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 old-style audio summaries for run "foo".
tf.reset_default_graph()
sess = tf.Session()
placeholder = tf.placeholder(tf.float32)
tf.summary.audio(name="baz", tensor=placeholder, sample_rate=44100)
merged_summary_op = tf.summary.merge_all()
foo_directory = os.path.join(self.log_dir, "foo")
writer = tf.summary.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 new-style audio summaries for run "bar".
tf.reset_default_graph()
sess = tf.Session()
audio_placeholder = tf.placeholder(tf.float32)
labels_placeholder = tf.placeholder(tf.string)
summary.op("quux", audio_placeholder, sample_rate=44100,
labels=labels_placeholder,
description="how do you pronounce that, anyway?")
merged_summary_op = tf.summary.merge_all()
bar_directory = os.path.join(self.log_dir, "bar")
writer = tf.summary.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={
audio_placeholder: numpy.random.rand(42, 11025, 1) * 2 - 1,
labels_placeholder: [
tf.compat.as_bytes('step **%s**, sample %s' % (step, sample))
for sample in xrange(42)
],
}), 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 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_new_style_audio(self):
audio = tf.reshape(tf.linspace(0.0, 100.0, 4 * 10 * 2), (4, 10, 2))
op = audio_summary.op('k488',
tf.cast(audio, tf.float32),
sample_rate=44100,
display_name='Piano Concerto No.23',
description='In **A major**.')
value = self._value_from_op(op)
assert value.HasField('tensor'), value
self._assert_noop(value)
def test_new_style_audio(self):
audio = tf.reshape(tf.linspace(0.0, 100.0, 4 * 10 * 2), (4, 10, 2))
op = audio_summary.op('k488',
tf.cast(audio, tf.float32),
sample_rate=44100,
display_name='Piano Concerto No.23',
description='In **A major**.')
value = self._value_from_op(op)
assert value.HasField('tensor'), value
self._assert_noop(value)
def testNewStyleAudioSummary(self):
"""Verify processing of tensorboard.plugins.audio.summary."""
event_sink = _EventGenerator(self, zero_out_timestamps=True)
writer = test_util.FileWriter(self.get_temp_dir())
writer.event_writer = event_sink
with self.test_session() as sess:
ipt = tf.random_normal(shape=[5, 441, 2])
with tf.name_scope('1'):
audio_summary.op('one', ipt, sample_rate=44100, max_outputs=1)
with tf.name_scope('2'):
audio_summary.op('two', ipt, sample_rate=44100, max_outputs=2)
with tf.name_scope('3'):
audio_summary.op('three', ipt, sample_rate=44100, max_outputs=3)
merged = tf.summary.merge_all()
writer.add_graph(sess.graph)
for i in xrange(10):
summ = sess.run(merged)
writer.add_summary(summ, global_step=i)
accumulator = ea.EventAccumulator(event_sink)
accumulator.Reload()
tags = [
u'1/one/audio_summary',
u'2/two/audio_summary',
u'3/three/audio_summary',
]
self.assertTagsEqual(accumulator.Tags(), {
ea.TENSORS: tags,
ea.GRAPH: True,
ea.META_GRAPH: False,
})
def testNewStyleAudioSummary(self):
"""Verify processing of tensorboard.plugins.audio.summary."""
