def test_audio(self):
with tf.compat.v1.Graph().as_default():
audio = tf.reshape(tf.linspace(0.0, 100.0, 4 * 10 * 2), (4, 10, 2))
old_op = tf.compat.v1.summary.audio("k488", audio, 44100)
old_value = self._value_from_op(old_op)
assert old_value.HasField("audio"), old_value
new_value = data_compat.migrate_value(old_value)
self.assertEqual("k488/audio/0", new_value.tag)
expected_metadata = audio_metadata.create_summary_metadata(
display_name="k488/audio/0",
description="",
encoding=audio_metadata.Encoding.Value("WAV"),
converted_to_tensor=True,
)
# Check serialized submessages...
plugin_content = audio_metadata.parse_plugin_metadata(
new_value.metadata.plugin_data.content)
expected_content = audio_metadata.parse_plugin_metadata(
expected_metadata.plugin_data.content)
self.assertEqual(plugin_content, expected_content)
# ...then check full metadata except plugin content, since
# serialized forms need not be identical.
new_value.metadata.plugin_data.content = (
expected_metadata.plugin_data.content)
self.assertEqual(expected_metadata, new_value.metadata)
self.assertTrue(new_value.HasField("tensor"))
data = tensor_util.make_ndarray(new_value.tensor)
self.assertEqual((1, 2), data.shape)
self.assertEqual(
tf.compat.as_bytes(old_value.audio.encoded_audio_string),
data[0][0])
self.assertEqual(b"", data[0][1]) # empty label
def send_requests(self, run_to_events):
"""Accepts a stream of TF events and sends batched write RPCs.
Each sent request will be at most `_MAX_REQUEST_LENGTH_BYTES`
bytes long.
Args:
run_to_events: Mapping from run name to generator of `tf.Event`
values, as returned by `LogdirLoader.get_run_events`.
Raises:
RuntimeError: If no progress can be made because even a single
point is too large (say, due to a gigabyte-long tag name).
"""
for (run_name, event, orig_value) in self._run_values(run_to_events):
value = data_compat.migrate_value(orig_value)
time_series_key = (run_name, value.tag)
# The metadata for a time series is memorized on the first event.
# If later events arrive with a mismatching plugin_name, they are
# ignored with a warning.
metadata = self._tag_metadata.get(time_series_key)
first_in_time_series = False
if metadata is None:
first_in_time_series = True
metadata = value.metadata
self._tag_metadata[time_series_key] = metadata
plugin_name = metadata.plugin_data.plugin_name
if value.HasField("metadata") and (
plugin_name != value.metadata.plugin_data.plugin_name
):
logger.warning(
"Mismatching plugin names for %s. Expected %s, found %s.",
time_series_key,
metadata.plugin_data.plugin_name,
value.metadata.plugin_data.plugin_name,
)
continue
if plugin_name not in self._allowed_plugins:
if first_in_time_series:
logger.info(
"Skipping time series %r with unsupported plugin name %r",
time_series_key,
plugin_name,
)
continue
if plugin_name == scalar_metadata.PLUGIN_NAME:
self._scalar_request_sender.add_event(
run_name, event, value, metadata
)
# TODO(nielsene): add Tensor plugin cases here
# TODO(soergel): add Graphs blob case here
self._scalar_request_sender.flush()
def test_empty_histogram(self):
with tf.compat.v1.Graph().as_default():
old_op = tf.compat.v1.summary.histogram("empty_yet_important",
tf.constant([]))
old_value = self._value_from_op(old_op)
assert old_value.HasField("histo"), old_value
new_value = data_compat.migrate_value(old_value)
self.assertEqual("empty_yet_important", new_value.tag)
expected_metadata = histogram_metadata.create_summary_metadata(
