import warnings
import optuna
with optuna._imports.try_import() as _imports:
from pytorch_lightning import LightningModule
from pytorch_lightning import Trainer
from pytorch_lightning.callbacks import Callback
if not _imports.is_successful():
Callback = object # type: ignore # NOQA
LightningModule = object # type: ignore # NOQA
Trainer = object # type: ignore # NOQA
[docs]class PyTorchLightningPruningCallback(Callback):
"""PyTorch Lightning callback to prune unpromising trials.
See `the example <https://github.com/optuna/optuna-examples/blob/
main/pytorch/pytorch_lightning_simple.py>`__
if you want to add a pruning callback which observes accuracy.
Args:
trial:
A :class:`~optuna.trial.Trial` corresponding to the current evaluation of the
objective function.
monitor:
An evaluation metric for pruning, e.g., ``val_loss`` or
``val_acc``. The metrics are obtained from the returned dictionaries from e.g.
``pytorch_lightning.LightningModule.training_step`` or
``pytorch_lightning.LightningModule.validation_epoch_end`` and the names thus depend on
how this dictionary is formatted.
"""
def __init__(self, trial: optuna.trial.Trial, monitor: str) -> None:
_imports.check()
super().__init__()
self._trial = trial
self.monitor = monitor
def on_validation_end(self, trainer: Trainer, pl_module: LightningModule) -> None:
epoch = pl_module.current_epoch
current_score = trainer.callback_metrics.get(self.monitor)
if current_score is None:
message = (
"The metric '{}' is not in the evaluation logs for pruning. "
"Please make sure you set the correct metric name.".format(self.monitor)
)
warnings.warn(message)
return
self._trial.report(current_score, step=epoch)
if self._trial.should_prune():
message = "Trial was pruned at epoch {}.".format(epoch)
raise optuna.TrialPruned(message)