def _get_experiment_components(
self,
stage: str = None
) -> Tuple[Model, Criterion, Optimizer, Scheduler, Device]:
"""
Inner method for `Experiment` components preparation.
Check available torch device, takes model from the experiment
and creates stage-specified criterion, optimizer, scheduler for it.
Args:
stage (str): experiment stage name of interest
like "pretraining" / "training" / "finetuning" / etc
Returns:
tuple: model, criterion, optimizer,
scheduler and device for a given stage and model
"""
utils.set_global_seed(self.experiment.initial_seed)
model = self.experiment.get_model(stage)
criterion, optimizer, scheduler = \
self.experiment.get_experiment_components(model, stage)
model, criterion, optimizer, scheduler, device = \
utils.process_components(
model=model,
criterion=criterion,
optimizer=optimizer,
scheduler=scheduler,
distributed_params=self.experiment.distributed_params,
device=self.device
)
return model, criterion, optimizer, scheduler, device
def _get_experiment_components(
experiment: IExperiment,
stage: str = None,
device: Device = None,
) -> Tuple[Model, Criterion, Optimizer, Scheduler, Device]:
"""
Inner method for `Experiment` components preparation.
Check available torch device, takes model from the experiment
and creates stage-specified criterion, optimizer, scheduler for it.
Args:
stage (str): experiment stage name of interest
like "pretrain" / "train" / "finetune" / etc
Returns:
tuple: model, criterion, optimizer,
scheduler and device for a given stage and model
"""
(
model,
criterion,
optimizer,
scheduler,
) = experiment.get_experiment_components(stage)
(
model,
criterion,
optimizer,
scheduler,
device,
) = utils.process_components(
model=model,
criterion=criterion,
optimizer=optimizer,
scheduler=scheduler,
distributed_params=experiment.distributed_params,
device=device,
)
return model, criterion, optimizer, scheduler, device
