lightning.pytorch.callbacks.lambda_function — PyTorch Lightning 2.5.1.post0 documentation (original) (raw)

Source code for lightning.pytorch.callbacks.lambda_function

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you may not use this file except in compliance with the License.

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r""" Lambda Callback ^^^^^^^^^^^^^^^

Create a simple callback on the fly using lambda functions.

"""

from typing import Callable, Optional

from lightning.pytorch.callbacks.callback import Callback

[docs]class LambdaCallback(Callback): r"""Create a simple callback on the fly using lambda functions.

Args:
    **kwargs: hooks supported by :class:`~lightning.pytorch.callbacks.callback.Callback`

Example::

    >>> from lightning.pytorch import Trainer
    >>> from lightning.pytorch.callbacks import LambdaCallback
    >>> trainer = Trainer(callbacks=[LambdaCallback(setup=lambda *args: print('setup'))])

"""

def __init__(
    self,
    setup: Optional[Callable] = None,
    teardown: Optional[Callable] = None,
    on_fit_start: Optional[Callable] = None,
    on_fit_end: Optional[Callable] = None,
    on_sanity_check_start: Optional[Callable] = None,
    on_sanity_check_end: Optional[Callable] = None,
    on_train_batch_start: Optional[Callable] = None,
    on_train_batch_end: Optional[Callable] = None,
    on_train_epoch_start: Optional[Callable] = None,
    on_train_epoch_end: Optional[Callable] = None,
    on_validation_epoch_start: Optional[Callable] = None,
    on_validation_epoch_end: Optional[Callable] = None,
    on_test_epoch_start: Optional[Callable] = None,
    on_test_epoch_end: Optional[Callable] = None,
    on_validation_batch_start: Optional[Callable] = None,
    on_validation_batch_end: Optional[Callable] = None,
    on_test_batch_start: Optional[Callable] = None,
    on_test_batch_end: Optional[Callable] = None,
    on_train_start: Optional[Callable] = None,
    on_train_end: Optional[Callable] = None,
    on_validation_start: Optional[Callable] = None,
    on_validation_end: Optional[Callable] = None,
    on_test_start: Optional[Callable] = None,
    on_test_end: Optional[Callable] = None,
    on_exception: Optional[Callable] = None,
    on_save_checkpoint: Optional[Callable] = None,
    on_load_checkpoint: Optional[Callable] = None,
    on_before_backward: Optional[Callable] = None,
    on_after_backward: Optional[Callable] = None,
    on_before_optimizer_step: Optional[Callable] = None,
    on_before_zero_grad: Optional[Callable] = None,
    on_predict_start: Optional[Callable] = None,
    on_predict_end: Optional[Callable] = None,
    on_predict_batch_start: Optional[Callable] = None,
    on_predict_batch_end: Optional[Callable] = None,
    on_predict_epoch_start: Optional[Callable] = None,
    on_predict_epoch_end: Optional[Callable] = None,
):
    for k, v in locals().items():
        if k == "self":
            continue
        if v is not None:
            setattr(self, k, v)