97 lines
4.0 KiB
Python
97 lines
4.0 KiB
Python
# Copyright The PyTorch Lightning team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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r"""
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Lambda Callback
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^^^^^^^^^^^^^^^
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Create a simple callback on the fly using lambda functions.
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"""
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from typing import Callable, Optional
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from pytorch_lightning.callbacks.base import Callback
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class LambdaCallback(Callback):
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r"""
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Create a simple callback on the fly using lambda functions.
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Args:
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**kwargs: hooks supported by :class:`~pytorch_lightning.callbacks.base.Callback`
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Example::
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>>> from pytorch_lightning import Trainer
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>>> from pytorch_lightning.callbacks import LambdaCallback
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>>> trainer = Trainer(callbacks=[LambdaCallback(setup=lambda *args: print('setup'))])
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"""
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def __init__(
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self,
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on_before_accelerator_backend_setup: Optional[Callable] = None,
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setup: Optional[Callable] = None,
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on_configure_sharded_model: Optional[Callable] = None,
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teardown: Optional[Callable] = None,
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on_init_start: Optional[Callable] = None,
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on_init_end: Optional[Callable] = None,
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on_fit_start: Optional[Callable] = None,
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on_fit_end: Optional[Callable] = None,
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on_sanity_check_start: Optional[Callable] = None,
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on_sanity_check_end: Optional[Callable] = None,
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on_train_batch_start: Optional[Callable] = None,
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on_train_batch_end: Optional[Callable] = None,
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on_train_epoch_start: Optional[Callable] = None,
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on_train_epoch_end: Optional[Callable] = None,
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on_validation_epoch_start: Optional[Callable] = None,
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on_validation_epoch_end: Optional[Callable] = None,
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on_test_epoch_start: Optional[Callable] = None,
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on_test_epoch_end: Optional[Callable] = None,
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on_epoch_start: Optional[Callable] = None,
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on_epoch_end: Optional[Callable] = None,
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on_batch_start: Optional[Callable] = None,
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on_validation_batch_start: Optional[Callable] = None,
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on_validation_batch_end: Optional[Callable] = None,
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on_test_batch_start: Optional[Callable] = None,
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on_test_batch_end: Optional[Callable] = None,
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on_batch_end: Optional[Callable] = None,
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on_train_start: Optional[Callable] = None,
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on_train_end: Optional[Callable] = None,
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on_pretrain_routine_start: Optional[Callable] = None,
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on_pretrain_routine_end: Optional[Callable] = None,
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on_validation_start: Optional[Callable] = None,
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on_validation_end: Optional[Callable] = None,
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on_test_start: Optional[Callable] = None,
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on_test_end: Optional[Callable] = None,
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on_keyboard_interrupt: Optional[Callable] = None,
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on_exception: Optional[Callable] = None,
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on_save_checkpoint: Optional[Callable] = None,
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on_load_checkpoint: Optional[Callable] = None,
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on_before_backward: Optional[Callable] = None,
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on_after_backward: Optional[Callable] = None,
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on_before_optimizer_step: Optional[Callable] = None,
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on_before_zero_grad: Optional[Callable] = None,
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on_predict_start: Optional[Callable] = None,
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on_predict_end: Optional[Callable] = None,
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on_predict_batch_start: Optional[Callable] = None,
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on_predict_batch_end: Optional[Callable] = None,
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on_predict_epoch_start: Optional[Callable] = None,
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on_predict_epoch_end: Optional[Callable] = None,
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):
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for k, v in locals().items():
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if k == "self":
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continue
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if v is not None:
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setattr(self, k, v)
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