Shortcuts

Source code for pytorch_lightning.loops.dataloader.evaluation_loop

# 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, Sequence

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 _OUT_DICT, ResultCollection
from pytorch_lightning.utilities.model_helpers import is_overridden
from pytorch_lightning.utilities.types import EPOCH_OUTPUT


[docs]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._outputs: List[EPOCH_OUTPUT] = [] self._max_batches: List[int] = [] 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
[docs] 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
[docs] 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()
[docs] def on_skip(self) -> List: return []
[docs] 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()
[docs] 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.training_type_plugin.process_dataloader(self.current_dataloader) self.data_fetcher = 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
[docs] def on_run_end(self) -> List[_OUT_DICT]: """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
[docs] def teardown(self) -> None: self._results.cpu() self.epoch_loop.teardown()
def _get_max_batches(self) -> List[int]: """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_val_dl: 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 the logger connector state self.trainer.logger_connector.reset_results() 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()

© Copyright Copyright (c) 2018-2023, William Falcon et al...

Built with Sphinx using a theme provided by Read the Docs.