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prediction_epoch_loop.py
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prediction_epoch_loop.py
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from collections import OrderedDict
from typing import Any, Dict, Iterator, List, Tuple
import torch
from deprecate import void
from pytorch_lightning.loops.base import Loop
from pytorch_lightning.overrides.distributed import IndexBatchSamplerWrapper
from pytorch_lightning.trainer.progress import Progress
from pytorch_lightning.utilities.apply_func import move_data_to_device
from pytorch_lightning.utilities.warnings import WarningCache
warning_cache = WarningCache()
class PredictionEpochLoop(Loop):
"""Loop performing prediction on arbitrary sequentially used dataloaders."""
def __init__(self) -> None:
super().__init__()
self.return_predictions = False
self.predictions: List[Any] = []
self.current_batch_indices: List[int] = []
self.batch_progress = Progress()
self._dl_max_batches = 0
self._num_dataloaders = 0
self._warning_cache = WarningCache()
self._seen_batch_indices: List[List[int]] = []
@property
def done(self) -> bool:
"""Ends prediction when the iteration count exceeds the total number of available batches."""
return self.batch_progress.current.completed >= self._dl_max_batches
@property
def should_store_predictions(self) -> bool:
"""Whether the predictions should be stored for later usage (e.g. aggregation or returning)"""
any_pred = any(cb.interval.on_epoch for cb in self.trainer.prediction_writer_callbacks)
return self.return_predictions or any_pred
def connect(self, **kwargs: "Loop") -> None:
raise NotImplementedError(f"{self.__class__.__name__} does not connect any child loops.")
def reset(self) -> None:
"""Resets the loops internal state."""
self._seen_batch_indices = []
self.predictions = []
self.batch_progress.reset_on_run()
def on_run_start( # type: ignore[override]
self,
dataloader_iter: Iterator,
dataloader_idx: int,
dl_max_batches: int,
num_dataloaders: int,
return_predictions: bool = False,
) -> None:
"""Prepares the loops internal state.
Args:
dataloader_iter: the iterator over the current dataloader
dataloader_idx: the index of the current dataloader
dl_max_batches: the maximum number of batches the current loader can produce
num_dataloaders: the total number of dataloaders
return_predictions: whether to return the obtained predictions
"""
void(dataloader_iter, dataloader_idx)
self._dl_max_batches = dl_max_batches
self._num_dataloaders = num_dataloaders
self.return_predictions = return_predictions
# this call requires that `self.return_predictions` is set
self._seen_batch_indices = self._get_batch_indices(dataloader_idx)
def advance( # type: ignore[override]
self,
dataloader_iter: Iterator,
dataloader_idx: int,
dl_max_batches: int,
num_dataloaders: int,
return_predictions: bool = False,
) -> None:
"""Runs one prediction step.
Args:
dataloader_iter: the iterator over the current dataloader
dataloader_idx: the index of the current dataloader
dl_max_batches: the maximum number of batches the current loader can produce
num_dataloaders: the total number of dataloaders
return_predictions: whether to return the obtained predictions
"""
batch_idx, batch = next(dataloader_iter)
self._seen_batch_indices = self._get_batch_indices(dataloader_idx)
# we need to truncate the list of batch indicies due to prefetching in the dataloader and Lightning
self._seen_batch_indices = self._seen_batch_indices[: (self.batch_progress.current.completed + 1)]
if batch is None:
raise StopIteration
batch = self.trainer._call_strategy_hook("batch_to_device", batch, dataloader_idx=dataloader_idx)
self.batch_progress.increment_ready()
self._predict_step(batch, batch_idx, dataloader_idx)
def on_run_end(self) -> Tuple[List[Any], List[List[int]]]:
"""Returns the predictions and the corresponding batch indices."""
predictions, all_batch_indices = self.predictions, self._seen_batch_indices
self.predictions, self._seen_batch_indices = [], [] # free memory
return predictions, all_batch_indices
def _predict_step(self, batch: Any, batch_idx: int, dataloader_idx: int) -> None:
"""Runs the actual predict step together with all the necessary bookkeeping and the hooks tied to the
predict step.
Args:
batch: the current batch to run the prediction on
batch_idx: the index of the current batch
dataloader_idx: the index of the dataloader producing the current batch
"""
# configure step_kwargs
step_kwargs = self._build_kwargs(batch, batch_idx, dataloader_idx)
# extract batch_indices and store them
self.current_batch_indices = self._seen_batch_indices[batch_idx] if self._seen_batch_indices else []
self.trainer._call_callback_hooks("on_predict_batch_start", batch, batch_idx, dataloader_idx)
self.trainer._call_lightning_module_hook("on_predict_batch_start", batch, batch_idx, dataloader_idx)
self.batch_progress.increment_started()
predictions = self.trainer._call_strategy_hook("predict_step", *step_kwargs.values())
self.batch_progress.increment_processed()
if predictions is None:
self._warning_cache.warn("predict returned None if it was on purpose, ignore this warning...")
self.trainer._call_callback_hooks("on_predict_batch_end", predictions, batch, batch_idx, dataloader_idx)
self.trainer._call_lightning_module_hook("on_predict_batch_end", predictions, batch, batch_idx, dataloader_idx)
self.batch_progress.increment_completed()
if self.should_store_predictions:
self.predictions.append(move_data_to_device(predictions, torch.device("cpu")))
def _build_kwargs(self, batch: Any, batch_idx: int, dataloader_idx: int) -> Dict[str, Any]:
"""Assembles the keyword arguments for the ``predict_step``
Args:
batch: the current batch to run the prediction on
batch_idx: the index of the current batch
dataloader_idx: the index of the dataloader producing the current batch
Returns:
the dictionary containing all the keyboard arguments for the predict step
"""
step_kwargs = OrderedDict([("batch", batch), ("batch_idx", batch_idx)])
if self._num_dataloaders > 1:
step_kwargs["dataloader_idx"] = dataloader_idx
return step_kwargs
def _get_batch_indices(self, dataloader_idx: int) -> List[List[int]]:
"""Returns a reference to the seen batch indices if the dataloader has a batch sampler wrapped by our
:class:`~pytorch_lightning.overrides.distributed.IndexBatchSamplerWrapper`."""
# the batch_sampler is not be defined in case of CombinedDataLoaders
batch_sampler = getattr(
self.trainer.predict_dataloaders[dataloader_idx], # type: ignore[has-type]
"batch_sampler",
None,
)
if isinstance(batch_sampler, IndexBatchSamplerWrapper) and self.should_store_predictions:
return batch_sampler.seen_batch_indices
warning_cache.warn("Lightning couldn't infer the indices fetched for your dataloader.")
return []