def cli_main(): # pragma: no-cover
pl.seed_everything(1234)
parser = ArgumentParser()
parser = pl.Trainer.add_argparse_args(parser)
parser.add_argument('--dataset', type=str, help='stl10, cifar10', default='cifar10')
parser.add_argument('--ckpt_path', type=str, help='path to ckpt')
parser.add_argument('--data_dir', type=str, help='path to ckpt', default=os.getcwd())
args = parser.parse_args()
# load the backbone
backbone = CPCV2.load_from_checkpoint(args.ckpt_path, strict=False)
if args.dataset == 'cifar10':
dm = CIFAR10DataModule.from_argparse_args(args)
dm.train_transforms = CPCTrainTransformsCIFAR10()
dm.val_transforms = CPCEvalTransformsCIFAR10()
dm.test_transforms = CPCEvalTransformsCIFAR10()
elif args.dataset == 'stl10':
dm = STL10DataModule.from_argparse_args(args)
dm.train_dataloader = dm.train_dataloader_labeled
dm.val_dataloader = dm.val_dataloader_labeled
dm.train_transforms = CPCTrainTransformsSTL10()
dm.val_transforms = CPCEvalTransformsSTL10()
dm.test_transforms = CPCEvalTransformsSTL10()
# finetune
tuner = SSLFineTuner(backbone, in_features=backbone.z_dim, num_classes=backbone.num_classes)
trainer = pl.Trainer.from_argparse_args(args, early_stop_callback=True)
trainer.fit(tuner, dm)
trainer.test(datamodule=dm)
def cli_main(): # pragma: no-cover
pl.seed_everything(1234)
parser = ArgumentParser()
parser = pl.Trainer.add_argparse_args(parser)
parser.add_argument('--dataset',
type=str,
help='stl10, cifar10',
default='cifar10')
parser.add_argument('--ckpt_path', type=str, help='path to ckpt')
parser.add_argument('--data_dir',
type=str,
help='path to ckpt',
default=os.getcwd())
args = parser.parse_args()
# load the backbone
backbone = SimCLR.load_from_checkpoint(args.ckpt_path, strict=False)
# init default datamodule
if args.dataset == 'cifar10':
dm = CIFAR10DataModule.from_argparse_args(args)
dm.train_transforms = SimCLRTrainDataTransform(32)
dm.val_transforms = SimCLREvalDataTransform(32)
dm.test_transforms = SimCLREvalDataTransform(32)
args.num_samples = dm.num_samples
elif args.dataset == 'stl10':
dm = STL10DataModule.from_argparse_args(args)
dm.train_dataloader = dm.train_dataloader_labeled
dm.val_dataloader = dm.val_dataloader_labeled
args.num_samples = dm.num_labeled_samples
(c, h, w) = dm.size()
dm.train_transforms = SimCLRTrainDataTransform(h)
dm.val_transforms = SimCLREvalDataTransform(h)
elif args.dataset == 'imagenet2012':
dm = ImagenetDataModule.from_argparse_args(args, image_size=196)
(c, h, w) = dm.size()
dm.train_transforms = SimCLRTrainDataTransform(h)
dm.val_transforms = SimCLREvalDataTransform(h)
# finetune
tuner = SSLFineTuner(backbone,
in_features=2048 * 2 * 2,
num_classes=dm.num_classes,
hidden_dim=None)
trainer = pl.Trainer.from_argparse_args(args, early_stop_callback=True)
trainer.fit(tuner, dm)
trainer.test(datamodule=dm)