mlconfig

Installation

$ pip install mlconfig

Usage

config.yaml

num_classes: 50model:
name: LeNetnum_classes: $num_classesoptimizer:
name: Adamlr: 1.e-3weight_decay: 1.e-4
...

main.py

importmlconfigfromtorchimportnn, optimfromtorchvisionimportmodelsmlconfig.register(optim.Adam)
@mlconfig.registerclassLeNet(nn.Module):
def__init__(self, num_classes):
super(LeNet, self).__init__()
self.num_classes=num_classesself.features=nn.Sequential(
nn.Conv2d(1, 6, 5, bias=False),
nn.ReLU(inplace=True),
nn.MaxPool2d(2, 2),
nn.Conv2d(6, 16, 5, bias=False),
nn.ReLU(inplace=True),
nn.MaxPool2d(2, 2),
)
self.classifier=nn.Sequential(
nn.Linear(16*5*5, 120),
nn.ReLU(inplace=True),
nn.Linear(120, 84),
nn.ReLU(inplace=True),
nn.Linear(84, self.num_classes),
)
defforward(self, x):
x=self.features(x)
x=x.view(x.size(0), -1)
x=self.classifier(x)
returnxdefmain():
config=mlconfig.load('config.yaml')
config.set_immutable()
model=config.model()
optimizer=config.optimizer(model.parameters())
...
if__name__=='__main__':
main()