def __init__(self) -> None:
super().__init__()
self.predictions: List[List[Any]] = []
self.epoch_batch_indices: List[List[int]] = []
self.epoch_loop = PredictionEpochLoop()
self._results = None # for `trainer._results` access
self._return_predictions: bool = False
def __init__(self):
super().__init__()
self.predictions: Optional[List[List[Any]]] = None
self.epoch_batch_indices: Optional[List[List[int]]] = None
self.progress = EpochLoopProgress()
self.epoch_loop = PredictionEpochLoop()
self._results = None # for `trainer._results` access
self._return_predictions: bool = False
class PredictionLoop(DataLoaderLoop):
"""Loop to run over dataloaders for prediction."""
def __init__(self) -> None:
super().__init__()
self.predictions: List[List[Any]] = []
self.epoch_batch_indices: List[List[int]] = []
self.epoch_loop = PredictionEpochLoop()
self._results = None # for `trainer._results` access
self._return_predictions: bool = False
@property
def return_predictions(self) -> bool:
"""Whether to return the predictions or not."""
return self._return_predictions
@return_predictions.setter
def return_predictions(self,
return_predictions: Optional[bool] = None) -> None:
# `DDPSpawnStrategy` plugins and derivatives don't support return predictions.
is_ddp_spawn = isinstance(self.trainer.strategy, DDPSpawnStrategy)
if return_predictions and is_ddp_spawn:
raise MisconfigurationException(
"`return_predictions` should be set to `False` when using the `DDPSpawnStrategy` or children class. "
f"Found {return_predictions} with training_type_plugin {type(self.trainer.strategy)}."
)
# For non `DDPSpawnStrategy` plugin, the `return_predictions` is True by default unless user decide otherwise.
self._return_predictions = not is_ddp_spawn if return_predictions is None else return_predictions
@property
def num_dataloaders(self) -> int:
"""Returns the number of prediction dataloaders."""
# case where user does:
# return dl1, dl2
dataloaders = self.dataloaders
length = len(dataloaders)
if len(dataloaders) > 0 and isinstance(dataloaders[0], (list, tuple)):
length = len(dataloaders[0])
return length
@property
def max_batches(self) -> List[int]:
"""The max number of batches this loop will run for each dataloader."""
return self.trainer.num_predict_batches
@property
def dataloaders(self) -> Sequence[DataLoader]:
"""Returns all prediction dataloaders."""
return self.trainer.predict_dataloaders
@property
def skip(self) -> bool:
return sum(self.max_batches) == 0
def connect(
self,
epoch_loop: PredictionEpochLoop) -> None: # type: ignore[override]
"""Connect the prediction epoch loop with this loop."""
self.epoch_loop = epoch_loop
def reset(self) -> None:
"""Resets the internal state of the loop for a new run."""
self.predictions = []
self.epoch_batch_indices = []
super().reset()
# when restarting, if we are running 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:
self.dataloader_progress.reset_on_run()
def on_run_start(self) -> None: # type: ignore[override]
"""Calls ``_on_predict_start`` hook."""
self._on_predict_start()
def advance(self, *args: Any, **kwargs: Any) -> None:
"""Predicts one entire dataloader."""
void(*args, **kwargs)
dataloader = self.current_dataloader
if (dataloader is not None and getattr(dataloader, "sampler", None)
and callable(getattr(dataloader.sampler, "set_epoch", None))):
# set seed for distributed sampler (enables shuffling for each epoch)
dataloader.sampler.set_epoch(
self.trainer.fit_loop.epoch_progress.current.processed)
dataloader = self.trainer.strategy.process_dataloader(dataloader)
dataloader_iter = enumerate(dataloader)
dl_max_batches = self.max_batches[self.current_dataloader_idx]
dl_predictions, dl_batch_indices = self.epoch_loop.run(
dataloader_iter, self.current_dataloader_idx, dl_max_batches,
self.num_dataloaders, self.return_predictions)
self.predictions.append(dl_predictions)
self.epoch_batch_indices.append(dl_batch_indices)
def on_run_end(self) -> Optional[_PREDICT_OUTPUT]:
"""Calls ``on_predict_epoch_end`` and ``on_predict_end`` hooks and returns results from all dataloaders."""
results = self._on_predict_epoch_end()
self._on_predict_end()
return results
def _on_predict_start(self) -> None:
"""Sets model to eval mode and disables gradients.
Also calls ``on_predict_start`` and ``on_predict_epoch_start`` hooks.
"""
# enable eval mode + no grads
self._on_predict_model_eval()
self.trainer.lightning_module.zero_grad()
# hook
self.trainer._call_callback_hooks("on_predict_start")
self.trainer._call_lightning_module_hook("on_predict_start")
self.trainer._call_strategy_hook("on_predict_start")
self.trainer._call_callback_hooks("on_predict_epoch_start")
self.trainer._call_lightning_module_hook("on_predict_epoch_start")
def _on_predict_epoch_end(self) -> Optional[_PREDICT_OUTPUT]:
"""Calls ``on_predict_epoch_end`` hook.
Returns:
the results for all dataloaders
"""
results = self.predictions
self.trainer._call_callback_hooks("on_predict_epoch_end", results)
self.trainer._call_lightning_module_hook("on_predict_epoch_end",
results)
if self.return_predictions:
return results[0] if self.num_dataloaders == 1 else results
def _on_predict_end(self) -> None:
"""Resets previous gradient status and calls ``on_predict_end`` hook."""