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:
ipt = tf.random_normal(shape=[5, 441, 2])
with tf.name_scope('1'):
audio_summary.op('one', ipt, sample_rate=44100, max_outputs=1)
with tf.name_scope('2'):
audio_summary.op('two', ipt, sample_rate=44100, max_outputs=2)
with tf.name_scope('3'):
audio_summary.op('three', ipt, sample_rate=44100, max_outputs=3)
merged = tf.summary.merge_all()
writer.add_graph(sess.graph)
for i in xrange(10):
summ = sess.run(merged)
writer.add_summary(summ, global_step=i)
accumulator = ea.EventAccumulator(event_sink)
accumulator.Reload()
tags = [
u'1/one/audio_summary',
u'2/two/audio_summary',
u'3/three/audio_summary',
]
self.assertTagsEqual(accumulator.Tags(), {
ea.TENSORS: tags,
ea.GRAPH: True,
ea.META_GRAPH: False,
})
def test_new_style_audio(self):
with tf.compat.v1.Graph().as_default():
audio = tf.reshape(tf.linspace(0.0, 100.0, 4 * 10 * 2), (4, 10, 2))
op = audio_summary.op(
"k488",
tf.cast(audio, tf.float32),
sample_rate=44100,
display_name="Piano Concerto No.23",
description="In **A major**.",
)
value = self._value_from_op(op)
assert value.HasField("tensor"), value
self._assert_noop(value)
def testNewStyleAudioSummary(self):
"""Verify processing of tensorboard.plugins.audio.summary."""
event_sink = _EventGenerator(self, zero_out_timestamps=True)
writer = test_util.FileWriter(self.get_temp_dir())
writer.event_writer = event_sink
with tf.compat.v1.Graph().as_default():
with self.test_session() as sess:
ipt = tf.random.normal(shape=[5, 441, 2])
with tf.name_scope("1"):
audio_summary.op("one",
ipt,
sample_rate=44100,
max_outputs=1)
with tf.name_scope("2"):
audio_summary.op("two",
ipt,
sample_rate=44100,
max_outputs=2)
with tf.name_scope("3"):
audio_summary.op("three",
ipt,
sample_rate=44100,
max_outputs=3)
merged = tf.compat.v1.summary.merge_all()
writer.add_graph(sess.graph)
for i in range(10):
summ = sess.run(merged)
writer.add_summary(summ, global_step=i)
accumulator = ea.EventAccumulator(event_sink)
accumulator.Reload()
tags = [
graph_metadata.RUN_GRAPH_NAME,
"1/one/audio_summary",
"2/two/audio_summary",
"3/three/audio_summary",
]
self.assertTagsEqual(
accumulator.Tags(),
{
ea.TENSORS: tags,
ea.GRAPH: True,
ea.META_GRAPH: False,
},
)
self.assertItemsEqual(
accumulator.ActivePlugins(),
[audio_metadata.PLUGIN_NAME, graph_metadata.PLUGIN_NAME],
)
def run(logdir, run_name, wave_name, wave_constructor):
"""Generate wave data of the given form.
The provided function `wave_constructor` should accept a scalar tensor
of type float32, representing the frequency (in Hz) at which to
construct a wave, and return a tensor of shape [1, _samples(), `n`]
representing audio data (for some number of channels `n`).
Waves will be generated at frequencies ranging from A4 to A5.
Arguments:
logdir: the top-level directory into which to write summary data
run_name: the name of this run; will be created as a subdirectory
under logdir
wave_name: the name of the wave being generated
wave_constructor: see above
"""
tf.compat.v1.reset_default_graph()
tf.compat.v1.set_random_seed(0)
# On each step `i`, we'll set this placeholder to `i`. This allows us
# to know "what time it is" at each step.
step_placeholder = tf.compat.v1.placeholder(tf.float32, shape=[])
# We want to linearly interpolate a frequency between A4 (440 Hz) and
# A5 (880 Hz).
with tf.name_scope("compute_frequency"):
f_min = 440.0
f_max = 880.0
t = step_placeholder / (FLAGS.steps - 1)
frequency = f_min * (1.0 - t) + f_max * t
# Let's log this frequency, just so that we can make sure that it's as
# expected.
tf.compat.v1.summary.scalar("frequency", frequency)