display_name="empty_yet_important", description="")
self.assertEqual(expected_metadata, new_value.metadata)
self.assertTrue(new_value.HasField("tensor"))
buckets = tensor_util.make_ndarray(new_value.tensor)
self.assertEmpty(buckets)
def test_histogram(self):
old_op = tf.summary.histogram('important_data',
tf.random_normal(shape=[23, 45]))
old_value = self._value_from_op(old_op)
assert old_value.HasField('histo'), old_value
new_value = data_compat.migrate_value(old_value)
self.assertEqual('important_data', new_value.tag)
expected_metadata = histogram_metadata.create_summary_metadata(
display_name='important_data', description='')
self.assertEqual(expected_metadata, new_value.metadata)
self.assertTrue(new_value.HasField('tensor'))
buckets = tf.make_ndarray(new_value.tensor)
self.assertEqual(old_value.histo.min, buckets[0][0])
self.assertEqual(old_value.histo.max, buckets[-1][1])
self.assertEqual(23 * 45, buckets[:, 2].astype(int).sum())
def test_scalar(self):
old_op = tf.summary.scalar('important_constants', tf.constant(0x5f3759df))
old_value = self._value_from_op(old_op)
assert old_value.HasField('simple_value'), old_value
new_value = data_compat.migrate_value(old_value)
self.assertEqual('important_constants', new_value.tag)
expected_metadata = scalar_metadata.create_summary_metadata(
display_name='important_constants',
description='')
self.assertEqual(expected_metadata, new_value.metadata)
self.assertTrue(new_value.HasField('tensor'))
data = tf.make_ndarray(new_value.tensor)
self.assertEqual((), data.shape)
low_precision_value = np.array(0x5f3759df).astype('float32').item()
self.assertEqual(low_precision_value, data.item())
def test_histogram(self):
old_op = tf.summary.histogram('important_data',
tf.random_normal(shape=[23, 45]))
old_value = self._value_from_op(old_op)
assert old_value.HasField('histo'), old_value
new_value = data_compat.migrate_value(old_value)
self.assertEqual('important_data', new_value.tag)
expected_metadata = histogram_metadata.create_summary_metadata(
display_name='important_data', description='')
self.assertEqual(expected_metadata, new_value.metadata)
self.assertTrue(new_value.HasField('tensor'))
buckets = tf.make_ndarray(new_value.tensor)
self.assertEqual(old_value.histo.min, buckets[0][0])
self.assertEqual(old_value.histo.max, buckets[-1][1])
self.assertEqual(23 * 45, buckets[:, 2].astype(int).sum())
def test_scalar(self):
old_op = tf.summary.scalar('important_constants',
tf.constant(0x5f3759df))
old_value = self._value_from_op(old_op)
assert old_value.HasField('simple_value'), old_value
new_value = data_compat.migrate_value(old_value)
self.assertEqual('important_constants', new_value.tag)
expected_metadata = scalar_metadata.create_summary_metadata(
display_name='important_constants', description='')
self.assertEqual(expected_metadata, new_value.metadata)
self.assertTrue(new_value.HasField('tensor'))
data = tf.make_ndarray(new_value.tensor)
self.assertEqual((), data.shape)
low_precision_value = np.array(0x5f3759df).astype('float32').item()
self.assertEqual(low_precision_value, data.item())
def test_scalar(self):
with tf.compat.v1.Graph().as_default():
old_op = tf.compat.v1.summary.scalar("important_constants",
tf.constant(0x5F3759DF))
old_value = self._value_from_op(old_op)
assert old_value.HasField("simple_value"), old_value
new_value = data_compat.migrate_value(old_value)
self.assertEqual("important_constants", new_value.tag)
