/
summary_ops_v2.py
1406 lines (1139 loc) · 50.5 KB
/
summary_ops_v2.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
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
# Copyright 2017 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.
# ==============================================================================
"""Operations to emit summaries."""
import abc
import collections
import functools
import os
import re
import threading
from tensorflow.core.framework import graph_pb2
from tensorflow.core.framework import summary_pb2
from tensorflow.core.protobuf import config_pb2
from tensorflow.python.eager import context
from tensorflow.python.eager import profiler as _profiler
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.framework import smart_cond
from tensorflow.python.framework import tensor_util
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import gen_resource_variable_ops
from tensorflow.python.ops import gen_summary_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import resource_variable_ops
from tensorflow.python.ops import summary_op_util
from tensorflow.python.platform import tf_logging as logging
from tensorflow.python.training import training_util
from tensorflow.python.training.tracking import tracking
from tensorflow.python.util import deprecation
from tensorflow.python.util import tf_contextlib
from tensorflow.python.util.tf_export import tf_export
# Name for graph collection of summary writer init ops, which is only exposed
# as a legacy API for tf.contrib.summary in TF 1.x.
_SUMMARY_WRITER_INIT_COLLECTION_NAME = "_SUMMARY_WRITER_V2"
class _SummaryState(threading.local):
def __init__(self):
super(_SummaryState, self).__init__()
self.is_recording = None
# TODO(slebedev): why a separate flag for DS and is it on by default?
self.is_recording_distribution_strategy = True
self.writer = None
self.step = None
_summary_state = _SummaryState()
class _SummaryContextManager:
"""Context manager to implement SummaryWriter.as_default()."""
# Note: this is a class so that it's possible to implement `set_as_default()`
# simply via `as_default().__enter__()`. We can't do that with @contextmanager
# because the `finally` block will be executed when the generator is GCed.
def __init__(self, writer, step=None):
self._writer = writer
self._step = step
self._old_writer = None
self._old_step = None
def __enter__(self):
self._old_writer = _summary_state.writer
_summary_state.writer = self._writer
if self._step is not None:
self._old_step = _summary_state.step
_summary_state.step = self._step
return self._writer
def __exit__(self, *exc):
# Flushes the summary writer in eager mode or in graph functions, but
# not in legacy graph mode (you're on your own there).
_summary_state.writer.flush()
_summary_state.writer = self._old_writer
if self._step is not None:
_summary_state.step = self._old_step
return False
def _should_record_summaries_internal(default_state):
"""Returns boolean Tensor if summaries should/shouldn't be recorded.
Now the summary condition is decided by logical "and" of below conditions:
First, summary writer must be set. Given this constraint is met,
ctx.summary_recording and ctx.summary_recording_distribution_strategy.
The former one is usually set by user, and the latter one is controlled
by DistributionStrategy (tf.distribute.ReplicaContext).
Args:
default_state: can be True or False. The default summary behavior when
summary writer is set and the user does not specify
ctx.summary_recording and ctx.summary_recording_distribution_strategy
is True.
"""
if _summary_state.writer is None:
return constant_op.constant(False)
if not callable(_summary_state.is_recording):
static_cond = tensor_util.constant_value(_summary_state.is_recording)
if static_cond is not None and not static_cond:
return constant_op.constant(False)
resolve = lambda x: x() if callable(x) else x
cond_distributed = resolve(_summary_state.is_recording_distribution_strategy)
cond = resolve(_summary_state.is_recording)
if cond is None:
cond = default_state
return math_ops.logical_and(cond_distributed, cond)
@tf_export("summary.should_record_summaries", v1=[])
def should_record_summaries():
"""Returns boolean Tensor which is True if summaries will be recorded.
If no default summary writer is currently registered, this always returns
False. Otherwise, this reflects the recording condition has been set via
`tf.summary.record_if()` (except that it may return False for some replicas
when using `tf.distribute.Strategy`). If no recording condition is active,
it defaults to True.