# Now, we pass this to the wave constructor to get our waveform. Doing
# so within a name scope means that any summaries that the wave
# constructor produces will be namespaced.
with tf.name_scope(wave_name):
waveform = wave_constructor(frequency)
# We also have the opportunity to annotate each audio clip with a
# label. This is a good place to include the frequency, because it'll
# be visible immediately next to the audio clip.
with tf.name_scope("compute_labels"):
samples = tf.shape(input=waveform)[0]
wave_types = tf.tile(["*Wave type:* `%s`." % wave_name], [samples])
frequencies = tf.strings.join([
"*Frequency:* ",
tf.tile([tf.as_string(frequency, precision=2)], [samples]),
" Hz.",
])
samples = tf.strings.join([
"*Sample:* ",
tf.as_string(tf.range(samples) + 1),
" of ",
tf.as_string(samples),
".",
])
labels = tf.strings.join([wave_types, frequencies, samples],
separator=" ")
# We can place a description next to the summary in TensorBoard. This
# is a good place to explain what the summary represents, methodology
# for creating it, etc. Let's include the source code of the function
# that generated the wave.
source = "\n".join(" %s" % line.rstrip()
for line in inspect.getsourcelines(wave_constructor)[0])
description = "A wave of type `%r`, generated via:\n\n%s" % (
wave_name,
source,
)
# Here's the crucial piece: we interpret this result as audio.
summary.op(
"waveform",
waveform,
FLAGS.sample_rate,
labels=labels,
display_name=wave_name,
description=description,
)
# Now, we can collect up all the summaries and begin the run.
summ = tf.compat.v1.summary.merge_all()
sess = tf.compat.v1.Session()
writer = tf.summary.FileWriter(os.path.join(logdir, run_name))
writer.add_graph(sess.graph)
sess.run(tf.compat.v1.global_variables_initializer())
for step in xrange(FLAGS.steps):
s = sess.run(summ, feed_dict={step_placeholder: float(step)})
writer.add_summary(s, global_step=step)
writer.close()
def audio(self, *args, **kwargs):
return tf.compat.v1.Summary.FromString(
summary.op(*args, **kwargs).numpy())
def test_requires_wav_in_op(self):
with six.assertRaisesRegex(self, ValueError, 'Unknown encoding'):
summary.op('k488', self.stereo, 44100, encoding='pptx')
def test_requires_rank_3_in_op(self):
with six.assertRaisesRegex(self, ValueError, 'must have rank 3'):
summary.op('k488', tf.constant([[1, 2, 3], [4, 5, 6]]), 44100)
def run(logdir, run_name, wave_name, wave_constructor):
"""Generate wave data of the given form.
The provided function `wave_constructor` should accept a scalar tensor
of type float32, representing the frequency (in Hz) at which to
construct a wave, and return a tensor of shape [1, _samples(), `n`]
representing audio data (for some number of channels `n`).
Waves will be generated at frequencies ranging from A4 to A5.
Arguments:
logdir: the top-level directory into which to write summary data
run_name: the name of this run; will be created as a subdirectory
under logdir
wave_name: the name of the wave being generated
wave_constructor: see above
"""
tf.reset_default_graph()
tf.set_random_seed(0)
# On each step `i`, we'll set this placeholder to `i`. This allows us
# to know "what time it is" at each step.
step_placeholder = tf.placeholder(tf.float32, shape=[])
# We want to linearly interpolate a frequency between A4 (440 Hz) and
# A5 (880 Hz).
with tf.name_scope('compute_frequency'):
f_min = 440.0
f_max = 880.0
t = step_placeholder / (FLAGS.steps - 1)
frequency = f_min * (1.0 - t) + f_max * t
# Let's log this frequency, just so that we can make sure that it's as
# expected.
tf.summary.scalar('frequency', frequency)
# Now, we pass this to the wave constructor to get our waveform. Doing
# so within a name scope means that any summaries that the wave
# constructor produces will be namespaced.
with tf.name_scope(wave_name):
waveform = wave_constructor(frequency)
