-
Notifications
You must be signed in to change notification settings - Fork 74k
/
tf_logging.py
326 lines (248 loc) · 9.1 KB
/
tf_logging.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
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# 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.
# ==============================================================================
"""Logging utilities."""
# pylint: disable=unused-import
# pylint: disable=g-bad-import-order
# pylint: disable=invalid-name
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import logging as _logging
import os as _os
import sys as _sys
import time as _time
import traceback as _traceback
from logging import DEBUG
from logging import ERROR
from logging import FATAL
from logging import INFO
from logging import WARN
import threading
import six
from tensorflow.python.util.tf_export import tf_export
# Don't use this directly. Use get_logger() instead.
_logger = None
_logger_lock = threading.Lock()
def _get_caller(offset=3):
"""Returns a code and frame object for the lowest non-logging stack frame."""
# Use sys._getframe(). This avoids creating a traceback object.
# pylint: disable=protected-access
f = _sys._getframe(offset)
# pylint: enable=protected-access
our_file = f.f_code.co_filename
f = f.f_back
while f:
code = f.f_code
if code.co_filename != our_file:
return code, f
f = f.f_back
return None, None
# The definition of `findCaller` changed in Python 3.2
if _sys.version_info.major >= 3 and _sys.version_info.minor >= 2:
def _logger_find_caller(stack_info=False): # pylint: disable=g-wrong-blank-lines
code, frame = _get_caller(4)
sinfo = None
if stack_info:
sinfo = '\n'.join(_traceback.format_stack())
if code:
return (code.co_filename, frame.f_lineno, code.co_name, sinfo)
else:
return '(unknown file)', 0, '(unknown function)', sinfo
else:
def _logger_find_caller(): # pylint: disable=g-wrong-blank-lines
code, frame = _get_caller(4)
if code:
return (code.co_filename, frame.f_lineno, code.co_name)
else:
return '(unknown file)', 0, '(unknown function)'
@tf_export('get_logger')
def get_logger():
"""Return TF logger instance."""
global _logger
# Use double-checked locking to avoid taking lock unnecessarily.
if _logger:
return _logger
_logger_lock.acquire()
try:
if _logger:
return _logger
# Scope the TensorFlow logger to not conflict with users' loggers.
logger = _logging.getLogger('tensorflow')
# Override findCaller on the logger to skip internal helper functions
logger.findCaller = _logger_find_caller
# Don't further configure the TensorFlow logger if the root logger is
# already configured. This prevents double logging in those cases.
if not _logging.getLogger().handlers:
# Determine whether we are in an interactive environment
_interactive = False
try:
# This is only defined in interactive shells.
if _sys.ps1: _interactive = True
except AttributeError:
# Even now, we may be in an interactive shell with `python -i`.
_interactive = _sys.flags.interactive
# If we are in an interactive environment (like Jupyter), set loglevel
# to INFO and pipe the output to stdout.
if _interactive:
logger.setLevel(INFO)
_logging_target = _sys.stdout
else:
_logging_target = _sys.stderr
# Add the output handler.
_handler = _logging.StreamHandler(_logging_target)
_handler.setFormatter(_logging.Formatter(_logging.BASIC_FORMAT, None))
logger.addHandler(_handler)
_logger = logger
return _logger
finally:
_logger_lock.release()
@tf_export(v1=['logging.log'])
def log(level, msg, *args, **kwargs):
get_logger().log(level, msg, *args, **kwargs)
@tf_export(v1=['logging.debug'])
def debug(msg, *args, **kwargs):
get_logger().debug(msg, *args, **kwargs)
@tf_export(v1=['logging.error'])
def error(msg, *args, **kwargs):
get_logger().error(msg, *args, **kwargs)
@tf_export(v1=['logging.fatal'])
def fatal(msg, *args, **kwargs):
get_logger().fatal(msg, *args, **kwargs)
@tf_export(v1=['logging.info'])
def info(msg, *args, **kwargs):
get_logger().info(msg, *args, **kwargs)
@tf_export(v1=['logging.warn'])
def warn(msg, *args, **kwargs):
get_logger().warning(msg, *args, **kwargs)
@tf_export(v1=['logging.warning'])
def warning(msg, *args, **kwargs):
get_logger().warning(msg, *args, **kwargs)
