def testSummaryMetadata_FirstMetadataWins(self):
logdir = self.get_temp_dir()
summary_metadata_1 = summary_pb2.SummaryMetadata(
display_name="current tagee",
summary_description="no",
plugin_data=summary_pb2.SummaryMetadata.PluginData(
plugin_name="outlet", content=b"120v"
),
)
self._writeMetadata(logdir, summary_metadata_1, nonce="1")
acc = ea.EventAccumulator(logdir)
acc.Reload()
summary_metadata_2 = summary_pb2.SummaryMetadata(
display_name="tagee of the future",
summary_description="definitely not",
plugin_data=summary_pb2.SummaryMetadata.PluginData(
plugin_name="plug", content=b"110v"
),
)
self._writeMetadata(logdir, summary_metadata_2, nonce="2")
acc.Reload()
self.assertProtoEquals(
summary_metadata_1, acc.SummaryMetadata("you_are_it")
)
def testNewStyleScalarSummary(self):
"""Verify processing of tensorboard.plugins.scalar.summary."""
event_sink = _EventGenerator(self, zero_out_timestamps=True)
writer = test_util.FileWriter(self.get_temp_dir())
writer.event_writer = event_sink
with self.test_session() as sess:
step = tf.compat.v1.placeholder(tf.float32, shape=[])
scalar_summary.op('accuracy', 1.0 - 1.0 / (step + tf.constant(1.0)))
scalar_summary.op('xent', 1.0 / (step + tf.constant(1.0)))
merged = tf.compat.v1.summary.merge_all()
writer.add_graph(sess.graph)
for i in xrange(10):
summ = sess.run(merged, feed_dict={step: float(i)})
writer.add_summary(summ, global_step=i)
accumulator = ea.EventAccumulator(event_sink)
accumulator.Reload()
tags = [
u'accuracy/scalar_summary',
u'xent/scalar_summary',
]
self.assertTagsEqual(accumulator.Tags(), {
ea.TENSORS: tags,
ea.GRAPH: True,
ea.META_GRAPH: False,
})
def testTFSummaryTensor(self):
"""Verify processing of tf.summary.tensor."""
event_sink = _EventGenerator(self, zero_out_timestamps=True)
writer = test_util.FileWriter(self.get_temp_dir())
writer.event_writer = event_sink
with tf.compat.v1.Graph().as_default():
with self.test_session() as sess:
tensor_summary = tf.compat.v1.summary.tensor_summary
tensor_summary("scalar", tf.constant(1.0))
tensor_summary("vector", tf.constant([1.0, 2.0, 3.0]))
tensor_summary("string", tf.constant(six.b("foobar")))
merged = tf.compat.v1.summary.merge_all()
summ = sess.run(merged)
writer.add_summary(summ, 0)
accumulator = ea.EventAccumulator(event_sink)
accumulator.Reload()
self.assertTagsEqual(accumulator.Tags(), {
ea.TENSORS: ["scalar", "vector", "string"],
})
scalar_proto = accumulator.Tensors("scalar")[0].tensor_proto
scalar = tensor_util.make_ndarray(scalar_proto)
vector_proto = accumulator.Tensors("vector")[0].tensor_proto
vector = tensor_util.make_ndarray(vector_proto)
string_proto = accumulator.Tensors("string")[0].tensor_proto
string = tensor_util.make_ndarray(string_proto)
self.assertTrue(np.array_equal(scalar, 1.0))
self.assertTrue(np.array_equal(vector, [1.0, 2.0, 3.0]))
self.assertTrue(np.array_equal(string, six.b("foobar")))
self.assertItemsEqual(accumulator.ActivePlugins(), [])
def testSessionLogStartMessageDiscardsExpiredEvents(self):
"""Test that SessionLog.START message discards expired events.
This discard logic is preferred over the out-of-order step discard logic,
but this logic can only be used for event protos which have the SessionLog
enum, which was introduced to event.proto for file_version >= brain.Event:2.
