-
Notifications
You must be signed in to change notification settings - Fork 4
/
pytorch_utils.py
176 lines (154 loc) · 6.7 KB
/
pytorch_utils.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
# Copyright 2020 Arkadip Bhattacharya
# 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.
import sys
import time
import collections
import numpy as np
class Progbar(object):
"""Displays a progress bar.
# Arguments
target: Total number of steps expected, None if unknown.
width: Progress bar width on screen.
verbose: Verbosity mode, 0 (silent), 1 (verbose), 2 (semi-verbose)
stateful_metrics: 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 progbar before display.
interval: Minimum visual progress update interval (in seconds).
"""
def __init__(self, target, width=30, verbose=1, interval=0.05,
stateful_metrics=None):
self.target = target
self.width = width
self.verbose = verbose
self.interval = interval
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)
self._total_width = 0
self._seen_so_far = 0
self._values = collections.OrderedDict()
self._start = time.time()
self._last_update = 0
def update(self, current, values=None):
"""Updates the progress bar.
# Arguments
current: Index of current step.
values: 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 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.floor(np.log10(self.target))) + 1
barstr = '%%%dd/%d [' % (numdigits, self.target)
bar = barstr % current
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:
info += ' %.0fs/step' % time_per_unit
elif time_per_unit >= 1e-3:
info += ' %.0fms/step' % (time_per_unit * 1e3)
else:
info += ' %.0fus/step' % (time_per_unit * 1e6)
for k in self._values:
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 None or current >= self.target:
for k in self._values:
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)