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Source code for lightning.pytorch.callbacks.lambda_function

# Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
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)