Image/MobileNetv1/code/model.py

'''
MobileNetv1 in PyTorch.

论文: "MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications"
参考: https://arxiv.org/abs/1704.04861

主要特点：
1. 使用深度可分离卷积(Depthwise Separable Convolution)减少参数量和计算量
2. 引入宽度乘子(Width Multiplier)和分辨率乘子(Resolution Multiplier)进一步压缩模型
3. 适用于移动设备和嵌入式设备的轻量级CNN架构
'''

import torch
import torch.nn as nn


class Block(nn.Module):
    '''深度可分离卷积块 (Depthwise Separable Convolution Block)
    
    包含:
    1. 深度卷积(Depthwise Conv): 对每个通道单独进行空间卷积
    2. 逐点卷积(Pointwise Conv): 1x1卷积实现通道混合
    
    Args:
        in_channels: 输入通道数
        out_channels: 输出通道数
        stride: 卷积步长
    '''
    def __init__(self, in_channels, out_channels, stride=1):
        super(Block, self).__init__()
        
        # 深度卷积 - 每个通道单独进行3x3卷积
        self.conv1 = nn.Conv2d(
            in_channels=in_channels,
            out_channels=in_channels,
            kernel_size=3,
            stride=stride,
            padding=1,
            groups=in_channels,  # groups=in_channels 即为深度可分离卷积
            bias=False
        )
        self.bn1 = nn.BatchNorm2d(in_channels)
        self.relu1 = nn.ReLU(inplace=True)

        # 逐点卷积 - 1x1卷积用于通道混合
        self.conv2 = nn.Conv2d(
            in_channels=in_channels,
            out_channels=out_channels,
            kernel_size=1,
            stride=1,
            padding=0,
            bias=False
        )
        self.bn2 = nn.BatchNorm2d(out_channels)
        self.relu2 = nn.ReLU(inplace=True)

    def forward(self, x):
        # 深度卷积
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu1(x)
        
        # 逐点卷积
        x = self.conv2(x)
        x = self.bn2(x)
        x = self.relu2(x)
        return x


class MobileNet(nn.Module):
    '''MobileNet v1网络
    
    Args:
        num_classes: 分类数量
        alpha: 宽度乘子，用于控制网络宽度(默认1.0)
        beta: 分辨率乘子，用于控制输入分辨率(默认1.0)
        init_weights: 是否初始化权重
    '''
    # 网络配置: (输出通道数, 步长)，步长默认为1
    cfg = [64, (128,2), 128, (256,2), 256, (512,2), 
           512, 512, 512, 512, 512, (1024,2), 1024]

    def __init__(self, num_classes=10, alpha=1.0, beta=1.0, init_weights=True):
        super(MobileNet, self).__init__()

        # 第一层标准卷积
        self.conv1 = nn.Sequential(
            nn.Conv2d(3, 32, kernel_size=3, stride=1, bias=False),
            nn.BatchNorm2d(32),
            nn.ReLU(inplace=True)
        )

        # 深度可分离卷积层
        self.layers = self._make_layers(in_channels=32)

        # 全局平均池化和分类器
        self.avg = nn.AdaptiveAvgPool2d(1)  # 自适应平均池化，输出大小为1x1
        self.linear = nn.Linear(1024, num_classes)

        # 初始化权重
        if init_weights:
            self._initialize_weights()

    def _make_layers(self, in_channels):
        '''构建深度可分离卷积层
        
        Args:
            in_channels: 输入通道数
        '''
        layers = []
        for x in self.cfg:
            out_channels = x if isinstance(x, int) else x[0]
            stride = 1 if isinstance(x, int) else x[1]
            layers.append(Block(in_channels, out_channels, stride))
            in_channels = out_channels
        return nn.Sequential(*layers)

    def forward(self, x):
        # 标准卷积
        x = self.conv1(x)
        
        # 深度可分离卷积层
        x = self.layers(x)
        
        # 全局平均池化和分类器
        x = self.avg(x)
        x = x.view(x.size(0), -1)
        x = self.linear(x)
        return x

    def _initialize_weights(self):
        '''初始化模型权重'''
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                # 使用kaiming初始化卷积层
                nn.init.kaiming_normal_(m.weight, mode='fan_out')
                if m.bias is not None:
                    nn.init.zeros_(m.bias)
            elif isinstance(m, nn.BatchNorm2d):
                # 初始化BN层
                nn.init.ones_(m.weight)
                nn.init.zeros_(m.bias)
            elif isinstance(m, nn.Linear):
                # 初始化全连接层
                nn.init.normal_(m.weight, 0, 0.01)
                nn.init.zeros_(m.bias)


def test():
    """测试函数"""
    net = MobileNet()
    x = torch.randn(2, 3, 32, 32)
    y = net(x)
    print(y.size())
    
    # 打印模型结构
    from torchinfo import summary
    device = 'cuda' if torch.cuda.is_available() else 'cpu'
    net = net.to(device)
    summary(net, (2, 3, 32, 32))

if __name__ == '__main__':
    test()