轻量级CNN模型mobilenet v1

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()

巴特西

轻量级CNN模型mobilenet v1

mobilenet v1

论文解读

代码实现

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