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.
import os
import shutil
import sys
from collections import ChainMap, OrderedDict
from typing import Any, Iterable, List, Optional, Sequence, Tuple, Type, Union
from lightning_utilities.core.apply_func import apply_to_collection
from torch import Tensor
from torch.utils.data.dataloader import DataLoader
import pytorch_lightning as pl
from pytorch_lightning.accelerators import CUDAAccelerator
from pytorch_lightning.callbacks.progress.rich_progress import _RICH_AVAILABLE
from pytorch_lightning.loops.dataloader import DataLoaderLoop
from pytorch_lightning.loops.epoch import EvaluationEpochLoop
from pytorch_lightning.loops.utilities import _set_sampler_epoch
from pytorch_lightning.trainer.connectors.logger_connector.result import _OUT_DICT, _ResultCollection
from pytorch_lightning.trainer.states import TrainerFn
from pytorch_lightning.utilities.exceptions import MisconfigurationException
from pytorch_lightning.utilities.fetching import (
AbstractDataFetcher,
DataFetcher,
DataLoaderIterDataFetcher,
InterBatchParallelDataFetcher,
)
from pytorch_lightning.utilities.rank_zero import rank_zero_warn
from pytorch_lightning.utilities.signature_utils import is_param_in_hook_signature
from pytorch_lightning.utilities.types import EPOCH_OUTPUT
if _RICH_AVAILABLE:
from rich import get_console
from rich.table import Column, Table
[docs]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[Union[int, float]] = []
self._has_run: bool = False
self._data_fetcher: Optional[AbstractDataFetcher] = None
@property
def num_dataloaders(self) -> int:
"""Returns the total number of dataloaders."""
# case where user does:
# return dl1, dl2
dataloaders = self.dataloaders
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:
return []
return dataloaders
@property
def prefetch_batches(self) -> int:
batches = self.trainer.num_test_batches if self.trainer.testing else self.trainer.num_val_batches
is_unsized = batches[self.current_dataloader_idx] == float("inf")
inter_batch_parallelism = os.getenv("PL_INTER_BATCH_PARALLELISM", "0") == "1"
return 1 if is_unsized or inter_batch_parallelism else 0
[docs] 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
[docs] 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()
[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."""
data_fetcher_cls = _select_data_fetcher_type(self.trainer)
self._data_fetcher = data_fetcher_cls(prefetch_batches=self.prefetch_batches)
# 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."""
dataloader_idx = self.current_dataloader_idx
dataloader = self.current_dataloader
def batch_to_device(batch: Any) -> Any:
batch = self.trainer.lightning_module._on_before_batch_transfer(batch, dataloader_idx=dataloader_idx)
batch = self.trainer._call_strategy_hook("batch_to_device", batch, dataloader_idx=dataloader_idx)
return batch
assert self._data_fetcher is not None
self._data_fetcher.setup(dataloader, batch_to_device=batch_to_device)
dl_max_batches = self._max_batches[dataloader_idx]
kwargs = OrderedDict()
if self.num_dataloaders > 1:
kwargs["dataloader_idx"] = dataloader_idx
dl_outputs = self.epoch_loop.run(self._data_fetcher, dl_max_batches, kwargs)
# 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_advance_start(self, *args: Any, **kwargs: Any) -> None:
if self.current_dataloader is not None:
_set_sampler_epoch(self.current_dataloader, self.trainer.fit_loop.epoch_progress.current.processed)
super().on_advance_start(*args, **kwargs)
[docs] 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()
[docs] 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
epoch_end_logged_outputs = self.trainer._logger_connector.update_eval_epoch_metrics()
all_logged_outputs = dict(ChainMap(*logged_outputs)) # list[dict] -> dict
all_logged_outputs.update(epoch_end_logged_outputs)
for dl_outputs in logged_outputs:
dl_outputs.update(epoch_end_logged_outputs)
# log metrics
self.trainer._logger_connector.log_eval_end_metrics(all_logged_outputs)
# 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
[docs] def teardown(self) -> None:
if self._data_fetcher is not None:
self._data_fetcher.teardown()
self._data_fetcher = None
self._results.cpu()
self.epoch_loop.teardown()
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:
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."""
dataloaders = None
if self.trainer.testing:
self.trainer.reset_test_dataloader()
dataloaders = self.trainer.test_dataloaders
elif self.trainer.val_dataloaders is None or self.trainer._data_connector._should_reload_val_dl:
self.trainer.reset_val_dataloader()
dataloaders = self.trainer.val_dataloaders
if dataloaders is not None:
self.epoch_loop._reset_dl_batch_idx(len(dataloaders))
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)
hook_name = "on_test_start" if self.trainer.testing else "on_validation_start"
self.trainer._call_callback_hooks(hook_name, *args, **kwargs)
self.trainer._call_lightning_module_hook(hook_name, *args, **kwargs)
self.trainer._call_strategy_hook(hook_name, *args, **kwargs)
def _on_evaluation_model_eval(self) -> None:
"""Sets model to eval mode."""
hook_name = "on_test_model_eval" if self.trainer.testing else "on_validation_model_eval"
self.trainer._call_lightning_module_hook(hook_name)
def _on_evaluation_model_train(self) -> None:
"""Sets model to train mode."""
