Loop Refactor 4/N - Remove Old Evaluation Loop (#8056)
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@ -143,6 +143,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
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* Refactored internal loop interface; added new classes `FitLoop`, `TrainingEpochLoop`, `TrainingBatchLoop` ([#7871](https://github.com/PyTorchLightning/pytorch-lightning/pull/7871))
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* Removed `pytorch_lightning/trainer/training_loop.py` ([#7985](https://github.com/PyTorchLightning/pytorch-lightning/pull/7985))
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* Refactored evaluation loop interface; added new classes `DataLoaderLoop`, `EvaluationDataLoaderLoop`, `EvaluationEpochLoop` ([#7990](https://github.com/PyTorchLightning/pytorch-lightning/pull/7990))
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* Removed `pytorch_lightning/trainer/evaluation_loop.py` ([#8056](https://github.com/PyTorchLightning/pytorch-lightning/pull/8056))
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- Refactored logging
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* Renamed and moved `core/step_result.py` to `trainer/connectors/logger_connector/result.py` ([#7736](https://github.com/PyTorchLightning/pytorch-lightning/pull/7736))
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@ -1,270 +0,0 @@
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# 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, List, Optional, Tuple, Union
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from torch.utils.data import DataLoader
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import pytorch_lightning as pl
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from pytorch_lightning.trainer.connectors.logger_connector.result import ResultCollection
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from pytorch_lightning.trainer.states import TrainerFn
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from pytorch_lightning.trainer.supporters import PredictionCollection
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from pytorch_lightning.utilities.model_helpers import is_overridden
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from pytorch_lightning.utilities.types import EPOCH_OUTPUT, STEP_OUTPUT
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from pytorch_lightning.utilities.warnings import WarningCache
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class EvaluationLoop(object):
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def __init__(self, trainer: 'pl.Trainer'):
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self.trainer: 'pl.Trainer' = trainer
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self.outputs: EPOCH_OUTPUT = []
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self.predictions: Optional[PredictionCollection] = None
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self.max_batches: Optional[List[Union[int, float]]] = None
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self.warning_cache = WarningCache()
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self.num_dataloaders: Optional[int] = None
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self._val_results = ResultCollection(training=False)
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self._test_results = ResultCollection(training=False)
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@property
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def results(self) -> Optional[ResultCollection]:
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if self.trainer.validating or self.trainer.sanity_checking:
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return self._val_results
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elif self.trainer.testing:
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return self._test_results
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return None
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def on_trainer_init(self) -> None:
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self.trainer.num_sanity_val_batches = []
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self.trainer.num_test_batches = []
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self.trainer.num_val_batches = []
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self.trainer.test_dataloaders = None
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self.trainer.val_dataloaders = None
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# .validate() and .test() set this when they load a checkpoint
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self.trainer.validated_ckpt_path = None
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self.trainer.tested_ckpt_path = None
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# when true, print evaluation results in .validate() and .test()
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self.trainer.verbose_evaluate = True
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def get_evaluation_dataloaders(self) -> Tuple[Optional[List[DataLoader]], List[Union[int, float]]]:
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model = self.trainer.lightning_module
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# select dataloaders
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if self.trainer.testing:
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self.trainer.reset_test_dataloader(model)
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dataloaders = self.trainer.test_dataloaders
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max_batches = self.trainer.num_test_batches
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else:
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# val
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if self.trainer.val_dataloaders is None or self.trainer.reload_dataloaders_every_epoch:
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self.trainer.reset_val_dataloader(model)
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if self.trainer.sanity_checking:
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self.trainer.num_sanity_val_batches = [
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min(self.trainer.num_sanity_val_steps, val_batches) for val_batches in self.trainer.num_val_batches
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]
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max_batches = self.trainer.num_sanity_val_batches
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else:
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max_batches = self.trainer.num_val_batches
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dataloaders = self.trainer.val_dataloaders
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return dataloaders, max_batches
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def should_skip_evaluation(self, max_batches: List[Union[int, float]]) -> bool:
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return sum(max_batches) == 0
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def on_evaluation_start(self, *args: Any, **kwargs: Any) -> None:
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self.should_track_batch_outputs_for_epoch_end: bool = self._should_track_batch_outputs_for_epoch_end()
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assert self.results is not None
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self.results.to(device=self.trainer.lightning_module.device)
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if self.trainer.testing:
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self.trainer.call_hook('on_test_start', *args, **kwargs)
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else:
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self.trainer.call_hook('on_validation_start', *args, **kwargs)
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def on_evaluation_model_eval(self) -> None:
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model_ref = self.trainer.lightning_module
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if self.trainer.testing:
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model_ref.on_test_model_eval()
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else:
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model_ref.on_validation_model_eval()
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def on_evaluation_model_train(self) -> None:
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model_ref = self.trainer.lightning_module
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if self.trainer.testing:
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model_ref.on_test_model_train()
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else:
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model_ref.on_validation_model_train()
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def on_evaluation_end(self, *args: Any, **kwargs: Any) -> None:
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if self.trainer.testing:
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self.trainer.call_hook('on_test_end', *args, **kwargs)
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else:
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self.trainer.call_hook('on_validation_end', *args, **kwargs)
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if self.trainer.state.fn != TrainerFn.FITTING:
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# summarize profile results
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self.trainer.profiler.describe()
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# reset any `torchmetrics.Metric` and the logger connector state
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self.trainer.logger_connector.reset(metrics=True)
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def reload_evaluation_dataloaders(self) -> None:
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model = self.trainer.lightning_module
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if self.trainer.testing:
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self.trainer.reset_test_dataloader(model)
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else:
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self.trainer.reset_val_dataloader(model)
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def setup(self, max_batches: List[Union[int, float]], dataloaders: List[DataLoader]) -> None:
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# bookkeeping
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self.outputs = []
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self.predictions = PredictionCollection(self.trainer.global_rank, self.trainer.world_size)
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# convert max_batches to list
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if isinstance(max_batches, int):
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max_batches = [max_batches] * len(dataloaders)
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self.max_batches = max_batches
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self.num_dataloaders = self._get_num_dataloaders(dataloaders)
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def on_evaluation_epoch_start(self, *args: Any, **kwargs: Any) -> None:
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self.trainer.logger_connector.on_epoch_start()
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self.trainer.call_hook('on_epoch_start', *args, **kwargs)
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if self.trainer.testing:
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self.trainer.call_hook('on_test_epoch_start', *args, **kwargs)
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else:
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self.trainer.call_hook('on_validation_epoch_start', *args, **kwargs)
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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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# 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 = (
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not self.trainer.testing and self._get_num_dataloaders(self.trainer.val_dataloaders) > 1
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)
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multiple_test_loaders = (self.trainer.testing and self._get_num_dataloaders(self.trainer.test_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 _get_num_dataloaders(self, dataloaders: Optional[List[DataLoader]]) -> int:
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# case where user does:
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# return dl1, dl2
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if dataloaders is not None:
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length = len(dataloaders)
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if len(dataloaders) > 0 and isinstance(dataloaders[0], (list, tuple)):
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length = len(dataloaders[0])
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return length
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else:
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return 0
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def evaluation_step(self, batch: Any, batch_idx: int, dataloader_idx: int) -> Optional[STEP_OUTPUT]:
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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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if self.trainer.testing:
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output = self.trainer.call_hook('test_step_end', *args, **kwargs)
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else:
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output = self.trainer.call_hook('validation_step_end', *args, **kwargs)
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return output
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def _should_track_batch_outputs_for_epoch_end(self) -> bool:
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model = self.trainer.lightning_module
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if self.trainer.testing:
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return is_overridden('test_epoch_end', model)
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else:
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return is_overridden('validation_epoch_end', model)
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def evaluation_epoch_end(self, outputs: EPOCH_OUTPUT) -> None:
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# inform logger the batch loop has finished
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self.trainer.logger_connector.epoch_end_reached()
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# call the model epoch end
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model = self.trainer.lightning_module
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# unset dataloader_idx in model
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model._current_dataloader_idx = None
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if self.trainer.testing:
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if is_overridden('test_epoch_end', model):
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model._current_fx_name = 'test_epoch_end'
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model.test_epoch_end(outputs)
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else:
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if is_overridden('validation_epoch_end', model):
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model._current_fx_name = 'validation_epoch_end'
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model.validation_epoch_end(outputs)
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def on_evaluation_batch_start(self, batch: Any, batch_idx: int, dataloader_idx: int) -> None:
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self.trainer.logger_connector.on_batch_start()
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# set dataloader_idx to model and track batch_size
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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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if self.trainer.testing:
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self.trainer.call_hook('on_test_batch_end', output, batch, batch_idx, dataloader_idx)
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else:
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self.trainer.call_hook('on_validation_batch_end', 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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# 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 on_evaluation_epoch_end(self) -> None:
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hook_name = "on_test_epoch_end" if self.trainer.testing else "on_validation_epoch_end"
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self.trainer.call_hook(hook_name)
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self.trainer.call_hook('on_epoch_end')
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self.trainer.logger_connector.on_epoch_end()
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@ -44,7 +44,6 @@ exclude_lines =
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# *metrics (94%+) are temporarily removed from testing while tests speed up
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omit =
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pytorch_lightning/cluster_environments/*.py
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pytorch_lightning/trainer/evaluation_loop.py
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pytorch_lightning/utilities/distributed.py
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pytorch_lightning/tuner/auto_gpu_select.py
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