"""
gen = _EventGenerator(self)
acc = ea.EventAccumulator(gen)
gen.AddEvent(
event_pb2.Event(wall_time=0, step=1, file_version='brain.Event:2'))
gen.AddScalarTensor('s1', wall_time=1, step=100, value=20)
gen.AddScalarTensor('s1', wall_time=1, step=200, value=20)
gen.AddScalarTensor('s1', wall_time=1, step=300, value=20)
gen.AddScalarTensor('s1', wall_time=1, step=400, value=20)
gen.AddScalarTensor('s2', wall_time=1, step=202, value=20)
gen.AddScalarTensor('s2', wall_time=1, step=203, value=20)
slog = event_pb2.SessionLog(status=event_pb2.SessionLog.START)
gen.AddEvent(
event_pb2.Event(wall_time=2, step=201, session_log=slog))
acc.Reload()
self.assertEqual([x.step for x in acc.Tensors('s1')], [100, 200])
self.assertEqual([x.step for x in acc.Tensors('s2')], [])
def _testTFSummaryTensor_SizeGuidance(self,
plugin_name,
tensor_size_guidance,
steps,
expected_count):
event_sink = _EventGenerator(self, zero_out_timestamps=True)
writer = test_util.FileWriter(self.get_temp_dir())
writer.event_writer = event_sink
with self.test_session() as sess:
summary_metadata = summary_pb2.SummaryMetadata(
plugin_data=summary_pb2.SummaryMetadata.PluginData(
plugin_name=plugin_name, content=b'{}'))
tf.compat.v1.summary.tensor_summary('scalar', tf.constant(1.0),
summary_metadata=summary_metadata)
merged = tf.compat.v1.summary.merge_all()
for step in xrange(steps):
writer.add_summary(sess.run(merged), global_step=step)
accumulator = ea.EventAccumulator(
event_sink, tensor_size_guidance=tensor_size_guidance)
accumulator.Reload()
tensors = accumulator.Tensors('scalar')
self.assertEqual(len(tensors), expected_count)
def testKeyError(self):
"""KeyError should be raised when accessing non-existing keys."""
gen = _EventGenerator(self)
acc = ea.EventAccumulator(gen)
acc.Reload()
with self.assertRaises(KeyError):
acc.Tensors('s1')
def testExpiredDataDiscardedAfterRestartForFileVersionLessThan2(self):
"""Tests that events are discarded after a restart is detected.
If a step value is observed to be lower than what was previously seen,
this should force a discard of all previous items with the same tag
that are outdated.
Only file versions < 2 use this out-of-order discard logic. Later versions
discard events based on the step value of SessionLog.START.
"""
warnings = []
self.stubs.Set(logger, 'warn', warnings.append)
gen = _EventGenerator(self)
acc = ea.EventAccumulator(gen)
gen.AddEvent(
event_pb2.Event(wall_time=0, step=0, file_version='brain.Event:1'))
gen.AddScalarTensor('s1', wall_time=1, step=100, value=20)
gen.AddScalarTensor('s1', wall_time=1, step=200, value=20)
gen.AddScalarTensor('s1', wall_time=1, step=300, value=20)
acc.Reload()
## Check that number of items are what they should be
self.assertEqual([x.step for x in acc.Tensors('s1')], [100, 200, 300])
gen.AddScalarTensor('s1', wall_time=1, step=101, value=20)
gen.AddScalarTensor('s1', wall_time=1, step=201, value=20)
gen.AddScalarTensor('s1', wall_time=1, step=301, value=20)
acc.Reload()
## Check that we have discarded 200 and 300 from s1
self.assertEqual([x.step for x in acc.Tensors('s1')], [100, 101, 201, 301])
def testPluginTagToContent_PluginsCannotJumpOnTheBandwagon(self):
# If there are multiple `SummaryMetadata` for a given tag, and the
# set of plugins in the `plugin_data` of second is different from
# that of the first, then the second set should be ignored.
logdir = self.get_temp_dir()
summary_metadata_1 = summary_pb2.SummaryMetadata(
display_name="current tagee",
summary_description="no",
plugin_data=summary_pb2.SummaryMetadata.PluginData(
plugin_name="outlet", content=b"120v"),
)
self._writeMetadata(logdir, summary_metadata_1, nonce="1")
acc = ea.EventAccumulator(logdir)
acc.Reload()
summary_metadata_2 = summary_pb2.SummaryMetadata(
display_name="tagee of the future",
summary_description="definitely not",
plugin_data=summary_pb2.SummaryMetadata.PluginData(
plugin_name="plug", content=b"110v"),
)
self._writeMetadata(logdir, summary_metadata_2, nonce="2")
acc.Reload()
self.assertEqual(acc.PluginTagToContent("outlet"),
{"you_are_it": b"120v"})
with self.assertRaisesRegex(KeyError, "plug"):
acc.PluginTagToContent("plug")
self.assertItemsEqual(acc.ActivePlugins(), ["outlet"])
def testGraphFromMetaGraphBecomesAvailable(self):
"""Test accumulator by writing values and then reading them."""
