235 lines
9.4 KiB
Python
235 lines
9.4 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.loops.utilities import _prepare_dataloader_iter
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from pytorch_lightning.trainer.progress import Progress
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from pytorch_lightning.utilities.fetching import AbstractDataFetcher
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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.dataloader: Optional[Iterator] = None
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self._dl_max_batches: 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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self.batch_progress = Progress()
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self.dataloader_iter: Optional[Iterator] = None
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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.batch_progress.current.completed >= self._dl_max_batches
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def connect(self, **kwargs: "Loop") -> None:
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raise NotImplementedError(f"{self.__class__.__name__} does not connect any child loops.")
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def reset(self) -> None:
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"""Resets the loop's internal state."""
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self._dl_max_batches = None
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self._num_dataloaders = None
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self.outputs = []
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if not self.restarting:
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self.batch_progress.current.reset()
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def on_run_start(
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self, data_fetcher: AbstractDataFetcher, dataloader_idx: int, dl_max_batches: int, 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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data_fetcher: the current data_fetcher wrapping 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_idx)
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self._dl_max_batches = dl_max_batches
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self._num_dataloaders = num_dataloaders
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self.dataloader_iter = _prepare_dataloader_iter(data_fetcher, self.batch_progress.current.ready)
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def advance(
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self, data_fetcher: AbstractDataFetcher, dataloader_idx: int, dl_max_batches: int, 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(data_fetcher, dl_max_batches, num_dataloaders)
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batch_idx, (batch, _) = next(self.dataloader_iter)
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if batch is None:
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raise StopIteration
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if not self.trainer.data_connector.evaluation_data_fetcher.store_on_device:
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with self.trainer.profiler.profile("evaluation_batch_to_device"):
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batch = self.trainer.accelerator.batch_to_device(batch, dataloader_idx=dataloader_idx)
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self.batch_progress.increment_ready()
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# hook
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self.on_evaluation_batch_start(batch, batch_idx, dataloader_idx)
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self.batch_progress.increment_started()
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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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self.batch_progress.increment_processed()
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# track loss history
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self.on_evaluation_batch_end(output, batch, batch_idx, dataloader_idx)
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self.batch_progress.increment_completed()
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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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outputs = self.outputs
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# free memory
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self.outputs = []
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return 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, output: Optional[STEP_OUTPUT], batch: Any, batch_idx: int, 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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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, outputs: List[STEP_OUTPUT], output: Optional[STEP_OUTPUT]
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) -> List[STEP_OUTPUT]:
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if output is not None:
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if 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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