def cli_main():
from pl_bolts.callbacks.self_supervised import SSLOnlineEvaluator
from pl_bolts.datamodules import CIFAR10DataModule, STL10DataModule, ImagenetDataModule
from pl_bolts.models.self_supervised.simclr import SimCLRTrainDataTransform, SimCLREvalDataTransform
seed_everything(1234)
parser = ArgumentParser()
# trainer args
parser = pl.Trainer.add_argparse_args(parser)
# model args
parser = BYOL.add_model_specific_args(parser)
args = parser.parse_args()
# pick data
dm = None
# init default datamodule
if args.dataset == 'cifar10':
dm = CIFAR10DataModule.from_argparse_args(args)
dm.train_transforms = SimCLRTrainDataTransform(32)
dm.val_transforms = SimCLREvalDataTransform(32)
args.num_classes = dm.num_classes
elif args.dataset == 'stl10':
dm = STL10DataModule.from_argparse_args(args)
dm.train_dataloader = dm.train_dataloader_mixed
dm.val_dataloader = dm.val_dataloader_mixed
(c, h, w) = dm.size()
dm.train_transforms = SimCLRTrainDataTransform(h)
dm.val_transforms = SimCLREvalDataTransform(h)
args.num_classes = dm.num_classes
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)
args.num_classes = dm.num_classes
model = BYOL(**args.__dict__)
def to_device(batch, device):
(x1, x2), y = batch
x1 = x1.to(device)
y = y.to(device)
return x1, y
# finetune in real-time
online_eval = SSLOnlineEvaluator(z_dim=2048, num_classes=dm.num_classes)
online_eval.to_device = to_device
trainer = pl.Trainer.from_argparse_args(args,
max_steps=300000,
callbacks=[online_eval])
trainer.fit(model, 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 = 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():
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 import LatentDimInterpolator, TensorboardGenerativeModelImageSampler
from pl_bolts.datamodules import ImagenetDataModule
pl.seed_everything(1234)
parser = ArgumentParser()
parser.add_argument('--dataset',
default='mnist',
type=str,
help='mnist, stl10, imagenet')
parser = pl.Trainer.add_argparse_args(parser)
parser = VAE.add_model_specific_args(parser)
parser = ImagenetDataModule.add_argparse_args(parser)
parser = MNISTDataModule.add_argparse_args(parser)
args = parser.parse_args()
# default is mnist
datamodule = None
if args.dataset == 'imagenet2012':
datamodule = ImagenetDataModule.from_argparse_args(args)
elif args.dataset == 'stl10':
datamodule = STL10DataModule.from_argparse_args(args)
callbacks = [
TensorboardGenerativeModelImageSampler(),
LatentDimInterpolator(interpolate_epoch_interval=5)
]
vae = VAE(**vars(args), datamodule=datamodule)
trainer = pl.Trainer.from_argparse_args(args,
callbacks=callbacks,
progress_bar_refresh_rate=10)
trainer.fit(vae)
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)
def cli_main():
from pl_bolts.callbacks.ssl_online import SSLOnlineEvaluator
from pl_bolts.datamodules import CIFAR10DataModule, STL10DataModule, ImagenetDataModule
parser = ArgumentParser()
# trainer args
parser = pl.Trainer.add_argparse_args(parser)
# model args
parser = SimCLR.add_model_specific_args(parser)
args = parser.parse_args()
# init default datamodule
if args.dataset == 'cifar10':
dm = CIFAR10DataModule.from_argparse_args(args)
dm.train_transforms = SimCLRTrainDataTransform(32)
dm.val_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_mixed
dm.val_dataloader = dm.val_dataloader_mixed
args.num_samples = dm.num_unlabeled_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)
model = SimCLR(**args.__dict__)
# finetune in real-time
def to_device(batch, device):
(x1, x2), y = batch
x1 = x1.to(device)
y = y.to(device)
return x1, y
online_eval = SSLOnlineEvaluator(z_dim=2048 * 2 * 2,
num_classes=dm.num_classes)
online_eval.to_device = to_device
trainer = pl.Trainer.from_argparse_args(args, callbacks=[online_eval])
trainer.fit(model, dm)
def cli_main():
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.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)
grid,
global_step=trainer.global_step)
# todo: covert to CLI func and add test
if __name__ == '__main__':
from pl_bolts.datamodules import ImagenetDataModule
parser = ArgumentParser()
parser = Trainer.add_argparse_args(parser)
parser = GAN.add_model_specific_args(parser)
parser = ImagenetDataModule.add_argparse_args(parser)
args = parser.parse_args()
# default is mnist
datamodule = None
if args.dataset == 'imagenet2012':
datamodule = ImagenetDataModule.from_argparse_args(args)
elif args.dataset == 'stl10':
datamodule = STL10DataModule.from_argparse_args(args)
gan = GAN(**vars(args), datamodule=datamodule)
callbacks = [ImageGenerator(), LatentDimInterpolator()]
# no val loop... thus we condition on loss and always save the last
checkpoint_cb = ModelCheckpoint(monitor='loss', save_last=True)
trainer = Trainer.from_argparse_args(args,
callbacks=callbacks,
checkpoint_callback=checkpoint_cb)
trainer.fit(gan)
