import torchbearer
from torchbearer.callbacks import Callback
from tqdm import tqdm
from collections import OrderedDict
from numbers import Number
def _format_num(n, precision):
# Adapted from https://github.com/tqdm/tqdm
f = ('{0:.' + str(precision) + 'g}').format(n).replace('+0', '+').replace('-0', '-')
n = str(n)
return f if len(f) < len(n) else n
def _format_metrics(metrics, precision):
# Adapted from https://github.com/tqdm/tqdm
postfix = OrderedDict([])
for key in sorted(metrics.keys()):
postfix[key] = metrics[key]
for key in postfix.keys():
if isinstance(postfix[key], Number):
postfix[key] = _format_num(postfix[key], precision)
elif not isinstance(postfix[key], str):
postfix[key] = str(postfix[key])
postfix = ', '.join(key + '=' + postfix[key].strip() for key in postfix.keys())
return postfix
[docs]class ConsolePrinter(Callback):
"""The ConsolePrinter callback simply outputs the training metrics to the console.
:param validation_label_letter: This is the letter displayed after the epoch number indicating the current phase of training
:type validation_label_letter: String
:param precision: Precision of the number format in significant figures
:type precision: int
"""
def __init__(self, validation_label_letter='v', precision=4):
super().__init__()
self.validation_label = validation_label_letter
self.precision = precision
def _step(self, state, letter, steps):
epoch_str = '{:d}/{:d}({:s}): '.format(state[torchbearer.EPOCH], state[torchbearer.MAX_EPOCHS], letter)
batch_str = '{:d}/{:d} '.format(state[torchbearer.BATCH], steps)
stats_str = _format_metrics(state[torchbearer.METRICS], self.precision)
print('\r' + epoch_str + batch_str + stats_str, end='')
def _end(self, state, letter):
epoch_str = '{:d}/{:d}({:s}): '.format(state[torchbearer.EPOCH], state[torchbearer.MAX_EPOCHS], letter)
stats_str = _format_metrics(state[torchbearer.METRICS], self.precision)
print('\r' + epoch_str + stats_str)
[docs] def on_step_training(self, state):
self._step(state, 't', state[torchbearer.STEPS])
[docs] def on_end_training(self, state):
self._end(state, 't')
[docs] def on_step_validation(self, state):
self._step(state, self.validation_label, state[torchbearer.STEPS])
[docs] def on_end_validation(self, state):
self._end(state, self.validation_label)
[docs]class Tqdm(Callback):
"""The Tqdm callback outputs the progress and metrics for training and validation loops to the console using TQDM. The given key is used to label validation output.
:param validation_label_letter: The letter to use for validation outputs.
:type validation_label_letter: str
:param precision: Precision of the number format in significant figures
:type precision: int
:param on_epoch: If True, output a single progress bar which tracks epochs
:type on_epoch: bool
:param tqdm_args: Any extra keyword args provided here will be passed through to the tqdm module constructor. See `github.com/tqdm/tqdm#parameters <https://github.com/tqdm/tqdm#parameters>`_ for more details.
"""
def __init__(self, tqdm_module=tqdm, validation_label_letter='v', precision=4, on_epoch=False, **tqdm_args):
self.tqdm_module = tqdm_module
self._loader = None
self.validation_label = validation_label_letter
self.precision = precision
self._on_epoch = on_epoch
self.tqdm_args = tqdm_args
def _on_start(self, state, letter):
bar_desc = '{:d}/{:d}({:s})'.format(state[torchbearer.EPOCH], state[torchbearer.MAX_EPOCHS], letter)
self._loader = self.tqdm_module(total=state[torchbearer.STEPS], desc=bar_desc, **self.tqdm_args)
def _update(self, state):
self._loader.update(1)
self._loader.set_postfix_str(_format_metrics(state[torchbearer.METRICS], self.precision))
def _close(self, state):
self._loader.set_postfix_str(_format_metrics(state[torchbearer.METRICS], self.precision))
self._loader.close()
[docs] def on_start(self, state):
if self._on_epoch:
n = len(state[torchbearer.HISTORY])
self._loader = self.tqdm_module(initial=n, total=state[torchbearer.MAX_EPOCHS], **self.tqdm_args)
if n > 0:
metrics = state[torchbearer.HISTORY][-1][1]
state[torchbearer.METRICS] = metrics
self._update(state)
[docs] def on_end_epoch(self, state):
if self._on_epoch:
self._update(state)
[docs] def on_end(self, state):
if self._on_epoch:
self._close(state)
[docs] def on_start_training(self, state):
"""Initialise the TQDM bar for this training phase.
:param state: The Model state
:type state: dict
"""
if not self._on_epoch:
self._on_start(state, 't')
[docs] def on_step_training(self, state):
"""Update the bar with the metrics from this step.
:param state: The Model state
:type state: dict
"""
if not self._on_epoch:
self._update(state)
[docs] def on_end_training(self, state):
"""Update the bar with the terminal training metrics and then close.
:param state: The Model state
:type state: dict
"""
if not self._on_epoch:
self._close(state)
[docs] def on_start_validation(self, state):
"""Initialise the TQDM bar for this validation phase.
:param state: The Model state
:type state: dict
"""
if not self._on_epoch:
self._on_start(state, self.validation_label)
[docs] def on_step_validation(self, state):
"""Update the bar with the metrics from this step.
:param state: The Model state
:type state: dict
"""
if not self._on_epoch:
self._update(state)
[docs] def on_end_validation(self, state):
"""Update the bar with the terminal validation metrics and then close.
:param state: The Model state
:type state: dict
"""
if not self._on_epoch:
self._close(state)