expected_metadata = scalar_metadata.create_summary_metadata(
display_name="important_constants", description="")
self.assertEqual(expected_metadata, new_value.metadata)
self.assertTrue(new_value.HasField("tensor"))
data = tensor_util.make_ndarray(new_value.tensor)
self.assertEqual((), data.shape)
low_precision_value = np.array(0x5F3759DF).astype("float32").item()
self.assertEqual(low_precision_value, data.item())
def test_histogram(self):
with tf.compat.v1.Graph().as_default():
old_op = tf.compat.v1.summary.histogram(
"important_data", tf.random.normal(shape=[23, 45]))
old_value = self._value_from_op(old_op)
assert old_value.HasField("histo"), old_value
new_value = data_compat.migrate_value(old_value)
self.assertEqual("important_data", new_value.tag)
expected_metadata = histogram_metadata.create_summary_metadata(
display_name="important_data", description="")
self.assertEqual(expected_metadata, new_value.metadata)
self.assertTrue(new_value.HasField("tensor"))
buckets = tensor_util.make_ndarray(new_value.tensor)
self.assertEqual(old_value.histo.min, buckets[0][0])
self.assertEqual(old_value.histo.max, buckets[-1][1])
self.assertEqual(23 * 45, buckets[:, 2].astype(int).sum())
def test_image(self):
old_op = tf.summary.image('mona_lisa',
tf.cast(tf.random_normal(shape=[1, 400, 200, 3]),
tf.uint8))
old_value = self._value_from_op(old_op)
assert old_value.HasField('image'), old_value
new_value = data_compat.migrate_value(old_value)
self.assertEqual('mona_lisa/image/0', new_value.tag)
expected_metadata = image_metadata.create_summary_metadata(
display_name='mona_lisa/image/0', description='')
self.assertEqual(expected_metadata, new_value.metadata)
self.assertTrue(new_value.HasField('tensor'))
(width, height, data) = tf.make_ndarray(new_value.tensor)
self.assertEqual(b'200', width)
self.assertEqual(b'400', height)
self.assertEqual(
tf.compat.as_bytes(old_value.image.encoded_image_string), data)
def test_image(self):
old_op = tf.summary.image(
'mona_lisa',
tf.cast(tf.random_normal(shape=[1, 400, 200, 3]), tf.uint8))
old_value = self._value_from_op(old_op)
assert old_value.HasField('image'), old_value
new_value = data_compat.migrate_value(old_value)
self.assertEqual('mona_lisa/image/0', new_value.tag)
expected_metadata = image_metadata.create_summary_metadata(
display_name='mona_lisa/image/0', description='')
self.assertEqual(expected_metadata, new_value.metadata)
self.assertTrue(new_value.HasField('tensor'))
(width, height, data) = tf.make_ndarray(new_value.tensor)
self.assertEqual(b'200', width)
self.assertEqual(b'400', height)
self.assertEqual(
tf.compat.as_bytes(old_value.image.encoded_image_string), data)
def test_image(self):
with tf.compat.v1.Graph().as_default():
old_op = tf.compat.v1.summary.image(
"mona_lisa",
tf.image.convert_image_dtype(
tf.random.normal(shape=[1, 400, 200, 3]),
tf.uint8,
saturate=True,
),
)
old_value = self._value_from_op(old_op)
assert old_value.HasField("image"), old_value
new_value = data_compat.migrate_value(old_value)
self.assertEqual("mona_lisa/image/0", new_value.tag)
expected_metadata = image_metadata.create_summary_metadata(
display_name="mona_lisa/image/0",
description="",
converted_to_tensor=True,
)
# Check serialized submessages...
plugin_content = image_metadata.parse_plugin_metadata(
new_value.metadata.plugin_data.content
)
expected_content = image_metadata.parse_plugin_metadata(
expected_metadata.plugin_data.content
)
self.assertEqual(plugin_content, expected_content)
