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 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 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()
