313 lines
14 KiB
Python
313 lines
14 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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from abc import ABC
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from copy import deepcopy
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from typing import Any, Dict, List, Optional, Type, Union
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import torch
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from packaging.version import Version
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import pytorch_lightning as pl
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from pytorch_lightning.callbacks import Callback
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from pytorch_lightning.utilities import rank_zero_warn
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from pytorch_lightning.utilities.signature_utils import is_param_in_hook_signature
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from pytorch_lightning.utilities.types import STEP_OUTPUT
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class TrainerCallbackHookMixin(ABC):
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# this is just a summary on variables used in this abstract class,
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# the proper values/initialisation should be done in child class
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callbacks: List[Callback] = []
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lightning_module: "pl.LightningModule"
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def on_before_accelerator_backend_setup(self) -> None:
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"""Called at the beginning of fit (train + validate), validate, test, or predict, or tune."""
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for callback in self.callbacks:
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callback.on_before_accelerator_backend_setup(self, self.lightning_module)
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def on_configure_sharded_model(self) -> None:
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"""Called at the beginning of fit (train + validate), validate, test, or predict, or tune."""
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for callback in self.callbacks:
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callback.on_configure_sharded_model(self, self.lightning_module)
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def setup(self, stage: Optional[str]) -> None:
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"""Called at the beginning of fit (train + validate), validate, test, or predict, or tune."""
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for callback in self.callbacks:
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callback.setup(self, self.lightning_module, stage=stage)
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def teardown(self, stage: Optional[str] = None) -> None:
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"""Called at the end of fit (train + validate), validate, test, or predict, or tune."""
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for callback in self.callbacks:
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callback.teardown(self, self.lightning_module, stage=stage)
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def on_init_start(self):
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"""Called when the trainer initialization begins, model has not yet been set."""
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for callback in self.callbacks:
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callback.on_init_start(self)
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def on_init_end(self):
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"""Called when the trainer initialization ends, model has not yet been set."""
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for callback in self.callbacks:
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callback.on_init_end(self)
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def on_fit_start(self):
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"""Called when the trainer initialization begins, model has not yet been set."""
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for callback in self.callbacks:
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callback.on_fit_start(self, self.lightning_module)
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def on_fit_end(self):
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"""Called when the trainer initialization begins, model has not yet been set."""
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for callback in self.callbacks:
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callback.on_fit_end(self, self.lightning_module)
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def on_sanity_check_start(self):
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"""Called when the validation sanity check starts."""
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for callback in self.callbacks:
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callback.on_sanity_check_start(self, self.lightning_module)
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def on_sanity_check_end(self):
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"""Called when the validation sanity check ends."""
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for callback in self.callbacks:
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callback.on_sanity_check_end(self, self.lightning_module)
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def on_train_epoch_start(self):
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"""Called when the epoch begins."""
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for callback in self.callbacks:
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callback.on_train_epoch_start(self, self.lightning_module)
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def on_train_epoch_end(self):
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"""Called when the epoch ends."""
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for callback in self.callbacks:
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callback.on_train_epoch_end(self, self.lightning_module)
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def on_validation_epoch_start(self):
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"""Called when the epoch begins."""
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for callback in self.callbacks:
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callback.on_validation_epoch_start(self, self.lightning_module)
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def on_validation_epoch_end(self):
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"""Called when the validation epoch ends."""
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for callback in self.callbacks:
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callback.on_validation_epoch_end(self, self.lightning_module)
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def on_test_epoch_start(self):
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"""Called when the epoch begins."""
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for callback in self.callbacks:
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callback.on_test_epoch_start(self, self.lightning_module)
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def on_test_epoch_end(self):
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"""Called when the test epoch ends."""
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for callback in self.callbacks:
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callback.on_test_epoch_end(self, self.lightning_module)
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def on_predict_epoch_start(self) -> None:
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"""Called when the epoch begins."""
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for callback in self.callbacks:
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callback.on_predict_epoch_start(self, self.lightning_module)
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def on_predict_epoch_end(self, outputs: List[Any]) -> None:
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"""Called when the epoch ends."""
