# -*- coding: utf-8 -*-
import warnings
from typing import Any, Mapping
import torch
from ignite.contrib.handlers.base_logger import BaseLogger, BaseOutputHandler
from ignite.engine import Engine, Events
from ignite.engine.events import CallableEventWithFilter
class _OutputHandler(BaseOutputHandler):
"""Helper handler to log engine's output and/or metrics
Args:
description (str): progress bar description.
metric_names (list of str, optional): list of metric names to plot or a string "all" to plot all available
metrics.
output_transform (callable, optional): output transform function to prepare `engine.state.output` as a number.
For example, `output_transform = lambda output: output`
This function can also return a dictionary, e.g `{'loss': loss1, 'another_loss': loss2}` to label the plot
with corresponding keys.
closing_event_name: event's name on which the progress bar is closed. Valid events are from
:class:`~ignite.engine.events.Events` or any `event_name` added by
:meth:`~ignite.engine.engine.Engine.register_events`.
"""
def __init__(
self, description, metric_names=None, output_transform=None, closing_event_name=Events.EPOCH_COMPLETED
):
if metric_names is None and output_transform is None:
# This helps to avoid 'Either metric_names or output_transform should be defined' of BaseOutputHandler
metric_names = []
super(_OutputHandler, self).__init__(description, metric_names, output_transform, global_step_transform=None)
self.closing_event_name = closing_event_name
@staticmethod
def get_max_number_events(event_name, engine):
if event_name in (Events.ITERATION_STARTED, Events.ITERATION_COMPLETED):
return engine.state.epoch_length
if event_name in (Events.EPOCH_STARTED, Events.EPOCH_COMPLETED):
return engine.state.max_epochs
return 1
def __call__(self, engine, logger, event_name):
pbar_total = self.get_max_number_events(event_name, engine)
if logger.pbar is None:
logger._reset(pbar_total=pbar_total)
desc = self.tag
max_num_of_closing_events = self.get_max_number_events(self.closing_event_name, engine)
if max_num_of_closing_events > 1:
global_step = engine.state.get_event_attrib_value(self.closing_event_name)
desc += " [{}/{}]".format(global_step, max_num_of_closing_events)
logger.pbar.set_description(desc)
metrics = self._setup_output_metrics(engine)
rendered_metrics = {}
for key, value in metrics.items():
if isinstance(value, torch.Tensor):
if value.ndimension() == 0:
rendered_metrics[key] = value.item()
elif value.ndimension() == 1:
for i, v in enumerate(value):
k = "{}_{}".format(key, i)
rendered_metrics[k] = v.item()
else:
warnings.warn("ProgressBar can not log " "tensor with {} dimensions".format(value.ndimension()))
else:
rendered_metrics[key] = value
if rendered_metrics:
logger.pbar.set_postfix(**rendered_metrics)
global_step = engine.state.get_event_attrib_value(event_name)
if pbar_total is not None:
global_step = (global_step - 1) % pbar_total + 1
logger.pbar.update(global_step - logger.pbar.n)
[docs]class ProgressBar(BaseLogger):
"""
TQDM progress bar handler to log training progress and computed metrics.
Args:
persist (bool, optional): set to ``True`` to persist the progress bar after completion (default = ``False``)
bar_format (str, optional): Specify a custom bar string formatting. May impact performance.
[default: '{desc}[{n_fmt}/{total_fmt}] {percentage:3.0f}%|{bar}{postfix} [{elapsed}<{remaining}]'].
Set to ``None`` to use ``tqdm`` default bar formatting: '{l_bar}{bar}{r_bar}', where
l_bar='{desc}: {percentage:3.0f}%|' and
r_bar='| {n_fmt}/{total_fmt} [{elapsed}<{remaining}, {rate_fmt}{postfix}]'. For more details on the
formatting, see `tqdm docs <https://tqdm.github.io/docs/tqdm/>`_.
**tqdm_kwargs: kwargs passed to tqdm progress bar.
By default, progress bar description displays "Epoch [5/10]" where 5 is the current epoch and 10 is the
number of epochs. If tqdm_kwargs defines `desc`, e.g. "Predictions", than the description is
"Predictions [5/10]" if number of epochs is more than one otherwise it is simply "Predictions".
