def setUp(self):
self.log_dir = tempfile.mkdtemp()
# We use numpy.random to generate audio. We seed to avoid non-determinism
# in this test.
numpy.random.seed(42)
# Create audio summaries for run foo.
ops.reset_default_graph()
sess = tf.Session()
placeholder = array_ops.placeholder(dtypes.float32)
summary_lib.audio(name="baz", tensor=placeholder, sample_rate=44100)
merged_summary_op = summary_lib.merge_all()
foo_directory = os.path.join(self.log_dir, "foo")
writer = FileWriter(foo_directory)
writer.add_graph(sess.graph)
for step in xrange(2):
# The floats (sample data) range from -1 to 1.
writer.add_summary(sess.run(merged_summary_op, feed_dict={
placeholder: numpy.random.rand(42, 22050) * 2 - 1
}), global_step=step)
writer.close()
# Create audio summaries for run bar.
ops.reset_default_graph()
sess = tf.Session()
placeholder = array_ops.placeholder(dtypes.float32)
summary_lib.audio(name="quux", tensor=placeholder, sample_rate=44100)
merged_summary_op = summary_lib.merge_all()
bar_directory = os.path.join(self.log_dir, "bar")
writer = summary_lib.FileWriter(bar_directory)
writer.add_graph(sess.graph)
for step in xrange(2):
# The floats (sample data) range from -1 to 1.
writer.add_summary(sess.run(merged_summary_op, feed_dict={
placeholder: numpy.random.rand(42, 11025) * 2 - 1
}), global_step=step)
writer.close()
# Start a server with the plugin.
multiplexer = event_multiplexer.EventMultiplexer({
"foo": foo_directory,
"bar": bar_directory,
})
context = base_plugin.TBContext(
logdir=self.log_dir, multiplexer=multiplexer)
self.plugin = audio_plugin.AudioPlugin(context)
wsgi_app = application.TensorBoardWSGIApp(
self.log_dir, [self.plugin], multiplexer, reload_interval=0)
self.server = werkzeug_test.Client(wsgi_app, wrappers.BaseResponse)
def test_audio(self):
op = summary_lib.audio('white_noise',
random_uniform(shape=[1, 44100],
minval=-1.0,
maxval=1.0),
sample_rate=44100)
value = self._value_from_op(op)
assert value.HasField('audio'), value
self._assert_noop(value)
def testAudioSummary(self):
with self.cached_session() as s:
i = array_ops.ones((5, 3, 4))
with ops.name_scope('outer'):
aud = summary_lib.audio('inner', i, 0.2, max_outputs=3)
summary_str = s.run(aud)
summary = summary_pb2.Summary()
summary.ParseFromString(summary_str)
values = summary.value
self.assertEqual(len(values), 3)
tags = sorted(v.tag for v in values)
expected = sorted('outer/inner/audio/{}'.format(i) for i in xrange(3))
self.assertEqual(tags, expected)
def testAudioSummary(self):
with self.cached_session() as s:
i = array_ops.ones((5, 3, 4))
with ops.name_scope('outer'):
aud = summary_lib.audio('inner', i, 0.2, max_outputs=3)
summary_str = s.run(aud)
summary = summary_pb2.Summary()
summary.ParseFromString(summary_str)
values = summary.value
self.assertEqual(len(values), 3)
tags = sorted(v.tag for v in values)
expected = sorted('outer/inner/audio/{}'.format(i) for i in xrange(3))
self.assertEqual(tags, expected)
def test_audio_summary_v2(self):
"""Tests audio v2 invocation."""
with test.mock.patch.object(
summary_v2, 'audio', autospec=True) as mock_audio_v2:
with summary_ops_v2.create_summary_file_writer(
self.get_temp_dir()).as_default(step=10):
i = array_ops.ones((5, 3, 4))
with ops.name_scope_v2('dolphin'):
tensor = summary_lib.audio('wave', i, 0.2, max_outputs=3)
# Returns empty string.
self.assertEqual(tensor.numpy(), b'')
self.assertEqual(tensor.dtype, dtypes.string)
mock_audio_v2.assert_called_once_with(
'dolphin/wave', data=i, sample_rate=0.2, step=10, max_outputs=3)
def test_audio_summary_v2__2d_tensor(self):
"""Tests audio v2 invocation with 2-D tensor input."""
with test.mock.patch.object(
summary_v2, 'audio', autospec=True) as mock_audio_v2:
with summary_ops_v2.create_summary_file_writer(
self.get_temp_dir()).as_default(step=11):
input_2d = array_ops.ones((5, 3))
tensor = summary_lib.audio('wave', input_2d, 0.2, max_outputs=3)
# Returns empty string.
self.assertEqual(tensor.numpy(), b'')
self.assertEqual(tensor.dtype, dtypes.string)
mock_audio_v2.assert_called_once_with(
'wave', data=test.mock.ANY, sample_rate=0.2, step=11, max_outputs=3)
input_3d = array_ops.ones((5, 3, 1)) # 3-D input tensor
self.assertAllEqual(mock_audio_v2.call_args[1]['data'], input_3d)
def testAudioSummary(self):
np.random.seed(7)
for channels in (1, 2, 5, 8):
with self.test_session(graph=ops.Graph()) as sess:
num_frames = 7
shape = (4, num_frames, channels)
# Generate random audio in the range [-1.0, 1.0).
const = 2.0 * np.random.random(shape) - 1.0
# Summarize
sample_rate = 8000
summ = summary.audio(
"snd", const, max_outputs=3, sample_rate=sample_rate)
value = sess.run(summ)
self.assertEqual([], summ.get_shape())
audio_summ = self._AsSummary(value)
# Check the rest of the proto
self._CheckProto(audio_summ, sample_rate, channels, num_frames)
def testAudioSummary(self):
np.random.seed(7)
for channels in (1, 2, 5, 8):
with self.session(graph=ops.Graph()) as sess:
num_frames = 7
shape = (4, num_frames, channels)
# Generate random audio in the range [-1.0, 1.0).
const = 2.0 * np.random.random(shape) - 1.0
# Summarize
sample_rate = 8000
summ = summary.audio(
"snd", const, max_outputs=3, sample_rate=sample_rate)
value = sess.run(summ)
self.assertEqual([], summ.get_shape())
audio_summ = self._AsSummary(value)
# Check the rest of the proto
self._CheckProto(audio_summ, sample_rate, channels, num_frames)
def testAudioSummaryWithInvalidSampleRate(self):
with self.assertRaises(errors.InvalidArgumentError):
invalid_sample_rate = [22000.0, 22000.0]
self.evaluate(summary_lib.audio('', [[1.0]], invalid_sample_rate))
