class RichProgressBar(ProgressBarBase):
"""Create a progress bar with `rich text formatting <https://github.com/willmcgugan/rich>`_.
Install it with pip:
.. code-block:: bash
pip install rich
.. code-block:: python
from pytorch_lightning import Trainer
from pytorch_lightning.callbacks import RichProgressBar
trainer = Trainer(callbacks=RichProgressBar())
Args:
refresh_rate: Determines at which rate (in number of batches) the progress bars get updated.
Set it to ``0`` to disable the display.
leave: Leaves the finished progress bar in the terminal at the end of the epoch. Default: False
theme: Contains styles used to stylize the progress bar.
console_kwargs: Args for constructing a `Console`
Raises:
ModuleNotFoundError:
If required `rich` package is not installed on the device.
Note:
PyCharm users will need to enable “emulate terminal” in output console option in
run/debug configuration to see styled output.
Reference: https://rich.readthedocs.io/en/latest/introduction.html#requirements
"""
def __init__(
self,
refresh_rate: int = 1,
leave: bool = False,
theme: RichProgressBarTheme = RichProgressBarTheme(),
console_kwargs: Optional[Dict[str, Any]] = None,
) -> None:
if not _RICH_AVAILABLE:
raise ModuleNotFoundError(
"`RichProgressBar` requires `rich` >= 10.2.2. Install it by running `pip install -U rich`."
)
super().__init__()
self._refresh_rate: int = refresh_rate
self._leave: bool = leave
self._console_kwargs = console_kwargs or {}
self._enabled: bool = True
self.progress: Optional[Progress] = None
self.val_sanity_progress_bar_id: Optional[int] = None
self._reset_progress_bar_ids()
self._metric_component = None
self._progress_stopped: bool = False
self.theme = theme
self._update_for_light_colab_theme()
@property
def refresh_rate(self) -> float:
return self._refresh_rate
@property
def is_enabled(self) -> bool:
return self._enabled and self.refresh_rate > 0
@property
def is_disabled(self) -> bool:
return not self.is_enabled
def _update_for_light_colab_theme(self) -> None:
if _detect_light_colab_theme():
attributes = ["description", "batch_progress", "metrics"]
for attr in attributes:
if getattr(self.theme, attr) == "white":
setattr(self.theme, attr, "black")
def disable(self) -> None:
self._enabled = False
def enable(self) -> None:
self._enabled = True
def _init_progress(self, trainer):
if self.is_enabled and (self.progress is None
or self._progress_stopped):
self._reset_progress_bar_ids()
self._console = Console(**self._console_kwargs)
self._console.clear_live()
self._metric_component = MetricsTextColumn(trainer,
self.theme.metrics)
self.progress = CustomProgress(
*self.configure_columns(trainer),
self._metric_component,
auto_refresh=False,
disable=self.is_disabled,
console=self._console,
)
self.progress.start()
# progress has started
self._progress_stopped = False
def refresh(self) -> None:
if self.progress:
self.progress.refresh()
def on_train_start(self, trainer, pl_module):
self._init_progress(trainer)
def on_predict_start(self, trainer, pl_module):
self._init_progress(trainer)
def on_test_start(self, trainer, pl_module):
self._init_progress(trainer)
def on_validation_start(self, trainer, pl_module):
self._init_progress(trainer)
def on_sanity_check_start(self, trainer, pl_module):
self._init_progress(trainer)
def on_sanity_check_end(self, trainer, pl_module):
if self.progress is not None:
self.progress.update(self.val_sanity_progress_bar_id,
advance=0,
visible=False)
self.refresh()
def on_train_epoch_start(self, trainer, pl_module):
total_train_batches = self.total_train_batches
total_val_batches = self.total_val_batches
if total_train_batches != float("inf"):
# val can be checked multiple times per epoch
val_checks_per_epoch = total_train_batches // trainer.val_check_batch
