mobilenetv2代码

这里是使用PyTorch实现的MobileNetV2代码示例：

import torch.nn as nn

class MobileNetV2(nn.Module):
    def __init__(self, num_classes=1000, input_size=224):
        super(MobileNetV2, self).__init__()
        self.num_classes = num_classes
        self.input_size = input_size

        self.features = nn.Sequential(
            nn.Conv2d(3, 32, kernel_size=3, stride=2, padding=1, bias=False),
            nn.BatchNorm2d(32),
            nn.ReLU(inplace=True),
            nn.Conv2d(32, 32, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(32),
            nn.ReLU(inplace=True),
            nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),
            nn.Conv2d(64, 64, kernel_size=3, stride=2, padding=1, bias=False),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),
            nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(128),
            nn.ReLU(inplace=True),
            nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(128),
            nn.ReLU(inplace=True),
            nn.Conv2d(128, 64, kernel_size=1, stride=1, padding=0, bias=False),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),
            nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1, bias=False),
            nn.BatchNorm2d(128),
            nn.ReLU(inplace=True),
            nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(256),
            nn.ReLU(inplace=True),
            nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(256),
            nn.ReLU(inplace=True),
            nn.Conv2d(256, 96, kernel_size=1, stride=1, padding=0, bias=False),
            nn.BatchNorm2d(96),
            nn.ReLU(inplace=True),
            nn.Conv2d(96, 256, kernel_size=3, stride=2, padding=1, bias=False),
            nn.BatchNorm2d(256),
            nn.ReLU(inplace=True),
            nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(512),
            nn.ReLU(inplace=True),
            nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(512),
            nn.ReLU(inplace=True),
            nn.Conv2d(512, 192, kernel_size=1, stride=1, padding=0, bias=False),
            nn.BatchNorm2d(192),
            nn.ReLU(inplace=True),
            nn.Conv2d(192, 512, kernel_size=3, stride=2, padding=1, bias=False),
            nn.BatchNorm2d(512),
            nn.ReLU(inplace=True),
            nn.Conv2d(512, 1024, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(1024),
            nn.ReLU(inplace=True),
            nn.Conv2d(1024, 1024, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(1024),
            nn.ReLU(inplace=True),
            nn.AdaptiveAvgPool2d((1, 1)),
        )
        self.classifier = nn.Sequential(
            nn.Dropout(0.2),
            nn.Linear(1024, self.num_classes),
        )

    def forward(self, x):
        x = self.features(x)
        x = x.view(x.size(0), -1)
        x = self.classifier(x)
        return x

此代码实现了MobileNetV2模型，包括输入大小和分类数。该模型的特征提取器由一系列卷积层和批归一化层组成，带有ReLU激活函数。分类器由一个dropout层和一个全连接层组成。在前向传递过程中，输入图像首先经过特征提取器，然后通过分类器输出预测结果。