257 lines
10 KiB
Python
257 lines
10 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 collections import OrderedDict
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from typing import Any, Dict, Iterator, List, Optional, Union
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from deprecate import void
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from torch import Tensor
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from pytorch_lightning.loops.base import Loop
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from pytorch_lightning.trainer.connectors.logger_connector.result import ResultCollection
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from pytorch_lightning.trainer.supporters import PredictionCollection
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from pytorch_lightning.utilities.memory import recursive_detach
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from pytorch_lightning.utilities.types import STEP_OUTPUT
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class EvaluationEpochLoop(Loop):
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"""
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This is the loop performing the evaluation. It mainly loops over the given dataloader and runs the validation
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or test step (depending on the trainer's current state).
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"""
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def __init__(self) -> None:
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super().__init__()
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self.predictions: Optional[PredictionCollection] = None
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self.dataloader: Optional[Iterator] = None
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self.dl_max_batches: Optional[int] = None
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self.dataloader_idx: Optional[int] = None
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self.num_dataloaders: Optional[int] = None
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self.outputs: List[STEP_OUTPUT] = []
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@property
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def done(self) -> bool:
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"""Returns ``True`` if the current iteration count reaches the number of dataloader batches."""
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return self.iteration_count >= self.dl_max_batches
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def reset(self) -> None:
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"""Resets the loop's internal state."""
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self.iteration_count = 0
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self.predictions = PredictionCollection(self.trainer.global_rank, self.trainer.world_size)
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self.dl_max_batches = None
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self.dataloader_idx = None
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self.num_dataloaders = None
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self.outputs = []
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def on_run_start(
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self,
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dataloader_iter: Iterator,
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dataloader_idx: int,
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dl_max_batches: int,
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num_dataloaders: int,
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) -> None:
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"""Adds the passed arguments to the loop's state if necessary
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Args:
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dataloader_iter: iterator over the dataloader
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dataloader_idx: index of the current dataloader
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dl_max_batches: maximum number of batches the dataloader can produce
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num_dataloaders: the total number of dataloaders
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"""
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void(dataloader_iter)
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self.dl_max_batches = dl_max_batches
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self.dataloader_idx = dataloader_idx
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self.num_dataloaders = num_dataloaders
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def advance(
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self,
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dataloader_iter: Iterator,
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dataloader_idx: int,
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dl_max_batches: int,
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num_dataloaders: int,
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) -> None:
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"""Calls the evaluation step with the corresponding hooks and updates the logger connector.
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Args:
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dataloader_iter: iterator over the dataloader
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dataloader_idx: index of the current dataloader
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dl_max_batches: maximum number of batches the dataloader can produce
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num_dataloaders: the total number of dataloaders
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Raises:
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StopIteration: If the current batch is None
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"""
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void(dl_max_batches, num_dataloaders)
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batch_idx, batch = next(dataloader_iter)
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if batch is None:
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raise StopIteration
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# hook
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self.on_evaluation_batch_start(batch, batch_idx, dataloader_idx)
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# lightning module methods
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with self.trainer.profiler.profile("evaluation_step_and_end"):
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output = self.evaluation_step(batch, batch_idx, dataloader_idx)
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output = self.evaluation_step_end(output)
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# hook + store predictions
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self.on_evaluation_batch_end(output, batch, batch_idx, dataloader_idx)
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# log batch metrics
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self.trainer.logger_connector.update_eval_step_metrics()
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# track epoch level outputs
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self.outputs = self._track_output_for_epoch_end(self.outputs, output)
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def on_run_end(self) -> List[STEP_OUTPUT]:
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"""Returns the outputs of the whole run"""
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return self.outputs
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def teardown(self) -> None:
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"""Frees memory of tracked outputs"""
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self.outputs = []
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def evaluation_step(self, batch: Any, batch_idx: int, dataloader_idx: int) -> Optional[STEP_OUTPUT]:
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"""The evaluation step (validation_step or test_step depending on the trainer's state).
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Args:
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batch: The current batch to run through the step.