total_val_batches = total_val_batches * val_checks_per_epoch
total_batches = total_train_batches + total_val_batches
train_description = self._get_train_description(trainer.current_epoch)
if self.main_progress_bar_id is not None and self._leave:
self._stop_progress()
self._init_progress(trainer)
if self.main_progress_bar_id is None:
self.main_progress_bar_id = self._add_task(total_batches,
train_description)
elif self.progress is not None:
self.progress.reset(self.main_progress_bar_id,
total=total_batches,
description=train_description,
visible=True)
self.refresh()
def on_validation_batch_start(self, trainer: "pl.Trainer",
pl_module: "pl.LightningModule", batch: Any,
batch_idx: int, dataloader_idx: int) -> None:
if not self.has_dataloader_changed(dataloader_idx):
return
if trainer.sanity_checking:
if self.val_sanity_progress_bar_id is not None:
self.progress.update(self.val_sanity_progress_bar_id,
advance=0,
visible=False)
self.val_sanity_progress_bar_id = self._add_task(
self.total_val_batches_current_dataloader,
self.sanity_check_description,
visible=False)
else:
if self.val_progress_bar_id is not None:
self.progress.update(self.val_progress_bar_id,
advance=0,
visible=False)
# TODO: remove old tasks when new onces are created
self.val_progress_bar_id = self._add_task(
self.total_val_batches_current_dataloader,
self.validation_description,
visible=False)
self.refresh()
def _add_task(self,
total_batches: int,
description: str,
visible: bool = True) -> Optional[int]:
if self.progress is not None:
return self.progress.add_task(
f"[{self.theme.description}]{description}",
total=total_batches,
visible=visible)
def _update(self,
progress_bar_id: int,
current: int,
total: Union[int, float],
visible: bool = True) -> None:
if self.progress is not None and self._should_update(current, total):
leftover = current % self.refresh_rate
advance = leftover if (current == total
and leftover != 0) else self.refresh_rate
self.progress.update(progress_bar_id,
advance=advance,
visible=visible)
self.refresh()
def _should_update(self, current: int, total: Union[int, float]) -> bool:
return self.is_enabled and (current % self.refresh_rate == 0
or current == total)
def on_validation_epoch_end(self, trainer, pl_module):
if self.val_progress_bar_id is not None and trainer.state.fn == "fit":
self.progress.update(self.val_progress_bar_id,
advance=0,
visible=False)
self.refresh()
def on_validation_end(self, trainer: "pl.Trainer",
pl_module: "pl.LightningModule") -> None:
if trainer.state.fn == "fit":
self._update_metrics(trainer, pl_module)
self.reset_dataloader_idx_tracker()
def on_test_end(self, trainer: "pl.Trainer",
pl_module: "pl.LightningModule") -> None:
self.reset_dataloader_idx_tracker()
def on_predict_end(self, trainer: "pl.Trainer",
pl_module: "pl.LightningModule") -> None:
self.reset_dataloader_idx_tracker()
def on_test_batch_start(self, trainer: "pl.Trainer",
pl_module: "pl.LightningModule", batch: Any,
batch_idx: int, dataloader_idx: int) -> None:
if not self.has_dataloader_changed(dataloader_idx):
return
if self.test_progress_bar_id is not None:
self.progress.update(self.test_progress_bar_id,
advance=0,
visible=False)
self.test_progress_bar_id = self._add_task(
self.total_test_batches_current_dataloader, self.test_description)
self.refresh()
def on_predict_batch_start(self, trainer: "pl.Trainer",
pl_module: "pl.LightningModule", batch: Any,
batch_idx: int, dataloader_idx: int) -> None:
if not self.has_dataloader_changed(dataloader_idx):
return
if self.predict_progress_bar_id is not None:
self.progress.update(self.predict_progress_bar_id,
advance=0,
visible=False)
self.predict_progress_bar_id = self._add_task(
self.total_predict_batches_current_dataloader,
self.predict_description)
self.refresh()
def on_train_batch_end(self, trainer, pl_module, outputs, batch,
