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)