_level_names = {
FATAL: 'FATAL',
ERROR: 'ERROR',
WARN: 'WARN',
INFO: 'INFO',
DEBUG: 'DEBUG',
}
# Mask to convert integer thread ids to unsigned quantities for logging
# purposes
_THREAD_ID_MASK = 2 * _sys.maxsize + 1
_log_prefix = None # later set to google2_log_prefix
# Counter to keep track of number of log entries per token.
_log_counter_per_token = {}
@tf_export(v1=['logging.TaskLevelStatusMessage'])
def TaskLevelStatusMessage(msg):
error(msg)
@tf_export(v1=['logging.flush'])
def flush():
raise NotImplementedError()
# Code below is taken from pyglib/logging
@tf_export(v1=['logging.vlog'])
def vlog(level, msg, *args, **kwargs):
get_logger().log(level, msg, *args, **kwargs)
def _GetNextLogCountPerToken(token):
"""Wrapper for _log_counter_per_token.
Args:
token: The token for which to look up the count.
Returns:
The number of times this function has been called with
*token* as an argument (starting at 0)
"""
global _log_counter_per_token # pylint: disable=global-variable-not-assigned
_log_counter_per_token[token] = 1 + _log_counter_per_token.get(token, -1)
return _log_counter_per_token[token]
@tf_export(v1=['logging.log_every_n'])
def log_every_n(level, msg, n, *args):
"""Log 'msg % args' at level 'level' once per 'n' times.
Logs the 1st call, (N+1)st call, (2N+1)st call, etc.
Not threadsafe.
Args:
level: The level at which to log.
msg: The message to be logged.
n: The number of times this should be called before it is logged.
*args: The args to be substituted into the msg.
"""
count = _GetNextLogCountPerToken(_GetFileAndLine())
log_if(level, msg, not (count % n), *args)
@tf_export(v1=['logging.log_first_n'])
def log_first_n(level, msg, n, *args): # pylint: disable=g-bad-name
"""Log 'msg % args' at level 'level' only first 'n' times.
Not threadsafe.
Args:
level: The level at which to log.
msg: The message to be logged.
n: The number of times this should be called before it is logged.
*args: The args to be substituted into the msg.
"""
count = _GetNextLogCountPerToken(_GetFileAndLine())
log_if(level, msg, count < n, *args)
@tf_export(v1=['logging.log_if'])
def log_if(level, msg, condition, *args):
"""Log 'msg % args' at level 'level' only if condition is fulfilled."""
if condition:
vlog(level, msg, *args)
def _GetFileAndLine():
"""Returns (filename, linenumber) for the stack frame."""
code, f = _get_caller()
if not code:
return ('<unknown>', 0)
return (code.co_filename, f.f_lineno)
def google2_log_prefix(level, timestamp=None, file_and_line=None):
"""Assemble a logline prefix using the google2 format."""
# pylint: disable=global-variable-not-assigned
global _level_names
# pylint: enable=global-variable-not-assigned
# Record current time
now = timestamp or _time.time()
now_tuple = _time.localtime(now)
now_microsecond = int(1e6 * (now % 1.0))
(filename, line) = file_and_line or _GetFileAndLine()
basename = _os.path.basename(filename)
# Severity string
severity = 'I'
if level in _level_names:
severity = _level_names[level][0]
s = '%c%02d%02d %02d:%02d:%02d.%06d %5d %s:%d] ' % (
severity,
now_tuple[1], # month
now_tuple[2], # day
now_tuple[3], # hour
now_tuple[4], # min
now_tuple[5], # sec
now_microsecond,
_get_thread_id(),
basename,
line)
return s
@tf_export(v1=['logging.get_verbosity'])
def get_verbosity():
"""Return how much logging output will be produced."""
return get_logger().getEffectiveLevel()
@tf_export(v1=['logging.set_verbosity'])
def set_verbosity(v):
"""Sets the threshold for what messages will be logged."""
get_logger().setLevel(v)
def _get_thread_id():
"""Get id of current thread, suitable for logging as an unsigned quantity."""
# pylint: disable=protected-access
thread_id = six.moves._thread.get_ident()
# pylint:enable=protected-access
return thread_id & _THREAD_ID_MASK
_log_prefix = google2_log_prefix
tf_export(v1=['logging.DEBUG']).export_constant(__name__, 'DEBUG')
tf_export(v1=['logging.ERROR']).export_constant(__name__, 'ERROR')
tf_export(v1=['logging.FATAL']).export_constant(__name__, 'FATAL')
tf_export(v1=['logging.INFO']).export_constant(__name__, 'INFO')
tf_export(v1=['logging.WARN']).export_constant(__name__, 'WARN')