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.callbacks.ssl_online import SSLOnlineEvaluator
from pl_bolts.datamodules import CIFAR10DataModule, STL10DataModule
from pl_bolts.datamodules.ssl_imagenet_datamodule import SSLImagenetDataModule
pl.seed_everything(1234)
parser = ArgumentParser()
parser = pl.Trainer.add_argparse_args(parser)
parser = CPCV2.add_model_specific_args(parser)
parser.add_argument('--dataset', default='cifar10', type=str)
parser.add_argument('--data_dir', default='.', type=str)
parser.add_argument('--meta_dir',
default='.',
type=str,
help='path to meta.bin for imagenet')
parser.add_argument('--num_workers', default=8, type=int)
parser.add_argument('--batch_size', type=int, default=128)
args = parser.parse_args()
datamodule = None
online_evaluator = SSLOnlineEvaluator()
if args.dataset == 'cifar10':
datamodule = CIFAR10DataModule.from_argparse_args(args)
datamodule.train_transforms = CPCTrainTransformsCIFAR10()
datamodule.val_transforms = CPCEvalTransformsCIFAR10()
args.patch_size = 8
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 = CPCTrainTransformsSTL10()
datamodule.val_transforms = CPCEvalTransformsSTL10()
args.patch_size = 16
# 16 GB RAM - 64
# 32 GB RAM - 144
args.batch_size = 144
def to_device(batch, device):
(_, _), (x2, y2) = batch
x2 = x2.to(device)
y2 = y2.to(device)
return x2, y2
online_evaluator.to_device = to_device
elif args.dataset == 'imagenet2012':
datamodule = SSLImagenetDataModule.from_argparse_args(args)
datamodule.train_transforms = CPCTrainTransformsImageNet128()
datamodule.val_transforms = CPCEvalTransformsImageNet128()
args.patch_size = 32
model = CPCV2(**vars(args))
trainer = pl.Trainer.from_argparse_args(args, callbacks=[online_evaluator])
trainer.fit(model, datamodule)
def cli_main():
from pl_bolts.datamodules import CIFAR10DataModule, STL10DataModule
from pl_bolts.datamodules.ssl_imagenet_datamodule import SSLImagenetDataModule
pl.seed_everything(1234)
parser = ArgumentParser()
parser = pl.Trainer.add_argparse_args(parser)
parser = CPCV2.add_model_specific_args(parser)
args = parser.parse_args()
args.online_ft = True
datamodule = None
online_evaluator = SSLOnlineEvaluator()
if args.dataset == 'cifar10':
datamodule = CIFAR10DataModule.from_argparse_args(args)
datamodule.train_transforms = CPCTrainTransformsCIFAR10()
datamodule.val_transforms = CPCEvalTransformsCIFAR10()
args.patch_size = 8
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 = CPCTrainTransformsSTL10()
datamodule.val_transforms = CPCEvalTransformsSTL10()
args.patch_size = 16
# 16 GB RAM - 64
# 32 GB RAM - 144
args.batch_size = 144
def to_device(batch, device):
(_, _), (x2, y2) = batch
x2 = x2.to(device)
y2 = y2.to(device)
return x2, y2
online_evaluator.to_device = to_device
elif args.dataset == 'imagenet2012':
datamodule = SSLImagenetDataModule.from_argparse_args(args)
datamodule.train_transforms = CPCTrainTransformsImageNet128()
datamodule.val_transforms = CPCEvalTransformsImageNet128()
args.patch_size = 32
model = CPCV2(**vars(args), datamodule=datamodule)
trainer = pl.Trainer.from_argparse_args(args, callbacks=[online_evaluator])
trainer.fit(model)
def cli_main():
from pl_bolts.callbacks.ssl_online import SSLOnlineEvaluator
from pl_bolts.datamodules import CIFAR10DataModule
from pl_bolts.datamodules.ssl_imagenet_datamodule import SSLImagenetDataModule
seed_everything(1234)
parser = ArgumentParser()
parser = Trainer.add_argparse_args(parser)
parser = CPC_v2.add_model_specific_args(parser)
parser.add_argument("--dataset", default="cifar10", type=str)
parser.add_argument("--data_dir", default=".", type=str)
parser.add_argument("--meta_dir",
default=".",
type=str,
help="path to meta.bin for imagenet")
parser.add_argument("--num_workers", default=8, type=int)
parser.add_argument("--hidden_mlp",
default=2048,
type=int,
help="hidden layer dimension in projection head")
parser.add_argument("--batch_size", type=int, default=128)
args = parser.parse_args()
datamodule = None
if args.dataset == "cifar10":
datamodule = CIFAR10DataModule.from_argparse_args(args)
datamodule.train_transforms = CPCTrainTransformsCIFAR10()
datamodule.val_transforms = CPCEvalTransformsCIFAR10()
args.patch_size = 8
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 = CPCTrainTransformsSTL10()
datamodule.val_transforms = CPCEvalTransformsSTL10()
args.patch_size = 16
elif args.dataset == "imagenet2012":
datamodule = SSLImagenetDataModule.from_argparse_args(args)
datamodule.train_transforms = CPCTrainTransformsImageNet128()
datamodule.val_transforms = CPCEvalTransformsImageNet128()
args.patch_size = 32
online_evaluator = SSLOnlineEvaluator(
drop_p=0.0,
hidden_dim=None,
z_dim=args.hidden_mlp,
num_classes=datamodule.num_classes,
dataset=args.dataset,
)
if args.dataset == "stl10":
# 16 GB RAM - 64
# 32 GB RAM - 144
args.batch_size = 144
def to_device(batch, device):
(_, _), (x2, y2) = batch
x2 = x2.to(device)
y2 = y2.to(device)
return x2, y2
online_evaluator.to_device = to_device
model = CPC_v2(**vars(args))
trainer = Trainer.from_argparse_args(args, callbacks=[online_evaluator])
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)