directory = os.path.join(self.get_temp_dir(), 'metagraph_test_values_dir')
if tf.gfile.IsDirectory(directory):
tf.gfile.DeleteRecursively(directory)
tf.gfile.MkDir(directory)
writer = tf.summary.FileWriter(directory, max_queue=100)
with tf.Graph().as_default() as graph:
_ = tf.constant([2.0, 1.0])
# Add a graph to the summary writer.
meta_graph_def = tf.train.export_meta_graph(graph_def=graph.as_graph_def(
add_shapes=True))
writer.add_meta_graph(meta_graph_def)
writer.flush()
# Verify that we can load those events properly
acc = ea.EventAccumulator(directory)
acc.Reload()
self.assertTagsEqual(acc.Tags(), {
ea.GRAPH: True,
ea.META_GRAPH: True,
})
self.assertProtoEquals(graph.as_graph_def(add_shapes=True), acc.Graph())
self.assertProtoEquals(meta_graph_def, acc.MetaGraph())
def testGraphFromMetaGraphBecomesAvailable(self):
"""Test accumulator by writing values and then reading them."""
directory = os.path.join(self.get_temp_dir(), 'metagraph_test_values_dir')
if tf.io.gfile.isdir(directory):
tf.io.gfile.rmtree(directory)
tf.io.gfile.mkdir(directory)
writer = test_util.FileWriter(directory, max_queue=100)
with tf.Graph().as_default() as graph:
_ = tf.constant([2.0, 1.0])
# Add a graph to the summary writer.
meta_graph_def = tf.compat.v1.train.export_meta_graph(graph_def=graph.as_graph_def(
add_shapes=True))
writer.add_meta_graph(meta_graph_def)
writer.flush()
# Verify that we can load those events properly
acc = ea.EventAccumulator(directory)
acc.Reload()
self.assertTagsEqual(acc.Tags(), {
ea.GRAPH: True,
ea.META_GRAPH: True,
})
expected_graph_def = graph_pb2.GraphDef.FromString(
graph.as_graph_def(add_shapes=True).SerializeToString())
self.assertProtoEquals(expected_graph_def, acc.Graph())
expected_meta_graph = meta_graph_pb2.MetaGraphDef.FromString(
meta_graph_def.SerializeToString())
self.assertProtoEquals(expected_meta_graph, acc.MetaGraph())
def testPluginTagToContent_PluginsCannotJumpOnTheBandwagon(self):
# If there are multiple `SummaryMetadata` for a given tag, and the
# set of plugins in the `plugin_data` of second is different from
# that of the first, then the second set should be ignored.
logdir = self.get_temp_dir()
summary_metadata_1 = summary_pb2.SummaryMetadata(
display_name='current tagee',
summary_description='no',
plugin_data=summary_pb2.SummaryMetadata.PluginData(
plugin_name='outlet', content=b'120v'))
self._writeMetadata(logdir, summary_metadata_1, nonce='1')
acc = ea.EventAccumulator(logdir)
acc.Reload()
summary_metadata_2 = summary_pb2.SummaryMetadata(
display_name='tagee of the future',
summary_description='definitely not',
plugin_data=summary_pb2.SummaryMetadata.PluginData(
plugin_name='plug', content=b'110v'))
self._writeMetadata(logdir, summary_metadata_2, nonce='2')
acc.Reload()
self.assertEqual(acc.PluginTagToContent('outlet'),
{'you_are_it': b'120v'})
with six.assertRaisesRegex(self, KeyError, 'plug'):
acc.PluginTagToContent('plug')
def testFirstEventTimestamp(self):
"""Test that FirstEventTimestamp() returns wall_time of the first event."""
gen = _EventGenerator(self)
acc = ea.EventAccumulator(gen)
gen.AddEvent(tf.Event(wall_time=10, step=20, file_version='brain.Event:2'))
gen.AddScalarTensor('s1', wall_time=30, step=40, value=20)
self.assertEqual(acc.FirstEventTimestamp(), 10)
def testTFSummaryTensor(self):
"""Verify processing of tf.summary.tensor."""
