''' 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()