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for callback in self.callbacks:
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callback.on_predict_epoch_end(self, self.lightning_module, outputs)
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def on_epoch_start(self):
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"""Called when either of train/val/test epoch begins."""
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for callback in self.callbacks:
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callback.on_epoch_start(self, self.lightning_module)
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def on_epoch_end(self):
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"""Called when either of train/val/test epoch ends."""
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for callback in self.callbacks:
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callback.on_epoch_end(self, self.lightning_module)
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def on_train_start(self):
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"""Called when the train begins."""
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for callback in self.callbacks:
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callback.on_train_start(self, self.lightning_module)
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def on_train_end(self):
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"""Called when the train ends."""
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for callback in self.callbacks:
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callback.on_train_end(self, self.lightning_module)
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def on_pretrain_routine_start(self) -> None:
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"""Called when the pre-train routine begins."""
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for callback in self.callbacks:
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callback.on_pretrain_routine_start(self, self.lightning_module)
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def on_pretrain_routine_end(self) -> None:
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"""Called when the pre-train routine ends."""
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for callback in self.callbacks:
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callback.on_pretrain_routine_end(self, self.lightning_module)
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def on_batch_start(self):
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"""Called when the training batch begins."""
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for callback in self.callbacks:
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callback.on_batch_start(self, self.lightning_module)
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def on_batch_end(self):
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"""Called when the training batch ends."""
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for callback in self.callbacks:
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callback.on_batch_end(self, self.lightning_module)
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# TODO: Update this in v1.7 (deprecation: #9816)
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def on_train_batch_start(self, batch, batch_idx, dataloader_idx=0):
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"""Called when the training batch begins."""
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for callback in self.callbacks:
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if is_param_in_hook_signature(callback.on_train_batch_start, "dataloader_idx", explicit=True):
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callback.on_train_batch_start(self, self.lightning_module, batch, batch_idx, 0)
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else:
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callback.on_train_batch_start(self, self.lightning_module, batch, batch_idx)
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# TODO: Update this in v1.7 (deprecation: #9816)
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def on_train_batch_end(self, outputs: STEP_OUTPUT, batch, batch_idx, dataloader_idx=0):
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"""Called when the training batch ends."""
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for callback in self.callbacks:
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if is_param_in_hook_signature(callback.on_train_batch_end, "dataloader_idx", explicit=True):
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callback.on_train_batch_end(self, self.lightning_module, outputs, batch, batch_idx, 0)
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else:
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callback.on_train_batch_end(self, self.lightning_module, outputs, batch, batch_idx)
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def on_validation_batch_start(self, batch, batch_idx, dataloader_idx):
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"""Called when the validation batch begins."""
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for callback in self.callbacks:
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callback.on_validation_batch_start(self, self.lightning_module, batch, batch_idx, dataloader_idx)
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def on_validation_batch_end(self, outputs: STEP_OUTPUT, batch, batch_idx, dataloader_idx):
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"""Called when the validation batch ends."""
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for callback in self.callbacks:
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callback.on_validation_batch_end(self, self.lightning_module, outputs, batch, batch_idx, dataloader_idx)
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def on_test_batch_start(self, batch, batch_idx, dataloader_idx):
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"""Called when the test batch begins."""
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for callback in self.callbacks:
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callback.on_test_batch_start(self, self.lightning_module, batch, batch_idx, dataloader_idx)
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def on_test_batch_end(self, outputs: STEP_OUTPUT, batch, batch_idx, dataloader_idx):
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"""Called when the test batch ends."""
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for callback in self.callbacks:
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callback.on_test_batch_end(self, self.lightning_module, outputs, batch, batch_idx, dataloader_idx)
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def on_predict_batch_start(self, batch: Any, batch_idx: int, dataloader_idx: int) -> None:
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"""Called when the predict batch begins."""
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for callback in self.callbacks:
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callback.on_predict_batch_start(self, self.lightning_module, batch, batch_idx, dataloader_idx)
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def on_predict_batch_end(self, outputs: STEP_OUTPUT, batch: Any, batch_idx: int, dataloader_idx: int) -> None:
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"""Called when the predict batch ends."""
