289 lines
12 KiB
Python
289 lines
12 KiB
Python
# Copyright The PyTorch Lightning team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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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.core.step_result import Result
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from pytorch_lightning.trainer.states import TrainerState
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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.signature_utils import is_param_in_hook_signature
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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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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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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 != TrainerState.FITTING:
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# summarize profile results
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self.trainer.profiler.describe()
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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.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_args(self, batch: Any, batch_idx: int, dataloader_idx: int) -> List[Union[Any, int]]:
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# make dataloader_idx arg in validation_step optional
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args = [batch, 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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args.append(dataloader_idx)
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return args
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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 args
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args = self._build_args(batch, batch_idx, dataloader_idx)
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model_ref = self.trainer.lightning_module
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model_ref._results = Result()
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if self.trainer.testing:
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model_ref._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(args)
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else:
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model_ref._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(args)
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# capture any logged information
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self.trainer.logger_connector.cache_logged_metrics()
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# track batch size for weighted average
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if isinstance(output, Result):
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output.track_batch_size(batch)
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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 evaluation_epoch_end(self, outputs: EPOCH_OUTPUT) -> None:
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# unset dataloder_idx in model
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self.trainer.logger_connector.evaluation_epoch_end()
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# call the model epoch end
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model = self.trainer.lightning_module
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if self.trainer.testing:
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if is_overridden('test_epoch_end', model=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=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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# capture logging
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self.trainer.logger_connector.cache_logged_metrics()
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def on_evaluation_batch_start(self, batch: Any, batch_idx: int, dataloader_idx: int) -> None:
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# set dataloader_idx to model and track batch_size
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self.trainer.logger_connector.on_evaluation_batch_start(batch, 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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# 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, Result) 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, outputs: Union[List[List[Dict]], List[Dict]]) -> None:
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model_ref = self.trainer.lightning_module
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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._reset_result_and_set_hook_fx_name(hook_name)
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with self.trainer.profiler.profile(hook_name):
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if hasattr(self.trainer, hook_name):
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on_evaluation_epoch_end_hook = getattr(self.trainer, hook_name)
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on_evaluation_epoch_end_hook(outputs)
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if is_overridden(hook_name, model_ref):
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model_hook_fx = getattr(model_ref, hook_name)
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if is_param_in_hook_signature(model_hook_fx, "outputs"):
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model_hook_fx(outputs)
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else:
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self.warning_cache.warn(
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f"`ModelHooks.{hook_name}` signature has changed in v1.3. `outputs` parameter has been added."
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" Support for the old signature will be removed in v1.5", DeprecationWarning
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)
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model_hook_fx()
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self.trainer._cache_logged_metrics()
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self.trainer.call_hook('on_epoch_end')
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def log_evaluation_step_metrics(self, batch_idx: int) -> None:
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if self.trainer.sanity_checking:
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return
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cached_results = self.trainer.logger_connector.cached_results
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if cached_results is not None:
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cached_batch_pbar_metrics, cached_batch_log_metrics = cached_results.update_logger_connector()
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if len(cached_batch_log_metrics) > 0:
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# make the metrics appear as a different line in the same graph
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metrics_by_epoch = {}
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for k, v in cached_batch_log_metrics.items():
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metrics_by_epoch[f'{k}/epoch_{self.trainer.current_epoch}'] = v
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self.trainer.logger_connector.log_metrics(metrics_by_epoch, {}, step=batch_idx)
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if len(cached_batch_pbar_metrics) > 0:
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self.trainer.logger_connector.add_progress_bar_metrics(cached_batch_pbar_metrics)
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