轻量级CNN模型mobilenet v1
- 2019 年 10 月 31 日
- 筆記
mobilenet v1
论文解读
论文地址:https://arxiv.org/abs/1704.04861
核心思想就是通过depthwise conv替代普通conv. 
有关depthwise conv可以参考https://www.cnblogs.com/sdu20112013/p/11759928.html
模型结构: 
类似于vgg这种堆叠的结构.
每一层的运算量 
可以看到,运算量并不是与参数数量绝对成正比,当然整体趋势而言,参数量更少的模型会运算更快.
代码实现
https://github.com/marvis/pytorch-mobilenet
网络结构:
class Net(nn.Module): def __init__(self): super(Net, self).__init__() def conv_bn(inp, oup, stride): return nn.Sequential( nn.Conv2d(inp, oup, 3, stride, 1, bias=False), nn.BatchNorm2d(oup), nn.ReLU(inplace=True) ) def conv_dw(inp, oup, stride): return nn.Sequential( nn.Conv2d(inp, inp, 3, stride, 1, groups=inp, bias=False), nn.BatchNorm2d(inp), nn.ReLU(inplace=True), nn.Conv2d(inp, oup, 1, 1, 0, bias=False), nn.BatchNorm2d(oup), nn.ReLU(inplace=True), ) self.model = nn.Sequential( conv_bn( 3, 32, 2), conv_dw( 32, 64, 1), conv_dw( 64, 128, 2), conv_dw(128, 128, 1), conv_dw(128, 256, 2), conv_dw(256, 256, 1), conv_dw(256, 512, 2), conv_dw(512, 512, 1), conv_dw(512, 512, 1), conv_dw(512, 512, 1), conv_dw(512, 512, 1), conv_dw(512, 512, 1), conv_dw(512, 1024, 2), conv_dw(1024, 1024, 1), nn.AvgPool2d(7), ) self.fc = nn.Linear(1024, 1000) def forward(self, x): x = self.model(x) x = x.view(-1, 1024) x = self.fc(x) return x 参考论文中的结构,第一层是普通的卷积层,后面接的都是可分离卷积.
这里注意groups参数的用法. 当groups=输入channel数目时,即对每个channel分别做卷积.默认groups=1,此时即为普通卷积. 
训练伪代码
# create model model = Net() # define loss function (criterion) and optimizer criterion = nn.CrossEntropyLoss().cuda() optimizer = torch.optim.SGD(model.parameters(), args.lr, momentum=args.momentum, weight_decay=args.weight_decay) # load data train_loader = torch.utils.data.DataLoader() # train for every epoch: input,target=get_from_data #前向传播得到预测值 output = model(input_var) #计算loss loss = criterion(output, target_var) #反向传播更新网络参数 optimizer.zero_grad() loss.backward() optimizer.step() 
