nn.BatchNorm2d(960)
        self.hswish2 = Hswish(inplace=True)

    def forward(self, x):
        # head
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.hswish(x)

        # body
        for f in self.features:
            x = f(x)

        # tail
        x = self.conv2(x)
        x = self.bn2(x)
        x = self.hswish2(x)

        return x


class YOLOv5(nn.Module):
    def __init__(self, num_classes=80):
        super(YOLOv5, self).__init__()

        self.head = Conv(960, 1024, 3, stride=1, padding=1)
        self.body = nn.Sequential(
            Residual(1024, 512, 1),
            Conv(512, 256, 1),
            nn.Upsample(scale_factor=2, mode='nearest'),
            Residual(512, 256, 1),
            Conv(256, 128, 1),
            nn.Upsample(scale_factor=2, mode='nearest'),
            Residual(256, 128, 1),
            Conv(128, 64, 1),
            nn.Upsample(scale_factor=2, mode='nearest'),
            Residual(128, 64, 1),
        )
        self.tail = nn.Conv2d(64, 3 * (5 + num_classes), 1)

    def forward(self, x):
        x = self.head(x)
        x = self.body(x)
        x = self.tail(x)
        return x


if __name__ == '__main__':
    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

    # load MobileNetV3
    mobilenetv3 = MobileNetV3().to(device)
    mobilenetv3.load_state_dict(torch.load('mobilenetv3_weights.pth'))

    # load YOLOv5
    yolov5 = YOLOv5().to(device)
    yolov5.load_state_dict(torch.load('yolov5_weights.pth'))

    # replace feature extractor of YOLOv5 with MobileNetV3
    yolov5.head = mobilenetv3.conv2
    yolov5.body = mobilenetv3.features

    # train YOLOv5 with MobileNetV3 feature extractor
    train_dataset = ...
    train_loader = ...
    optimizer = ...
    loss_fn = ...
    for epoch in range(num_epochs):
        for batch_idx, (data, targets) in enumerate(train_loader):
            data, targets = data.to(device), targets.to(device)
            optimizer.zero_grad()
            outputs = yolov5(data)
            loss = loss_fn(outputs, targets)
            loss.backward()
            optimizer.step()