mobilenet代码pytorch实现

以下是MobileNet的PyTorch实现示例代码：

import torch
import torch.nn as nn

class DepthwiseSeparableConv2d(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size=1, stride=1, padding=0):
        super(DepthwiseSeparableConv2d, self).__init__()
        self.depthwise = nn.Conv2d(in_channels, in_channels, kernel_size, stride, padding, groups=in_channels)
        self.pointwise = nn.Conv2d(in_channels, out_channels, 1)
        self.bn = nn.BatchNorm2d(out_channels)
        self.relu = nn.ReLU(inplace=True)

    def forward(self, x):
        x = self.depthwise(x)
        x = self.pointwise(x)
        x = self.bn(x)
        x = self.relu(x)
        return x

class MobileNet(nn.Module):
    def __init__(self, num_classes=1000):
        super(MobileNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 32, 3, stride=2, padding=1)
        self.bn1 = nn.BatchNorm2d(32)
        self.relu1 = nn.ReLU(inplace=True)
        self.features = nn.Sequential(
            DepthwiseSeparableConv2d(32, 64, 3, 1, 1),
            DepthwiseSeparableConv2d(64, 128, 3, 2, 1),
            DepthwiseSeparableConv2d(128, 128, 3, 1, 1),
            DepthwiseSeparableConv2d(128, 256, 3, 2, 1),
            DepthwiseSeparableConv2d(256, 256, 3, 1, 1),
            DepthwiseSeparableConv2d(256, 512, 3, 2, 1),
            DepthwiseSeparableConv2d(512, 512, 3, 1, 1),
            DepthwiseSeparableConv2d(512, 512, 3, 1, 1),
            DepthwiseSeparableConv2d(512, 512, 3, 1, 1),
            DepthwiseSeparableConv2d(512, 512, 3, 1, 1),
            DepthwiseSeparableConv2d(512, 512, 3, 1, 1),
            DepthwiseSeparableConv2d(512, 1024, 3, 2, 1),
            DepthwiseSeparableConv2d(1024, 1024, 3, 1, 1),
        )
        self.avgpool = nn.AdaptiveAvgPool2d(1)
        self.fc = nn.Linear(1024, num_classes)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu1(x)
        x = self.features(x)
        x = self.avgpool(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)
        return x

以上代码中，`DepthwiseSeparableConv2d`实现了深度可分离卷积，`MobileNet`则是MobileNet的主体模型，包含了多个深度可分离卷积层。在`MobileNet`的`__init__`方法中，首先定义了一个标准的卷积层`conv1`，然后定义了多个深度可分离卷积层，最后有一个自适应平均池化层`avgpool`和一个全连接层`fc`。在`forward`方法中，先经过标准卷积层、批归一化和ReLU激活函数，然后通过多个深度可分离卷积层和自适应平均池化层，最后通过全连接层得到最终的输出。