"""
return _should_record_summaries_internal(default_state=True)
# Legacy symbol used by tf.contrib.summary.should_record_summaries.
def _legacy_contrib_should_record_summaries():
"""Returns boolean Tensor which is true if summaries should be recorded."""
return _should_record_summaries_internal(default_state=False)
@tf_export("summary.record_if", v1=[])
@tf_contextlib.contextmanager
def record_if(condition):
"""Sets summary recording on or off per the provided boolean value.
The provided value can be a python boolean, a scalar boolean Tensor, or
or a callable providing such a value; if a callable is passed it will be
invoked on-demand to determine whether summary writing will occur. Note that
when calling record_if() in an eager mode context, if you intend to provide a
varying condition like `step % 100 == 0`, you must wrap this in a
callable to avoid immediate eager evaluation of the condition. In particular,
using a callable is the only way to have your condition evaluated as part of
the traced body of an @tf.function that is invoked from within the
`record_if()` context.
Args:
condition: can be True, False, a bool Tensor, or a callable providing such.
Yields:
Returns a context manager that sets this value on enter and restores the
previous value on exit.
"""
old = _summary_state.is_recording
try:
_summary_state.is_recording = condition
yield
finally:
_summary_state.is_recording = old
def has_default_writer():
"""Returns a boolean indicating whether a default summary writer exists."""
return _summary_state.writer is not None
# TODO(apassos) consider how to handle local step here.
def record_summaries_every_n_global_steps(n, global_step=None):
"""Sets the should_record_summaries Tensor to true if global_step % n == 0."""
if global_step is None:
global_step = training_util.get_or_create_global_step()
with ops.device("cpu:0"):
should = lambda: math_ops.equal(global_step % n, 0)
if not context.executing_eagerly():
should = should()
return record_if(should)
def always_record_summaries():
"""Sets the should_record_summaries Tensor to always true."""
return record_if(True)
def never_record_summaries():
"""Sets the should_record_summaries Tensor to always false."""
return record_if(False)
@tf_export("summary.experimental.get_step", v1=[])
def get_step():
"""Returns the default summary step for the current thread.
Returns:
The step set by `tf.summary.experimental.set_step()` if one has been set,
otherwise None.
"""
return _summary_state.step
@tf_export("summary.experimental.set_step", v1=[])
def set_step(step):
"""Sets the default summary step for the current thread.
For convenience, this function sets a default value for the `step` parameter
used in summary-writing functions elsewhere in the API so that it need not
be explicitly passed in every such invocation. The value can be a constant
or a variable, and can be retrieved via `tf.summary.experimental.get_step()`.
Note: when using this with @tf.functions, the step value will be captured at
the time the function is traced, so changes to the step outside the function
will not be reflected inside the function unless using a `tf.Variable` step.
Args:
step: An `int64`-castable default step value, or None to unset.
"""
_summary_state.step = step
@tf_export("summary.SummaryWriter", v1=[])
class SummaryWriter(metaclass=abc.ABCMeta):
"""Interface representing a stateful summary writer object."""
def set_as_default(self, step=None):
"""Enables this summary writer for the current thread.
For convenience, if `step` is not None, this function also sets a default
value for the `step` parameter used in summary-writing functions elsewhere
in the API so that it need not be explicitly passed in every such
invocation. The value can be a constant or a variable.
Note: when setting `step` in a @tf.function, the step value will be
captured at the time the function is traced, so changes to the step outside
the function will not be reflected inside the function unless using
a `tf.Variable` step.
Args:
step: An `int64`-castable default step value, or `None`. When not `None`,
the current step is modified to the given value. When `None`, the
current step is not modified.
"""
self.as_default(step).__enter__()
def as_default(self, step=None):
"""Returns a context manager that enables summary writing.
For convenience, if `step` is not None, this function also sets a default
value for the `step` parameter used in summary-writing functions elsewhere
in the API so that it need not be explicitly passed in every such
invocation. The value can be a constant or a variable.
Note: when setting `step` in a @tf.function, the step value will be
captured at the time the function is traced, so changes to the step outside
the function will not be reflected inside the function unless using
a `tf.Variable` step.
