def cli_main():
parser = ArgumentParser()
# trainer args
parser = pl.Trainer.add_argparse_args(parser)
# model args
parser = MocoV2.add_model_specific_args(parser)
args = parser.parse_args()
if args.dataset == 'cifar10':
datamodule = CIFAR10DataModule.from_argparse_args(args)
datamodule.train_transforms = Moco2TrainCIFAR10Transforms()
datamodule.val_transforms = Moco2EvalCIFAR10Transforms()
elif args.dataset == 'stl10':
datamodule = STL10DataModule.from_argparse_args(args)
datamodule.train_dataloader = datamodule.train_dataloader_mixed
datamodule.val_dataloader = datamodule.val_dataloader_mixed
datamodule.train_transforms = Moco2TrainSTL10Transforms()
datamodule.val_transforms = Moco2EvalSTL10Transforms()
elif args.dataset == 'imagenet2012':
datamodule = SSLImagenetDataModule.from_argparse_args(args)
datamodule.train_transforms = Moco2TrainImagenetTransforms()
datamodule.val_transforms = Moco2EvalImagenetTransforms()
model = MocoV2(**args.__dict__, datamodule=datamodule)
trainer = pl.Trainer.from_argparse_args(args)
trainer.fit(model)
def cli_main():
from pl_bolts.datamodules import CIFAR10DataModule, SSLImagenetDataModule, STL10DataModule
parser = ArgumentParser()
# trainer args
parser = pl.Trainer.add_argparse_args(parser)
# model args
parser = MocoV2.add_model_specific_args(parser)
args = parser.parse_args()
if args.dataset == 'cifar10':
datamodule = CIFAR10DataModule.from_argparse_args(args)
datamodule.train_transforms = Moco2TrainCIFAR10Transforms()
datamodule.val_transforms = Moco2EvalCIFAR10Transforms()
elif args.dataset == 'stl10':
datamodule = STL10DataModule.from_argparse_args(args)
datamodule.train_dataloader = datamodule.train_dataloader_mixed
datamodule.val_dataloader = datamodule.val_dataloader_mixed
datamodule.train_transforms = Moco2TrainSTL10Transforms()
datamodule.val_transforms = Moco2EvalSTL10Transforms()
elif args.dataset == 'imagenet2012':
datamodule = SSLImagenetDataModule.from_argparse_args(args)
datamodule.train_transforms = Moco2TrainImagenetTransforms()
datamodule.val_transforms = Moco2EvalImagenetTransforms()
else:
# replace with your own dataset, otherwise CIFAR-10 will be used by default if `None` passed in
datamodule = None
model = MocoV2(**args.__dict__)
wandb_logger = WandbLogger(name='Baseline', project='MocoV2')
trainer = pl.Trainer.from_argparse_args(args, logger=wandb_logger)
trainer.fit(model, datamodule=datamodule)
wandb.finish()
def cli_main():
from pl_bolts.datamodules import CIFAR10DataModule, SSLImagenetDataModule, STL10DataModule
parser = ArgumentParser()
# trainer args
parser = Trainer.add_argparse_args(parser)
# model args
parser = Moco_v2.add_model_specific_args(parser)
args = parser.parse_args()
if args.dataset == "cifar10":
datamodule = CIFAR10DataModule.from_argparse_args(args)
datamodule.train_transforms = Moco2TrainCIFAR10Transforms()
datamodule.val_transforms = Moco2EvalCIFAR10Transforms()
elif args.dataset == "stl10":
datamodule = STL10DataModule.from_argparse_args(args)
datamodule.train_dataloader = datamodule.train_dataloader_mixed
datamodule.val_dataloader = datamodule.val_dataloader_mixed
datamodule.train_transforms = Moco2TrainSTL10Transforms()
datamodule.val_transforms = Moco2EvalSTL10Transforms()
elif args.dataset == "imagenet2012":
datamodule = SSLImagenetDataModule.from_argparse_args(args)
datamodule.train_transforms = Moco2TrainImagenetTransforms()
datamodule.val_transforms = Moco2EvalImagenetTransforms()
else:
# replace with your own dataset, otherwise CIFAR-10 will be used by default if `None` passed in
datamodule = None
model = Moco_v2(**args.__dict__)
trainer = Trainer.from_argparse_args(args)
trainer.fit(model, datamodule=datamodule)
parser = ArgumentParser()
# trainer args
parser = pl.Trainer.add_argparse_args(parser)
# model args
parser = MocoV2.add_model_specific_args(parser)
args = parser.parse_args()
if args.dataset == 'cifar10':
datamodule = CIFAR10DataModule.from_argparse_args(args)
datamodule.train_transforms = Moco2TrainCIFAR10Transforms()
datamodule.val_transforms = Moco2EvalCIFAR10Transforms()
elif args.dataset == 'stl10':
datamodule = STL10DataModule.from_argparse_args(args)
datamodule.train_dataloader = datamodule.train_dataloader_mixed
datamodule.val_dataloader = datamodule.val_dataloader_mixed
datamodule.train_transforms = Moco2TrainSTL10Transforms()
datamodule.val_transforms = Moco2EvalSTL10Transforms()
elif args.dataset == 'imagenet2012':
datamodule = SSLImagenetDataModule.from_argparse_args(args)
datamodule.train_transforms = Moco2TrainImagenetTransforms()
datamodule.val_transforms = Moco2EvalImagenetTransforms()
model = MocoV2(**args.__dict__, datamodule=datamodule)
trainer = pl.Trainer.from_argparse_args(args)
trainer.fit(model)
def train_transform():
return Moco2TrainImagenetTransforms(height=224).train_transform
def train_transform():
transform = Moco2TrainImagenetTransforms(height=224).train_transform
return ApplyN(transform=transform, n=2)