Source code for pytorch_lightning.callbacks.progress
# Copyright The PyTorch Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Progress Bars
=============
Use or override one of the progress bar callbacks.
"""
import importlib
import io
import math
import os
import sys
# check if ipywidgets is installed before importing tqdm.auto
# to ensure it won't fail and a progress bar is displayed
from typing import Optional, Union
if importlib.util.find_spec("ipywidgets") is not None:
from tqdm.auto import tqdm as _tqdm
else:
from tqdm import tqdm as _tqdm
from pytorch_lightning.callbacks import Callback
_PAD_SIZE = 5
[docs]class tqdm(_tqdm):
"""
Custom tqdm progressbar where we append 0 to floating points/strings to prevent the progress bar from flickering
"""
[docs] @staticmethod
def format_num(n) -> str:
"""Add additional padding to the formatted numbers"""
should_be_padded = isinstance(n, (float, str))
if not isinstance(n, str):
n = _tqdm.format_num(n)
if should_be_padded and "e" not in n:
if "." not in n and len(n) < _PAD_SIZE:
try:
_ = float(n)
except ValueError:
return n
n += "."
n += "0" * (_PAD_SIZE - len(n))
return n
[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):
super().on_train_batch_end(trainer, pl_module, outputs) # 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_start(self, trainer, pl_module):
self._train_batch_idx = trainer.fit_loop.batch_idx
[docs] def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_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]class ProgressBar(ProgressBarBase):
r"""
This is the default progress bar used by Lightning. It prints to `stdout` using the
:mod:`tqdm` package and shows up to four different bars:
- **sanity check progress:** the progress during the sanity check run
- **main progress:** shows training + validation progress combined. It also accounts for
multiple validation runs during training when
:paramref:`~pytorch_lightning.trainer.trainer.Trainer.val_check_interval` is used.
- **validation progress:** only visible during validation;
shows total progress over all validation datasets.
- **test progress:** only active when testing; shows total progress over all test datasets.
For infinite datasets, the progress bar never ends.
If you want to customize the default ``tqdm`` progress bars used by Lightning, you can override
specific methods of the callback class and pass your custom implementation to the
:class:`~pytorch_lightning.trainer.trainer.Trainer`:
Example::
class LitProgressBar(ProgressBar):
def init_validation_tqdm(self):
bar = super().init_validation_tqdm()
bar.set_description('running validation ...')
return bar
bar = LitProgressBar()
trainer = Trainer(callbacks=[bar])
Args:
refresh_rate:
Determines at which rate (in number of batches) the progress bars get updated.
Set it to ``0`` to disable the display. By default, the
:class:`~pytorch_lightning.trainer.trainer.Trainer` uses this implementation of the progress
bar and sets the refresh rate to the value provided to the
:paramref:`~pytorch_lightning.trainer.trainer.Trainer.progress_bar_refresh_rate` argument in the
:class:`~pytorch_lightning.trainer.trainer.Trainer`.
process_position:
Set this to a value greater than ``0`` to offset the progress bars by this many lines.
This is useful when you have progress bars defined elsewhere and want to show all of them
together. This corresponds to
:paramref:`~pytorch_lightning.trainer.trainer.Trainer.process_position` in the
:class:`~pytorch_lightning.trainer.trainer.Trainer`.
"""
def __init__(self, refresh_rate: int = 1, process_position: int = 0):
super().__init__()
self._refresh_rate = refresh_rate
self._process_position = process_position
self._enabled = True
self.main_progress_bar = None
self.val_progress_bar = None
self.test_progress_bar = None
self.predict_progress_bar = None
def __getstate__(self):
# can't pickle the tqdm objects
state = self.__dict__.copy()
state["main_progress_bar"] = None
state["val_progress_bar"] = None
state["test_progress_bar"] = None
state["predict_progress_bar"] = None
return state
@property
def refresh_rate(self) -> int:
return self._refresh_rate
@property
def process_position(self) -> int:
return self._process_position
@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
[docs] def init_sanity_tqdm(self) -> tqdm:
"""Override this to customize the tqdm bar for the validation sanity run."""
bar = tqdm(
desc="Validation sanity check",
position=(2 * self.process_position),
disable=self.is_disabled,
leave=False,
dynamic_ncols=True,
file=sys.stdout,
)
return bar
[docs] def init_train_tqdm(self) -> tqdm:
"""Override this to customize the tqdm bar for training."""
bar = tqdm(
desc="Training",
initial=self.train_batch_idx,
position=(2 * self.process_position),
disable=self.is_disabled,
leave=True,
dynamic_ncols=True,
file=sys.stdout,
smoothing=0,
)
return bar
[docs] def init_predict_tqdm(self) -> tqdm:
"""Override this to customize the tqdm bar for predicting."""
bar = tqdm(
desc="Predicting",
initial=self.train_batch_idx,
position=(2 * self.process_position),
disable=self.is_disabled,
leave=True,
dynamic_ncols=True,
file=sys.stdout,
smoothing=0,
)
return bar
[docs] def init_validation_tqdm(self) -> tqdm:
"""Override this to customize the tqdm bar for validation."""
