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 = tf.SummaryMetadata(
display_name='current tagee',
summary_description='no',
plugin_data=tf.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 = tf.SummaryMetadata(
display_name='tagee of the future',
summary_description='definitely not',
plugin_data=tf.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 test_session_start_pb(self):
start_time_secs = 314160
session_start_info = plugin_data_pb2.SessionStartInfo(
model_uri="//model/uri",
group_name="session_group",
start_time_secs=start_time_secs)
session_start_info.hparams["param1"].string_value = "string"
# TODO: Fix nondeterminism.
# session_start_info.hparams["param2"].number_value = 5.0
# session_start_info.hparams["param3"].bool_value = False
self.assertEqual(
summary.session_start_pb(
hparams={
"param1": "string",
# "param2":5,
# "param3":False,
},
model_uri="//model/uri",
group_name="session_group",
start_time_secs=start_time_secs),
tf.Summary(value=[
tf.Summary.Value(
tag="_hparams_/session_start_info",
metadata=tf.SummaryMetadata(
plugin_data=tf.SummaryMetadata.PluginData(
plugin_name="hparams",
content=(plugin_data_pb2.HParamsPluginData(
version=0,
session_start_info=session_start_info).
SerializeToString()))))
]))
def add_3dvolume(self, volume, tag, global_step=None, walltime=None):
filename = tag + "_"
if global_step is None:
filename += datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')
else:
filename += str(global_step)
if isinstance(volume, torch.Tensor):
volume = volume.detach().cpu().numpy()
img = ants.from_numpy(volume)
ants.image_write(img, os.path.join(self._log_dir,
filename + ".nii.gz"))
plugin_data = tf.SummaryMetadata.PluginData(
plugin_name="tb_3d_volume_plugin",
content=TextPluginData(version=0).SerializeToString())
metadata = tf.SummaryMetadata(plugin_data=plugin_data)
tensor = TensorProto(
dtype='DT_STRING',
string_val=[filename.encode(encoding='utf_8')],
tensor_shape=TensorShapeProto(dim=[TensorShapeProto.Dim(size=1)]))
summary = summary_pb2.Summary(value=[
summary_pb2.Summary.Value(
tag=tag, metadata=metadata, tensor=tensor)
])
self._file_writer.add_summary(summary,
global_step=global_step,
walltime=walltime)
self._file_writer.flush()
def _testTFSummaryTensor_SizeGuidance(self,
plugin_name,
tensor_size_guidance,
steps,
expected_count):
event_sink = _EventGenerator(self, zero_out_timestamps=True)
writer = tf.summary.FileWriter(self.get_temp_dir())
writer.event_writer = event_sink
with self.test_session() as sess:
summary_metadata = tf.SummaryMetadata(
plugin_data=tf.SummaryMetadata.PluginData(plugin_name=plugin_name,
content=b'{}'))
tf.summary.tensor_summary('scalar', tf.constant(1.0),
summary_metadata=summary_metadata)
merged = tf.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 testSummaryMetadata(self):
logdir = self.get_temp_dir()
summary_metadata = tf.SummaryMetadata(display_name='current tagee',
summary_description='no')
summary_metadata.plugin_data.plugin_name = 'outlet'
self._writeMetadata(logdir, summary_metadata)
acc = ea.EventAccumulator(logdir)
acc.Reload()
self.assertProtoEquals(summary_metadata,
acc.SummaryMetadata('you_are_it'))
def testSummaryMetadata_FirstMetadataWins(self):
logdir = self.get_temp_dir()
summary_metadata_1 = tf.SummaryMetadata(
display_name='current tagee',
summary_description='no',
plugin_data=tf.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 = tf.SummaryMetadata(
display_name='tagee of the future',
summary_description='definitely not',
plugin_data=tf.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 create_summary_metadata(display_name, description):
"""Create a `tf.SummaryMetadata` proto for text plugin data.
Returns:
A `tf.SummaryMetadata` protobuf object.