Examples:
Simple progress bar
.. code-block:: python
trainer = create_supervised_trainer(model, optimizer, loss)
pbar = ProgressBar()
pbar.attach(trainer)
# Progress bar will looks like
# Epoch [2/50]: [64/128] 50%|█████ [06:17<12:34]
Log output to a file instead of stderr (tqdm's default output)
.. code-block:: python
trainer = create_supervised_trainer(model, optimizer, loss)
log_file = open("output.log", "w")
pbar = ProgressBar(file=log_file)
pbar.attach(trainer)
Attach metrics that already have been computed at :attr:`~ignite.engine.events.Events.ITERATION_COMPLETED`
(such as :class:`~ignite.metrics.RunningAverage`)
.. code-block:: python
trainer = create_supervised_trainer(model, optimizer, loss)
RunningAverage(output_transform=lambda x: x).attach(trainer, 'loss')
pbar = ProgressBar()
pbar.attach(trainer, ['loss'])
# Progress bar will looks like
# Epoch [2/50]: [64/128] 50%|█████ , loss=0.123 [06:17<12:34]
Directly attach the engine's output
.. code-block:: python
trainer = create_supervised_trainer(model, optimizer, loss)
pbar = ProgressBar()
pbar.attach(trainer, output_transform=lambda x: {'loss': x})
# Progress bar will looks like
# Epoch [2/50]: [64/128] 50%|█████ , loss=0.123 [06:17<12:34]
Note:
When adding attaching the progress bar to an engine, it is recommend that you replace
every print operation in the engine's handlers triggered every iteration with
``pbar.log_message`` to guarantee the correct format of the stdout.
Note:
When using inside jupyter notebook, `ProgressBar` automatically uses `tqdm_notebook`. For correct rendering,
please install `ipywidgets <https://ipywidgets.readthedocs.io/en/stable/user_install.html#installation>`_.
Due to `tqdm notebook bugs <https://github.com/tqdm/tqdm/issues/594>`_, bar format may be needed to be set
to an empty string value.
"""
_events_order = [
Events.STARTED,
Events.EPOCH_STARTED,
Events.ITERATION_STARTED,
Events.ITERATION_COMPLETED,
Events.EPOCH_COMPLETED,
Events.COMPLETED,
]
def __init__(
self,
persist=False,
bar_format="{desc}[{n_fmt}/{total_fmt}] {percentage:3.0f}%|{bar}{postfix} [{elapsed}<{remaining}]",
**tqdm_kwargs
):
try:
from tqdm.autonotebook import tqdm
except ImportError:
raise RuntimeError(
"This contrib module requires tqdm to be installed. "
"Please install it with command: \n pip install tqdm"
)
self.pbar_cls = tqdm
self.pbar = None
self.persist = persist
self.bar_format = bar_format
self.tqdm_kwargs = tqdm_kwargs
def _reset(self, pbar_total):
self.pbar = self.pbar_cls(
total=pbar_total, leave=self.persist, bar_format=self.bar_format, initial=1, **self.tqdm_kwargs
)
def _close(self, engine):
if self.pbar is not None:
# https://github.com/tqdm/notebook.py#L240-L250
# issue #1115 : notebook backend of tqdm checks if n < total (error or KeyboardInterrupt)
# and the bar persists in 'danger' mode
if self.pbar.total is not None:
self.pbar.n = self.pbar.total
self.pbar.close()
self.pbar = None
@staticmethod
def _compare_lt(event1, event2):
i1 = ProgressBar._events_order.index(event1)
i2 = ProgressBar._events_order.index(event2)
return i1 < i2
[docs] def log_message(self, message):
"""
Logs a message, preserving the progress bar correct output format.
Args:
message (str): string you wish to log.
"""
from tqdm import tqdm
tqdm.write(message, file=self.tqdm_kwargs.get("file", None))
[docs] def attach(
self,
engine,
metric_names=None,
output_transform=None,
event_name=Events.ITERATION_COMPLETED,
closing_event_name=Events.EPOCH_COMPLETED,
):
"""
Attaches the progress bar to an engine object.
Args:
engine (Engine): engine object.
metric_names (list of str, optional): list of metric names to plot or a string "all" to plot all available
metrics.
output_transform (callable, optional): a function to select what you want to print from the engine's
output. This function may return either a dictionary with entries in the format of ``{name: value}``,
or a single scalar, which will be displayed with the default name `output`.
event_name: event's name on which the progress bar advances. Valid events are from
:class:`~ignite.engine.events.Events`.
closing_event_name: event's name on which the progress bar is closed. Valid events are from
:class:`~ignite.engine.events.Events`.
Note: accepted output value types are numbers, 0d and 1d torch tensors and strings
"""
desc = self.tqdm_kwargs.get("desc", "Epoch")
if event_name not in engine._allowed_events:
raise ValueError("Logging event {} is not in allowed events for this engine".format(event_name.name))
if isinstance(closing_event_name, CallableEventWithFilter):
if closing_event_name.filter != CallableEventWithFilter.default_event_filter:
raise ValueError("Closing Event should not be a filtered event")
if not self._compare_lt(event_name, closing_event_name):
raise ValueError(
"Logging event {} should be called before closing event {}".format(event_name, closing_event_name)
)
log_handler = _OutputHandler(desc, metric_names, output_transform, closing_event_name=closing_event_name)
super(ProgressBar, self).attach(engine, log_handler, event_name)
engine.add_event_handler(closing_event_name, self._close)
[docs] def attach_opt_params_handler(self, engine: Engine, event_name: str, *args: Any, **kwargs: Mapping):
"""Intentionally empty"""
pass
def _create_output_handler(self, *args, **kwargs):
return _OutputHandler(*args, **kwargs)
def _create_opt_params_handler(self, *args, **kwargs):
"""Intentionally empty"""
pass