batch_idx):
self._update(self.main_progress_bar_id, self.train_batch_idx,
self.total_train_batches)
self._update_metrics(trainer, pl_module)
self.refresh()
def on_train_epoch_end(self, trainer: "pl.Trainer",
pl_module: "pl.LightningModule") -> None:
self._update_metrics(trainer, pl_module)
def on_validation_batch_end(self, trainer, pl_module, outputs, batch,
batch_idx, dataloader_idx):
if trainer.sanity_checking:
self._update(self.val_sanity_progress_bar_id, self.val_batch_idx,
self.total_val_batches_current_dataloader)
elif self.val_progress_bar_id is not None:
# check to see if we should update the main training progress bar
if self.main_progress_bar_id is not None:
# TODO: Use total val_processed here just like TQDM in a follow-up
self._update(self.main_progress_bar_id, self.val_batch_idx,
self.total_val_batches_current_dataloader)
self._update(self.val_progress_bar_id, self.val_batch_idx,
self.total_val_batches_current_dataloader)
self.refresh()
def on_test_batch_end(self, trainer, pl_module, outputs, batch, batch_idx,
dataloader_idx):
self._update(self.test_progress_bar_id, self.test_batch_idx,
self.total_test_batches_current_dataloader)
self.refresh()
def on_predict_batch_end(self, trainer, pl_module, outputs, batch,
batch_idx, dataloader_idx):
self._update(self.predict_progress_bar_id, self.predict_batch_idx,
self.total_predict_batches_current_dataloader)
self.refresh()
def _get_train_description(self, current_epoch: int) -> str:
train_description = f"Epoch {current_epoch}"
if len(self.validation_description) > len(train_description):
# Padding is required to avoid flickering due of uneven lengths of "Epoch X"
# and "Validation" Bar description
num_digits = len(str(current_epoch))
required_padding = (len(self.validation_description) -
len(train_description) + 1) - num_digits
for _ in range(required_padding):
train_description += " "
return train_description
def _stop_progress(self) -> None:
if self.progress is not None:
self.progress.stop()
# # signals for progress to be re-initialized for next stages
self._progress_stopped = True
def _reset_progress_bar_ids(self):
self.main_progress_bar_id: Optional[int] = None
self.val_progress_bar_id: Optional[int] = None
self.test_progress_bar_id: Optional[int] = None
self.predict_progress_bar_id: Optional[int] = None
def _update_metrics(self, trainer, pl_module) -> None:
metrics = self.get_metrics(trainer, pl_module)
if self._metric_component:
self._metric_component.update(metrics)
def teardown(self,
trainer,
pl_module,
stage: Optional[str] = None) -> None:
self._stop_progress()
def on_exception(self, trainer, pl_module,
exception: BaseException) -> None:
self._stop_progress()
@property
def val_progress_bar(self) -> Task:
return self.progress.tasks[self.val_progress_bar_id]
@property
def val_sanity_check_bar(self) -> Task:
return self.progress.tasks[self.val_sanity_progress_bar_id]
@property
def main_progress_bar(self) -> Task:
return self.progress.tasks[self.main_progress_bar_id]
@property
def test_progress_bar(self) -> Task:
return self.progress.tasks[self.test_progress_bar_id]
def configure_columns(self, trainer) -> list:
return [
TextColumn("[progress.description]{task.description}"),
CustomBarColumn(
complete_style=self.theme.progress_bar,
finished_style=self.theme.progress_bar_finished,
pulse_style=self.theme.progress_bar_pulse,
),
BatchesProcessedColumn(style=self.theme.batch_progress),
CustomTimeColumn(style=self.theme.time),
ProcessingSpeedColumn(style=self.theme.processing_speed),
]
class KrakenTrainProgressBar(ProgressBarBase):
"""
Adaptation of the default ptl rich progress bar to fit with kraken (segtrain, train) output.
Args:
refresh_rate: Determines at which rate (in number of batches) the progress bars get updated.
