marvis / Pytorch Mobilenet
PyTorch MobileNet Implementation of "MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications"
Stars: ✭ 443
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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
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