320 lines
12 KiB
Python
320 lines
12 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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Abstract base class used to build new callbacks.
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"""
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import abc
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from typing import Any, Dict, List, Optional, Type
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import torch
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from torch.optim import Optimizer
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import pytorch_lightning as pl
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from pytorch_lightning.utilities.types import STEP_OUTPUT
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class Callback(abc.ABC):
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r"""
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Abstract base class used to build new callbacks.
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Subclass this class and override any of the relevant hooks
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"""
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@property
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def state_key(self) -> str:
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"""
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Identifier for the state of the callback. Used to store and retrieve a callback's state from the
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checkpoint dictionary by ``checkpoint["callbacks"][state_key]``. Implementations of a callback need to
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provide a unique state key if 1) the callback has state and 2) it is desired to maintain the state of
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multiple instances of that callback.
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"""
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return self.__class__.__qualname__
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@property
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def _legacy_state_key(self) -> Type["Callback"]:
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"""State key for checkpoints saved prior to version 1.5.0."""
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return type(self)
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def _generate_state_key(self, **kwargs: Any) -> str:
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"""
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Formats a set of key-value pairs into a state key string with the callback class name prefixed.
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Useful for defining a :attr:`state_key`.
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Args:
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**kwargs: A set of key-value pairs. Must be serializable to :class:`str`.
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"""
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return f"{self.__class__.__qualname__}{repr(kwargs)}"
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def on_configure_sharded_model(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
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"""Called before configure sharded model"""
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def on_before_accelerator_backend_setup(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
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"""Called before accelerator is being setup"""
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pass
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def setup(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", stage: Optional[str] = None) -> None:
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"""Called when fit, validate, test, predict, or tune begins"""
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pass
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def teardown(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", stage: Optional[str] = None) -> None:
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"""Called when fit, validate, test, predict, or tune ends"""
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pass
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def on_init_start(self, trainer: "pl.Trainer") -> None:
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"""Called when the trainer initialization begins, model has not yet been set."""
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pass
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def on_init_end(self, trainer: "pl.Trainer") -> None:
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"""Called when the trainer initialization ends, model has not yet been set."""
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pass
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def on_fit_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
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"""Called when fit begins"""
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pass
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def on_fit_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
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"""Called when fit ends"""
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pass
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def on_sanity_check_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
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"""Called when the validation sanity check starts."""
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pass
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def on_sanity_check_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
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"""Called when the validation sanity check ends."""
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pass
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def on_train_batch_start(
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self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", batch: Any, batch_idx: int, dataloader_idx: int
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) -> None:
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"""Called when the train batch begins."""
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pass
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def on_train_batch_end(
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self,
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trainer: "pl.Trainer",
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pl_module: "pl.LightningModule",
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outputs: STEP_OUTPUT,
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batch: Any,
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batch_idx: int,
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dataloader_idx: int,
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) -> None:
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"""Called when the train batch ends."""
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pass
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def on_train_epoch_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
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"""Called when the train epoch begins."""
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pass
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def on_train_epoch_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
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"""Called when the train epoch ends.
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To access all batch outputs at the end of the epoch, either:
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1. Implement `training_epoch_end` in the `LightningModule` and access outputs via the module OR
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2. Cache data across train batch hooks inside the callback implementation to post-process in this hook.
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"""
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pass
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def on_validation_epoch_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
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"""Called when the val epoch begins."""
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pass
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def on_validation_epoch_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
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"""Called when the val epoch ends."""
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pass
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def on_test_epoch_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
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"""Called when the test epoch begins."""
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pass
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def on_test_epoch_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
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"""Called when the test epoch ends."""
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pass
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def on_predict_epoch_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
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"""Called when the predict epoch begins."""
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pass
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def on_predict_epoch_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", outputs: List[Any]) -> None:
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"""Called when the predict epoch ends."""
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pass
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def on_epoch_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
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"""Called when either of train/val/test epoch begins."""
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pass
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def on_epoch_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
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"""Called when either of train/val/test epoch ends."""
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pass
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def on_batch_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
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"""Called when the training batch begins."""