For example, `step` can be used as:
```python
with writer_a.as_default(step=10):
tf.summary.scalar(tag, value) # Logged to writer_a with step 10
with writer_b.as_default(step=20):
tf.summary.scalar(tag, value) # Logged to writer_b with step 20
tf.summary.scalar(tag, value) # Logged to writer_a with step 10
```
Args:
step: An `int64`-castable default step value, or `None`. When not `None`,
the current step is captured, replaced by a given one, and the original
one is restored when the context manager exits. When `None`, the current
step is not modified (and not restored when the context manager exits).
Returns:
The context manager.
"""
return _SummaryContextManager(self, step)
def init(self):
"""Initializes the summary writer."""
raise NotImplementedError()
def flush(self):
"""Flushes any buffered data."""
raise NotImplementedError()
def close(self):
"""Flushes and closes the summary writer."""
raise NotImplementedError()
class _ResourceSummaryWriter(SummaryWriter):
"""Implementation of SummaryWriter using a SummaryWriterInterface resource."""
def __init__(self, create_fn, init_op_fn):
self._resource = create_fn()
self._init_op = init_op_fn(self._resource)
self._closed = False
if context.executing_eagerly():
self._set_up_resource_deleter()
else:
ops.add_to_collection(_SUMMARY_WRITER_INIT_COLLECTION_NAME, self._init_op)
# Extension point to be overridden by subclasses to customize deletion.
def _set_up_resource_deleter(self):
self._resource_deleter = resource_variable_ops.EagerResourceDeleter(
handle=self._resource, handle_device="cpu:0")
def set_as_default(self, step=None):
"""See `SummaryWriter.set_as_default`."""
if context.executing_eagerly() and self._closed:
raise RuntimeError(f"SummaryWriter {self!r} is already closed")
super().set_as_default(step)
def as_default(self, step=None):
"""See `SummaryWriter.as_default`."""
if context.executing_eagerly() and self._closed:
raise RuntimeError(f"SummaryWriter {self!r} is already closed")
return super().as_default(step)
def init(self):
"""See `SummaryWriter.init`."""
if context.executing_eagerly() and self._closed:
raise RuntimeError(f"SummaryWriter {self!r} is already closed")
return self._init_op
def flush(self):
"""See `SummaryWriter.flush`."""
if context.executing_eagerly() and self._closed:
return
with ops.device("cpu:0"):
return gen_summary_ops.flush_summary_writer(self._resource)
def close(self):
"""See `SummaryWriter.close`."""
if context.executing_eagerly() and self._closed:
return
try:
with ops.control_dependencies([self.flush()]):
with ops.device("cpu:0"):
return gen_summary_ops.close_summary_writer(self._resource)
finally:
if context.executing_eagerly():
self._closed = True
class _MultiMetaclass(
type(_ResourceSummaryWriter), type(tracking.TrackableResource)):
pass
class _TrackableResourceSummaryWriter(
_ResourceSummaryWriter,
tracking.TrackableResource,
metaclass=_MultiMetaclass):
"""A `_ResourceSummaryWriter` subclass that implements `TrackableResource`."""
def __init__(self, create_fn, init_op_fn):
# Resolve multiple inheritance via explicit calls to __init__() on parents.
tracking.TrackableResource.__init__(self, device="/CPU:0")
self._create_fn = create_fn
self._init_op_fn = init_op_fn
# Pass .resource_handle into _ResourceSummaryWriter parent class rather than
# create_fn, to ensure it accesses the resource handle only through the
# cached property so that everything is using a single resource handle.
_ResourceSummaryWriter.__init__(
self, create_fn=lambda: self.resource_handle, init_op_fn=init_op_fn)
# Override for TrackableResource implementation.
def _create_resource(self):
return self._create_fn()
# Override for TrackableResource implementation.
def _initialize(self):
return self._init_op_fn(self.resource_handle)
# Override for TrackableResource implementation.
def _destroy_resource(self):
gen_resource_variable_ops.destroy_resource_op(
self.resource_handle, ignore_lookup_error=True)
def _set_up_resource_deleter(self):
# Override to suppress ResourceSummaryWriter implementation; we don't need
# the deleter since TrackableResource.__del__() handles it for us.
pass
class _LegacyResourceSummaryWriter(SummaryWriter):
"""Legacy resource-backed SummaryWriter for tf.contrib.summary."""
