Source code for optuna.integration.pytorch_ignite

import optuna
from optuna import type_checking

if type_checking.TYPE_CHECKING:
    from optuna.trial import Trial  # NOQA

try:
    from ignite.engine import Engine  # NOQA

    _available = True
except ImportError as e:
    _import_error = e
    # PyTorchIgnitePruningHandler is disabled because pytorch-ignite is not available.
    _available = False


[docs]class PyTorchIgnitePruningHandler(object): """PyTorch Ignite handler to prune unpromising trials. See `the example <https://github.com/optuna/optuna/blob/master/ examples/pytorch_ignite_simple.py>`__ if you want to add a pruning handler which observes validation accuracy. Args: trial: A :class:`~optuna.trial.Trial` corresponding to the current evaluation of the objective function. metric: A name of metric for pruning, e.g., ``accuracy`` and ``loss``. trainer: A trainer engine of PyTorch Ignite. Please refer to `ignite.engine.Engine reference <https://pytorch.org/ignite/engine.html#ignite.engine.Engine>`_ for further details. """ def __init__(self, trial, metric, trainer): # type: (Trial, str, Engine) -> None self._trial = trial self._metric = metric self._trainer = trainer def __call__(self, engine): # type: (Engine) -> None score = engine.state.metrics[self._metric] self._trial.report(score, self._trainer.state.epoch) if self._trial.should_prune(): message = "Trial was pruned at {} epoch.".format(self._trainer.state.epoch) raise optuna.TrialPruned(message)
def _check_pytorch_ignite_availability(): # type: () -> None if not _available: raise ImportError( "PyTorch Ignite is not available. Please install PyTorch Ignite to use this feature. " "PyTorch Ignite can be installed by executing `$ pip install pytorch-ignite`. " "For further information, please refer to the installation guide of PyTorch Ignite. " "(The actual import error is as follows: " + str(_import_error) + ")" )