823 lines
31 KiB
Python
823 lines
31 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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"""Various hooks to be used in the Lightning code."""
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from typing import Any, Dict, List, Optional
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import torch
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from torch.optim.optimizer import Optimizer
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from pytorch_lightning.utilities import move_data_to_device
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from pytorch_lightning.utilities.types import EVAL_DATALOADERS, STEP_OUTPUT, TRAIN_DATALOADERS
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class ModelHooks:
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"""Hooks to be used in LightningModule."""
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def on_fit_start(self) -> None:
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"""Called at the very beginning of fit.
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If on DDP it is called on every process
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"""
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def on_fit_end(self) -> None:
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"""Called at the very end of fit.
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If on DDP it is called on every process
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"""
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def on_train_start(self) -> None:
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"""Called at the beginning of training after sanity check."""
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def on_train_end(self) -> None:
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"""Called at the end of training before logger experiment is closed."""
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def on_validation_start(self) -> None:
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"""Called at the beginning of validation."""
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def on_validation_end(self) -> None:
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"""Called at the end of validation."""
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def on_test_start(self) -> None:
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"""Called at the beginning of testing."""
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def on_test_end(self) -> None:
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"""Called at the end of testing."""
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def on_predict_start(self) -> None:
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"""Called at the beginning of predicting."""
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def on_predict_end(self) -> None:
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"""Called at the end of predicting."""
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def on_pretrain_routine_start(self) -> None:
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"""Called at the beginning of the pretrain routine (between fit and train start).
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- fit
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- pretrain_routine start
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- pretrain_routine end
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- training_start
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"""
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def on_pretrain_routine_end(self) -> None:
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"""Called at the end of the pretrain routine (between fit and train start).
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- fit
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- pretrain_routine start
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- pretrain_routine end
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- training_start
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"""
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def on_train_batch_start(self, batch: Any, batch_idx: int, unused: int = 0) -> None:
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"""Called in the training loop before anything happens for that batch.
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If you return -1 here, you will skip training for the rest of the current epoch.
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Args:
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batch: The batched data as it is returned by the training DataLoader.
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batch_idx: the index of the batch
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unused: Deprecated argument. Will be removed in v1.7.
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"""
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def on_train_batch_end(self, outputs: STEP_OUTPUT, batch: Any, batch_idx: int, unused: int = 0) -> None:
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"""Called in the training loop after the batch.
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Args:
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outputs: The outputs of training_step_end(training_step(x))
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batch: The batched data as it is returned by the training DataLoader.
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batch_idx: the index of the batch
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unused: Deprecated argument. Will be removed in v1.7.
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"""
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def on_validation_batch_start(self, batch: Any, batch_idx: int, dataloader_idx: int) -> None:
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"""Called in the validation loop before anything happens for that batch.
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Args:
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batch: The batched data as it is returned by the validation DataLoader.
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batch_idx: the index of the batch
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dataloader_idx: the index of the dataloader
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"""
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def on_validation_batch_end(
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self, outputs: Optional[STEP_OUTPUT], batch: Any, batch_idx: int, dataloader_idx: int
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) -> None:
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"""Called in the validation loop after the batch.
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Args:
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outputs: The outputs of validation_step_end(validation_step(x))
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batch: The batched data as it is returned by the validation DataLoader.
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batch_idx: the index of the batch
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dataloader_idx: the index of the dataloader
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"""
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def on_test_batch_start(self, batch: Any, batch_idx: int, dataloader_idx: int) -> None:
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"""Called in the test loop before anything happens for that batch.
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Args:
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batch: The batched data as it is returned by the test DataLoader.
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batch_idx: the index of the batch
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dataloader_idx: the index of the dataloader
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"""
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def on_test_batch_end(
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self, outputs: Optional[STEP_OUTPUT], batch: Any, batch_idx: int, dataloader_idx: int
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) -> None:
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"""Called in the test loop after the batch.
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Args:
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outputs: The outputs of test_step_end(test_step(x))
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batch: The batched data as it is returned by the test DataLoader.