# The main progress bar doesn't exist in `trainer.validate()`
has_main_bar = self.main_progress_bar is not None
bar = tqdm(
desc="Validating",
position=(2 * self.process_position + has_main_bar),
disable=self.is_disabled,
leave=False,
dynamic_ncols=True,
file=sys.stdout,
)
return bar
[docs] def init_test_tqdm(self) -> tqdm:
"""Override this to customize the tqdm bar for testing."""
bar = tqdm(
desc="Testing",
position=(2 * self.process_position),
disable=self.is_disabled,
leave=True,
dynamic_ncols=True,
file=sys.stdout,
)
return bar
[docs] def on_sanity_check_start(self, trainer, pl_module):
super().on_sanity_check_start(trainer, pl_module)
self.val_progress_bar = self.init_sanity_tqdm()
self.main_progress_bar = tqdm(disable=True) # dummy progress bar
[docs] def on_sanity_check_end(self, trainer, pl_module):
super().on_sanity_check_end(trainer, pl_module)
self.main_progress_bar.close()
self.val_progress_bar.close()
[docs] def on_train_start(self, trainer, pl_module):
super().on_train_start(trainer, pl_module)
self.main_progress_bar = self.init_train_tqdm()
[docs] def on_train_epoch_start(self, trainer, pl_module):
super().on_train_epoch_start(trainer, pl_module)
total_train_batches = self.total_train_batches
total_val_batches = self.total_val_batches
if total_train_batches != float("inf") and total_val_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
reset(self.main_progress_bar, total_batches)
self.main_progress_bar.set_description(f"Epoch {trainer.current_epoch}")
[docs] def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx):
super().on_train_batch_end(trainer, pl_module, outputs, batch, batch_idx, dataloader_idx)
total_batches = self.total_train_batches + self.total_val_batches
total_batches = convert_inf(total_batches)
if self._should_update(self.train_batch_idx, total_batches):
self._update_bar(self.main_progress_bar)
self.main_progress_bar.set_postfix(trainer.progress_bar_dict)
[docs] def on_validation_start(self, trainer, pl_module):
super().on_validation_start(trainer, pl_module)
if trainer.sanity_checking:
reset(self.val_progress_bar, sum(trainer.num_sanity_val_batches))
else:
self._update_bar(self.main_progress_bar) # fill up remaining
self.val_progress_bar = self.init_validation_tqdm()
reset(self.val_progress_bar, self.total_val_batches)
[docs] def on_validation_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx):
super().on_validation_batch_end(trainer, pl_module, outputs, batch, batch_idx, dataloader_idx)
if self._should_update(self.val_batch_idx, convert_inf(self.total_val_batches)):
self._update_bar(self.val_progress_bar)
self._update_bar(self.main_progress_bar)
[docs] def on_validation_end(self, trainer, pl_module):
super().on_validation_end(trainer, pl_module)
if self.main_progress_bar is not None:
self.main_progress_bar.set_postfix(trainer.progress_bar_dict)
self.val_progress_bar.close()
[docs] def on_train_end(self, trainer, pl_module):
super().on_train_end(trainer, pl_module)
self.main_progress_bar.close()
[docs] def on_test_start(self, trainer, pl_module):
super().on_test_start(trainer, pl_module)
self.test_progress_bar = self.init_test_tqdm()
self.test_progress_bar.total = convert_inf(self.total_test_batches)
[docs] def on_test_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx):
super().on_test_batch_end(trainer, pl_module, outputs, batch, batch_idx, dataloader_idx)
if self._should_update(self.test_batch_idx, self.total_test_batches):
self._update_bar(self.test_progress_bar)
[docs] def on_test_end(self, trainer, pl_module):
super().on_test_end(trainer, pl_module)
self.test_progress_bar.close()
[docs] def on_predict_epoch_start(self, trainer, pl_module):
super().on_predict_epoch_start(trainer, pl_module)
self.predict_progress_bar = self.init_predict_tqdm()
self.predict_progress_bar.total = convert_inf(self.total_predict_batches)
[docs] def on_predict_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx):
super().on_predict_batch_end(trainer, pl_module, outputs, batch, batch_idx, dataloader_idx)
if self._should_update(self.predict_batch_idx, self.total_predict_batches):
self._update_bar(self.predict_progress_bar)
[docs] def print(
self, *args, sep: str = " ", end: str = os.linesep, file: Optional[io.TextIOBase] = None, nolock: bool = False
):
active_progress_bar = None
if self.main_progress_bar is not None and not self.main_progress_bar.disable:
active_progress_bar = self.main_progress_bar
elif self.val_progress_bar is not None and not self.val_progress_bar.disable:
active_progress_bar = self.val_progress_bar
elif self.test_progress_bar is not None and not self.test_progress_bar.disable:
active_progress_bar = self.test_progress_bar
elif self.predict_progress_bar is not None and not self.predict_progress_bar.disable:
active_progress_bar = self.predict_progress_bar
if active_progress_bar is not None:
s = sep.join(map(str, args))
active_progress_bar.write(s, end=end, file=file, nolock=nolock)
def _should_update(self, current, total) -> bool:
return self.is_enabled and (current % self.refresh_rate == 0 or current == total)
def _update_bar(self, bar: Optional[tqdm]) -> None:
"""Updates the bar by the refresh rate without overshooting."""
if bar is None:
return
if bar.total is not None:
delta = min(self.refresh_rate, bar.total - bar.n)
else:
# infinite / unknown size
delta = self.refresh_rate
if delta > 0:
bar.update(delta)
[docs]def convert_inf(x: Optional[Union[int, float]]) -> Optional[Union[int, float]]:
"""The tqdm doesn't support inf/nan values. We have to convert it to None."""
if x is None or math.isinf(x) or math.isnan(x):
return None
return x
[docs]def reset(bar: tqdm, total: Optional[int] = None) -> None:
"""Resets the tqdm bar to 0 progress with a new total, unless it is disabled."""
if not bar.disable:
bar.reset(total=convert_inf(total))