event_sink = _EventGenerator(self, zero_out_timestamps=True)
writer = test_util.FileWriter(self.get_temp_dir())
writer.event_writer = event_sink
with self.test_session() as sess:
tf.compat.v1.summary.tensor_summary('scalar', tf.constant(1.0))
tf.compat.v1.summary.tensor_summary('vector', tf.constant([1.0, 2.0, 3.0]))
tf.compat.v1.summary.tensor_summary('string', tf.constant(six.b('foobar')))
merged = tf.compat.v1.summary.merge_all()
summ = sess.run(merged)
writer.add_summary(summ, 0)
accumulator = ea.EventAccumulator(event_sink)
accumulator.Reload()
self.assertTagsEqual(accumulator.Tags(), {
ea.TENSORS: ['scalar', 'vector', 'string'],
})
scalar_proto = accumulator.Tensors('scalar')[0].tensor_proto
scalar = tensor_util.make_ndarray(scalar_proto)
vector_proto = accumulator.Tensors('vector')[0].tensor_proto
vector = tensor_util.make_ndarray(vector_proto)
string_proto = accumulator.Tensors('string')[0].tensor_proto
string = tensor_util.make_ndarray(string_proto)
self.assertTrue(np.array_equal(scalar, 1.0))
self.assertTrue(np.array_equal(vector, [1.0, 2.0, 3.0]))
self.assertTrue(np.array_equal(string, six.b('foobar')))
def testNewStyleAudioSummary(self):
"""Verify processing of tensorboard.plugins.audio.summary."""
event_sink = _EventGenerator(self, zero_out_timestamps=True)
writer = test_util.FileWriter(self.get_temp_dir())
writer.event_writer = event_sink
with self.test_session() as sess:
ipt = tf.random.normal(shape=[5, 441, 2])
with tf.name_scope('1'):
audio_summary.op('one', ipt, sample_rate=44100, max_outputs=1)
with tf.name_scope('2'):
audio_summary.op('two', ipt, sample_rate=44100, max_outputs=2)
with tf.name_scope('3'):
audio_summary.op('three', ipt, sample_rate=44100, max_outputs=3)
merged = tf.compat.v1.summary.merge_all()
writer.add_graph(sess.graph)
for i in xrange(10):
summ = sess.run(merged)
writer.add_summary(summ, global_step=i)
accumulator = ea.EventAccumulator(event_sink)
accumulator.Reload()
tags = [
u'1/one/audio_summary',
u'2/two/audio_summary',
u'3/three/audio_summary',
]
self.assertTagsEqual(accumulator.Tags(), {
ea.TENSORS: tags,
ea.GRAPH: True,
ea.META_GRAPH: False,
})
def enable_runs(self, runs):
for run in runs:
self._multiplexer._accumulators[run] = event_accumulator.EventAccumulator(
os.path.join(self.logdir, run),
size_guidance=self._multiplexer._size_guidance,
tensor_size_guidance=self._multiplexer._tensor_size_guidance,
purge_orphaned_data=self._multiplexer.purge_orphaned_data)
self._multiplexer._paths[run] = os.path.join(self.logdir, run)
def testFirstEventTimestampLoadsEvent(self):
"""Test that FirstEventTimestamp() doesn't discard the loaded event."""
gen = _EventGenerator(self)
acc = ea.EventAccumulator(gen)
gen.AddEvent(tf.Event(wall_time=1, step=2, file_version='brain.Event:2'))
self.assertEqual(acc.FirstEventTimestamp(), 1)
acc.Reload()
self.assertEqual(acc.file_version, 2.0)
def readGraphDef(self, logdir=None):
'''function to read graph from drive'''
if logdir != None:
pass
else:
accumulator = event_accumulator.EventAccumulator(self._logdir)
accumulator.Reload()
self._graphDef = accumulator.Graph()
return
def testReload(self):
"""EventAccumulator contains suitable tags after calling Reload."""
