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progress_bar.py
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# The code in this file has been referenced from
# https://github.com/yueyericardo/pkbar/blob/master/pkbar/pkbar.py
import sys
import time
import numpy as np
class ProgressBar:
"""Progress Bar.
Args:
target (int): Total number of steps expected, None if unknown.
width (int, optional): Progress bar width on screen. (default: 30)
verbose (int, optional): Verbosity mode - 0 (silent), 1 (verbose),
2 (semi-verbose). (default: 1)
interval (float, optional): Minimum visual progress update interval
(in seconds). (default: 0.05)
stateful_metrics (optional): Iterable of string names of metrics
that should *not* be averaged over time. Metrics in this list
will be displayed as-is. All others will be averaged by the
ProgressBar before display. (default: None)
unit_name (str, optional): Display name for step counts.
(default: 'step')
"""
def __init__(
self, target, width=30, verbose=1, interval=0.05,
stateful_metrics=None, unit_name='step'
):
self.target = target
self.width = width
self.verbose = verbose
self.interval = interval
self.unit_name = unit_name
if stateful_metrics:
self.stateful_metrics = set(stateful_metrics)
else:
self.stateful_metrics = set()
self._dynamic_display = (
(hasattr(sys.stdout, 'isatty') and sys.stdout.isatty())
or 'ipykernel' in sys.modules or 'posix' in sys.modules
)
self._total_width = 0
self._seen_so_far = 0
self._values = {}
self._values_order = []
self._start = time.time()
self._last_update = 0
def update(self, current, values=None):
"""Updates the progress bar.
Args:
current (int): Index of current step.
values (list, optional): List of tuples:
`(name, value_for_last_step)`.
If `name` is in `stateful_metrics`,
`value_for_last_step` will be displayed as-is.
Else, an average of the metric over time will be displayed.
"""
values = values or []
for k, v in values:
# if torch tensor, convert it to numpy
if str(type(v)) == "<class 'torch.Tensor'>":
v = v.detach().cpu().numpy()
if k not in self._values_order:
self._values_order.append(k)
if k not in self.stateful_metrics:
if k not in self._values:
self._values[k] = [
v * (current - self._seen_so_far),
current - self._seen_so_far
]
else:
self._values[k][0] += v * (current - self._seen_so_far)
self._values[k][1] += (current - self._seen_so_far)
else:
# Stateful metrics output a numeric value. This representation
# means 'take an average from a single value' but keeps the
# numeric formatting.
self._values[k] = [v, 1]
self._seen_so_far = current
now = time.time()
info = ' - %.0fs' % (now - self._start)
if self.verbose == 1:
if (now - self._last_update < self.interval
and self.target is not None and current < self.target):
return
prev_total_width = self._total_width
if self._dynamic_display:
sys.stdout.write('\b' * prev_total_width)
sys.stdout.write('\r')
else:
sys.stdout.write('\n')
if self.target is not None:
numdigits = int(np.log10(self.target)) + 1
bar = ('%' + str(numdigits) + 'd/%d [') % (current, self.target)
prog = float(current) / self.target
prog_width = int(self.width * prog)
if prog_width > 0:
bar += ('=' * (prog_width - 1))
if current < self.target:
bar += '>'
else:
bar += '='
bar += ('.' * (self.width - prog_width))
bar += ']'
else:
bar = '%7d/Unknown' % current
self._total_width = len(bar)
sys.stdout.write(bar)
if current:
time_per_unit = (now - self._start) / current
else:
time_per_unit = 0
if self.target is not None and current < self.target:
eta = time_per_unit * (self.target - current)
if eta > 3600:
eta_format = '%d:%02d:%02d' % (
eta // 3600, (eta % 3600) // 60, eta % 60
)
elif eta > 60:
eta_format = '%d:%02d' % (eta // 60, eta % 60)
else:
eta_format = '%ds' % eta
info = ' - ETA: %s' % eta_format
else:
if time_per_unit >= 1 or time_per_unit == 0:
info += ' %.0fs/%s' % (time_per_unit, self.unit_name)
elif time_per_unit >= 1e-3:
info += ' %.0fms/%s' % (time_per_unit * 1e3, self.unit_name)
else:
info += ' %.0fus/%s' % (time_per_unit * 1e6, self.unit_name)
for k in self._values_order:
info += '%s:' % k
if isinstance(self._values[k], list):
avg = np.mean(self._values[k][0] / max(1, self._values[k][1]))
if abs(avg) > 1e-3:
info += ' %.4f' % avg
else:
info += ' %.4e' % avg
else:
info += ' %s' % self._values[k]
self._total_width += len(info)
if prev_total_width > self._total_width:
info += (' ' * (prev_total_width - self._total_width))
if self.target is not None and current >= self.target:
info += '\n'
sys.stdout.write(info)
sys.stdout.flush()
elif self.verbose == 2:
if self.target is not None and current >= self.target:
numdigits = int(np.log10(self.target)) + 1
count = ('%' + str(numdigits) + 'd/%d') % (current, self.target)
info = count + info
for k in self._values_order:
info += ' - %s:' % k
avg = np.mean(self._values[k][0] / max(1, self._values[k][1]))
if avg > 1e-3:
info += ' %.4f' % avg
else:
info += ' %.4e' % avg
info += '\n'
sys.stdout.write(info)
sys.stdout.flush()
self._last_update = now
def add(self, n, values=None):
self.update(self._seen_so_far + n, values)