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batch_idx: the index of the batch
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dataloader_idx: the index of the dataloader
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"""
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def on_predict_batch_start(self, batch: Any, batch_idx: int, dataloader_idx: int) -> None:
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"""Called in the predict loop before anything happens for that batch.
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Args:
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batch: The batched data as it is returned by the test DataLoader.
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batch_idx: the index of the batch
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dataloader_idx: the index of the dataloader
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"""
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def on_predict_batch_end(self, outputs: Optional[Any], batch: Any, batch_idx: int, dataloader_idx: int) -> None:
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"""Called in the predict loop after the batch.
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Args:
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outputs: The outputs of predict_step_end(test_step(x))
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batch: The batched data as it is returned by the test DataLoader.
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batch_idx: the index of the batch
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dataloader_idx: the index of the dataloader
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"""
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def on_validation_model_eval(self) -> None:
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"""Sets the model to eval during the val loop."""
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self.trainer.model.eval()
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def on_validation_model_train(self) -> None:
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"""Sets the model to train during the val loop."""
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self.trainer.model.train()
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def on_test_model_train(self) -> None:
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"""Sets the model to train during the test loop."""
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self.trainer.model.train()
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def on_test_model_eval(self) -> None:
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"""Sets the model to eval during the test loop."""
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self.trainer.model.eval()
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def on_predict_model_eval(self) -> None:
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"""Sets the model to eval during the predict loop."""
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self.trainer.model.eval()
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def on_epoch_start(self) -> None:
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"""Called when either of train/val/test epoch begins."""
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def on_epoch_end(self) -> None:
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"""Called when either of train/val/test epoch ends."""
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def on_train_epoch_start(self) -> None:
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"""Called in the training loop at the very beginning of the epoch."""
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def on_train_epoch_end(self) -> None:
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"""Called in the training loop at the very end of the epoch.
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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 OR
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2. Cache data across steps on the attribute(s) of the `LightningModule` and access them in this hook
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"""
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def on_validation_epoch_start(self) -> None:
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"""Called in the validation loop at the very beginning of the epoch."""
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def on_validation_epoch_end(self) -> None:
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"""Called in the validation loop at the very end of the epoch."""
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def on_test_epoch_start(self) -> None:
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"""Called in the test loop at the very beginning of the epoch."""
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def on_test_epoch_end(self) -> None:
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"""Called in the test loop at the very end of the epoch."""
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def on_predict_epoch_start(self) -> None:
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"""Called at the beginning of predicting."""
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def on_predict_epoch_end(self, results: List[Any]) -> None:
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"""Called at the end of predicting."""
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def on_before_zero_grad(self, optimizer: Optimizer) -> None:
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"""Called after ``training_step()`` and before ``optimizer.zero_grad()``.
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Called in the training loop after taking an optimizer step and before zeroing grads.
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Good place to inspect weight information with weights updated.
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This is where it is called::
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for optimizer in optimizers:
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out = training_step(...)
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model.on_before_zero_grad(optimizer) # < ---- called here
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optimizer.zero_grad()
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backward()
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Args:
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optimizer: The optimizer for which grads should be zeroed.
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"""
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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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Args:
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loss: Loss divided by number of batches for gradient accumulation and scaled if using native AMP.
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"""
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pass
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def on_after_backward(self) -> None:
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"""Called after ``loss.backward()`` and before optimizers are stepped.
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Note:
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If using native AMP, the gradients will not be unscaled at this point.
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Use the ``on_before_optimizer_step`` if you need the unscaled gradients.
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"""
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def on_before_optimizer_step(self, optimizer: Optimizer, optimizer_idx: int) -> None:
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"""Called before ``optimizer.step()``.
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The hook is only called if gradients do not need to be accumulated.
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See: :paramref:`~pytorch_lightning.trainer.Trainer.accumulate_grad_batches`.
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If using native AMP, the loss will be unscaled before calling this hook.
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See these `docs <https://pytorch.org/docs/stable/notes/amp_examples.html#working-with-unscaled-gradients>`__
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for more information on the scaling of gradients.
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If clipping gradients, the gradients will not have been clipped yet.