# ...then check full metadata except plugin content, since
# serialized forms need not be identical.
new_value.metadata.plugin_data.content = (
expected_metadata.plugin_data.content
)
self.assertEqual(expected_metadata, new_value.metadata)
self.assertTrue(new_value.HasField("tensor"))
(width, height, data) = tensor_util.make_ndarray(new_value.tensor)
self.assertEqual(b"200", width)
self.assertEqual(b"400", height)
self.assertEqual(
tf.compat.as_bytes(old_value.image.encoded_image_string), data
)
def test_audio(self):
audio = tf.reshape(tf.linspace(0.0, 100.0, 4 * 10 * 2), (4, 10, 2))
old_op = tf.summary.audio('k488', audio, 44100)
old_value = self._value_from_op(old_op)
assert old_value.HasField('audio'), old_value
new_value = data_compat.migrate_value(old_value)
self.assertEqual('k488/audio/0', new_value.tag)
expected_metadata = audio_metadata.create_summary_metadata(
display_name='k488/audio/0',
description='',
encoding=audio_metadata.Encoding.Value('WAV'))
self.assertEqual(expected_metadata, new_value.metadata)
self.assertTrue(new_value.HasField('tensor'))
data = tf.make_ndarray(new_value.tensor)
self.assertEqual((1, 2), data.shape)
self.assertEqual(tf.compat.as_bytes(old_value.audio.encoded_audio_string),
data[0][0])
self.assertEqual(b'', data[0][1]) # empty label
def test_audio(self):
audio = tf.reshape(tf.linspace(0.0, 100.0, 4 * 10 * 2), (4, 10, 2))
old_op = tf.summary.audio('k488', audio, 44100)
old_value = self._value_from_op(old_op)
assert old_value.HasField('audio'), old_value
new_value = data_compat.migrate_value(old_value)
self.assertEqual('k488/audio/0', new_value.tag)
expected_metadata = audio_metadata.create_summary_metadata(
display_name='k488/audio/0',
description='',
encoding=audio_metadata.Encoding.Value('WAV'))
self.assertEqual(expected_metadata, new_value.metadata)
self.assertTrue(new_value.HasField('tensor'))
data = tf.make_ndarray(new_value.tensor)
self.assertEqual((1, 2), data.shape)
self.assertEqual(tf.compat.as_bytes(old_value.audio.encoded_audio_string),
data[0][0])
self.assertEqual(b'', data[0][1]) # empty label
def test_single_value_histogram(self):
with tf.compat.v1.Graph().as_default():
old_op = tf.compat.v1.summary.histogram("single_value_data",
tf.constant([1] * 1024))
old_value = self._value_from_op(old_op)
assert old_value.HasField("histo"), old_value
new_value = data_compat.migrate_value(old_value)
self.assertEqual("single_value_data", new_value.tag)
expected_metadata = histogram_metadata.create_summary_metadata(
display_name="single_value_data", description="")
self.assertEqual(expected_metadata, new_value.metadata)
self.assertTrue(new_value.HasField("tensor"))
buckets = tensor_util.make_ndarray(new_value.tensor)
# Only one bucket is kept.
self.assertEqual((1, 3), buckets.shape)
self.assertEqual(1, buckets[0][0])
self.assertEqual(1, buckets[-1][1])
self.assertEqual(1024, buckets[0][2])
def test_histogram_with_extremal_values(self):
with tf.compat.v1.Graph().as_default():
old_op = tf.compat.v1.summary.histogram("extremal_values",
tf.constant([-1e20, 1e20]))
old_value = self._value_from_op(old_op)
assert old_value.HasField("histo"), old_value
new_value = data_compat.migrate_value(old_value)
self.assertEqual("extremal_values", new_value.tag)
expected_metadata = histogram_metadata.create_summary_metadata(
display_name="extremal_values", description="")
self.assertEqual(expected_metadata, new_value.metadata)
self.assertTrue(new_value.HasField("tensor"))
buckets = tensor_util.make_ndarray(new_value.tensor)
for bucket in buckets:
# No `backwards` buckets.
self.assertLessEqual(bucket[0], bucket[1])
self.assertEqual(old_value.histo.min, buckets[0][0])
self.assertEqual(old_value.histo.max, buckets[-1][1])
self.assertEqual(2, buckets[:, 2].astype(int).sum())
def _process_event(self, event, tagged_data):
"""Processes a single tf.Event and records it in tagged_data."""
event_type = event.WhichOneof('what')
# Handle the most common case first.
if event_type == 'summary':
for value in event.summary.value:
value = data_compat.migrate_value(value)
tag, metadata, values = tagged_data.get(value.tag, (None, None, []))
values.append((event.step, event.wall_time, value.tensor))
if tag is None:
# Store metadata only from the first event.
tagged_data[value.tag] = sqlite_writer.TagData(
value.tag, value.metadata, values)
elif event_type == 'file_version':
pass # TODO: reject file version < 2 (at loader level)
elif event_type == 'session_log':
if event.session_log.status == event_pb2.SessionLog.START:
pass # TODO: implement purging via sqlite writer truncation method
elif event_type in ('graph_def', 'meta_graph_def'):
pass # TODO: support graphs
elif event_type == 'tagged_run_metadata':
pass # TODO: support run metadata
def _process_event(self, event, tagged_data):
"""Processes a single tf.Event and records it in tagged_data."""