def __init__(self, resource, init_op_fn):
self._resource = resource
self._init_op_fn = init_op_fn
init_op = self.init()
if context.executing_eagerly():
self._resource_deleter = resource_variable_ops.EagerResourceDeleter(
handle=self._resource, handle_device="cpu:0")
else:
ops.add_to_collection(_SUMMARY_WRITER_INIT_COLLECTION_NAME, init_op)
def init(self):
"""See `SummaryWriter.init`."""
return self._init_op_fn(self._resource)
def flush(self):
"""See `SummaryWriter.flush`."""
with ops.device("cpu:0"):
return gen_summary_ops.flush_summary_writer(self._resource)
def close(self):
"""See `SummaryWriter.close`."""
with ops.control_dependencies([self.flush()]):
with ops.device("cpu:0"):
return gen_summary_ops.close_summary_writer(self._resource)
class _NoopSummaryWriter(SummaryWriter):
"""A summary writer that does nothing, for create_noop_writer()."""
def set_as_default(self, step=None):
pass
@tf_contextlib.contextmanager
def as_default(self, step=None):
yield
def init(self):
pass
def flush(self):
pass
def close(self):
pass
@tf_export(v1=["summary.initialize"])
def initialize(
graph=None, # pylint: disable=redefined-outer-name
session=None):
"""Initializes summary writing for graph execution mode.
This operation is a no-op when executing eagerly.
This helper method provides a higher-level alternative to using
`tf.contrib.summary.summary_writer_initializer_op` and
`tf.contrib.summary.graph`.
Most users will also want to call `tf.compat.v1.train.create_global_step`
which can happen before or after this function is called.
Args:
graph: A `tf.Graph` or `tf.compat.v1.GraphDef` to output to the writer.
This function will not write the default graph by default. When
writing to an event log file, the associated step will be zero.
session: So this method can call `tf.Session.run`. This defaults
to `tf.compat.v1.get_default_session`.
Raises:
RuntimeError: If the current thread has no default
`tf.contrib.summary.SummaryWriter`.
ValueError: If session wasn't passed and no default session.
"""
if context.executing_eagerly():
return
if _summary_state.writer is None:
raise RuntimeError("No default tf.contrib.summary.SummaryWriter found")
if session is None:
session = ops.get_default_session()
if session is None:
raise ValueError("Argument `session must be passed if no default "
"session exists")
session.run(summary_writer_initializer_op())
if graph is not None:
data = _serialize_graph(graph)
x = array_ops.placeholder(dtypes.string)
session.run(graph_v1(x, 0), feed_dict={x: data})
@tf_export("summary.create_file_writer", v1=[])
def create_file_writer_v2(logdir,
max_queue=None,
flush_millis=None,
filename_suffix=None,
name=None,
experimental_trackable=False):
"""Creates a summary file writer for the given log directory.
Args:
logdir: a string specifying the directory in which to write an event file.
max_queue: the largest number of summaries to keep in a queue; will
flush once the queue gets bigger than this. Defaults to 10.
flush_millis: the largest interval between flushes. Defaults to 120,000.
filename_suffix: optional suffix for the event file name. Defaults to `.v2`.
name: a name for the op that creates the writer.
experimental_trackable: a boolean that controls whether the returned writer
will be a `TrackableResource`, which makes it compatible with SavedModel
when used as a `tf.Module` property.
Returns:
A SummaryWriter object.