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batch_idx: The index of the current batch
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dataloader_idx: the index of the dataloader producing the current batch
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Returns:
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the outputs of the step
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"""
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# configure step_kwargs
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step_kwargs = self._build_kwargs(batch, batch_idx, dataloader_idx)
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if self.trainer.testing:
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self.trainer.lightning_module._current_fx_name = "test_step"
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with self.trainer.profiler.profile("test_step"):
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output = self.trainer.accelerator.test_step(step_kwargs)
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else:
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self.trainer.lightning_module._current_fx_name = "validation_step"
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with self.trainer.profiler.profile("validation_step"):
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output = self.trainer.accelerator.validation_step(step_kwargs)
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return output
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def evaluation_step_end(self, *args: Any, **kwargs: Any) -> Optional[STEP_OUTPUT]:
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"""Calls the `{validation/test}_step_end` hook"""
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hook_name = "test_step_end" if self.trainer.testing else "validation_step_end"
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output = self.trainer.call_hook(hook_name, *args, **kwargs)
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return output
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def on_evaluation_batch_start(self, batch: Any, batch_idx: int, dataloader_idx: int) -> None:
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"""Calls the ``on_{validation/test}_batch_start`` hook.
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Args:
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batch: The current batch to run through the step
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batch_idx: The index of the current batch
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dataloader_idx: The index of the dataloader producing the current batch
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Raises:
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AssertionError: If the number of dataloaders is None (has not yet been set).
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"""
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self.trainer.logger_connector.on_batch_start()
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assert self.num_dataloaders is not None
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self.trainer.logger_connector.on_evaluation_batch_start(batch, batch_idx, dataloader_idx, self.num_dataloaders)
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if self.trainer.testing:
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self.trainer.call_hook("on_test_batch_start", batch, batch_idx, dataloader_idx)
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else:
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self.trainer.call_hook("on_validation_batch_start", batch, batch_idx, dataloader_idx)
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def on_evaluation_batch_end(
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self,
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output: 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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"""The ``on_{validation/test}_batch_end`` hook.
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Args:
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output: The output of the performed step
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batch: The input batch for the step
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batch_idx: The index of the current batch
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dataloader_idx: Index of the dataloader producing the current batch
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"""
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hook_name = "on_test_batch_end" if self.trainer.testing else "on_validation_batch_end"
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self.trainer.call_hook(hook_name, output, batch, batch_idx, dataloader_idx)
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self.trainer.logger_connector.on_batch_end()
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# store predicitons if do_write_predictions and track eval loss history
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self.store_predictions(output, batch_idx, dataloader_idx)
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def store_predictions(self, output: Optional[STEP_OUTPUT], batch_idx: int, dataloader_idx: int) -> None:
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"""Stores the predictions in the prediction collection (only if running in test mode)
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Args:
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output: the outputs of the current step
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batch_idx: the index of the current batch
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dataloader_idx: the index of the dataloader producing the current batch
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"""
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# Add step predictions to prediction collection to write later
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if output is not None and self.predictions is not None:
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if isinstance(output, ResultCollection) and self.trainer.testing:
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self.predictions.add(output.pop("predictions", None))
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# track debug metrics
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self.trainer.dev_debugger.track_eval_loss_history(batch_idx, dataloader_idx, output)
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def _build_kwargs(self, batch: Any, batch_idx: int, dataloader_idx: int) -> Dict[str, Union[Any, int]]:
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"""Helper function to build the arguments for the current step
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Args:
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batch: The current batch to run through the step
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batch_idx: the index of the current batch
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dataloader_idx: the index of the dataloader producing the current batch
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Returns:
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the keyword arguments to pass to the step function
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"""
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# make dataloader_idx arg in validation_step optional
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step_kwargs = OrderedDict([("batch", batch), ("batch_idx", batch_idx)])
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multiple_val_loaders = not self.trainer.testing and self.num_dataloaders > 1
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multiple_test_loaders = self.trainer.testing and self.num_dataloaders > 1
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if multiple_test_loaders or multiple_val_loaders:
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step_kwargs["dataloader_idx"] = dataloader_idx
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return step_kwargs
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def _track_output_for_epoch_end(
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self,
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outputs: List[Union[ResultCollection, Dict, Tensor]],
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output: Optional[Union[ResultCollection, Dict, Tensor]],
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) -> List[Union[ResultCollection, Dict, Tensor]]:
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if output is not None:
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if isinstance(output, ResultCollection):
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output = output.detach()
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if self.trainer.move_metrics_to_cpu:
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output = output.cpu()
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elif isinstance(output, dict):
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output = recursive_detach(output, to_cpu=self.trainer.move_metrics_to_cpu)
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elif isinstance(output, Tensor) and output.is_cuda and self.trainer.move_metrics_to_cpu:
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output = output.cpu()
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outputs.append(output)
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return outputs
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