gen = _EventGenerator(self)
acc = ea.EventAccumulator(gen)
acc.Reload()
self.assertTagsEqual(acc.Tags(), {})
gen.AddScalarTensor("s1", wall_time=1, step=10, value=50)
gen.AddScalarTensor("s2", wall_time=1, step=10, value=80)
acc.Reload()
self.assertTagsEqual(acc.Tags(), {ea.TENSORS: ["s1", "s2"],})
def testSummaryMetadata(self):
logdir = self.get_temp_dir()
summary_metadata = tf.SummaryMetadata(
display_name='current tagee',
summary_description='no',
plugin_data=tf.SummaryMetadata.PluginData(plugin_name='outlet'))
self._writeMetadata(logdir, summary_metadata)
acc = ea.EventAccumulator(logdir)
acc.Reload()
self.assertProtoEquals(summary_metadata,
acc.SummaryMetadata('you_are_it'))
def testOnlySummaryEventsTriggerDiscards(self):
"""Test that file version event does not trigger data purge."""
gen = _EventGenerator(self)
acc = ea.EventAccumulator(gen)
gen.AddScalarTensor("s1", wall_time=1, step=100, value=20)
ev1 = event_pb2.Event(wall_time=2, step=0, file_version="brain.Event:1")
graph_bytes = tf.compat.v1.GraphDef().SerializeToString()
ev2 = event_pb2.Event(wall_time=3, step=0, graph_def=graph_bytes)
gen.AddEvent(ev1)
gen.AddEvent(ev2)
acc.Reload()
self.assertEqual([x.step for x in acc.Tensors("s1")], [100])
def testNonValueEvents(self):
"""Non-value events in the generator don't cause early exits."""
gen = _EventGenerator(self)
acc = ea.EventAccumulator(gen)
gen.AddScalarTensor("s1", wall_time=1, step=10, value=20)
gen.AddEvent(
event_pb2.Event(wall_time=2, step=20, file_version="nots2")
)
gen.AddScalarTensor("s3", wall_time=3, step=100, value=1)
acc.Reload()
self.assertTagsEqual(acc.Tags(), {ea.TENSORS: ["s1", "s3"],})
def testSummaryMetadata(self):
logdir = self.get_temp_dir()
summary_metadata = summary_pb2.SummaryMetadata(
display_name="current tagee",
summary_description="no",
plugin_data=summary_pb2.SummaryMetadata.PluginData(
plugin_name="outlet"),
)
self._writeMetadata(logdir, summary_metadata)
acc = ea.EventAccumulator(logdir)
acc.Reload()
self.assertProtoEquals(summary_metadata,
acc.SummaryMetadata("you_are_it"))
def testReloadPopulatesFirstEventTimestamp(self):
"""Test that Reload() means FirstEventTimestamp() won't load events."""
gen = _EventGenerator(self)
acc = ea.EventAccumulator(gen)
gen.AddEvent(tf.Event(wall_time=1, step=2, file_version='brain.Event:2'))
acc.Reload()
def _Die(*args, **kwargs): # pylint: disable=unused-argument
raise RuntimeError('Load() should not be called')
self.stubs.Set(gen, 'Load', _Die)
self.assertEqual(acc.FirstEventTimestamp(), 1)
def testNewStyleAudioSummary(self):
"""Verify processing of tensorboard.plugins.audio.summary."""
event_sink = _EventGenerator(self, zero_out_timestamps=True)
writer = test_util.FileWriter(self.get_temp_dir())
writer.event_writer = event_sink
with tf.compat.v1.Graph().as_default():
with self.test_session() as sess:
ipt = tf.random.normal(shape=[5, 441, 2])
with tf.name_scope("1"):
audio_summary.op("one",
ipt,
sample_rate=44100,
max_outputs=1)
with tf.name_scope("2"):
audio_summary.op("two",
ipt,
sample_rate=44100,
max_outputs=2)
with tf.name_scope("3"):
audio_summary.op("three",
ipt,
sample_rate=44100,
max_outputs=3)
merged = tf.compat.v1.summary.merge_all()
writer.add_graph(sess.graph)
for i in range(10):
summ = sess.run(merged)
writer.add_summary(summ, global_step=i)
accumulator = ea.EventAccumulator(event_sink)
accumulator.Reload()
tags = [
graph_metadata.RUN_GRAPH_NAME,
"1/one/audio_summary",
"2/two/audio_summary",
"3/three/audio_summary",
]
self.assertTagsEqual(
accumulator.Tags(),
{
ea.TENSORS: tags,
ea.GRAPH: True,
ea.META_GRAPH: False,
},
)
self.assertItemsEqual(
accumulator.ActivePlugins(),
[audio_metadata.PLUGIN_NAME, graph_metadata.PLUGIN_NAME],
)
def AddRun(self, path, name=None):
"""Add a run to the multiplexer.