event_type = event.WhichOneof('what')
# Handle the most common case first.
if event_type == 'summary':
for value in event.summary.value:
value = data_compat.migrate_value(value)
tag, metadata, values = tagged_data.get(value.tag, (None, None, []))
values.append((event.step, event.wall_time, value.tensor))
if tag is None:
# Store metadata only from the first event.
tagged_data[value.tag] = sqlite_writer.TagData(
value.tag, value.metadata, values)
elif event_type == 'file_version':
pass # TODO: reject file version < 2 (at loader level)
elif event_type == 'session_log':
if event.session_log.status == tf.SessionLog.START:
pass # TODO: implement purging via sqlite writer truncation method
elif event_type in ('graph_def', 'meta_graph_def'):
pass # TODO: support graphs
elif event_type == 'tagged_run_metadata':
pass # TODO: support run metadata
def test_audio(self):
with tf.compat.v1.Graph().as_default():
audio = tf.reshape(tf.linspace(0.0, 100.0, 4 * 10 * 2), (4, 10, 2))
old_op = tf.compat.v1.summary.audio("k488", audio, 44100)
old_value = self._value_from_op(old_op)
assert old_value.HasField("audio"), old_value
new_value = data_compat.migrate_value(old_value)
self.assertEqual("k488/audio/0", new_value.tag)
expected_metadata = audio_metadata.create_summary_metadata(
display_name="k488/audio/0",
description="",
encoding=audio_metadata.Encoding.Value("WAV"),
)
self.assertEqual(expected_metadata, new_value.metadata)
self.assertTrue(new_value.HasField("tensor"))
data = tensor_util.make_ndarray(new_value.tensor)
self.assertEqual((1, 2), data.shape)
self.assertEqual(
tf.compat.as_bytes(old_value.audio.encoded_audio_string),
data[0][0])
self.assertEqual(b"", data[0][1]) # empty label
def test_image(self):
with tf.compat.v1.Graph().as_default():
old_op = tf.compat.v1.summary.image(
"mona_lisa",
tf.image.convert_image_dtype(
tf.random.normal(shape=[1, 400, 200, 3]),
tf.uint8,
saturate=True,
),
)
old_value = self._value_from_op(old_op)
assert old_value.HasField("image"), old_value
new_value = data_compat.migrate_value(old_value)
self.assertEqual("mona_lisa/image/0", new_value.tag)
expected_metadata = image_metadata.create_summary_metadata(
display_name="mona_lisa/image/0", description="")
self.assertEqual(expected_metadata, new_value.metadata)
self.assertTrue(new_value.HasField("tensor"))
(width, height, data) = tensor_util.make_ndarray(new_value.tensor)
self.assertEqual(b"200", width)
self.assertEqual(b"400", height)
self.assertEqual(
tf.compat.as_bytes(old_value.image.encoded_image_string), data)
def _ProcessEvent(self, event):
"""Called whenever an event is loaded."""
if self._first_event_timestamp is None:
self._first_event_timestamp = event.wall_time
if event.HasField('file_version'):
new_file_version = _ParseFileVersion(event.file_version)
if self.file_version and self.file_version != new_file_version:
## This should not happen.
tf.logging.warn(
('Found new file_version for event.proto. This will '
'affect purging logic for TensorFlow restarts. '
'Old: {0} New: {1}').format(self.file_version,
new_file_version))
self.file_version = new_file_version
self._MaybePurgeOrphanedData(event)
## Process the event.
# GraphDef and MetaGraphDef are handled in a special way:
# If no graph_def Event is available, but a meta_graph_def is, and it
# contains a graph_def, then use the meta_graph_def.graph_def as our graph.
# If a graph_def Event is available, always prefer it to the graph_def
# inside the meta_graph_def.
if event.HasField('graph_def'):
if self._graph is not None:
tf.logging.warn(
('Found more than one graph event per run, or there was '
'a metagraph containing a graph_def, as well as one or '
'more graph events. Overwriting the graph with the '
'newest event.'))
self._graph = event.graph_def
self._graph_from_metagraph = False
elif event.HasField('meta_graph_def'):
if self._meta_graph is not None:
tf.logging.warn(
('Found more than one metagraph event per run. '
'Overwriting the metagraph with the newest event.'))
