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Source code for pytorch_lightning.callbacks.progress.base

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from typing import Any, Dict, Optional, Union

import pytorch_lightning as pl
from pytorch_lightning.callbacks import Callback
from pytorch_lightning.utilities.logger import _version
from pytorch_lightning.utilities.rank_zero import rank_zero_warn


[docs]class ProgressBarBase(Callback): r""" The base class for progress bars in Lightning. It is a :class:`~pytorch_lightning.callbacks.Callback` that keeps track of the batch progress in the :class:`~pytorch_lightning.trainer.trainer.Trainer`. You should implement your highly custom progress bars with this as the base class. Example:: class LitProgressBar(ProgressBarBase): def __init__(self): super().__init__() # don't forget this :) self.enable = True def disable(self): self.enable = False def on_train_batch_end(self, trainer, pl_module, outputs, batch_idx): super().on_train_batch_end(trainer, pl_module, outputs, batch_idx) # don't forget this :) percent = (self.train_batch_idx / self.total_train_batches) * 100 sys.stdout.flush() sys.stdout.write(f'{percent:.01f} percent complete \r') bar = LitProgressBar() trainer = Trainer(callbacks=[bar]) """ def __init__(self) -> None: self._trainer: Optional["pl.Trainer"] = None self._current_eval_dataloader_idx: Optional[int] = None @property def trainer(self) -> "pl.Trainer": if self._trainer is None: raise TypeError(f"The `{self.__class__.__name__}._trainer` reference has not been set yet.") return self._trainer @property def sanity_check_description(self) -> str: return "Sanity Checking" @property def train_description(self) -> str: return "Training" @property def validation_description(self) -> str: return "Validation" @property def test_description(self) -> str: return "Testing" @property def predict_description(self) -> str: return "Predicting" @property def _val_processed(self) -> int: # use total in case validation runs more than once per training epoch return self.trainer.fit_loop.epoch_loop.val_loop.epoch_loop.batch_progress.total.processed @property def train_batch_idx(self) -> int: """The number of batches processed during training. Use this to update your progress bar. """ return self.trainer.fit_loop.epoch_loop.batch_progress.current.processed @property def val_batch_idx(self) -> int: """The number of batches processed during validation. Use this to update your progress bar. """ if self.trainer.state.fn == "fit": loop = self.trainer.fit_loop.epoch_loop.val_loop else: loop = self.trainer.validate_loop current_batch_idx = loop.epoch_loop.batch_progress.current.processed return current_batch_idx @property def test_batch_idx(self) -> int: """The number of batches processed during testing. Use this to update your progress bar. """ return self.trainer.test_loop.epoch_loop.batch_progress.current.processed @property def predict_batch_idx(self) -> int: """The number of batches processed during prediction. Use this to update your progress bar. """ return self.trainer.predict_loop.epoch_loop.batch_progress.current.processed @property def total_train_batches(self) -> Union[int, float]: """The total number of training batches, which may change from epoch to epoch. Use this to set the total number of iterations in the progress bar. Can return ``inf`` if the training dataloader is of infinite size. """ return self.trainer.num_training_batches @property def total_val_batches_current_dataloader(self) -> Union[int, float]: """The total number of validation batches, which may change from epoch to epoch for current dataloader. Use this to set the total number of iterations in the progress bar. Can return ``inf`` if the validation dataloader is of infinite size. """ assert self._current_eval_dataloader_idx is not None if self.trainer.sanity_checking: return self.trainer.num_sanity_val_batches[self._current_eval_dataloader_idx] return self.trainer.num_val_batches[self._current_eval_dataloader_idx] @property def total_test_batches_current_dataloader(self) -> Union[int, float]: """The total number of testing batches, which may change from epoch to epoch for current dataloader. Use this to set the total number of iterations in the progress bar. Can return ``inf`` if the test dataloader is of infinite size. """ assert self._current_eval_dataloader_idx is not None return self.trainer.num_test_batches[self._current_eval_dataloader_idx] @property def total_predict_batches_current_dataloader(self) -> Union[int, float]: """The total number of prediction batches, which may change from epoch to epoch for current dataloader. Use this to set the total number of iterations in the progress bar. Can return ``inf`` if the predict dataloader is of infinite size. """ assert self._current_eval_dataloader_idx