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Args:
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optimizer: Current optimizer being used.
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optimizer_idx: Index of the current optimizer being used.
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Example::
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def on_before_optimizer_step(self, optimizer, optimizer_idx):
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# example to inspect gradient information in tensorboard
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if self.trainer.global_step % 25 == 0: # don't make the tf file huge
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for k, v in self.named_parameters():
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self.logger.experiment.add_histogram(
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tag=k, values=v.grad, global_step=self.trainer.global_step
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)
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"""
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def on_post_move_to_device(self) -> None:
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"""Called in the ``parameter_validation`` decorator after
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:meth:`~pytorch_lightning.core.LightningModule.to` is called. This is a good place to tie weights between
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modules after moving them to a device. Can be used when training models with weight sharing properties on
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TPU.
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Addresses the handling of shared weights on TPU:
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https://github.com/pytorch/xla/blob/master/TROUBLESHOOTING.md#xla-tensor-quirks
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Example::
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def on_post_move_to_device(self):
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self.decoder.weight = self.encoder.weight
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"""
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def configure_sharded_model(self) -> None:
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"""Hook to create modules in a distributed aware context. This is useful for when using sharded plugins,
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where we'd like to shard the model instantly, which is useful for extremely large models which can save
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memory and initialization time.
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This hook is called during each of fit/val/test/predict stages in the same process, so ensure that
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implementation of this hook is idempotent.
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"""
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class DataHooks:
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"""Hooks to be used for data related stuff."""
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def __init__(self) -> None:
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"""
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Attributes:
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prepare_data_per_node:
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If True, each LOCAL_RANK=0 will call prepare data.
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Otherwise only NODE_RANK=0, LOCAL_RANK=0 will prepare data.
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allow_zero_length_dataloader_with_multiple_devices:
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If True, dataloader with zero length within local rank is allowed.
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Default value is False.
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"""
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super().__init__()
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self.prepare_data_per_node: bool = True
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self.allow_zero_length_dataloader_with_multiple_devices: bool = False
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def prepare_data(self) -> None:
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"""Use this to download and prepare data.
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.. warning:: DO NOT set state to the model (use `setup` instead)
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since this is NOT called on every GPU in DDP/TPU
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Example::
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def prepare_data(self):
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# good
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download_data()
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tokenize()
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etc()
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# bad
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self.split = data_split
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self.some_state = some_other_state()
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In DDP prepare_data can be called in two ways (using Trainer(prepare_data_per_node)):
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1. Once per node. This is the default and is only called on LOCAL_RANK=0.
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2. Once in total. Only called on GLOBAL_RANK=0.
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Example::
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# DEFAULT
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# called once per node on LOCAL_RANK=0 of that node
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Trainer(prepare_data_per_node=True)
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# call on GLOBAL_RANK=0 (great for shared file systems)
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Trainer(prepare_data_per_node=False)
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Note:
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Setting ``prepare_data_per_node`` with the trainer flag is deprecated and will be removed in v1.7.0.
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Please set ``prepare_data_per_node`` in LightningDataModule or LightningModule directly instead.
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This is called before requesting the dataloaders:
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.. code-block:: python
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model.prepare_data()
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initialize_distributed()
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model.setup(stage)
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model.train_dataloader()
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model.val_dataloader()
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model.test_dataloader()
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"""
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def setup(self, stage: Optional[str] = None) -> None:
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"""Called at the beginning of fit (train + validate), validate, test, and predict. This is a good hook when
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you need to build models dynamically or adjust something about them. This hook is called on every process
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when using DDP.
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Args:
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stage: either ``'fit'``, ``'validate'``, ``'test'``, or ``'predict'``
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Example::
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class LitModel(...):
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def __init__(self):
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self.l1 = None
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def prepare_data(self):
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download_data()
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tokenize()
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# don't do this
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self.something = else
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def setup(stage):
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data = Load_data(...)
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self.l1 = nn.Linear(28, data.num_classes)
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"""
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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, predict, or tune.