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for callback in self.callbacks:
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callback.on_predict_batch_end(self, self.lightning_module, outputs, batch, batch_idx, dataloader_idx)
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def on_validation_start(self):
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"""Called when the validation loop begins."""
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for callback in self.callbacks:
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callback.on_validation_start(self, self.lightning_module)
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def on_validation_end(self):
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"""Called when the validation loop ends."""
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for callback in self.callbacks:
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callback.on_validation_end(self, self.lightning_module)
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def on_test_start(self):
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"""Called when the test begins."""
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for callback in self.callbacks:
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callback.on_test_start(self, self.lightning_module)
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def on_test_end(self):
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"""Called when the test ends."""
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for callback in self.callbacks:
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callback.on_test_end(self, self.lightning_module)
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def on_predict_start(self) -> None:
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"""Called when predict begins."""
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for callback in self.callbacks:
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callback.on_predict_start(self, self.lightning_module)
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def on_predict_end(self) -> None:
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"""Called when predict ends."""
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for callback in self.callbacks:
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callback.on_predict_end(self, self.lightning_module)
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def on_keyboard_interrupt(self):
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r"""
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.. deprecated:: v1.5
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This callback hook was deprecated in v1.5 in favor of `on_exception` and will be removed in v1.7.
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Called when any trainer execution is interrupted by KeyboardInterrupt.
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"""
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for callback in self.callbacks:
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callback.on_keyboard_interrupt(self, self.lightning_module)
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def on_exception(self, exception: BaseException) -> None:
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"""Called when any trainer execution is interrupted by an exception."""
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for callback in self.callbacks:
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callback.on_exception(self, self.lightning_module, exception)
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def on_save_checkpoint(self, checkpoint: Dict[str, Any]) -> Dict[str, dict]:
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"""Called when saving a model checkpoint."""
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callback_states = {}
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for callback in self.callbacks:
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state = callback.on_save_checkpoint(self, self.lightning_module, checkpoint)
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if state:
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callback_states[callback.state_key] = state
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return callback_states
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def on_load_checkpoint(self, checkpoint: Dict[str, Any]) -> None:
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"""Called when loading a model checkpoint."""
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# Todo: the `callback_states` are dropped with TPUSpawn as they
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# can't be saved using `xm.save`
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# https://github.com/pytorch/xla/issues/2773
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callback_states: Dict[Union[Type, str], Dict] = checkpoint.get("callbacks")
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if callback_states is None:
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return
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is_legacy_ckpt = Version(checkpoint["pytorch-lightning_version"]) < Version("1.5.0dev")
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current_callbacks_keys = {cb._legacy_state_key if is_legacy_ckpt else cb.state_key for cb in self.callbacks}
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difference = callback_states.keys() - current_callbacks_keys
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if difference:
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rank_zero_warn(
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"Be aware that when using `ckpt_path`,"
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" callbacks used to create the checkpoint need to be provided during `Trainer` instantiation."
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f" Please add the following callbacks: {list(difference)}.",
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UserWarning,
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)
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for callback in self.callbacks:
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state = callback_states.get(callback.state_key, callback_states.get(callback._legacy_state_key))
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if state:
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state = deepcopy(state)
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callback.on_load_checkpoint(self, self.lightning_module, state)
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def on_before_backward(self, loss: torch.Tensor) -> None:
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"""Called before ``loss.backward()``."""
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for callback in self.callbacks:
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callback.on_before_backward(self, self.lightning_module, loss)
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def on_after_backward(self):
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"""Called after loss.backward() and before optimizers do anything."""
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for callback in self.callbacks:
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callback.on_after_backward(self, self.lightning_module)
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def on_before_optimizer_step(self, optimizer, optimizer_idx):
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"""Called after on_after_backward() once the gradient is accumulated and before optimizer.step()."""
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for callback in self.callbacks:
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callback.on_before_optimizer_step(self, self.lightning_module, optimizer, optimizer_idx)
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def on_before_zero_grad(self, optimizer):
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"""Called after optimizer.step() and before optimizer.zero_grad()."""
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for callback in self.callbacks:
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callback.on_before_zero_grad(self, self.lightning_module, optimizer)
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