def test_serialize_tf_linspace_numpy(self):
# Should be subsumed by `test_serialize_numpy_scalars`; separate
# test because it's a common use case.
hparams = {
"f_default": tf.linspace(1.0, 2.0, 5).numpy()[0],
"f32": tf.cast(tf.linspace(1.0, 2.0, 5), tf.float32).numpy()[0],
"f64": tf.cast(tf.linspace(1.0, 2.0, 5), tf.float64).numpy()[0],
}
hp.hparams_pb(hparams)
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_audio(self):
audio = tf.reshape(tf.linspace(0.0, 100.0, 4 * 10 * 2), (4, 10, 2))
old_op = tf.summary.audio('k488', audio, 44100)
old_value = self._value_from_op(old_op)
assert old_value.HasField('audio'), old_value
new_value = data_compat.migrate_value(old_value)
self.assertEqual('k488/audio/0', new_value.tag)
expected_metadata = audio_metadata.create_summary_metadata(
display_name='k488/audio/0',
description='',
encoding=audio_metadata.Encoding.Value('WAV'))
self.assertEqual(expected_metadata, new_value.metadata)
self.assertTrue(new_value.HasField('tensor'))
data = tf.make_ndarray(new_value.tensor)
self.assertEqual((1, 2), data.shape)
self.assertEqual(tf.compat.as_bytes(old_value.audio.encoded_audio_string),
data[0][0])
self.assertEqual(b'', data[0][1]) # empty label
def run_sobel(logdir, verbose=False):
"""Run a Sobel edge detection demonstration.
See the summary description for more details.
Arguments:
logdir: Directory into which to write event logs.
verbose: Boolean; whether to log any output.
"""
if verbose:
tf.logging.info('--- Starting run: sobel')
tf.reset_default_graph()
tf.set_random_seed(0)
image = get_image(verbose=verbose)
kernel_radius = tf.placeholder(shape=(), dtype=tf.int32)
with tf.name_scope('horizontal_kernel'):
kernel_side_length = kernel_radius * 2 + 1
# Drop off influence for pixels further away from the center.
weighting_kernel = (
1.0 - tf.abs(tf.linspace(-1.0, 1.0, num=kernel_side_length)))
differentiation_kernel = tf.linspace(-1.0, 1.0, num=kernel_side_length)
horizontal_kernel = tf.matmul(tf.expand_dims(weighting_kernel, 1),
tf.expand_dims(differentiation_kernel, 0))
with tf.name_scope('vertical_kernel'):
vertical_kernel = tf.transpose(horizontal_kernel)
float_image = tf.cast(image, tf.float32)
dx = convolve(float_image, horizontal_kernel, name='convolve_dx')
dy = convolve(float_image, vertical_kernel, name='convolve_dy')
gradient_magnitude = tf.norm([dx, dy], axis=0, name='gradient_magnitude')
with tf.name_scope('normalized_gradient'):
normalized_gradient = gradient_magnitude / tf.reduce_max(gradient_magnitude)
with tf.name_scope('output_image'):
output_image = tf.cast(255 * normalized_gradient, tf.uint8)
summ = image_summary.op(
'sobel', tf.stack([output_image]),
display_name='Sobel edge detection',
description=(u'Demonstration of [Sobel edge detection]. The step '
'parameter adjusts the radius of the kernel. '
'The kernel can be of arbitrary size, and considers '
u'nearby pixels with \u2113\u2082-linear falloff.\n\n'
# (that says ``$\ell_2$-linear falloff'')
'Edge detection is done on a per-channel basis, so '
'you can observe which edges are “mostly red '
'edges,” for instance.\n\n'
'For practical edge detection, a small kernel '
'(usually not more than more than *r*=2) is best.\n\n'
'[Sobel edge detection]: %s\n\n'
"%s"
% ('https://en.wikipedia.org/wiki/Sobel_operator',
IMAGE_CREDIT)))
with tf.Session() as sess:
sess.run(image.initializer)
writer = tf.summary.FileWriter(os.path.join(logdir, 'sobel'))
writer.add_graph(sess.graph)
for step in xrange(8):
if verbose:
tf.logging.info("--- sobel: step: %s" % step)
feed_dict = {kernel_radius: step}
run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
run_metadata = tf.RunMetadata()
s = sess.run(summ, feed_dict=feed_dict,
options=run_options, run_metadata=run_metadata)
writer.add_summary(s, global_step=step)
writer.add_run_metadata(run_metadata, 'step_%04d' % step)
writer.close()