emperorq / pytorch-mobilenet

PyTorch MobileNet Implementation of "MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications"

Implementation of MobileNet, modified from https://github.com/pytorch/examples/tree/master/imagenet. imagenet data is processed as described here

nohup python main.py -a mobilenet ImageNet-Folder > log.txt &

Results

• sgd : top1 68.848 top5 88.740 download
• rmsprop: top1 0.104 top5 0.494
• rmsprop init from sgd : top1 69.526 top5 88.978 donwload
• paper: top1 70.6

Benchmark:

Titan-X, batchsize = 16

  resnet18 : 0.004030
   alexnet : 0.001395
     vgg16 : 0.002310
squeezenet : 0.009848
 mobilenet : 0.073611

Titan-X, batchsize = 1

  resnet18 : 0.003688
   alexnet : 0.001179
     vgg16 : 0.002055
squeezenet : 0.003385
 mobilenet : 0.076977

---

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