is not None return self.trainer.num_predict_batches[self._current_eval_dataloader_idx] @property def total_val_batches(self) -> Union[int, float]: """The total number of validation batches, which may change from epoch to epoch for all val dataloaders. Use this to set the total number of iterations in the progress bar. Can return ``inf`` if the predict dataloader is of infinite size. """ return sum(self.trainer.num_val_batches) if self.trainer.fit_loop.epoch_loop._should_check_val_epoch() else 0 @property def total_batches_current_epoch(self) -> Union[int, float]: total_train_batches = self.total_train_batches total_val_batches = self.total_val_batches assert self._trainer is not None if total_train_batches != float("inf") and total_val_batches != float("inf"): # val can be checked multiple times per epoch val_check_batch = self.trainer.val_check_batch if self.trainer.check_val_every_n_epoch is None: train_batches_processed = self.trainer.fit_loop.total_batch_idx + 1 val_checks_per_epoch = ((train_batches_processed + total_train_batches) // val_check_batch) - ( train_batches_processed // val_check_batch ) else: val_checks_per_epoch = total_train_batches // val_check_batch total_val_batches = total_val_batches * val_checks_per_epoch return total_train_batches + total_val_batches def has_dataloader_changed(self, dataloader_idx: int) -> bool: old_dataloader_idx = self._current_eval_dataloader_idx self._current_eval_dataloader_idx = dataloader_idx return old_dataloader_idx != dataloader_idx def reset_dataloader_idx_tracker(self) -> None: self._current_eval_dataloader_idx = None
[docs] def disable(self) -> None: """You should provide a way to disable the progress bar.""" raise NotImplementedError
[docs] def enable(self) -> None: """You should provide a way to enable the progress bar. The :class:`~pytorch_lightning.trainer.trainer.Trainer` will call this in e.g. pre-training routines like the :ref:`learning rate finder <advanced/training_tricks:Learning Rate Finder>`. to temporarily enable and disable the main progress bar. """ raise NotImplementedError
[docs] def print(self, *args: Any, **kwargs: Any) -> None: """You should provide a way to print without breaking the progress bar.""" print(*args, **kwargs)
[docs] def setup(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", stage: str) -> None: self._trainer = trainer if not trainer.is_global_zero: self.disable()
[docs] def get_metrics( self, trainer: "pl.Trainer", pl_module: "pl.LightningModule" ) -> Dict[str, Union[int, str, float, Dict[str, float]]]: r""" Combines progress bar metrics collected from the trainer with standard metrics from get_standard_metrics. Implement this to override the items displayed in the progress bar. Here is an example of how to override the defaults: .. code-block:: python def get_metrics(self, trainer, model): # don't show the version number items = super().get_metrics(trainer, model) items.pop("v_num", None) return items Return: Dictionary with the items to be displayed in the progress bar. """ standard_metrics = get_standard_metrics(trainer, pl_module) pbar_metrics = trainer.progress_bar_metrics duplicates = list(standard_metrics.keys() & pbar_metrics.keys()) if duplicates: rank_zero_warn( f"The progress bar already tracks a metric with the name(s) '{', '.join(duplicates)}' and" f" `self.log('{duplicates[0]}', ..., prog_bar=True)` will overwrite this value. " " If this is undesired, change the name or override `get_metrics()` in the progress bar callback.", ) return {**standard_metrics, **pbar_metrics}
def get_standard_metrics(trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> Dict[str, Union[int, str]]: r""" Returns several standard metrics displayed in the progress bar, including the average loss value, split index of BPTT (if used) and the version of the experiment when using a logger. .. code-block:: Epoch 1: 4%|▎ | 40/1095 [00:03<01:37, 10.84it/s, loss=4.501, v_num=10] Return: Dictionary with the standard metrics to be displayed in the progress bar. """ # call .item() only once but store elements without graphs running_train_loss = trainer.fit_loop.running_loss.mean() avg_training_loss = None if running_train_loss is not None: avg_training_loss = running_train_loss.cpu().item() elif pl_module.automatic_optimization: avg_training_loss = float("NaN") items_dict: Dict[str, Union[int, str]] = {} if avg_training_loss is not None: items_dict["loss"] = f"{avg_training_loss:.3g}" if pl_module.truncated_bptt_steps > 0: items_dict["split_idx"] = trainer.fit_loop.split_idx if trainer.loggers: version = _version(trainer.loggers) if version is not None: if isinstance(version, str): # show last 4 places of long version strings version = version[-4:] items_dict["v_num"] = version return items_dict

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