hook_name = "on_test_model_train" if self.trainer.testing else "on_validation_model_train"
self.trainer._call_lightning_module_hook(hook_name)
def _on_evaluation_end(self, *args: Any, **kwargs: Any) -> None:
"""Runs ``on_{validation/test}_end`` hook."""
hook_name = "on_test_end" if self.trainer.testing else "on_validation_end"
self.trainer._call_callback_hooks(hook_name, *args, **kwargs)
self.trainer._call_lightning_module_hook(hook_name, *args, **kwargs)
self.trainer._call_strategy_hook(hook_name, *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 the ``on_{validation/test}_epoch_start`` hooks."""
self.trainer._logger_connector.on_epoch_start()
hook_name = "on_test_epoch_start" if self.trainer.testing else "on_validation_epoch_start"
self.trainer._call_callback_hooks(hook_name, *args, **kwargs)
self.trainer._call_lightning_module_hook(hook_name, *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
hook_name = "test_epoch_end" if self.trainer.testing else "validation_epoch_end"
self.trainer._call_lightning_module_hook(hook_name, 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._logger_connector.on_epoch_end()
@staticmethod
def _get_keys(data: dict) -> Iterable[Tuple[str, ...]]:
for k, v in data.items():
if isinstance(v, dict):
for new_key in apply_to_collection(v, dict, EvaluationLoop._get_keys):
yield (k, *new_key) # this need to be in parenthesis for older python versions
else:
yield k,
@staticmethod
def _find_value(data: dict, target: Iterable[str]) -> Optional[Any]:
target_start, *rest = target
if target_start not in data:
return None
result = data[target_start]
if not rest:
return result
return EvaluationLoop._find_value(result, rest)
@staticmethod
def _print_results(results: List[_OUT_DICT], stage: str) -> None:
# remove the dl idx suffix
results = [{k.split("/dataloader_idx_")[0]: v for k, v in result.items()} for result in results]
metrics_paths = {k for keys in apply_to_collection(results, dict, EvaluationLoop._get_keys) for k in keys}
if not metrics_paths:
return
metrics_strs = [":".join(metric) for metric in metrics_paths]
# sort both lists based on metrics_strs
metrics_strs, metrics_paths = zip(*sorted(zip(metrics_strs, metrics_paths)))
headers = [f"DataLoader {i}" for i in range(len(results))]
# fallback is useful for testing of printed output
term_size = shutil.get_terminal_size(fallback=(120, 30)).columns or 120
max_length = int(min(max(len(max(metrics_strs, key=len)), len(max(headers, key=len)), 25), term_size / 2))
rows: List[List[Any]] = [[] for _ in metrics_paths]
for result in results:
for metric, row in zip(metrics_paths, rows):
val = EvaluationLoop._find_value(result, metric)
if val is not None:
if isinstance(val, Tensor):
val = val.item() if val.numel() == 1 else val.tolist()
row.append(f"{val}")
else:
row.append(" ")
# keep one column with max length for metrics
num_cols = int((term_size - max_length) / max_length)
for i in range(0, len(headers), num_cols):
table_headers = headers[i : (i + num_cols)]
table_rows = [row[i : (i + num_cols)] for row in rows]
table_headers.insert(0, f"{stage} Metric".capitalize())
if _RICH_AVAILABLE:
columns = [Column(h, justify="center", style="magenta", width=max_length) for h in table_headers]
columns[0].style = "cyan"
table = Table(*columns)
for metric, row in zip(metrics_strs, table_rows):
row.insert(0, metric)
table.add_row(*row)
console = get_console()
console.print(table)
else:
row_format = f"{{:^{max_length}}}" * len(table_headers)
half_term_size = int(term_size / 2)
try:
# some terminals do not support this character
if sys.stdout.encoding is not None:
"─".encode(sys.stdout.encoding)
except UnicodeEncodeError:
bar_character = "-"
else:
bar_character = "─"
bar = bar_character * term_size
lines = [bar, row_format.format(*table_headers).rstrip(), bar]
for metric, row in zip(metrics_strs, table_rows):
# deal with column overflow
if len(metric) > half_term_size:
while len(metric) > half_term_size:
row_metric = metric[:half_term_size]
metric = metric[half_term_size:]
lines.append(row_format.format(row_metric, *row).rstrip())
lines.append(row_format.format(metric, " ").rstrip())
else:
lines.append(row_format.format(metric, *row).rstrip())
lines.append(bar)
print(os.linesep.join(lines))
def _select_data_fetcher_type(trainer: "pl.Trainer") -> Type[AbstractDataFetcher]:
lightning_module = trainer.lightning_module
step_fx_name = "test_step" if trainer.testing else "validation_step"
step_fx = getattr(lightning_module, step_fx_name)
if is_param_in_hook_signature(step_fx, "dataloader_iter", explicit=True):
rank_zero_warn(
f"Found `dataloader_iter` argument in the `{step_fx_name}`. Note that the support for "
"this signature is experimental and the behavior is subject to change."
)
return DataLoaderIterDataFetcher
elif os.getenv("PL_INTER_BATCH_PARALLELISM", "0") == "1":
if not isinstance(trainer.accelerator, CUDAAccelerator):
raise MisconfigurationException("Inter batch parallelism is available only when using Nvidia GPUs.")
return InterBatchParallelDataFetcher
return DataFetcher
