289 lines
12 KiB
Python
289 lines
12 KiB
Python
# 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, Union
|
|
|
|
import torch
|
|
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.trainer.states import RunningStage, TrainerFn
|
|
from pytorch_lightning.utilities.types import EPOCH_OUTPUT
|
|
|
|
|
|
class EvaluationLoop(DataLoaderLoop):
|
|
"""Loops over all dataloaders for evaluation."""
|
|
|
|
def __init__(self, verbose: bool = True) -> None:
|
|
super().__init__()
|
|
self.epoch_loop = EvaluationEpochLoop()
|
|
self.verbose = verbose
|
|
|
|
self._results = _ResultCollection(training=False)
|
|
self._outputs: List[EPOCH_OUTPUT] = []
|
|
self._logged_outputs: List[_OUT_DICT] = []
|
|
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."""
|
|
dataloaders = self.trainer.test_dataloaders if self.trainer.testing else self.trainer.val_dataloaders
|
|
if dataloaders is None:
|
|
raise RuntimeError("Dataloaders should be available.")
|
|
return dataloaders
|
|
|
|
def connect(self, epoch_loop: EvaluationEpochLoop) -> None: # type: ignore[override]
|
|
"""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 = []
|
|
self._logged_outputs = []
|
|
|
|
if isinstance(self._max_batches, int):
|
|
self._max_batches = [self._max_batches] * len(self.dataloaders)
|
|
|
|
super().reset()
|
|
# when restarting, if we are running `validate` or `test` twice, since there's no concept of `max_epochs` we
|
|
# need to reset the current state when the loop has finished running
|
|
if self.done and self.trainer.state.fn != TrainerFn.FITTING:
|
|
self.dataloader_progress.reset_on_run()
|
|
|
|
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 = 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 if self.num_dataloaders > 1 else None, dl_max_batches
|
|
)
|
|
|
|
# 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_advance_end(self) -> None:
|
|
self.trainer.logger_connector.epoch_end_reached()
|
|
|
|
self._logged_outputs.append(self.trainer.logger_connector.update_eval_epoch_metrics())
|
|
|
|
super().on_advance_end()
|
|
|
|
def on_run_end(self) -> List[_OUT_DICT]:
|
|
"""Runs the ``_on_evaluation_epoch_end`` hook."""
|
|
# if `done` returned True before any iterations were done, this won't have been called in `on_advance_end`
|
|
self.trainer.logger_connector.epoch_end_reached()
|
|
|
|
# hook
|
|
self._evaluation_epoch_end(self._outputs)
|
|
self._outputs = [] # free memory
|
|
|
|
# hook
|
|
self._on_evaluation_epoch_end()
|
|
|
|
logged_outputs, self._logged_outputs = self._logged_outputs, [] # free memory
|
|
# include any logged outputs on epoch_end
|
|
if self.num_dataloaders < 2: # TODO: remove this check
|
|
epoch_end_logged_outputs = self.trainer.logger_connector.update_eval_epoch_metrics()
|
|
for dl_outputs in logged_outputs:
|
|
dl_outputs.update(epoch_end_logged_outputs)
|
|
|
|
# log metrics
|
|
self.trainer.logger_connector.log_eval_end_metrics()
|
|
|
|
# hook
|
|
self._on_evaluation_end()
|
|
|
|
# enable train mode again
|
|
self._on_evaluation_model_train()
|
|
|
|
if self.verbose and self.trainer.is_global_zero:
|
|
assert self.trainer.state.stage is not None
|
|
self._print_results(logged_outputs, self.trainer.state.stage)
|
|
|
|
return logged_outputs
|
|
|
|
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_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_callback_hooks("on_test_start", *args, **kwargs)
|
|
self.trainer._call_lightning_module_hook("on_test_start", *args, **kwargs)
|
|
self.trainer._call_strategy_hook("on_test_start", *args, **kwargs)
|
|
else:
|
|
self.trainer._call_callback_hooks("on_validation_start", *args, **kwargs)
|
|
self.trainer._call_lightning_module_hook("on_validation_start", *args, **kwargs)
|
|
self.trainer._call_strategy_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_lightning_module_hook("on_test_model_eval")
|
|
else:
|
|
self.trainer._call_lightning_module_hook("on_validation_model_eval")
|
|
|
|
def _on_evaluation_model_train(self) -> None:
|
|
"""Sets model to train mode."""
|
|
if self.trainer.testing:
|
|
self.trainer._call_lightning_module_hook("on_test_model_train")
|
|
else:
|
|
self.trainer._call_lightning_module_hook("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_callback_hooks("on_test_end", *args, **kwargs)
|
|
self.trainer._call_lightning_module_hook("on_test_end", *args, **kwargs)
|
|
self.trainer._call_strategy_hook("on_test_end", *args, **kwargs)
|
|
else:
|
|
self.trainer._call_callback_hooks("on_validation_end", *args, **kwargs)
|
|
self.trainer._call_lightning_module_hook("on_validation_end", *args, **kwargs)
|
|
self.trainer._call_strategy_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_callback_hooks("on_epoch_start", *args, **kwargs)
|
|
self.trainer._call_lightning_module_hook("on_epoch_start", *args, **kwargs)
|
|
|
|
if self.trainer.testing:
|
|
self.trainer._call_callback_hooks("on_test_epoch_start", *args, **kwargs)
|
|
self.trainer._call_lightning_module_hook("on_test_epoch_start", *args, **kwargs)
|
|
else:
|
|
self.trainer._call_callback_hooks("on_validation_epoch_start", *args, **kwargs)
|
|
self.trainer._call_lightning_module_hook("on_validation_epoch_start", *args, **kwargs)
|
|
|
|
def _evaluation_epoch_end(self, outputs: List[EPOCH_OUTPUT]) -> None:
|
|
"""Runs ``{validation/test}_epoch_end``"""
|
|
self.trainer.logger_connector._evaluation_epoch_end()
|
|
|
|
# with a single dataloader don't pass a 2D list
|
|
output_or_outputs: Union[EPOCH_OUTPUT, List[EPOCH_OUTPUT]] = (
|
|
outputs[0] if len(outputs) > 0 and self.num_dataloaders == 1 else outputs
|
|
)
|
|
|
|
# call the model epoch end
|
|
if self.trainer.testing:
|
|
self.trainer._call_lightning_module_hook("test_epoch_end", output_or_outputs)
|
|
else:
|
|
self.trainer._call_lightning_module_hook("validation_epoch_end", output_or_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_callback_hooks(hook_name)
|
|
self.trainer._call_lightning_module_hook(hook_name)
|
|
|
|
self.trainer._call_callback_hooks("on_epoch_end")
|
|
self.trainer._call_lightning_module_hook("on_epoch_end")
|
|
self.trainer.logger_connector.on_epoch_end()
|
|
|
|
def _print_results(self, results: List[_OUT_DICT], stage: RunningStage) -> None:
|
|
# TODO: this could be updated to look nicer
|
|
from pprint import pprint
|
|
|
|
print("-" * 80)
|
|
for i, metrics_dict in enumerate(results):
|
|
print(f"DATALOADER:{i} {stage.upper()} RESULTS")
|
|
pprint(
|
|
{
|
|
k: (v.item() if v.numel() == 1 else v.tolist()) if isinstance(v, torch.Tensor) else v
|
|
for k, v in metrics_dict.items()
|
|
}
|
|
)
|
|
print("-" * 80)
|