用pytorch实现mobielnetv3代码

当然可以！以下是一个使用PyTorch实现MobileNetV3的示例代码：

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

class ConvBNActivation(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size=1, stride=1, padding=0, groups=1):
        super(ConvBNActivation, self).__init__()
        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding, groups=groups, bias=False)
        self.bn = nn.BatchNorm2d(out_channels)
        self.activation = nn.ReLU6(inplace=True)

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

class InvertedResidual(nn.Module):
    def __init__(self, in_channels, out_channels, stride, expansion_factor):
        super(InvertedResidual, self).__init__()
        self.stride = stride
        hidden_dim = int(round(in_channels * expansion_factor))

        layers = []
        if expansion_factor != 1:
            layers.append(ConvBNActivation(in_channels, hidden_dim, kernel_size=1))

        layers.extend([
            ConvBNActivation(hidden_dim, hidden_dim, kernel_size=3, stride=stride, padding=1, groups=hidden_dim),
            nn.Conv2d(hidden_dim, out_channels, kernel_size=1, bias=False),
            nn.BatchNorm2d(out_channels)
        ])

        self.conv = nn.Sequential(*layers)
        self.use_res_connect = stride == 1 and in_channels == out_channels

    def forward(self, x):
        if self.use_res_connect:
            return x + self.conv(x)
        else:
            return self.conv(x)

class MobileNetV3(nn.Module):
    def __init__(self, num_classes=1000, width_multiplier=1.0):
        super(MobileNetV3, self).__init__()
        input_channels = int(16 * width_multiplier)

        # 第一个卷积层
        layers = [
            ConvBNActivation(3, input_channels, kernel_size=3, stride=2, padding=1)
        ]

        # 中间的Inverted Residual块
        layers.extend([
            InvertedResidual(input_channels, int(16 * width_multiplier), stride=1, expansion_factor=1),
            InvertedResidual(int(16 * width_multiplier), int(24 * width_multiplier), stride=2, expansion_factor=6),
            InvertedResidual(int(24 * width_multiplier), int(24 * width_multiplier), stride=1, expansion_factor=6),
            InvertedResidual(int(24 * width_multiplier), int(40 * width_multiplier), stride=2, expansion_factor=6),
            InvertedResidual(int(40 * width_multiplier), int(40 * width_multiplier), stride=1, expansion_factor=6),
            InvertedResidual(int(40 * width_multiplier), int(40 * width_multiplier), stride=1, expansion_factor=6),
            InvertedResidual(int(40 * width_multiplier), int(80 * width_multiplier), stride=2, expansion_factor=6),
            InvertedResidual(int(80 * width_multiplier), int(80 * width_multiplier), stride=1, expansion_factor=6),
            InvertedResidual(int(80 * width_multiplier), int(80 * width_multiplier), stride=1, expansion_factor=6),
            InvertedResidual(int(80 * width_multiplier), int(112 * width_multiplier), stride=1, expansion_factor=6),
            InvertedResidual(int(112 * width_multiplier), int(112 * width_multiplier), stride=1, expansion_factor=6),
            InvertedResidual(int(112 * width_multiplier), int(160 * width_multiplier), stride=2, expansion_factor=6),
            InvertedResidual(int(160 * width_multiplier), int(160 * width_multiplier), stride=1, expansion_factor=6),
            InvertedResidual(int(160 * width_multiplier), int(160 * width_multiplier), stride=1, expansion_factor=6)
        ])

        # 最后的卷积层
        layers.extend([
            ConvBNActivation(int(160 * width_multiplier), int(960 * width_multiplier), kernel_size=1),
            nn.AdaptiveAvgPool2d(1),
            nn.Conv2d(int(960 * width_multiplier), int(1280 * width_multiplier), kernel_size=1),
            nn.Hardswish(inplace=True)
        ])

        # 分类器
        self.classifier = nn.Sequential(
            nn.Dropout(0.2),
            nn.Linear(int(1280 * width_multiplier), num_classes)
        )

        self.features = nn.Sequential(*layers)

    def forward(self, x):
        x = self.features(x)
        x = torch.flatten(x, 1)
        x = self.classifier(x)
        return x

# 测试网络结构
model = MobileNetV3()
print(model)

这是一个基本的MobileNetV3网络实现，包括了ConvBNActivation、InvertedResidual和MobileNetV3三个模块。请注意，这只是一个示例代码，你可以根据自己的需求进行修改和扩展。