If the name is not specified, it is the same as the path.
If a run by that name exists, and we are already watching the right path,
do nothing. If we are watching a different path, replace the event
accumulator.
If `Reload` has been called, it will `Reload` the newly created
accumulators.
Args:
path: Path to the event files (or event directory) for given run.
name: Name of the run to add. If not provided, is set to path.
Returns:
The `EventMultiplexer`.
"""
name = name or path
accumulator = None
with self._accumulators_mutex:
if name not in self._accumulators or self._paths[name] != path:
if name in self._paths and self._paths[name] != path:
# TODO(@decentralion) - Make it impossible to overwrite an old path
# with a new path (just give the new path a distinct name)
logger.warning(
"Conflict for name %s: old path %s, new path %s",
name,
self._paths[name],
path,
)
logger.info("Constructing EventAccumulator for %s", path)
accumulator = event_accumulator.EventAccumulator(
path,
size_guidance=self._size_guidance,
tensor_size_guidance=self._tensor_size_guidance,
purge_orphaned_data=self.purge_orphaned_data,
event_file_active_filter=self._event_file_active_filter,
detect_file_replacement=self._detect_file_replacement,
)
self._accumulators[name] = accumulator
self._paths[name] = path
if accumulator:
if self._reload_called:
accumulator.Reload()
return self
def testNewStyleImageSummary(self):
"""Verify processing of tensorboard.plugins.image.summary."""
event_sink = _EventGenerator(self, zero_out_timestamps=True)
writer = test_util.FileWriter(self.get_temp_dir())
writer.event_writer = event_sink
with tf.compat.v1.Graph().as_default():
with self.test_session() as sess:
ipt = tf.ones([10, 4, 4, 3], tf.uint8)
# This is an interesting example, because the old tf.image_summary op
# would throw an error here, because it would be tag reuse.
# Using the tf node name instead allows argument re-use to the image
# summary.
with tf.name_scope("1"):
image_summary.op("images", ipt, max_outputs=1)
with tf.name_scope("2"):
image_summary.op("images", ipt, max_outputs=2)
with tf.name_scope("3"):
image_summary.op("images", ipt, max_outputs=3)
merged = tf.compat.v1.summary.merge_all()
writer.add_graph(sess.graph)
for i in range(10):
summ = sess.run(merged)
writer.add_summary(summ, global_step=i)
accumulator = ea.EventAccumulator(event_sink)
accumulator.Reload()
tags = [
graph_metadata.RUN_GRAPH_NAME,
"1/images/image_summary",
"2/images/image_summary",
"3/images/image_summary",
]
self.assertTagsEqual(
accumulator.Tags(),
{
ea.TENSORS: tags,
ea.GRAPH: True,
ea.META_GRAPH: False,
},
)
self.assertItemsEqual(
accumulator.ActivePlugins(),
[image_metadata.PLUGIN_NAME, graph_metadata.PLUGIN_NAME],
)
def testEventsDiscardedPerTagAfterRestartForFileVersionLessThan2(self):
"""Tests that event discards after restart, only affect the misordered
tag.
If a step value is observed to be lower than what was previously seen,
this should force a discard of all previous items that are outdated, but
only for the out of order tag. Other tags should remain unaffected.
Only file versions < 2 use this out-of-order discard logic. Later versions
discard events based on the step value of SessionLog.START.