"""
if logdir is None:
raise ValueError("Argument `logdir` cannot be None")
inside_function = ops.inside_function()
with ops.name_scope(name, "create_file_writer") as scope, ops.device("cpu:0"):
# Run init inside an init_scope() to hoist it out of tf.functions.
with ops.init_scope():
if context.executing_eagerly():
_check_create_file_writer_args(
inside_function,
logdir=logdir,
max_queue=max_queue,
flush_millis=flush_millis,
filename_suffix=filename_suffix)
logdir = ops.convert_to_tensor(logdir, dtype=dtypes.string)
if max_queue is None:
max_queue = constant_op.constant(10)
if flush_millis is None:
flush_millis = constant_op.constant(2 * 60 * 1000)
if filename_suffix is None:
filename_suffix = constant_op.constant(".v2")
def create_fn():
# Use unique shared_name to prevent resource sharing in eager mode, but
# otherwise use a fixed shared_name to allow SavedModel TF 1.x loading.
if context.executing_eagerly():
shared_name = context.anonymous_name()
else:
shared_name = ops.name_from_scope_name(scope) # pylint: disable=protected-access
return gen_summary_ops.summary_writer(
shared_name=shared_name, name=name)
init_op_fn = functools.partial(
gen_summary_ops.create_summary_file_writer,
logdir=logdir,
max_queue=max_queue,
flush_millis=flush_millis,
filename_suffix=filename_suffix)
if experimental_trackable:
return _TrackableResourceSummaryWriter(
create_fn=create_fn, init_op_fn=init_op_fn)
else:
return _ResourceSummaryWriter(
create_fn=create_fn, init_op_fn=init_op_fn)
def create_file_writer(logdir,
max_queue=None,
flush_millis=None,
filename_suffix=None,
name=None):
"""Creates a summary file writer in the current context under the given name.
Args:
logdir: a string, or None. If a string, creates a summary file writer
which writes to the directory named by the string. If None, returns
a mock object which acts like a summary writer but does nothing,
useful to use as a context manager.
max_queue: the largest number of summaries to keep in a queue; will
flush once the queue gets bigger than this. Defaults to 10.
flush_millis: the largest interval between flushes. Defaults to 120,000.
filename_suffix: optional suffix for the event file name. Defaults to `.v2`.
name: Shared name for this SummaryWriter resource stored to default
Graph. Defaults to the provided logdir prefixed with `logdir:`. Note: if a
summary writer resource with this shared name already exists, the returned
SummaryWriter wraps that resource and the other arguments have no effect.
Returns:
Either a summary writer or an empty object which can be used as a
summary writer.
"""
if logdir is None:
return _NoopSummaryWriter()
logdir = str(logdir)
with ops.device("cpu:0"):
if max_queue is None:
max_queue = constant_op.constant(10)
if flush_millis is None:
flush_millis = constant_op.constant(2 * 60 * 1000)
if filename_suffix is None:
filename_suffix = constant_op.constant(".v2")
if name is None:
name = "logdir:" + logdir
resource = gen_summary_ops.summary_writer(shared_name=name)
return _LegacyResourceSummaryWriter(
resource=resource,
init_op_fn=functools.partial(
gen_summary_ops.create_summary_file_writer,
logdir=logdir,
max_queue=max_queue,
flush_millis=flush_millis,
filename_suffix=filename_suffix))
@tf_export("summary.create_noop_writer", v1=[])
def create_noop_writer():
"""Returns a summary writer that does nothing.
This is useful as a placeholder in code that expects a context manager.
"""
return _NoopSummaryWriter()
def _cleanse_string(name, pattern, value):
if isinstance(value, str) and pattern.search(value) is None:
raise ValueError(f"{name} ({value}) must match {pattern.pattern}")
return ops.convert_to_tensor(value, dtypes.string)
def _nothing():
"""Convenient else branch for when summaries do not record."""
return constant_op.constant(False)
@tf_export(v1=["summary.all_v2_summary_ops"])
def all_v2_summary_ops():
"""Returns all V2-style summary ops defined in the current default graph.
This includes ops from TF 2.0 tf.summary and TF 1.x tf.contrib.summary (except
for `tf.contrib.summary.graph` and `tf.contrib.summary.import_event`), but
does *not* include TF 1.x tf.summary ops.