Set it to ``0`` to disable the display.
leave: Leaves the finished progress bar in the terminal at the end of the epoch. Default: False
console_kwargs: Args for constructing a `Console`
"""
def __init__(self,
refresh_rate: int = 1,
leave: bool = True,
console_kwargs: Optional[Dict[str, Any]] = None) -> None:
super().__init__()
self._refresh_rate: int = refresh_rate
self._leave: bool = leave
self._console_kwargs = console_kwargs or {}
self._enabled: bool = True
self.progress: Optional[Progress] = None
self.val_sanity_progress_bar_id: Optional[int] = None
self._reset_progress_bar_ids()
self._metric_component = None
self._progress_stopped: bool = False
@property
def refresh_rate(self) -> float:
return self._refresh_rate
@property
def is_enabled(self) -> bool:
return self._enabled and self.refresh_rate > 0
@property
def is_disabled(self) -> bool:
return not self.is_enabled
def disable(self) -> None:
self._enabled = False
def enable(self) -> None:
self._enabled = True
@property
def sanity_check_description(self) -> str:
return "Validation Sanity Check"
@property
def validation_description(self) -> str:
return "Validation"
@property
def test_description(self) -> str:
return "Testing"
def _init_progress(self, trainer):
if self.is_enabled and (self.progress is None
or self._progress_stopped):
self._reset_progress_bar_ids()
self._console = Console(**self._console_kwargs)
self._console.clear_live()
columns = self.configure_columns(trainer)
self._metric_component = MetricsTextColumn(trainer)
columns.append(self._metric_component)
if trainer.early_stopping_callback:
self._early_stopping_component = EarlyStoppingColumn(trainer)
columns.append(self._early_stopping_component)
self.progress = Progress(*columns,
auto_refresh=False,
disable=self.is_disabled,
console=self._console)
self.progress.start()
# progress has started
self._progress_stopped = False
def refresh(self) -> None:
if self.progress:
self.progress.refresh()
def on_train_start(self, trainer, pl_module):
self._init_progress(trainer)
def on_test_start(self, trainer, pl_module):
self._init_progress(trainer)
def on_validation_start(self, trainer, pl_module):
self._init_progress(trainer)
def on_sanity_check_start(self, trainer, pl_module):
self._init_progress(trainer)
def on_sanity_check_end(self, trainer, pl_module):
if self.progress is not None:
self.progress.update(self.val_sanity_progress_bar_id,
advance=0,
visible=False)
self.refresh()
def on_train_epoch_start(self, trainer, pl_module):
total_train_batches = self.total_train_batches
total_val_batches = self.total_val_batches
if total_train_batches != float("inf"):
# val can be checked multiple times per epoch
val_checks_per_epoch = total_train_batches // trainer.val_check_batch
total_val_batches = total_val_batches * val_checks_per_epoch
total_batches = total_train_batches + total_val_batches
train_description = f"stage {trainer.current_epoch}/{trainer.max_epochs if pl_module.hparams.quit == 'dumb' else '∞'}"
if len(self.validation_description) > len(train_description):
# Padding is required to avoid flickering due of uneven lengths of "Epoch X"
# and "Validation" Bar description
num_digits = len(str(trainer.current_epoch))
required_padding = (len(self.validation_description) -
len(train_description) + 1) - num_digits
for _ in range(required_padding):
train_description += " "
if self.main_progress_bar_id is not None and self._leave:
self._stop_progress()
self._init_progress(trainer)
if self.main_progress_bar_id is None:
self.main_progress_bar_id = self._add_task(total_batches,
train_description)
elif self.progress is not None:
self.progress.reset(self.main_progress_bar_id,
total=total_batches,
description=train_description,
visible=True)
self.refresh()
def on_validation_epoch_start(self, trainer, pl_module):
if trainer.sanity_checking:
self.val_sanity_progress_bar_id = self._add_task(
self.total_val_batches, self.sanity_check_description)