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Args:
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stage: either ``'fit'``, ``'validate'``, ``'test'``, or ``'predict'``
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"""
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def train_dataloader(self) -> TRAIN_DATALOADERS:
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"""Implement one or more PyTorch DataLoaders for training.
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Return:
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A collection of :class:`torch.utils.data.DataLoader` specifying training samples.
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In the case of multiple dataloaders, please see this :ref:`page <multiple-training-dataloaders>`.
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The dataloader you return will not be reloaded unless you set
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:paramref:`~pytorch_lightning.trainer.Trainer.reload_dataloaders_every_n_epochs` to
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a positive integer.
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For data processing use the following pattern:
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- download in :meth:`prepare_data`
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- process and split in :meth:`setup`
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However, the above are only necessary for distributed processing.
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.. warning:: do not assign state in prepare_data
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- :meth:`~pytorch_lightning.trainer.Trainer.fit`
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- ...
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- :meth:`prepare_data`
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- :meth:`setup`
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- :meth:`train_dataloader`
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Note:
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Lightning adds the correct sampler for distributed and arbitrary hardware.
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There is no need to set it yourself.
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Example::
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# single dataloader
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def train_dataloader(self):
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transform = transforms.Compose([transforms.ToTensor(),
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transforms.Normalize((0.5,), (1.0,))])
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dataset = MNIST(root='/path/to/mnist/', train=True, transform=transform,
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download=True)
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loader = torch.utils.data.DataLoader(
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dataset=dataset,
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batch_size=self.batch_size,
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shuffle=True
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)
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return loader
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# multiple dataloaders, return as list
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def train_dataloader(self):
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mnist = MNIST(...)
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cifar = CIFAR(...)
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mnist_loader = torch.utils.data.DataLoader(
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dataset=mnist, batch_size=self.batch_size, shuffle=True
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)
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cifar_loader = torch.utils.data.DataLoader(
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dataset=cifar, batch_size=self.batch_size, shuffle=True
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)
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# each batch will be a list of tensors: [batch_mnist, batch_cifar]
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return [mnist_loader, cifar_loader]
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# multiple dataloader, return as dict
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def train_dataloader(self):
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mnist = MNIST(...)
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cifar = CIFAR(...)
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mnist_loader = torch.utils.data.DataLoader(
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dataset=mnist, batch_size=self.batch_size, shuffle=True
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)
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cifar_loader = torch.utils.data.DataLoader(
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dataset=cifar, batch_size=self.batch_size, shuffle=True
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)
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# each batch will be a dict of tensors: {'mnist': batch_mnist, 'cifar': batch_cifar}
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return {'mnist': mnist_loader, 'cifar': cifar_loader}
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"""
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raise NotImplementedError("`train_dataloader` must be implemented to be used with the Lightning Trainer")
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def test_dataloader(self) -> EVAL_DATALOADERS:
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r"""
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Implement one or multiple PyTorch DataLoaders for testing.
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The dataloader you return will not be reloaded unless you set
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:paramref:`~pytorch_lightning.trainer.Trainer.reload_dataloaders_every_n_epochs` to
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a postive integer.
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For data processing use the following pattern:
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- download in :meth:`prepare_data`
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- process and split in :meth:`setup`
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However, the above are only necessary for distributed processing.
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.. warning:: do not assign state in prepare_data
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- :meth:`~pytorch_lightning.trainer.Trainer.fit`
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- ...
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- :meth:`prepare_data`
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- :meth:`setup`
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- :meth:`train_dataloader`
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- :meth:`val_dataloader`
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- :meth:`test_dataloader`
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Note:
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Lightning adds the correct sampler for distributed and arbitrary hardware.