Returns:
List of summary ops, or None if called under eager execution.
"""
if context.executing_eagerly():
return None
return ops.get_collection(ops.GraphKeys._SUMMARY_COLLECTION) # pylint: disable=protected-access
def summary_writer_initializer_op():
"""Graph-mode only. Returns the list of ops to create all summary writers.
Returns:
The initializer ops.
Raises:
RuntimeError: If in Eager mode.
"""
if context.executing_eagerly():
raise RuntimeError(
"tf.contrib.summary.summary_writer_initializer_op is only "
"supported in graph mode.")
return ops.get_collection(_SUMMARY_WRITER_INIT_COLLECTION_NAME)
_INVALID_SCOPE_CHARACTERS = re.compile(r"[^-_/.A-Za-z0-9]")
@tf_export("summary.experimental.summary_scope", v1=[])
@tf_contextlib.contextmanager
def summary_scope(name, default_name="summary", values=None):
"""Experimental context manager for use when defining a custom summary op.
This behaves similarly to `tf.name_scope`, except that it returns a generated
summary tag in addition to the scope name. The tag is structurally similar to
the scope name - derived from the user-provided name, prefixed with enclosing
name scopes if any - but we relax the constraint that it be uniquified, as
well as the character set limitation (so the user-provided name can contain
characters not legal for scope names; in the scope name these are removed).
This makes the summary tag more predictable and consistent for the user.
For example, to define a new summary op called `my_op`:
```python
def my_op(name, my_value, step):
with tf.summary.summary_scope(name, "MyOp", [my_value]) as (tag, scope):
my_value = tf.convert_to_tensor(my_value)
return tf.summary.write(tag, my_value, step=step)
```
Args:
name: string name for the summary.
default_name: Optional; if provided, used as default name of the summary.
values: Optional; passed as `values` parameter to name_scope.
Yields:
A tuple `(tag, scope)` as described above.
"""
name = name or default_name
current_scope = ops.get_name_scope()
tag = current_scope + "/" + name if current_scope else name
# Strip illegal characters from the scope name, and if that leaves nothing,
# use None instead so we pick up the default name.
name = _INVALID_SCOPE_CHARACTERS.sub("", name) or None
with ops.name_scope(name, default_name, values, skip_on_eager=False) as scope:
yield tag, scope
@tf_export("summary.write", v1=[])
def write(tag, tensor, step=None, metadata=None, name=None):
"""Writes a generic summary to the default SummaryWriter if one exists.
This exists primarily to support the definition of type-specific summary ops
like scalar() and image(), and is not intended for direct use unless defining
a new type-specific summary op.
Args:
tag: string tag used to identify the summary (e.g. in TensorBoard), usually
generated with `tf.summary.summary_scope`
tensor: the Tensor holding the summary data to write or a callable that
returns this Tensor. If a callable is passed, it will only be called when
a default SummaryWriter exists and the recording condition specified by
`record_if()` is met.
step: Explicit `int64`-castable monotonic step value for this summary. If
omitted, this defaults to `tf.summary.experimental.get_step()`, which must
not be None.
metadata: Optional SummaryMetadata, as a proto or serialized bytes
name: Optional string name for this op.
Returns:
True on success, or false if no summary was written because no default
summary writer was available.
Raises:
ValueError: if a default writer exists, but no step was provided and
`tf.summary.experimental.get_step()` is None.
"""
with ops.name_scope(name, "write_summary") as scope:
if _summary_state.writer is None:
return constant_op.constant(False)
if step is None:
step = get_step()
if metadata is None:
serialized_metadata = b""
elif hasattr(metadata, "SerializeToString"):
serialized_metadata = metadata.SerializeToString()
else:
serialized_metadata = metadata
def record():
"""Record the actual summary and return True."""