"""
warnings = []
self.stubs.Set(logger, "warn", warnings.append)
gen = _EventGenerator(self)
acc = ea.EventAccumulator(gen)
gen.AddEvent(
event_pb2.Event(wall_time=0, step=0, file_version="brain.Event:1")
)
gen.AddScalarTensor("s1", wall_time=1, step=100, value=20)
gen.AddScalarTensor("s2", wall_time=1, step=101, value=20)
gen.AddScalarTensor("s1", wall_time=1, step=200, value=20)
gen.AddScalarTensor("s2", wall_time=1, step=201, value=20)
gen.AddScalarTensor("s1", wall_time=1, step=300, value=20)
gen.AddScalarTensor("s2", wall_time=1, step=301, value=20)
gen.AddScalarTensor("s1", wall_time=1, step=101, value=20)
gen.AddScalarTensor("s3", wall_time=1, step=101, value=20)
gen.AddScalarTensor("s1", wall_time=1, step=201, value=20)
gen.AddScalarTensor("s1", wall_time=1, step=301, value=20)
acc.Reload()
## Check that we have discarded 200 and 300 for s1
self.assertEqual(
[x.step for x in acc.Tensors("s1")], [100, 101, 201, 301]
)
## Check that s1 discards do not affect s2 (written before out-of-order)
## or s3 (written after out-of-order).
## i.e. check that only events from the out of order tag are discarded
self.assertEqual([x.step for x in acc.Tensors("s2")], [101, 201, 301])
self.assertEqual([x.step for x in acc.Tensors("s3")], [101])
def testSummaryMetadata_FirstMetadataWins(self):
logdir = self.get_temp_dir()
summary_metadata_1 = summary_pb2.SummaryMetadata(
display_name='current tagee',
summary_description='no',
plugin_data=summary_pb2.SummaryMetadata.PluginData(
plugin_name='outlet', content=b'120v'))
self._writeMetadata(logdir, summary_metadata_1, nonce='1')
acc = ea.EventAccumulator(logdir)
acc.Reload()
summary_metadata_2 = summary_pb2.SummaryMetadata(
display_name='tagee of the future',
summary_description='definitely not',
plugin_data=summary_pb2.SummaryMetadata.PluginData(
plugin_name='plug', content=b'110v'))
self._writeMetadata(logdir, summary_metadata_2, nonce='2')
acc.Reload()
self.assertProtoEquals(summary_metadata_1,
acc.SummaryMetadata('you_are_it'))
def testOrphanedDataNotDiscardedIfFlagUnset(self):
"""Tests that events are not discarded if purge_orphaned_data is false.
"""
gen = _EventGenerator(self)
acc = ea.EventAccumulator(gen, purge_orphaned_data=False)
gen.AddEvent(tf.Event(wall_time=0, step=0, file_version='brain.Event:1'))
gen.AddScalarTensor('s1', wall_time=1, step=100, value=20)
gen.AddScalarTensor('s1', wall_time=1, step=200, value=20)
gen.AddScalarTensor('s1', wall_time=1, step=300, value=20)
acc.Reload()
## Check that number of items are what they should be
self.assertEqual([x.step for x in acc.Tensors('s1')], [100, 200, 300])
gen.AddScalarTensor('s1', wall_time=1, step=101, value=20)
gen.AddScalarTensor('s1', wall_time=1, step=201, value=20)
gen.AddScalarTensor('s1', wall_time=1, step=301, value=20)
acc.Reload()
## Check that we have discarded 200 and 300 from s1
self.assertEqual([x.step for x in acc.Tensors('s1')],
[100, 200, 300, 101, 201, 301])
def testNewStyleImageSummary(self):
"""Verify processing of tensorboard.plugins.image.summary."""
event_sink = _EventGenerator(self, zero_out_timestamps=True)
writer = test_util.FileWriter(self.get_temp_dir())
writer.event_writer = event_sink
with self.test_session() as sess:
ipt = tf.ones([10, 4, 4, 3], tf.uint8)
# This is an interesting example, because the old tf.image_summary op
# would throw an error here, because it would be tag reuse.
# Using the tf node name instead allows argument re-use to the image
# summary.
with tf.name_scope('1'):
image_summary.op('images', ipt, max_outputs=1)
with tf.name_scope('2'):
image_summary.op('images', ipt, max_outputs=2)
with tf.name_scope('3'):
image_summary.op('images', ipt, max_outputs=3)
merged = tf.summary.merge_all()
writer.add_graph(sess.graph)
for i in xrange(10):
summ = sess.run(merged)
writer.add_summary(summ, global_step=i)
accumulator = ea.EventAccumulator(event_sink)
accumulator.Reload()
tags = [
u'1/images/image_summary',
u'2/images/image_summary',
u'3/images/image_summary',
]
self.assertTagsEqual(accumulator.Tags(), {
ea.TENSORS: tags,
ea.GRAPH: True,
ea.META_GRAPH: False,
})