# clear memory. the predictions are extracted in `on_predict_epoch_end`.
self.predictions = []
self.epoch_batch_indices = []
# hook
self.trainer._call_callback_hooks("on_predict_end")
self.trainer._call_lightning_module_hook("on_predict_end")
self.trainer._call_strategy_hook("on_predict_end")
def _on_predict_model_eval(self) -> None:
"""Calls ``on_predict_model_eval`` hook."""
model_ref = self.trainer.lightning_module
model_ref.on_predict_model_eval()
class PredictionLoop(DataLoaderLoop):
"""Loop to run over dataloaders for prediction"""
def __init__(self):
super().__init__()
self.predictions: Optional[List[List[Any]]] = None
self.epoch_batch_indices: Optional[List[List[int]]] = None
self.epoch_loop = PredictionEpochLoop()
self._results = None # for `trainer._results` access
self._return_predictions: bool = False
@property
def return_predictions(self) -> bool:
"""Whether to return the predictions or not"""
return self._return_predictions
@return_predictions.setter
def return_predictions(self,
return_predictions: Optional[bool] = None) -> None:
# `DDPSpawnPlugin` plugins and derivatives don't support return predictions.
is_ddp_spawn = isinstance(self.trainer.training_type_plugin,
DDPSpawnPlugin)
if return_predictions and is_ddp_spawn:
raise MisconfigurationException(
"`return_predictions` should be set to `False` when using the `DDPSpawnPlugin` or children class. "
f"Found {return_predictions} with training_type_plugin {type(self.trainer.training_type_plugin)}."
)
# For non `DDPSpawnPlugin` plugin, the `return_predictions` is True by default unless user decide otherwise.
self._return_predictions = not is_ddp_spawn if return_predictions is None else return_predictions
@property
def num_dataloaders(self) -> int:
"""Returns the number of prediction dataloaders"""
# case where user does:
# return dl1, dl2
dataloaders = self.dataloaders
length = len(dataloaders)
if len(dataloaders) > 0 and isinstance(dataloaders[0], (list, tuple)):
length = len(dataloaders[0])
return length
@property
def max_batches(self) -> List[int]:
"""The max number of batches this loop will run for each dataloader."""
max_batches = self.trainer.num_predict_batches
if isinstance(max_batches, int):
max_batches = [max_batches] * len(self.dataloaders)
return max_batches
@property
def dataloaders(self) -> Sequence[DataLoader]:
"""Returns all prediction dataloaders"""
return self.trainer.predict_dataloaders
@property
def skip(self) -> bool:
return sum(self.max_batches) == 0
def connect(self, epoch_loop: PredictionEpochLoop):
"""Connect the prediction epoch loop with this loop."""
self.epoch_loop = epoch_loop
def reset(self) -> None:
"""Resets the internal state of the loop for a new run"""
super().reset()
self.predictions = []
self.epoch_batch_indices = []
def on_run_start(self) -> None:
"""Calls ``on_predict_start`` hook"""
self.on_predict_start()
def advance(self, *args: Any, **kwargs: Any) -> None:
"""Predicts one entire dataloader"""
void(*args, **kwargs)
dataloader = self.trainer.accelerator.process_dataloader(
self.current_dataloader)
dataloader_iter = enumerate(dataloader)
dl_max_batches = self.max_batches[self.current_dataloader_idx]
dl_predictions, dl_batch_indices = self.epoch_loop.run(
dataloader_iter, self.current_dataloader_idx, dl_max_batches,
self.num_dataloaders, self.return_predictions)
self.predictions.append(dl_predictions)
self.epoch_batch_indices.append(dl_batch_indices)
def on_run_end(self) -> Union[List[Any], List[List[Any]]]:
"""Calls ``on_predict_epoch_end`` and ``on_predict_end`` hooks and returns results from all dataloaders"""
results = self.on_predict_epoch_end()
self.on_predict_end()
return results
def on_predict_start(self) -> None:
"""
Sets model to eval mode and disables gradients. Also calls ``on_predict_start`` and
``on_predict_epoch_start`` hooks.
"""
# enable eval mode + no grads
self.on_predict_model_eval()
self.trainer.lightning_module.zero_grad()
# hook
self.trainer.call_hook("on_predict_start")
self.trainer.call_hook("on_predict_epoch_start")
def on_predict_epoch_end(self) -> Optional[_PREDICT_OUTPUT]:
"""Calls ``on_predict_epoch_end`` hook.
Returns:
the results for all dataloaders
"""
results = self.predictions
self.trainer.call_hook("on_predict_epoch_end", results)
if self.return_predictions:
return results[0] if self.num_dataloaders == 1 else results
def on_predict_end(self) -> None:
"""Resets previous gradient status and calls ``on_predict_end`` hook"""
# clear memory. the predictions are extracted in `on_predict_epoch_end`.
self.predictions = []
self.epoch_batch_indices = []
# hook
self.trainer.call_hook("on_predict_end")
def on_predict_model_eval(self):
"""Calls ``on_predict_model_eval`` hook"""
model_ref = self.trainer.lightning_module
model_ref.on_predict_model_eval()
def __init__(self):
super().__init__()
self.epoch_loop: PredictionEpochLoop = PredictionEpochLoop()
self.predictions: Optional[List[List[Any]]] = None
self.epoch_batch_indices: Optional[List[List[int]]] = None
self._return_predictions: bool = False