if step is None:
raise ValueError("No step set. Please specify one either through the "
"`step` argument or through "
"tf.summary.experimental.set_step()")
# Note the identity to move the tensor to the CPU.
with ops.device("cpu:0"):
summary_tensor = tensor() if callable(tensor) else array_ops.identity(
tensor)
write_summary_op = gen_summary_ops.write_summary(
_summary_state.writer._resource, # pylint: disable=protected-access
step,
summary_tensor,
tag,
serialized_metadata,
name=scope)
with ops.control_dependencies([write_summary_op]):
return constant_op.constant(True)
op = smart_cond.smart_cond(
should_record_summaries(), record, _nothing, name="summary_cond")
if not context.executing_eagerly():
ops.add_to_collection(ops.GraphKeys._SUMMARY_COLLECTION, op) # pylint: disable=protected-access
return op
@tf_export("summary.experimental.write_raw_pb", v1=[])
def write_raw_pb(tensor, step=None, name=None):
"""Writes a summary using raw `tf.compat.v1.Summary` protocol buffers.
Experimental: this exists to support the usage of V1-style manual summary
writing (via the construction of a `tf.compat.v1.Summary` protocol buffer)
with the V2 summary writing API.
Args:
tensor: the string Tensor holding one or more serialized `Summary` protobufs
step: Explicit `int64`-castable monotonic step value for this summary. If
omitted, this defaults to `tf.summary.experimental.get_step()`, which must
not be None.
name: Optional string name for this op.
Returns:
True on success, or false if no summary was written because no default
summary writer was available.
Raises:
ValueError: if a default writer exists, but no step was provided and
`tf.summary.experimental.get_step()` is None.
"""
with ops.name_scope(name, "write_raw_pb") as scope:
if _summary_state.writer is None:
return constant_op.constant(False)
if step is None:
step = get_step()
if step is None:
raise ValueError("No step set. Please specify one either through the "
"`step` argument or through "
"tf.summary.experimental.set_step()")
def record():
"""Record the actual summary and return True."""
# Note the identity to move the tensor to the CPU.
with ops.device("cpu:0"):
raw_summary_op = gen_summary_ops.write_raw_proto_summary(
_summary_state.writer._resource, # pylint: disable=protected-access
step,
array_ops.identity(tensor),
name=scope)
with ops.control_dependencies([raw_summary_op]):
return constant_op.constant(True)
with ops.device("cpu:0"):
op = smart_cond.smart_cond(
should_record_summaries(), record, _nothing, name="summary_cond")
if not context.executing_eagerly():
ops.add_to_collection(ops.GraphKeys._SUMMARY_COLLECTION, op) # pylint: disable=protected-access
return op
def summary_writer_function(name, tensor, function, family=None):
"""Helper function to write summaries.
Args:
name: name of the summary
tensor: main tensor to form the summary
function: function taking a tag and a scope which writes the summary
family: optional, the summary's family
Returns:
The result of writing the summary.
"""
name_scope = ops.get_name_scope()
if name_scope:
# Add a slash to allow reentering the name scope.
name_scope += "/"
def record():
with ops.name_scope(name_scope), summary_op_util.summary_scope(
name, family, values=[tensor]) as (tag, scope):
with ops.control_dependencies([function(tag, scope)]):
return constant_op.constant(True)
if _summary_state.writer is None:
return control_flow_ops.no_op()
with ops.device("cpu:0"):
op = smart_cond.smart_cond(
_legacy_contrib_should_record_summaries(), record, _nothing, name="")
if not context.executing_eagerly():
ops.add_to_collection(ops.GraphKeys._SUMMARY_COLLECTION, op) # pylint: disable=protected-access
return op
def generic(name, tensor, metadata=None, family=None, step=None):
"""Writes a tensor summary if possible."""
def function(tag, scope):
if metadata is None:
serialized_metadata = constant_op.constant("")
elif hasattr(metadata, "SerializeToString"):
serialized_metadata = constant_op.constant(metadata.SerializeToString())
else:
serialized_metadata = metadata
# Note the identity to move the tensor to the CPU.
return gen_summary_ops.write_summary(
_summary_state.writer._resource, # pylint: disable=protected-access
_choose_step(step),
array_ops.identity(tensor),
tag,
serialized_metadata,
name=scope)
return summary_writer_function(name, tensor, function, family=family)
def scalar(name, tensor, family=None, step=None):
"""Writes a scalar summary if possible.