# We also have the opportunity to annotate each audio clip with a
# label. This is a good place to include the frequency, because it'll
# be visible immediately next to the audio clip.
with tf.name_scope('compute_labels'):
samples = tf.shape(waveform)[0]
wave_types = tf.tile(["*Wave type:* `%s`." % wave_name], [samples])
frequencies = tf.string_join([
"*Frequency:* ",
tf.tile([tf.as_string(frequency, precision=2)], [samples]),
" Hz.",
])
samples = tf.string_join([
"*Sample:* ", tf.as_string(tf.range(samples) + 1),
" of ", tf.as_string(samples), ".",
])
labels = tf.string_join([wave_types, frequencies, samples], separator=" ")
# We can place a description next to the summary in TensorBoard. This
# is a good place to explain what the summary represents, methodology
# for creating it, etc. Let's include the source code of the function
# that generated the wave.
source = '\n'.join(' %s' % line.rstrip()
for line in inspect.getsourcelines(wave_constructor)[0])
description = ("A wave of type `%r`, generated via:\n\n%s"
% (wave_name, source))
# Here's the crucial piece: we interpret this result as audio.
summary.op('waveform', waveform, FLAGS.sample_rate,
labels=labels,
display_name=wave_name,
description=description)
# Now, we can collect up all the summaries and begin the run.
summ = tf.summary.merge_all()
sess = tf.Session()
writer = tf.summary.FileWriter(os.path.join(logdir, run_name))
writer.add_graph(sess.graph)
sess.run(tf.global_variables_initializer())
for step in xrange(FLAGS.steps):
s = sess.run(summ, feed_dict={step_placeholder: float(step)})
writer.add_summary(s, global_step=step)
writer.close()
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 old-style audio summaries for run "foo".
tf.compat.v1.reset_default_graph()
sess = tf.compat.v1.Session()
placeholder = tf.compat.v1.placeholder(tf.float32)
tf.compat.v1.summary.audio(name="baz", tensor=placeholder, sample_rate=44100)
merged_summary_op = tf.compat.v1.summary.merge_all()
foo_directory = os.path.join(self.log_dir, "foo")
with test_util.FileWriterCache.get(foo_directory) as writer:
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)
# Create new-style audio summaries for run "bar".
tf.compat.v1.reset_default_graph()
sess = tf.compat.v1.Session()
audio_placeholder = tf.compat.v1.placeholder(tf.float32)
labels_placeholder = tf.compat.v1.placeholder(tf.string)
summary.op("quux", audio_placeholder, sample_rate=44100,
labels=labels_placeholder,
description="how do you pronounce that, anyway?")
merged_summary_op = tf.compat.v1.summary.merge_all()
bar_directory = os.path.join(self.log_dir, "bar")
with test_util.FileWriterCache.get(bar_directory) as writer:
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={
audio_placeholder: numpy.random.rand(42, 11025, 1) * 2 - 1,
labels_placeholder: [
tf.compat.as_bytes('step **%s**, sample %s' % (step, sample))
for sample in xrange(42)
],
}), global_step=step)
# 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)
# Setting a reload interval of -1 disables reloading. We disable reloading
# because we seek to block tests from running til after one reload finishes.
# This setUp method thus manually reloads the multiplexer. TensorBoard would
# otherwise reload in a non-blocking thread.
wsgi_app = application.TensorBoardWSGIApp(
self.log_dir, [self.plugin], multiplexer, reload_interval=-1,
path_prefix='')
self.server = werkzeug_test.Client(wsgi_app, wrappers.BaseResponse)
multiplexer.Reload()
def test_requires_wav_in_op(self):
with six.assertRaisesRegex(self, ValueError, 'Unknown encoding'):
summary.op('k488', self.stereo, 44100, encoding='pptx')
def test_requires_rank_3_in_op(self):
with six.assertRaisesRegex(self, ValueError, 'must have rank 3'):
summary.op('k488', tf.constant([[1, 2, 3], [4, 5, 6]]), 44100)
