def test_audio(self):
logdir = self.get_temp_dir()
steps = (0, 1, 2)
with test_util.FileWriter(logdir) as writer:
for step in steps:
event = event_pb2.Event()
event.step = step
event.wall_time = 456.75 * step
audio = tf.reshape(tf.linspace(0.0, 100.0, 4 * 10 * 2),
(4, 10, 2))
audio_pb = audio_summary.pb(
"foo",
audio,
labels=["one", "two", "three", "four"],
sample_rate=44100,
display_name="bar",
description="baz",
)
writer.add_summary(audio_pb.SerializeToString(),
global_step=step)
files = os.listdir(logdir)
self.assertLen(files, 1)
event_file = os.path.join(logdir, files[0])
loader = event_file_loader.RawEventFileLoader(event_file)
input_events = [event_pb2.Event.FromString(x) for x in loader.Load()]
new_events = []
initial_metadata = {}
for input_event in input_events:
migrated = self._migrate_event(input_event,
initial_metadata=initial_metadata)
new_events.extend(migrated)
self.assertLen(new_events, 4)
self.assertEqual(new_events[0].WhichOneof("what"), "file_version")
for step in steps:
with self.subTest("step %d" % step):
new_event = new_events[step + 1]
self.assertLen(new_event.summary.value, 1)
value = new_event.summary.value[0]
tensor = tensor_util.make_ndarray(value.tensor)
self.assertEqual(tensor.shape,
(3, )) # 4 clipped to max_outputs=3
self.assertStartsWith(tensor[0], b"RIFF")
self.assertStartsWith(tensor[1], b"RIFF")
if step == min(steps):
metadata = value.metadata
self.assertEqual(
metadata.data_class,
summary_pb2.DATA_CLASS_BLOB_SEQUENCE,
)
self.assertEqual(
metadata.plugin_data.plugin_name,
audio_metadata.PLUGIN_NAME,
)
else:
self.assertFalse(value.HasField("metadata"))
def test_graph_def(self):
# Create a `GraphDef` and write it to disk as an event.
logdir = self.get_temp_dir()
writer = test_util.FileWriter(logdir)
graph_def = graph_pb2.GraphDef()
graph_def.node.add(name="alice", op="Person")
graph_def.node.add(name="bob", op="Person")
graph_def.node.add(
name="friendship", op="Friendship", input=["alice", "bob"]
)
writer.add_graph(graph=None, graph_def=graph_def, global_step=123)
writer.flush()
# Read in the `Event` containing the written `graph_def`.
files = os.listdir(logdir)
self.assertLen(files, 1)
event_file = os.path.join(logdir, files[0])
self.assertIn("tfevents", event_file)
loader = event_file_loader.RawEventFileLoader(event_file)
events = [event_pb2.Event.FromString(x) for x in loader.Load()]
self.assertLen(events, 2)
self.assertEqual(events[0].WhichOneof("what"), "file_version")
self.assertEqual(events[1].WhichOneof("what"), "graph_def")
old_event = events[1]
new_events = self._migrate_event(old_event)
self.assertLen(new_events, 2)
self.assertIs(new_events[0], old_event)
new_event = new_events[1]
self.assertEqual(new_event.WhichOneof("what"), "summary")
self.assertLen(new_event.summary.value, 1)
tensor = tensor_util.make_ndarray(new_event.summary.value[0].tensor)
self.assertEqual(
new_event.summary.value[0].metadata.data_class,
summary_pb2.DATA_CLASS_BLOB_SEQUENCE,
)
self.assertEqual(
new_event.summary.value[0].metadata.plugin_data.plugin_name,
graphs_metadata.PLUGIN_NAME,
)
self.assertEqual(tensor.shape, (1,))
new_graph_def_bytes = tensor[0]
self.assertIsInstance(new_graph_def_bytes, bytes)
self.assertGreaterEqual(len(new_graph_def_bytes), 16)
new_graph_def = graph_pb2.GraphDef.FromString(new_graph_def_bytes)
self.assertProtoEquals(graph_def, new_graph_def)
def _LoaderForTestFile(self, filename):
return event_file_loader.RawEventFileLoader(
os.path.join(self.get_temp_dir(), filename))