Source code for pytorch_lightning.callbacks.progress.base
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from typing import Dict, Union
import pytorch_lightning as pl
from pytorch_lightning.callbacks import Callback
from pytorch_lightning.utilities 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):
self._trainer = None
self._train_batch_idx = 0
self._val_batch_idx = 0
self._test_batch_idx = 0
self._predict_batch_idx = 0
@property
def trainer(self):
return self._trainer
@property
def train_batch_idx(self) -> int:
"""The current batch index being processed during training.
Use this to update your progress bar.
"""
return self._train_batch_idx
@property
def val_batch_idx(self) -> int:
"""The current batch index being processed during validation.
Use this to update your progress bar.
"""
return self._val_batch_idx
@property
def test_batch_idx(self) -> int:
"""The current batch index being processed during testing.
Use this to update your progress bar.
"""
return self._test_batch_idx
@property
def predict_batch_idx(self) -> int:
"""The current batch index being processed during predicting.
Use this to update your progress bar.
"""
return self._predict_batch_idx
@property
def total_train_batches(self) -> int:
"""The total number of training batches during training, 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(self) -> int:
"""The total number of validation batches during validation, 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 validation
dataloader is of infinite size.
"""
total_val_batches = 0
if self.trainer.enable_validation:
is_val_epoch = (self.trainer.current_epoch + 1) % self.trainer.check_val_every_n_epoch == 0
total_val_batches = sum(self.trainer.num_val_batches) if is_val_epoch else 0
return total_val_batches
@property
def total_test_batches(self) -> int:
"""The total number of testing batches during testing, 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 test dataloader is
of infinite size.
"""
return sum(self.trainer.num_test_batches)
@property
def total_predict_batches(self) -> int:
"""The total number of predicting batches during testing, 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 predict dataloader
is of infinite size.
"""
return sum(self.trainer.num_predict_batches)
[docs] def disable(self):
"""You should provide a way to disable the progress bar.
The :class:`~pytorch_lightning.trainer.trainer.Trainer` will call this to disable the
output on processes that have a rank different from 0, e.g., in multi-node training.
"""
raise NotImplementedError
[docs] def enable(self):
"""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/lr_finder:Learning Rate Finder>`
to temporarily enable and disable the main progress bar.
"""
raise NotImplementedError
[docs] def print(self, *args, **kwargs):
"""You should provide a way to print without breaking the progress bar."""
print(*args, **kwargs)
[docs] def on_train_epoch_start(self, trainer, pl_module):
self._train_batch_idx = trainer.fit_loop.epoch_loop.batch_progress.current.completed
[docs] def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx):
self._train_batch_idx += 1
[docs] def on_validation_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx):
self._val_batch_idx += 1
[docs] def on_test_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx):
self._test_batch_idx += 1
[docs] def on_predict_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx):
self._predict_batch_idx += 1
[docs] def get_metrics(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> Dict[str, Union[int, str]]:
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 = pl_module.get_progress_bar_dict()
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.",
UserWarning,
)
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 = {}
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.logger is not None and trainer.logger.version is not None:
version = trainer.logger.version
# show last 4 places of long version strings
version = version[-4:] if isinstance(version, str) else version
items_dict["v_num"] = version
return items_dict