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There is no need to set it yourself.
|
|
|
|
Return:
|
|
A :class:`torch.utils.data.DataLoader` or a sequence of them specifying testing samples.
|
|
|
|
Example::
|
|
|
|
def test_dataloader(self):
|
|
transform = transforms.Compose([transforms.ToTensor(),
|
|
transforms.Normalize((0.5,), (1.0,))])
|
|
dataset = MNIST(root='/path/to/mnist/', train=False, transform=transform,
|
|
download=True)
|
|
loader = torch.utils.data.DataLoader(
|
|
dataset=dataset,
|
|
batch_size=self.batch_size,
|
|
shuffle=False
|
|
)
|
|
|
|
return loader
|
|
|
|
# can also return multiple dataloaders
|
|
def test_dataloader(self):
|
|
return [loader_a, loader_b, ..., loader_n]
|
|
|
|
Note:
|
|
If you don't need a test dataset and a :meth:`test_step`, you don't need to implement
|
|
this method.
|
|
|
|
Note:
|
|
In the case where you return multiple test dataloaders, the :meth:`test_step`
|
|
will have an argument ``dataloader_idx`` which matches the order here.
|
|
"""
|
|
raise NotImplementedError("`test_dataloader` must be implemented to be used with the Lightning Trainer")
|
|
|
|
def val_dataloader(self) -> EVAL_DATALOADERS:
|
|
r"""
|
|
Implement one or multiple PyTorch DataLoaders for validation.
|
|
|
|
The dataloader you return will not be reloaded unless you set
|
|
:paramref:`~pytorch_lightning.trainer.Trainer.reload_dataloaders_every_n_epochs` to
|
|
a positive integer.
|
|
|
|
It's recommended that all data downloads and preparation happen in :meth:`prepare_data`.
|
|
|
|
- :meth:`~pytorch_lightning.trainer.Trainer.fit`
|
|
- ...
|
|
- :meth:`prepare_data`
|
|
- :meth:`train_dataloader`
|
|
- :meth:`val_dataloader`
|
|
- :meth:`test_dataloader`
|
|
|
|
Note:
|
|
Lightning adds the correct sampler for distributed and arbitrary hardware
|
|
There is no need to set it yourself.
|
|
|
|
Return:
|
|
A :class:`torch.utils.data.DataLoader` or a sequence of them specifying validation samples.
|
|
|
|
Examples::
|
|
|
|
def val_dataloader(self):
|
|
transform = transforms.Compose([transforms.ToTensor(),
|
|
transforms.Normalize((0.5,), (1.0,))])
|
|
dataset = MNIST(root='/path/to/mnist/', train=False,
|
|
transform=transform, download=True)
|
|
loader = torch.utils.data.DataLoader(
|
|
dataset=dataset,
|
|
batch_size=self.batch_size,
|
|
shuffle=False
|
|
)
|
|
|
|
return loader
|
|
|
|
# can also return multiple dataloaders
|
|
def val_dataloader(self):
|
|
return [loader_a, loader_b, ..., loader_n]
|
|
|
|
Note:
|
|
If you don't need a validation dataset and a :meth:`validation_step`, you don't need to
|
|
implement this method.
|
|
|
|
Note:
|
|
In the case where you return multiple validation dataloaders, the :meth:`validation_step`
|
|
will have an argument ``dataloader_idx`` which matches the order here.
|
|
"""
|
|
raise NotImplementedError("`val_dataloader` must be implemented to be used with the Lightning Trainer")
|
|
|
|
def predict_dataloader(self) -> EVAL_DATALOADERS:
|
|
r"""
|
|
Implement one or multiple PyTorch DataLoaders for prediction.
|
|
|
|
It's recommended that all data downloads and preparation happen in :meth:`prepare_data`.
|
|
|
|
- :meth:`~pytorch_lightning.trainer.Trainer.fit`
|
|
- ...
|
|
- :meth:`prepare_data`
|
|
- :meth:`train_dataloader`
|
|
- :meth:`val_dataloader`
|
|
- :meth:`test_dataloader`
|
|
|
|
Note:
|
|
Lightning adds the correct sampler for distributed and arbitrary hardware
|
|
There is no need to set it yourself.
|
|
|
|
Return:
|
|
A :class:`torch.utils.data.DataLoader` or a sequence of them specifying prediction samples.
|
|
|
|
Note:
|
|
In the case where you return multiple prediction dataloaders, the :meth:`predict`
|
|
will have an argument ``dataloader_idx`` which matches the order here.