self._meta_graph = event.meta_graph_def
if self._graph is None or self._graph_from_metagraph:
# We may have a graph_def in the metagraph. If so, and no
# graph_def is directly available, use this one instead.
meta_graph = tf.MetaGraphDef()
meta_graph.ParseFromString(self._meta_graph)
if meta_graph.graph_def:
if self._graph is not None:
tf.logging.warn((
'Found multiple metagraphs containing graph_defs,'
'but did not find any graph events. Overwriting the '
'graph with the newest metagraph version.'))
self._graph_from_metagraph = True
self._graph = meta_graph.graph_def.SerializeToString()
elif event.HasField('tagged_run_metadata'):
tag = event.tagged_run_metadata.tag
if tag in self._tagged_metadata:
tf.logging.warn(
'Found more than one "run metadata" event with tag ' +
tag + '. Overwriting it with the newest event.')
self._tagged_metadata[tag] = event.tagged_run_metadata.run_metadata
elif event.HasField('summary'):
for value in event.summary.value:
value = data_compat.migrate_value(value)
if value.HasField('metadata'):
tag = value.tag
# We only store the first instance of the metadata. This check
# is important: the `FileWriter` does strip metadata from all
# values except the first one per each tag, but a new
# `FileWriter` is created every time a training job stops and
# restarts. Hence, we must also ignore non-initial metadata in
# this logic.
if tag not in self.summary_metadata:
self.summary_metadata[tag] = value.metadata
plugin_data = value.metadata.plugin_data
if plugin_data.plugin_name:
self._plugin_to_tag_to_content[
plugin_data.plugin_name][tag] = (
plugin_data.content)
else:
tf.logging.warn((
'This summary with tag %r is oddly not associated with a '
'plugin.'), tag)
for summary_type, summary_func in SUMMARY_TYPES.items():
if value.HasField(summary_type):
datum = getattr(value, summary_type)
tag = value.tag
if summary_type == 'tensor' and not tag:
# This tensor summary was created using the old method that used
# plugin assets. We must still continue to support it.
tag = value.node_name
getattr(self, summary_func)(tag, event.wall_time,
event.step, datum)
def _assert_noop(self, value):
original_pbtxt = value.SerializeToString()
result = data_compat.migrate_value(value)
self.assertEqual(value, result)
self.assertEqual(original_pbtxt, value.SerializeToString())
def build_requests(self, run_to_events):
"""Converts a stream of TF events to a stream of outgoing requests.
Each yielded request will be at most `_MAX_REQUEST_LENGTH_BYTES`
bytes long.
Args:
run_to_events: Mapping from run name to generator of `tf.Event`
values, as returned by `LogdirLoader.get_run_events`.
Yields:
A finite stream of `WriteScalarRequest` objects.
Raises:
RuntimeError: If no progress can be made because even a single
point is too large (say, due to a gigabyte-long tag name).
"""
self._new_request()
runs = {} # cache: map from run name to `Run` proto in request
tags = (
{}
) # cache: map from `(run, tag)` to `Tag` proto in run in request
work_items = peekable_iterator.PeekableIterator(
self._run_values(run_to_events))
while work_items.has_next():
(run_name, event, orig_value) = work_items.peek()
value = data_compat.migrate_value(orig_value)
time_series_key = (run_name, value.tag)
metadata = self._tag_metadata.get(time_series_key)
if metadata is None:
plugin_name = value.metadata.plugin_data.plugin_name
if plugin_name == scalar_metadata.PLUGIN_NAME:
metadata = value.metadata
else:
metadata = _RequestBuilder._NON_SCALAR_TIME_SERIES
self._tag_metadata[time_series_key] = metadata
if metadata is _RequestBuilder._NON_SCALAR_TIME_SERIES:
next(work_items)
continue
try:
run_proto = runs.get(run_name)
if run_proto is None:
run_proto = self._create_run(run_name)
runs[run_name] = run_proto
tag_proto = tags.get((run_name, value.tag))
if tag_proto is None:
tag_proto = self._create_tag(run_proto, value.tag,
metadata)
tags[(run_name, value.tag)] = tag_proto
self._create_point(tag_proto, event, value)
next(work_items)
except _OutOfSpaceError:
# Flush request and start a new one.
request_to_emit = self._prune_request()
if request_to_emit is None:
raise RuntimeError(
"Could not make progress uploading data")
self._new_request()
runs.clear()
tags.clear()
yield request_to_emit
final_request = self._prune_request()
if final_request is not None:
yield final_request
def _ProcessEvent(self, event):
"""Called whenever an event is loaded."""