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pass
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def on_validation_batch_start(
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self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", batch: Any, batch_idx: int, dataloader_idx: int
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) -> None:
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"""Called when the validation batch begins."""
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pass
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def on_validation_batch_end(
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self,
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trainer: "pl.Trainer",
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pl_module: "pl.LightningModule",
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outputs: Optional[STEP_OUTPUT],
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batch: Any,
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batch_idx: int,
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dataloader_idx: int,
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) -> None:
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"""Called when the validation batch ends."""
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pass
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def on_test_batch_start(
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self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", batch: Any, batch_idx: int, dataloader_idx: int
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) -> None:
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"""Called when the test batch begins."""
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pass
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def on_test_batch_end(
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self,
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trainer: "pl.Trainer",
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pl_module: "pl.LightningModule",
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outputs: Optional[STEP_OUTPUT],
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batch: Any,
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batch_idx: int,
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dataloader_idx: int,
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) -> None:
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"""Called when the test batch ends."""
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pass
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def on_predict_batch_start(
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self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", batch: Any, batch_idx: int, dataloader_idx: int
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) -> None:
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"""Called when the predict batch begins."""
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pass
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def on_predict_batch_end(
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self,
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trainer: "pl.Trainer",
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pl_module: "pl.LightningModule",
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outputs: Any,
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batch: Any,
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batch_idx: int,
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dataloader_idx: int,
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) -> None:
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"""Called when the predict batch ends."""
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pass
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def on_batch_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
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"""Called when the training batch ends."""
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pass
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def on_train_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
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"""Called when the train begins."""
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pass
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def on_train_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
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"""Called when the train ends."""
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pass
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def on_pretrain_routine_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
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"""Called when the pretrain routine begins."""
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pass
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def on_pretrain_routine_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
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"""Called when the pretrain routine ends."""
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pass
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def on_validation_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
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"""Called when the validation loop begins."""
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pass
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def on_validation_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
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"""Called when the validation loop ends."""
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pass
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def on_test_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
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"""Called when the test begins."""
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pass
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def on_test_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
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"""Called when the test ends."""
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pass
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def on_predict_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
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"""Called when the predict begins."""
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pass
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def on_predict_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
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"""Called when predict ends."""
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pass
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def on_keyboard_interrupt(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
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"""Called when the training is interrupted by ``KeyboardInterrupt``."""
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pass
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def on_save_checkpoint(
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self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", checkpoint: Dict[str, Any]
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) -> dict:
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"""
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Called when saving a model checkpoint, use to persist state.
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Args:
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trainer: the current :class:`~pytorch_lightning.trainer.Trainer` instance.
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pl_module: the current :class:`~pytorch_lightning.core.lightning.LightningModule` instance.
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checkpoint: the checkpoint dictionary that will be saved.
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Returns:
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The callback state.
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"""
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pass
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def on_load_checkpoint(
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self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", callback_state: Dict[str, Any]
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) -> None:
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"""Called when loading a model checkpoint, use to reload state.
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Args:
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trainer: the current :class:`~pytorch_lightning.trainer.Trainer` instance.
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pl_module: the current :class:`~pytorch_lightning.core.lightning.LightningModule` instance.
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callback_state: the callback state returned by ``on_save_checkpoint``.
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Note:
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The ``on_load_checkpoint`` won't be called with an undefined state.
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If your ``on_load_checkpoint`` hook behavior doesn't rely on a state,
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you will still need to override ``on_save_checkpoint`` to return a ``dummy state``.
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"""
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pass
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def on_before_backward(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", loss: torch.Tensor) -> None:
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"""Called before ``loss.backward()``."""
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pass
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def on_after_backward(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
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"""Called after ``loss.backward()`` and before optimizers are stepped."""
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pass
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def on_before_optimizer_step(
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self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", optimizer: Optimizer, opt_idx: int
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) -> None:
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"""Called before ``optimizer.step()``."""
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pass
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def on_before_zero_grad(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", optimizer: Optimizer) -> None:
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"""Called after ``optimizer.step()`` and before ``optimizer.zero_grad()``."""
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pass
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