|
|
"""
|
|
raise NotImplementedError("`predict_dataloader` must be implemented to be used with the Lightning Trainer")
|
|
|
|
def on_train_dataloader(self) -> None:
|
|
"""Called before requesting the train dataloader.
|
|
|
|
.. deprecated:: v1.5
|
|
:meth:`on_train_dataloader` is deprecated and will be removed in v1.7.0.
|
|
Please use :meth:`train_dataloader()` directly.
|
|
"""
|
|
|
|
def on_val_dataloader(self) -> None:
|
|
"""Called before requesting the val dataloader.
|
|
|
|
.. deprecated:: v1.5
|
|
:meth:`on_val_dataloader` is deprecated and will be removed in v1.7.0.
|
|
Please use :meth:`val_dataloader()` directly.
|
|
"""
|
|
|
|
def on_test_dataloader(self) -> None:
|
|
"""Called before requesting the test dataloader.
|
|
|
|
.. deprecated:: v1.5
|
|
:meth:`on_test_dataloader` is deprecated and will be removed in v1.7.0.
|
|
Please use :meth:`test_dataloader()` directly.
|
|
"""
|
|
|
|
def on_predict_dataloader(self) -> None:
|
|
"""Called before requesting the predict dataloader.
|
|
|
|
.. deprecated:: v1.5
|
|
:meth:`on_predict_dataloader` is deprecated and will be removed in v1.7.0.
|
|
Please use :meth:`predict_dataloader()` directly.
|
|
"""
|
|
|
|
def transfer_batch_to_device(self, batch: Any, device: torch.device, dataloader_idx: int) -> Any:
|
|
"""Override this hook if your :class:`~torch.utils.data.DataLoader` returns tensors wrapped in a custom
|
|
data structure.
|
|
|
|
The data types listed below (and any arbitrary nesting of them) are supported out of the box:
|
|
|
|
- :class:`torch.Tensor` or anything that implements `.to(...)`
|
|
- :class:`list`
|
|
- :class:`dict`
|
|
- :class:`tuple`
|
|
- :class:`torchtext.data.batch.Batch`
|
|
|
|
For anything else, you need to define how the data is moved to the target device (CPU, GPU, TPU, ...).
|
|
|
|
Note:
|
|
This hook should only transfer the data and not modify it, nor should it move the data to
|
|
any other device than the one passed in as argument (unless you know what you are doing).
|
|
To check the current state of execution of this hook you can use
|
|
``self.trainer.training/testing/validating/predicting`` so that you can
|
|
add different logic as per your requirement.
|
|
|
|
Note:
|
|
This hook only runs on single GPU training and DDP (no data-parallel).
|
|
Data-Parallel support will come in near future.
|
|
|
|
Args:
|
|
batch: A batch of data that needs to be transferred to a new device.
|
|
device: The target device as defined in PyTorch.
|
|
dataloader_idx: The index of the dataloader to which the batch belongs.
|
|
|
|
Returns:
|
|
A reference to the data on the new device.
|
|
|
|
Example::
|
|
|
|
def transfer_batch_to_device(self, batch, device, dataloader_idx):
|
|
if isinstance(batch, CustomBatch):
|
|
# move all tensors in your custom data structure to the device
|
|
batch.samples = batch.samples.to(device)
|
|
batch.targets = batch.targets.to(device)
|
|
elif dataloader_idx == 0:
|
|
# skip device transfer for the first dataloader or anything you wish
|
|
pass
|
|
else:
|
|
batch = super().transfer_batch_to_device(data, device)
|
|
return batch
|
|
|
|
Raises:
|
|
MisconfigurationException:
|
|
If using data-parallel, ``Trainer(strategy='dp')``.
|
|
|
|
See Also:
|
|
- :meth:`move_data_to_device`
|
|
- :meth:`apply_to_collection`
|
|
"""
|
|
return move_data_to_device(batch, device)
|
|
|
|
def on_before_batch_transfer(self, batch: Any, dataloader_idx: int) -> Any:
|
|
"""Override to alter or apply batch augmentations to your batch before it is transferred to the device.