Unlike `tf.contrib.summary.generic` this op may change the dtype
depending on the writer, for both practical and efficiency concerns.
Args:
name: An arbitrary name for this summary.
tensor: A `tf.Tensor` Must be one of the following types:
`float32`, `float64`, `int32`, `int64`, `uint8`, `int16`,
`int8`, `uint16`, `half`, `uint32`, `uint64`.
family: Optional, the summary's family.
step: The `int64` monotonic step variable, which defaults
to `tf.compat.v1.train.get_global_step`.
Returns:
The created `tf.Operation` or a `tf.no_op` if summary writing has
not been enabled for this context.
"""
def function(tag, scope):
# Note the identity to move the tensor to the CPU.
return gen_summary_ops.write_scalar_summary(
_summary_state.writer._resource, # pylint: disable=protected-access
_choose_step(step),
tag,
array_ops.identity(tensor),
name=scope)
return summary_writer_function(name, tensor, function, family=family)
def histogram(name, tensor, family=None, step=None):
"""Writes a histogram summary if possible."""
def function(tag, scope):
# Note the identity to move the tensor to the CPU.
return gen_summary_ops.write_histogram_summary(
_summary_state.writer._resource, # pylint: disable=protected-access
_choose_step(step),
tag,
array_ops.identity(tensor),
name=scope)
return summary_writer_function(name, tensor, function, family=family)
def image(name, tensor, bad_color=None, max_images=3, family=None, step=None):
"""Writes an image summary if possible."""
def function(tag, scope):
bad_color_ = (constant_op.constant([255, 0, 0, 255], dtype=dtypes.uint8)
if bad_color is None else bad_color)
# Note the identity to move the tensor to the CPU.
return gen_summary_ops.write_image_summary(
_summary_state.writer._resource, # pylint: disable=protected-access
_choose_step(step),
tag,
array_ops.identity(tensor),
bad_color_,
max_images,
name=scope)
return summary_writer_function(name, tensor, function, family=family)
def audio(name, tensor, sample_rate, max_outputs, family=None, step=None):
"""Writes an audio summary if possible."""
def function(tag, scope):
# Note the identity to move the tensor to the CPU.
return gen_summary_ops.write_audio_summary(
_summary_state.writer._resource, # pylint: disable=protected-access
_choose_step(step),
tag,
array_ops.identity(tensor),
sample_rate=sample_rate,
max_outputs=max_outputs,
name=scope)
return summary_writer_function(name, tensor, function, family=family)
def graph_v1(param, step=None, name=None):
"""Writes a TensorFlow graph to the summary interface.
The graph summary is, strictly speaking, not a summary. Conditions
like `tf.summary.should_record_summaries` do not apply. Only
a single graph can be associated with a particular run. If multiple
graphs are written, then only the last one will be considered by
TensorBoard.
When not using eager execution mode, the user should consider passing
the `graph` parameter to `tf.compat.v1.summary.initialize` instead of
calling this function. Otherwise special care needs to be taken when
using the graph to record the graph.
Args:
param: A `tf.Tensor` containing a serialized graph proto. When
eager execution is enabled, this function will automatically
coerce `tf.Graph`, `tf.compat.v1.GraphDef`, and string types.
step: The global step variable. This doesn't have useful semantics
for graph summaries, but is used anyway, due to the structure of
event log files. This defaults to the global step.
name: A name for the operation (optional).
Returns:
The created `tf.Operation` or a `tf.no_op` if summary writing has
not been enabled for this context.
Raises:
TypeError: If `param` isn't already a `tf.Tensor` in graph mode.
"""
if not context.executing_eagerly() and not isinstance(param, ops.Tensor):
raise TypeError("graph() needs a argument `param` to be tf.Tensor "
"(e.g. tf.placeholder) in graph mode, but received "
f"param={param} of type {type(param).__name__}.")
writer = _summary_state.writer
if writer is None: