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():
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():
parser = ArgumentParser()
# trainer args
parser = pl.Trainer.add_argparse_args(parser)
# model args
parser = ImageGPT.add_model_specific_args(parser)
args = parser.parse_args()
if args.dataset == "fashion_mnist":
datamodule = FashionMNISTDataModule.from_argparse_args(args)
elif args.dataset == "imagenet128":
datamodule = ImagenetDataModule.from_argparse_args(args)
model = ImageGPT(**args.__dict__, datamodule=datamodule)
trainer = pl.Trainer.from_argparse_args(args)
trainer.fit(model)
parser.add_argument("--classify", action="store_true", default=False)
parser.add_argument("--batch_size", type=int, default=64)
parser.add_argument("--learning_rate", type=float, default=1e-2)
parser.add_argument("--steps", type=int, default=25_000)
return parser
# todo: covert to CLI func and add test
if __name__ == "__main__":
from argparse import ArgumentParser
parser = ArgumentParser()
# trainer args
parser = pl.Trainer.add_argparse_args(parser)
# model args
parser = ImageGPT.add_model_specific_args(parser)
args = parser.parse_args()
if args.dataset == "fashion_mnist":
datamodule = FashionMNISTDataModule.from_argparse_args(args)
elif args.dataset == "imagenet128":
datamodule = ImagenetDataModule.from_argparse_args(args)
model = ImageGPT(**args.__dict__, datamodule=datamodule)
trainer = pl.Trainer.from_argparse_args(args)
trainer.fit(model)
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)
