''' MobileNetV3 in PyTorch. 论文: "Searching for MobileNetV3" 参考: https://arxiv.org/abs/1905.02244 主要特点： 1. 引入基于NAS的网络架构搜索 2. 使用改进的SE注意力机块 3. 使用h-swish激活函数 4. 重新设计了网络的最后几层 5. 提供了Large和Small两个版本 ''' import torch import torch.nn as nn import torch.nn.functional as F def get_activation(name): '''获取激活函数 Args: name: 激活函数名称 ('relu' 或 'hardswish') ''' if name == 'relu': return nn.ReLU(inplace=True) elif name == 'hardswish': return nn.Hardswish(inplace=True) else: raise NotImplementedError class SEModule(nn.Module): '''Squeeze-and-Excitation模块 通过全局平均池化和两层全连接网络学习通道注意力权重 Args: channel: 输入通道数 reduction: 降维比例 ''' def __init__(self, channel, reduction=4): super(SEModule, self).__init__() self.avg_pool = nn.AdaptiveAvgPool2d(1) self.fc = nn.Sequential( nn.Linear(channel, channel // reduction, bias=False), nn.ReLU(inplace=True), nn.Linear(channel // reduction, channel, bias=False), nn.Hardsigmoid(inplace=True) ) def forward(self, x): b, c, _, _ = x.size() y = self.avg_pool(x).view(b, c) # squeeze y = self.fc(y).view(b, c, 1, 1) # excitation return x * y.expand_as(x) # scale class Bottleneck(nn.Module): '''MobileNetV3 Bottleneck 包含: 1. Expansion layer (1x1 conv) 2. Depthwise layer (3x3 or 5x5 depthwise conv) 3. SE module (optional) 4. Projection layer (1x1 conv) Args: in_channels: 输入通道数 exp_channels: 扩展层通道数 out_channels: 输出通道数 kernel_size: 深度卷积核大小 stride: 步长 use_SE: 是否使用SE模块 activation: 激活函数类型 use_residual: 是否使用残差连接 ''' def __init__(self, in_channels, exp_channels, out_channels, kernel_size, stride, use_SE, activation, use_residual=True): super(Bottleneck, self).__init__() self.use_residual = use_residual and stride == 1 and in_channels == out_channels padding = (kernel_size - 1) // 2 layers = [] # Expansion layer if exp_channels != in_channels: layers.extend([ nn.Conv2d(in_channels, exp_channels, 1, bias=False), nn.BatchNorm2d(exp_channels), get_activation(activation) ]) # Depthwise conv layers.extend([ nn.Conv2d( exp_channels, exp_channels, kernel_size, stride, padding, groups=exp_channels, bias=False ), nn.BatchNorm2d(exp_channels), get_activation(activation) ]) # SE module if use_SE: layers.append(SEModule(exp_channels)) # Projection layer layers.extend([ nn.Conv2d(exp_channels, out_channels, 1, bias=False), nn.BatchNorm2d(out_channels) ]) self.conv = nn.Sequential(*layers) def forward(self, x): if self.use_residual: return x + self.conv(x) else: return self.conv(x) class MobileNetV3(nn.Module): '''MobileNetV3网络 Args: num_classes: 分类数量 mode: 'large' 或 'small'，选择网络版本 ''' def __init__(self, num_classes=10, mode='small'): super(MobileNetV3, self).__init__() if mode == 'large': # MobileNetV3-Large架构 self.config = [ # k, exp, out, SE, activation, stride [3, 16, 16, False, 'relu', 1], [3, 64, 24, False, 'relu', 2], [3, 72, 24, False, 'relu', 1], [5, 72, 40, True, 'relu', 2], [5, 120, 40, True, 'relu', 1], [5, 120, 40, True, 'relu', 1], [3, 240, 80, False, 'hardswish', 2], [3, 200, 80, False, 'hardswish', 1], [3, 184, 80, False, 'hardswish', 1], [3, 184, 80, False, 'hardswish', 1], [3, 480, 112, True, 'hardswish', 1], [3, 672, 112, True, 'hardswish', 1], [5, 672, 160, True, 'hardswish', 2], [5, 960, 160, True, 'hardswish', 1], [5, 960, 160, True, 'hardswish', 1], ] init_conv_out = 16 final_conv_out = 960 else: # MobileNetV3-Small架构 self.config = [ # k, exp, out, SE, activation, stride [3, 16, 16, True, 'relu', 2], [3, 72, 24, False, 'relu', 2], [3, 88, 24, False, 'relu', 1], [5, 96, 40, True, 'hardswish', 2], [5, 240, 40, True, 'hardswish', 1], [5, 240, 40, True, 'hardswish', 1], [5, 120, 48, True, 'hardswish', 1], [5, 144, 48, True, 'hardswish', 1], [5, 288, 96, True, 'hardswish', 2], [5, 576, 96, True, 'hardswish', 1], [5, 576, 96, True, 'hardswish', 1], ] init_conv_out = 16 final_conv_out = 576 # 第一层卷积 self.conv_stem = nn.Sequential( nn.Conv2d(3, init_conv_out, 3, 2, 1, bias=False), nn.BatchNorm2d(init_conv_out), get_activation('hardswish') ) # 构建Bottleneck层 features = [] in_channels = init_conv_out for k, exp, out, se, activation, stride in self.config: features.append( Bottleneck(in_channels, exp, out, k, stride, se, activation) ) in_channels = out self.features = nn.Sequential(*features) # 最后的卷积层 self.conv_head = nn.Sequential( nn.Conv2d(in_channels, final_conv_out, 1, bias=False), nn.BatchNorm2d(final_conv_out), get_activation('hardswish') ) # 分类器 self.avgpool = nn.AdaptiveAvgPool2d(1) self.classifier = nn.Sequential( nn.Linear(final_conv_out, num_classes) ) # 初始化权重 self._initialize_weights() def _initialize_weights(self): '''初始化模型权重''' for m in self.modules(): if isinstance(m, nn.Conv2d): 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): nn.init.ones_(m.weight) nn.init.zeros_(m.bias) elif isinstance(m, nn.Linear): nn.init.normal_(m.weight, 0, 0.01) if m.bias is not None: nn.init.zeros_(m.bias) def forward(self, x): x = self.conv_stem(x) x = self.features(x) x = self.conv_head(x) x = self.avgpool(x) x = x.view(x.size(0), -1) x = self.classifier(x) return x def test(): """测试函数""" # 测试Large版本 net_large = MobileNetV3(mode='large') x = torch.randn(2, 3, 32, 32) y = net_large(x) print('Large output size:', y.size()) # 测试Small版本 net_small = MobileNetV3(mode='small') y = net_small(x) print('Small output size:', y.size()) # 打印模型结构 from torchinfo import summary device = 'cuda' if torch.cuda.is_available() else 'cpu' net_small = net_small.to(device) summary(net_small, (2, 3, 32, 32)) if __name__ == '__main__': test()