# configure trainer
trainer = Trainer(
experiment=exp,
checkpoint_callback=checkpoint,
#early_stop_callback=early_stop,
# distributed_backend='dp',
#gpus=[0],
nb_sanity_val_steps=2,
gradient_clip=0.5)
# train model
trainer.fit(model)
if __name__ == '__main__':
# use default args given by lightning
root_dir = os.path.split(os.path.dirname(
sys.modules['__main__'].__file__))[0]
parent_parser = HyperOptArgumentParser(strategy='random_search',
add_help=False)
add_default_args(parent_parser, root_dir)
# allow model to overwrite or extend args
parser = ContrastivePredictiveSystem.add_model_specific_args(
parent_parser, root_dir)
hyperparams = parser.parse_args()
# train model
main(hyperparams)
)
trainer.fit(model)
if __name__ == "__main__":
SEED = 2538
torch.manual_seed(SEED)
np.random.seed(SEED)
# use default args given by lightning
root_dir = os.getcwd()
parent_parser = HyperOptArgumentParser(strategy="random_search",
add_help=False)
add_default_args(parent_parser, root_dir, rand_seed=SEED)
parent_parser.add_argument(
"--dist_backend",
type=str,
default="dp",
help=
"When using multiple GPUs set Trainer(distributed_backend=dp) (or ddp)",
)
# allow model to overwrite or extend args
parser = AutoregressiveFaceVAE.add_model_specific_args(
parent_parser, root_dir)
hyperparams = parser.parse_args()
print(hyperparams)
# train model
main(hyperparams)
def test_training(self):
# use default args given by lightning
root_dir = '/Volumes/Elements/Projekte/Immersions'
parent_parser = HyperOptArgumentParser(strategy='random_search', add_help=False)
add_default_args(parent_parser, root_dir)
# allow model to overwrite or extend args
parser = ContrastivePredictiveSystem.add_model_specific_args(parent_parser, root_dir)
hparams = parser.parse_args()
name = 'immersions_scalogram_resnet_test'
version = 0
hparams.log_dir = '/Volumes/Elements/Projekte/Immersions/logs'
hparams.checkpoint_dir = '/Volumes/Elements/Projekte/Immersions/checkpoints'
hparams.training_set_path = '/Volumes/Elements/Datasets/Immersions/house_data_mp3/training'
hparams.validation_set_path = '/Volumes/Elements/Datasets/Immersions/house_data_mp3/validation'
hparams.dummy_datasets = False
hparams.batch_size = 64
hparams.learning_rate = 2e-4
hparams.warmup_steps = 1000
hparams.annealing_steps = 100000
# init experiment
exp = Experiment(
name=name,
debug=False,
save_dir=hparams.log_dir,
version=version,
autosave=False,
description='test demo'
)
# set the hparams for the experiment
exp.argparse(hparams)
exp.save()
# build model
model = ContrastivePredictiveSystem(hparams)
# callbacks
early_stop = EarlyStopping(
monitor=hparams.early_stop_metric,
patience=hparams.early_stop_patience,
verbose=True,
mode=hparams.early_stop_mode
)
checkpoint = ModelCheckpoint(
filepath=hparams.checkpoint_dir,
save_best_only=False,
verbose=True,
monitor=hparams.model_save_monitor_value,
mode=hparams.model_save_monitor_mode
)
# configure trainer
trainer = Trainer(
experiment=exp,
checkpoint_callback=checkpoint,
# early_stop_callback=early_stop,
# distributed_backend='dp',
gpus=[0],
nb_sanity_val_steps=5,
val_check_interval=0.2,
train_percent_check=0.01,
max_nb_epochs=1
)
# train model
trainer.fit(model)