optuna.integration.fastaiv2 源代码

from packaging import version

import optuna
from optuna._imports import try_import

with try_import() as _imports:
    import fastai

    if version.parse(fastai.__version__) < version.parse("2.0.0"):
        raise ImportError(
            f"You don't have fastai V2 installed! Fastai version: {fastai.__version__}"

    from fastai.callback.core import CancelFitException
    from fastai.callback.tracker import TrackerCallback

if not _imports.is_successful():
    TrackerCallback = object  # NOQA

[文档]class FastAIV2PruningCallback(TrackerCallback): """FastAI callback to prune unpromising trials for fastai. .. note:: This callback is for fastai>=2.0. See `the example <https://github.com/optuna/optuna/blob/master/ examples/fastai/fastaiv2_simple.py>`__ if you want to add a pruning callback which monitors validation loss of a ``Learner``. Example: Register a pruning callback to ``learn.fit`` and ``learn.fit_one_cycle``. .. code:: learn = cnn_learner(dls, resnet18, metrics=[error_rate]) learn.fit(n_epochs, cbs=[FastAIPruningCallback(trial)]) # Monitor "valid_loss" learn.fit_one_cycle( n_epochs, lr_max, cbs=[FastAIPruningCallback(trial, monitor="error_rate")], # Monitor "error_rate" ) Args: trial: A :class:`~optuna.trial.Trial` corresponding to the current evaluation of the objective function. monitor: An evaluation metric for pruning, e.g. ``valid_loss`` or ``accuracy``. Please refer to `fastai.callback.TrackerCallback reference <https://docs.fast.ai/callback.tracker#TrackerCallback>`_ for further details. """ # Implementation notes: it's a subclass of TrackerCallback to benefit from it. For example, # when to run (after the Recorder callback), when not to (like with lr_find), etc. def __init__(self, trial: optuna.Trial, monitor: str = "valid_loss"): super().__init__(monitor=monitor) _imports.check() self.trial = trial def after_epoch(self) -> None: super().after_epoch() # self.idx is set by TrackTrackerCallback self.trial.report(self.recorder.final_record[self.idx], step=self.epoch) if self.trial.should_prune(): raise CancelFitException() def after_fit(self) -> None: super().after_fit() if self.trial.should_prune(): raise optuna.TrialPruned(f"Trial was pruned at epoch {self.epoch}.")
FastAIPruningCallback = FastAIV2PruningCallback