else:
self.val_progress_bar_id = self._add_task(
self.total_val_batches,
self.validation_description,
visible=False)
self.refresh()
def _add_task(self,
total_batches: int,
description: str,
visible: bool = True) -> Optional[int]:
if self.progress is not None:
return self.progress.add_task(f"{description}",
total=total_batches,
visible=visible)
def _update(self,
progress_bar_id: int,
current: int,
total: Union[int, float],
visible: bool = True) -> None:
if self.progress is not None and self._should_update(current, total):
leftover = current % self.refresh_rate
advance = leftover if (current == total
and leftover != 0) else self.refresh_rate
self.progress.update(progress_bar_id,
advance=advance,
visible=visible)
self.refresh()
def _should_update(self, current: int, total: Union[int, float]) -> bool:
return self.is_enabled and (current % self.refresh_rate == 0
or current == total)
def on_validation_epoch_end(self, trainer, pl_module):
if self.val_progress_bar_id is not None and trainer.state.fn == "fit":
self.progress.update(self.val_progress_bar_id,
advance=0,
visible=False)
self.refresh()
def on_validation_end(self, trainer: "pl.Trainer",
pl_module: "pl.LightningModule") -> None:
if trainer.state.fn == "fit":
self._update_metrics(trainer, pl_module)
def on_test_epoch_start(self, trainer, pl_module):
self.test_progress_bar_id = self._add_task(self.total_test_batches,
self.test_description)
self.refresh()
def on_train_batch_end(self, trainer, pl_module, outputs, batch,
batch_idx):
self._update(self.main_progress_bar_id, self.train_batch_idx,
self.total_train_batches)
self._update_metrics(trainer, pl_module)
self.refresh()
def on_train_epoch_end(self, trainer: "pl.Trainer",
pl_module: "pl.LightningModule") -> None:
self._update_metrics(trainer, pl_module)
def on_validation_batch_end(self, trainer, pl_module, outputs, batch,
batch_idx, dataloader_idx):
if trainer.sanity_checking:
self._update(self.val_sanity_progress_bar_id, self.val_batch_idx,
self.total_val_batches)
elif self.val_progress_bar_id is not None:
# check to see if we should update the main training progress bar
if self.main_progress_bar_id is not None:
self._update(self.main_progress_bar_id, self.val_batch_idx,
self.total_val_batches)
self._update(self.val_progress_bar_id, self.val_batch_idx,
self.total_val_batches)
self.refresh()
def on_test_batch_end(self, trainer, pl_module, outputs, batch, batch_idx,
dataloader_idx):
self._update(self.test_progress_bar_id, self.test_batch_idx,
self.total_test_batches)
self.refresh()
def _stop_progress(self) -> None:
if self.progress is not None:
self.progress.stop()
# # signals for progress to be re-initialized for next stages
self._progress_stopped = True
def _reset_progress_bar_ids(self):
self.main_progress_bar_id: Optional[int] = None
self.val_progress_bar_id: Optional[int] = None
self.test_progress_bar_id: Optional[int] = None
def _update_metrics(self, trainer, pl_module) -> None:
metrics = self.get_metrics(trainer, pl_module)
metrics.pop('loss', None)
metrics.pop('val_metric', None)
if self._metric_component:
self._metric_component.update(metrics)
def teardown(self,
trainer,
pl_module,
stage: Optional[str] = None) -> None:
self._stop_progress()
def on_exception(self, trainer, pl_module,
exception: BaseException) -> None:
self._stop_progress()
@property
def val_progress_bar(self) -> Task:
return self.progress.tasks[self.val_progress_bar_id]
@property
def val_sanity_check_bar(self) -> Task:
return self.progress.tasks[self.val_sanity_progress_bar_id]
@property
def main_progress_bar(self) -> Task:
return self.progress.tasks[self.main_progress_bar_id]
@property
def test_progress_bar(self) -> Task:
return self.progress.tasks[self.test_progress_bar_id]
def configure_columns(self, trainer) -> list:
return [
TextColumn("[progress.description]{task.description}"),
BarColumn(),
BatchesProcessedColumn(),
TimeRemainingColumn(),
TimeElapsedColumn()
]
