def compute_and_check_summary_pb(self,
name,
audio,
max_outputs=3,
display_name=None,
description=None,
audio_tensor=None,
feed_dict=None):
"""Use both `op` and `pb` to get a summary, asserting validity.
"Validity" means that the `op` and `pb` functions must return the
same protobufs, and also that each encoded audio value appears to be
a valid WAV file. If either of these conditions fails, the test will
immediately fail. Otherwise, the valid protobuf will be returned.
Returns:
A `Summary` protocol buffer.
"""
if audio_tensor is None:
audio_tensor = tf.constant(audio)
op = summary.op(name, audio_tensor, self.samples_per_second,
max_outputs=max_outputs,
display_name=display_name, description=description)
pb = summary.pb(name, audio, self.samples_per_second,
max_outputs=max_outputs,
display_name=display_name, description=description)
pb_via_op = self.pb_via_op(op, feed_dict=feed_dict)
self.assertProtoEquals(pb, pb_via_op)
audios = tf.make_ndarray(pb.value[0].tensor)[:, 0].tolist()
invalid_audios = [x for x in audios
if not x.startswith(b'RIFF')]
self.assertFalse(invalid_audios)
return pb
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 audio(self, *args, **kwargs):
return summary.pb(*args, **kwargs)
def test_requires_wav_in_pb(self):
with six.assertRaisesRegex(self, ValueError, 'Unknown encoding'):
summary.pb('k488', self.stereo, 44100, encoding='pptx')
def test_requires_rank_3_in_pb(self):
with six.assertRaisesRegex(self, ValueError, 'must have rank 3'):
summary.pb('k488', np.array([[1, 2, 3], [4, 5, 6]]), 44100)
def test_requires_wav_in_pb(self):
with six.assertRaisesRegex(self, ValueError, 'Unknown encoding'):
summary.pb('k488', self.stereo, 44100, encoding='pptx')
def test_requires_rank_3_in_pb(self):
with six.assertRaisesRegex(self, ValueError, 'must have rank 3'):
summary.pb('k488', np.array([[1, 2, 3], [4, 5, 6]]), 44100)