"""
content = plugin_data_pb2.TextPluginData(version=PROTO_VERSION)
metadata = tf.SummaryMetadata(display_name=display_name,
summary_description=description,
plugin_data=tf.SummaryMetadata.PluginData(
plugin_name=PLUGIN_NAME,
content=content.SerializeToString()))
return metadata
def test_fully_populated_tensor(self):
metadata = tf.SummaryMetadata(
plugin_data=tf.SummaryMetadata.PluginData(
plugin_name='font_of_wisdom',
content=b'adobe_garamond'))
op = tf.summary.tensor_summary(
name='tensorpocalypse',
tensor=tf.constant([[0.0, 2.0], [float('inf'), float('nan')]]),
display_name='TENSORPOCALYPSE',
summary_description='look on my works ye mighty and despair',
summary_metadata=metadata)
value = self._value_from_op(op)
assert value.HasField('tensor'), value
self._assert_noop(value)
def create_summary_metadata(hparams_plugin_data_pb):
"""Creates a tf.SummaryMetadata holding a copy of the given
HParamsPluginData message in its plugin_data.content field.
Sets the version field of the hparams_plugin_data_pb copy to
PLUGIN_DATA_VERSION.
"""
if not isinstance(hparams_plugin_data_pb,
plugin_data_pb2.HParamsPluginData):
raise TypeError(
'Needed an instance of plugin_data_pb2.HParamsPluginData.'
' Got: %s' % type(hparams_plugin_data_pb))
content = plugin_data_pb2.HParamsPluginData()
content.CopyFrom(hparams_plugin_data_pb)
content.version = PLUGIN_DATA_VERSION
return tf.SummaryMetadata(plugin_data=tf.SummaryMetadata.PluginData(
plugin_name=PLUGIN_NAME, content=content.SerializeToString()))
def test_session_end_pb(self):
end_time_secs = 1234.0
self.assertEqual(
summary.session_end_pb(api_pb2.STATUS_SUCCESS, end_time_secs),
tf.Summary(value=[
tf.Summary.Value(
tag="_hparams_/session_end_info",
metadata=tf.SummaryMetadata(
plugin_data=tf.SummaryMetadata.PluginData(
plugin_name="hparams",
content=(plugin_data_pb2.HParamsPluginData(
version=0,
session_end_info=(
plugin_data_pb2.SessionEndInfo(
status=api_pb2.STATUS_SUCCESS,
end_time_secs=end_time_secs,
))).SerializeToString()))))
]))
def create_summary_metadata(display_name, description, num_thresholds):
"""Create a `tf.SummaryMetadata` proto for pr_curves plugin data.
Arguments:
display_name: The display name used in TensorBoard.
description: The description to show in TensorBoard.
num_thresholds: The number of thresholds to use for PR curves.
Returns:
A `tf.SummaryMetadata` protobuf object.
"""
pr_curve_plugin_data = plugin_data_pb2.PrCurvePluginData(
version=PROTO_VERSION, num_thresholds=num_thresholds)
content = pr_curve_plugin_data.SerializeToString()
return tf.SummaryMetadata(display_name=display_name,
summary_description=description,
plugin_data=tf.SummaryMetadata.PluginData(
plugin_name=PLUGIN_NAME, content=content))
def test_experiment_pb(self):
hparam_infos = [
api_pb2.HParamInfo(name="param1",
display_name="display_name1",
description="foo",
type=api_pb2.DATA_TYPE_STRING,
domain_discrete=struct_pb2.ListValue(values=[
struct_pb2.Value(string_value='a'),
struct_pb2.Value(string_value='b')
])),
api_pb2.HParamInfo(name="param2",
display_name="display_name2",
description="bar",
type=api_pb2.DATA_TYPE_FLOAT64,
domain_interval=api_pb2.Interval(
min_value=-100.0, max_value=100.0))
]
metric_infos = [
api_pb2.MetricInfo(name=api_pb2.MetricName(tag="loss"),
dataset_type=api_pb2.DATASET_VALIDATION),
api_pb2.MetricInfo(name=api_pb2.MetricName(group="train/",
tag="acc"),
dataset_type=api_pb2.DATASET_TRAINING),
]
time_created_secs = 314159.0
self.assertEqual(
summary.experiment_pb(hparam_infos,
metric_infos,
time_created_secs=time_created_secs),
tf.Summary(value=[
tf.Summary.Value(
tag="_hparams_/experiment",
metadata=tf.SummaryMetadata(
plugin_data=tf.SummaryMetadata.PluginData(
plugin_name="hparams",
content=(plugin_data_pb2.HParamsPluginData(
version=0,
experiment=api_pb2.Experiment(
time_created_secs=time_created_secs,
hparam_infos=hparam_infos,
metric_infos=metric_infos)).
SerializeToString()))))
]))
def _create_summary_metadata():
return tf.SummaryMetadata(
plugin_data=tf.SummaryMetadata.PluginData(
plugin_name=metadata.PLUGIN_NAME))
