# Copyright The PyTorch Lightning team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import Any, List, Optional, Sequence, Union from deprecate.utils import void from torch.utils.data.dataloader import DataLoader from pytorch_lightning.loops.dataloader import DataLoaderLoop from pytorch_lightning.loops.epoch import EvaluationEpochLoop from pytorch_lightning.trainer.connectors.logger_connector.result import ResultCollection from pytorch_lightning.utilities.model_helpers import is_overridden from pytorch_lightning.utilities.types import EPOCH_OUTPUT class EvaluationLoop(DataLoaderLoop): """Loops over all dataloaders for evaluation.""" def __init__(self): super().__init__() self.outputs: List[EPOCH_OUTPUT] = [] self.epoch_loop = EvaluationEpochLoop() self._results = ResultCollection(training=False) self._max_batches: Optional[Union[int, Sequence[int]]] = None self._has_run: bool = False @property def num_dataloaders(self) -> int: """Returns the total number of dataloaders""" # case where user does: # return dl1, dl2 dataloaders = self.dataloaders if dataloaders is None: return 0 length = len(dataloaders) if length > 0 and isinstance(dataloaders[0], (list, tuple)): length = len(dataloaders[0]) return length @property def dataloaders(self) -> Sequence[DataLoader]: """Returns the validation or test dataloaders""" if self.trainer.testing: return self.trainer.test_dataloaders return self.trainer.val_dataloaders def connect(self, epoch_loop: EvaluationEpochLoop): """Connect the evaluation epoch loop with this loop.""" self.epoch_loop = epoch_loop @property def done(self) -> bool: """Returns whether all dataloaders are processed or evaluation should be skipped altogether""" return super().done or self.skip @property def skip(self) -> bool: """Returns whether the evaluation should be skipped.""" max_batches = self.get_max_batches() return sum(max_batches) == 0 def reset(self) -> None: """Resets the internal state of the loop""" self._max_batches = self.get_max_batches() # bookkeeping self.outputs = [] if isinstance(self._max_batches, int): self._max_batches = [self._max_batches] * len(self.dataloaders) super().reset() def on_skip(self) -> List: return [] def on_run_start(self, *args: Any, **kwargs: Any) -> None: """Runs the ``on_evaluation_model_eval``, ``on_evaluation_start`` and ``on_evaluation_epoch_start`` hooks""" void(*args, **kwargs) # hook self.on_evaluation_model_eval() self.trainer.lightning_module.zero_grad() self.on_evaluation_start() self.on_evaluation_epoch_start() def advance(self, *args: Any, **kwargs: Any) -> None: """Performs evaluation on one single dataloader""" void(*args, **kwargs) dataloader_idx: int = self.current_dataloader_idx dataloader = self.trainer.accelerator.process_dataloader(self.current_dataloader) dataloader = self.trainer.data_connector.get_profiled_dataloader(dataloader, dataloader_idx=dataloader_idx) dl_max_batches = self._max_batches[dataloader_idx] dl_outputs = self.epoch_loop.run(dataloader, dataloader_idx, dl_max_batches, self.num_dataloaders) # store batch level output per dataloader self.outputs.append(dl_outputs) if not self.trainer.sanity_checking: # indicate the loop has run self._has_run = True def on_run_end(self) -> Any: """Runs the ``on_evaluation_epoch_end`` hook""" outputs = self.outputs # free memory self.outputs = [] # with a single dataloader don't pass a 2D list if len(outputs) > 0 and self.num_dataloaders == 1: outputs = outputs[0] # lightning module method self.evaluation_epoch_end(outputs) # hook self.on_evaluation_epoch_end() # log epoch metrics eval_loop_results = self.trainer.logger_connector.update_eval_epoch_metrics() # hook self.on_evaluation_end() # enable train mode again self.on_evaluation_model_train() return eval_loop_results def get_max_batches(self) -> List[Union[int, float]]: """Returns the max number of batches for each dataloader""" if self.trainer.testing: max_batches = self.trainer.num_test_batches else: if self.trainer.sanity_checking: self.trainer.num_sanity_val_batches = [ min(self.trainer.num_sanity_val_steps, val_batches) for val_batches in self.trainer.num_val_batches ] max_batches = self.trainer.num_sanity_val_batches else: max_batches = self.trainer.num_val_batches return max_batches def reload_evaluation_dataloaders(self) -> None: """Reloads dataloaders if necessary""" if self.trainer.testing: self.trainer.reset_test_dataloader() elif self.trainer.val_dataloaders is None or self.trainer._should_reload_dl_epoch: self.trainer.reset_val_dataloader() def on_evaluation_start(self, *args: Any, **kwargs: Any) -> None: """Runs ``on_{validation/test}_start`` hooks""" assert self._results is not None self._results.to(device=self.trainer.lightning_module.device) if self.trainer.testing: self.trainer.call_hook("on_test_start", *args, **kwargs) else: self.trainer.call_hook("on_validation_start", *args, **kwargs) def on_evaluation_model_eval(self) -> None: """Sets model to eval mode""" if self.trainer.testing: self.trainer.call_hook("on_test_model_eval") else: self.trainer.call_hook("on_validation_model_eval") def on_evaluation_model_train(self) -> None: """Sets model to train mode""" model_ref = self.trainer.lightning_module if self.trainer.testing: model_ref.on_test_model_train() else: model_ref.on_validation_model_train() def on_evaluation_end(self, *args: Any, **kwargs: Any) -> None: """Runs ``on_{validation/test}_end`` hook""" if self.trainer.testing: self.trainer.call_hook("on_test_end", *args, **kwargs) else: self.trainer.call_hook("on_validation_end", *args, **kwargs) # reset any `torchmetrics.Metric` and the logger connector state self.trainer.logger_connector.reset(metrics=True) def on_evaluation_epoch_start(self, *args: Any, **kwargs: Any) -> None: """Runs ``on_epoch_start`` and ``on_{validation/test}_epoch_start`` hooks""" self.trainer.logger_connector.on_epoch_start() self.trainer.call_hook("on_epoch_start", *args, **kwargs) if self.trainer.testing: self.trainer.call_hook("on_test_epoch_start", *args, **kwargs) else: self.trainer.call_hook("on_validation_epoch_start", *args, **kwargs) def evaluation_epoch_end(self, outputs: EPOCH_OUTPUT) -> None: """Runs ``{validation/test}_epoch_end``""" # inform logger the batch loop has finished self.trainer.logger_connector.epoch_end_reached() # call the model epoch end model = self.trainer.lightning_module # unset dataloader_idx in model model._current_dataloader_idx = None if self.trainer.testing: if is_overridden("test_epoch_end", model): model._current_fx_name = "test_epoch_end" model.test_epoch_end(outputs) else: if is_overridden("validation_epoch_end", model): model._current_fx_name = "validation_epoch_end" model.validation_epoch_end(outputs) def on_evaluation_epoch_end(self) -> None: """Runs ``on_{validation/test}_epoch_end`` hook""" hook_name = "on_test_epoch_end" if self.trainer.testing else "on_validation_epoch_end" self.trainer.call_hook(hook_name) self.trainer.call_hook("on_epoch_end") self.trainer.logger_connector.on_epoch_end() def teardown(self) -> None: self._results.cpu() self.epoch_loop.teardown()