# Set random seed

# NOTE: this must be done before any class initialisation,

# hence also before the call to parser.instantiate_classes()

seed_everything(hparams.seed_everything, workers=True)

# Construct the experiment directory

experiment_subdir = construct_experiment_subdir(hparams)

if hparams.trainer.default_root_dir is None:

experiment_dir = f'./{experiment_subdir}'

else:

experiment_dir = f'{hparams.trainer.default_root_dir}/{experiment_subdir}'

# Instantiate dynamic object classes

hparams = parser.instantiate_classes(hparams)

# Get the instantiated data

datamodule = hparams.data

# Get the instantiated model

model = hparams.model

# Instantiate the trainer

trainer_args = { **(hparams.trainer.as_dict()), "default_root_dir": experiment_dir }

if hparams.add_checkpoint_callback:

checkpoint_callback = ModelCheckpoint(save_top_k=1,

save_last=True,

monitor="loss/val")

if trainer_args['callbacks'] is not None:

trainer_args['callbacks'].append(checkpoint_callback)

else:

trainer_args['callbacks'] = [checkpoint_callback]

trainer = pl.Trainer(**trainer_args)

# The instantiation steps might be different for different models.

# Hence we reset the seed before training such that the seed at the start of lightning setup is the same.

reset_seed()

# Begin fitting