def main(args):
# create experiment dir
experiment_dir = CKPT_DIR / args.experiment_name
experiment_dir.mkdir(parents=True, exist_ok=True)
# logger
logger = pl_loggers.WandbLogger(
name=args.experiment_name,
save_dir=LOG_DIR,
)
# early stop call back
early_stop = EarlyStopping(monitor='val_loss',
patience=5,
strict=False,
verbose=False,
mode='min')
# checkpoint
checkpoint_callback = ModelCheckpoint(filepath=experiment_dir,
save_top_k=3,
verbose=True,
monitor='val_loss',
mode='min',
prefix='')
model = LightningModel(args)
trainer = Trainer.from_argparse_args(
args,
logger=logger,
early_stop_callback=early_stop,
checkpoint_callback=checkpoint_callback)
trainer.fit(model)
def main(args):
# logger
logger = pl_loggers.WandbLogger(name=None, save_dir=None, experiment=None)
# early stop call back
early_stop = EarlyStopping(monitor='val_loss',
patience=5,
strict=False,
verbose=False,
mode='min')
model = LightningModel(args)
trainer = Trainer.from_argparse_args(args,
logger=logger,
early_stop_callback=early_stop)
trainer.fit(model)
def main(args):
model = LightningModel.load_from_checkpoint(args.checkpoint_path)
trainer = Trainer.from_argparse_args(args)
trainer.test(model)
from argparse import ArgumentParser
from args import BaseArgParser
from lightning import LightningModel
from pytorch_lightning import Trainer, seed_everything
from pytorch_lightning import loggers as pl_loggers
from pytorch_lightning.callbacks import EarlyStopping
seed_everything(6)
def main(args):
model = LightningModel.load_from_checkpoint(args.checkpoint_path)
trainer = Trainer.from_argparse_args(args)
trainer.test(model)
if __name__ == '__main__':
parser = BaseArgParser().get_parser()
parser.add_argument('--checkpoint_path', type=str)
# add model specific args
parser = LightningModel.add_model_specific_args(parser)
# add all the available trainer options to argparse
parser = Trainer.add_argparse_args(parser)
args = parser.parse_args()
main(args)