# Copyright The PyTorch Lightning 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""" Abstract base class used to build new callbacks. """ import abc from typing import Any, Dict, List, Optional from pytorch_lightning.core.lightning import LightningModule class Callback(abc.ABC): r""" Abstract base class used to build new callbacks. Subclass this class and override any of the relevant hooks """ def on_configure_sharded_model(self, trainer, pl_module: LightningModule) -> None: """Called before configure sharded model""" def on_before_accelerator_backend_setup(self, trainer, pl_module: LightningModule) -> None: """Called before accelerator is being setup""" pass def setup(self, trainer, pl_module: LightningModule, stage: Optional[str] = None) -> None: """Called when fit, validate, test, predict, or tune begins""" pass def teardown(self, trainer, pl_module: LightningModule, stage: Optional[str] = None) -> None: """Called when fit, validate, test, predict, or tune ends""" pass def on_init_start(self, trainer) -> None: """Called when the trainer initialization begins, model has not yet been set.""" pass def on_init_end(self, trainer) -> None: """Called when the trainer initialization ends, model has not yet been set.""" pass def on_fit_start(self, trainer, pl_module: LightningModule) -> None: """Called when fit begins""" pass def on_fit_end(self, trainer, pl_module: LightningModule) -> None: """Called when fit ends""" pass def on_sanity_check_start(self, trainer, pl_module: LightningModule) -> None: """Called when the validation sanity check starts.""" pass def on_sanity_check_end(self, trainer, pl_module: LightningModule) -> None: """Called when the validation sanity check ends.""" pass def on_train_batch_start( self, trainer, pl_module: LightningModule, batch: Any, batch_idx: int, dataloader_idx: int ) -> None: """Called when the train batch begins.""" pass def on_train_batch_end( self, trainer, pl_module: LightningModule, outputs: Any, batch: Any, batch_idx: int, dataloader_idx: int ) -> None: """Called when the train batch ends.""" pass def on_train_epoch_start(self, trainer, pl_module: LightningModule) -> None: """Called when the train epoch begins.""" pass def on_train_epoch_end(self, trainer, pl_module: LightningModule, outputs: List[Any]) -> None: """Called when the train epoch ends.""" pass def on_validation_epoch_start(self, trainer, pl_module: LightningModule) -> None: """Called when the val epoch begins.""" pass def on_validation_epoch_end(self, trainer, pl_module: LightningModule, outputs: List[Any]) -> None: """Called when the val epoch ends.""" pass def on_test_epoch_start(self, trainer, pl_module: LightningModule) -> None: """Called when the test epoch begins.""" pass def on_test_epoch_end(self, trainer, pl_module: LightningModule, outputs: List[Any]) -> None: """Called when the test epoch ends.""" pass def on_epoch_start(self, trainer, pl_module: LightningModule) -> None: """Called when either of train/val/test epoch begins.""" pass def on_epoch_end(self, trainer, pl_module: LightningModule) -> None: """Called when either of train/val/test epoch ends.""" pass def on_batch_start(self, trainer, pl_module: LightningModule) -> None: """Called when the training batch begins.""" pass def on_validation_batch_start( self, trainer, pl_module: LightningModule, batch: Any, batch_idx: int, dataloader_idx: int ) -> None: """Called when the validation batch begins.""" pass def on_validation_batch_end( self, trainer, pl_module: LightningModule, outputs: Any, batch: Any, batch_idx: int, dataloader_idx: int ) -> None: """Called when the validation batch ends.""" pass def on_test_batch_start( self, trainer, pl_module: LightningModule, batch: Any, batch_idx: int, dataloader_idx: int ) -> None: """Called when the test batch begins.""" pass def on_test_batch_end( self, trainer, pl_module: LightningModule, outputs: Any, batch: Any, batch_idx: int, dataloader_idx: int ) -> None: """Called when the test batch ends.""" pass def on_batch_end(self, trainer, pl_module: LightningModule) -> None: """Called when the training batch ends.""" pass def on_train_start(self, trainer, pl_module: LightningModule) -> None: """Called when the train begins.""" pass def on_train_end(self, trainer, pl_module: LightningModule) -> None: """Called when the train ends.""" pass def on_pretrain_routine_start(self, trainer, pl_module: LightningModule) -> None: """Called when the pretrain routine begins.""" pass def on_pretrain_routine_end(self, trainer, pl_module: LightningModule) -> None: """Called when the pretrain routine ends.""" pass def on_validation_start(self, trainer, pl_module: LightningModule) -> None: """Called when the validation loop begins.""" pass def on_validation_end(self, trainer, pl_module: LightningModule) -> None: """Called when the validation loop ends.""" pass def on_test_start(self, trainer, pl_module: LightningModule) -> None: """Called when the test begins.""" pass def on_test_end(self, trainer, pl_module: LightningModule) -> None: """Called when the test ends.""" pass def on_keyboard_interrupt(self, trainer, pl_module: LightningModule) -> None: """Called when the training is interrupted by ``KeyboardInterrupt``.""" pass def on_save_checkpoint(self, trainer, pl_module: LightningModule, checkpoint: Dict[str, Any]) -> dict: """ Called when saving a model checkpoint, use to persist state. Args: trainer: the current Trainer instance. pl_module: the current LightningModule instance. checkpoint: the checkpoint dictionary that will be saved. Returns: The callback state. """ pass def on_load_checkpoint(self, callback_state: Dict[str, Any]) -> None: """Called when loading a model checkpoint, use to reload state. Args: callback_state: the callback state returned by ``on_save_checkpoint``. """ pass def on_after_backward(self, trainer, pl_module: LightningModule) -> None: """Called after ``loss.backward()`` and before optimizers do anything.""" pass def on_before_zero_grad(self, trainer, pl_module: LightningModule, optimizer) -> None: """Called after ``optimizer.step()`` and before ``optimizer.zero_grad()``.""" pass