if self._first_event_timestamp is None:
self._first_event_timestamp = event.wall_time
if event.HasField('file_version'):
new_file_version = _ParseFileVersion(event.file_version)
if self.file_version and self.file_version != new_file_version:
## This should not happen.
tf.logging.warn(('Found new file_version for event.proto. This will '
'affect purging logic for TensorFlow restarts. '
'Old: {0} New: {1}').format(self.file_version,
new_file_version))
self.file_version = new_file_version
self._MaybePurgeOrphanedData(event)
## Process the event.
# GraphDef and MetaGraphDef are handled in a special way:
# If no graph_def Event is available, but a meta_graph_def is, and it
# contains a graph_def, then use the meta_graph_def.graph_def as our graph.
# If a graph_def Event is available, always prefer it to the graph_def
# inside the meta_graph_def.
if event.HasField('graph_def'):
if self._graph is not None:
tf.logging.warn(
('Found more than one graph event per run, or there was '
'a metagraph containing a graph_def, as well as one or '
'more graph events. Overwriting the graph with the '
'newest event.'))
self._graph = event.graph_def
self._graph_from_metagraph = False
elif event.HasField('meta_graph_def'):
if self._meta_graph is not None:
tf.logging.warn(('Found more than one metagraph event per run. '
'Overwriting the metagraph with the newest event.'))
self._meta_graph = event.meta_graph_def
if self._graph is None or self._graph_from_metagraph:
# We may have a graph_def in the metagraph. If so, and no
# graph_def is directly available, use this one instead.
meta_graph = tf.MetaGraphDef()
meta_graph.ParseFromString(self._meta_graph)
if meta_graph.graph_def:
if self._graph is not None:
tf.logging.warn(
('Found multiple metagraphs containing graph_defs,'
'but did not find any graph events. Overwriting the '
'graph with the newest metagraph version.'))
self._graph_from_metagraph = True
self._graph = meta_graph.graph_def.SerializeToString()
elif event.HasField('tagged_run_metadata'):
tag = event.tagged_run_metadata.tag
if tag in self._tagged_metadata:
tf.logging.warn('Found more than one "run metadata" event with tag ' +
tag + '. Overwriting it with the newest event.')
self._tagged_metadata[tag] = event.tagged_run_metadata.run_metadata
elif event.HasField('summary'):
for value in event.summary.value:
value = data_compat.migrate_value(value)
if value.HasField('metadata'):
tag = value.tag
# We only store the first instance of the metadata. This check
# is important: the `FileWriter` does strip metadata from all
# values except the first one per each tag, but a new
# `FileWriter` is created every time a training job stops and
# restarts. Hence, we must also ignore non-initial metadata in
# this logic.
if tag not in self.summary_metadata:
self.summary_metadata[tag] = value.metadata
plugin_data = value.metadata.plugin_data
if plugin_data.plugin_name:
self._plugin_to_tag_to_content[plugin_data.plugin_name][tag] = (
plugin_data.content)
else:
tf.logging.warn(
('This summary with tag %r is oddly not associated with a '
'plugin.'), tag)
for summary_type, summary_func in SUMMARY_TYPES.items():
if value.HasField(summary_type):
datum = getattr(value, summary_type)
tag = value.tag
if summary_type == 'tensor' and not tag:
# This tensor summary was created using the old method that used
# plugin assets. We must still continue to support it.
tag = value.node_name
getattr(self, summary_func)(tag, event.wall_time, event.step, datum)
def _assert_noop(self, value): original_pbtxt = value.SerializeToString() result = data_compat.migrate_value(value) self.assertEqual(value, result) self.assertEqual(original_pbtxt, value.SerializeToString())