|
|
|
|
Note:
|
|
To check the current state of execution of this hook you can use
|
|
``self.trainer.training/testing/validating/predicting`` so that you can
|
|
add different logic as per your requirement.
|
|
|
|
Note:
|
|
This hook only runs on single GPU training and DDP (no data-parallel).
|
|
Data-Parallel support will come in near future.
|
|
|
|
Args:
|
|
batch: A batch of data that needs to be altered or augmented.
|
|
dataloader_idx: The index of the dataloader to which the batch belongs.
|
|
|
|
Returns:
|
|
A batch of data
|
|
|
|
Example::
|
|
|
|
def on_before_batch_transfer(self, batch, dataloader_idx):
|
|
batch['x'] = transforms(batch['x'])
|
|
return batch
|
|
|
|
Raises:
|
|
MisconfigurationException:
|
|
If using data-parallel, ``Trainer(strategy='dp')``.
|
|
|
|
See Also:
|
|
- :meth:`on_after_batch_transfer`
|
|
- :meth:`transfer_batch_to_device`
|
|
"""
|
|
return batch
|
|
|
|
def on_after_batch_transfer(self, batch: Any, dataloader_idx: int) -> Any:
|
|
"""Override to alter or apply batch augmentations to your batch after it is transferred to the device.
|
|
|
|
Note:
|
|
To check the current state of execution of this hook you can use
|
|
``self.trainer.training/testing/validating/predicting`` so that you can
|
|
add different logic as per your requirement.
|
|
|
|
Note:
|
|
This hook only runs on single GPU training and DDP (no data-parallel).
|
|
Data-Parallel support will come in near future.
|
|
|
|
Args:
|
|
batch: A batch of data that needs to be altered or augmented.
|
|
dataloader_idx: The index of the dataloader to which the batch belongs.
|
|
|
|
Returns:
|
|
A batch of data
|
|
|
|
Example::
|
|
|
|
def on_after_batch_transfer(self, batch, dataloader_idx):
|
|
batch['x'] = gpu_transforms(batch['x'])
|
|
return batch
|
|
|
|
Raises:
|
|
MisconfigurationException:
|
|
If using data-parallel, ``Trainer(strategy='dp')``.
|
|
|
|
See Also:
|
|
- :meth:`on_before_batch_transfer`
|
|
- :meth:`transfer_batch_to_device`
|
|
"""
|
|
return batch
|
|
|
|
|
|
class CheckpointHooks:
|
|
"""Hooks to be used with Checkpointing."""
|
|
|
|
def on_load_checkpoint(self, checkpoint: Dict[str, Any]) -> None:
|
|
r"""
|
|
Called by Lightning to restore your model.
|
|
If you saved something with :meth:`on_save_checkpoint` this is your chance to restore this.
|
|
|
|
Args:
|
|
checkpoint: Loaded checkpoint
|
|
|
|
Example::
|
|
|
|
def on_load_checkpoint(self, checkpoint):
|
|
# 99% of the time you don't need to implement this method
|
|
self.something_cool_i_want_to_save = checkpoint['something_cool_i_want_to_save']
|
|
|
|
Note:
|
|
Lightning auto-restores global step, epoch, and train state including amp scaling.
|
|
There is no need for you to restore anything regarding training.
|
|
"""
|
|
|
|
def on_save_checkpoint(self, checkpoint: Dict[str, Any]) -> None:
|
|
r"""
|
|
Called by Lightning when saving a checkpoint to give you a chance to store anything
|
|
else you might want to save.
|
|
|
|
Args:
|
|
checkpoint: The full checkpoint dictionary before it gets dumped to a file.
|
|
Implementations of this hook can insert additional data into this dictionary.
|
|
|
|
Example::
|
|
|
|
def on_save_checkpoint(self, checkpoint):
|
|
# 99% of use cases you don't need to implement this method
|
|
checkpoint['something_cool_i_want_to_save'] = my_cool_pickable_object
|
|
|
|
Note:
|
|
Lightning saves all aspects of training (epoch, global step, etc...)
|
|
including amp scaling.
|
|
There is no need for you to store anything about training.
|
|
|
|
"""
|