def generate_run(self, run_name, include_graph):
"""Create a run with a text summary, metadata, and optionally a graph."""
tf.reset_default_graph()
k1 = tf.constant(math.pi, name='k1')
k2 = tf.constant(math.e, name='k2')
result = (k1**k2) - k1
expected = tf.constant(20.0, name='expected')
error = tf.abs(result - expected, name='error')
message_prefix_value = 'error ' * 1000
true_length = len(message_prefix_value)
assert true_length > self._MESSAGE_PREFIX_LENGTH_LOWER_BOUND, true_length
message_prefix = tf.constant(message_prefix_value,
name='message_prefix')
error_message = tf.string_join(
[message_prefix,
tf.as_string(error, name='error_string')],
name='error_message')
summary_message = tf.summary.text('summary_message', error_message)
sess = tf.Session()
writer = tf.summary.FileWriter(os.path.join(self.logdir, run_name))
if include_graph:
writer.add_graph(sess.graph)
options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
run_metadata = tf.RunMetadata()
s = sess.run(summary_message,
options=options,
run_metadata=run_metadata)
writer.add_summary(s)
writer.add_run_metadata(run_metadata, self._METADATA_TAG)
writer.close()
def test_run_metadata(self):
self.set_up_with_runs()
(metadata_pbtxt,
mime_type) = self.plugin.run_metadata_impl(self._RUN_WITH_GRAPH,
self._METADATA_TAG)
self.assertEqual(mime_type, 'text/x-protobuf')
text_format.Parse(metadata_pbtxt, tf.RunMetadata())
def RunMetadata(self, tag):
"""Given a tag, return the associated session.run() metadata.
Args:
tag: A string tag associated with the event.
Raises:
ValueError: If the tag is not found.
Returns:
The metadata in form of `RunMetadata` proto.
"""
if tag not in self._tagged_metadata:
raise ValueError('There is no run metadata with this tag name')
run_metadata = tf.RunMetadata()
run_metadata.ParseFromString(self._tagged_metadata[tag])
return run_metadata
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()
def run_box_to_gaussian(logdir, verbose=False):
"""Run a box-blur-to-Gaussian-blur 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: box_to_gaussian')
tf.reset_default_graph()
tf.set_random_seed(0)
image = get_image(verbose=verbose)
blur_radius = tf.placeholder(shape=(), dtype=tf.int32)
with tf.name_scope('filter'):
blur_side_length = blur_radius * 2 + 1
pixel_filter = tf.ones((blur_side_length, blur_side_length))
pixel_filter = (pixel_filter
/ tf.cast(tf.size(pixel_filter), tf.float32)) # normalize
iterations = 4
images = [tf.cast(image, tf.float32) / 255.0]
for _ in xrange(iterations):
images.append(convolve(images[-1], pixel_filter))
with tf.name_scope('convert_to_uint8'):
images = tf.stack(
[tf.cast(255 * tf.clip_by_value(image_, 0.0, 1.0), tf.uint8)
for image_ in images])
summ = image_summary.op(
'box_to_gaussian', images, max_outputs=iterations,
display_name='Gaussian blur as a limit process of box blurs',
description=('Demonstration of forming a Gaussian blur by '
'composing box blurs, each of which can be expressed '
'as a 2D convolution.\n\n'
'A Gaussian blur is formed by convolving a Gaussian '
'kernel over an image. But a Gaussian kernel is '
'itself the limit of convolving a constant kernel '
'with itself many times. Thus, while applying '
'a box-filter convolution just once produces '
'results that are noticeably different from those '
'of a Gaussian blur, repeating the same convolution '
'just a few times causes the result to rapidly '
'converge to an actual Gaussian blur.\n\n'
'Here, the step value controls the blur radius, '
'and the image sample controls the number of times '
'that the convolution is applied (plus one). '
'So, when *sample*=1, the original image is shown; '
'*sample*=2 shows a box blur; and a hypothetical '
'*sample*=∞ would show a true Gaussian blur.\n\n'
'This is one ingredient in a recipe to compute very '
'fast Gaussian blurs. The other pieces require '
'special treatment for the box blurs themselves '
'(decomposition to dual one-dimensional box blurs, '
'each of which is computed with a sliding window); '
'we don’t perform those optimizations here.\n\n'
'[Here are some slides describing the full process.]'
'(%s)\n\n'
'%s'
% ('http://elynxsdk.free.fr/ext-docs/Blur/Fast_box_blur.pdf',
IMAGE_CREDIT)))
with tf.Session() as sess:
sess.run(image.initializer)
writer = tf.summary.FileWriter(os.path.join(logdir, 'box_to_gaussian'))
writer.add_graph(sess.graph)
for step in xrange(8):
if verbose:
tf.logging.info('--- box_to_gaussian: step: %s' % step)
feed_dict = {blur_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()
def testScalarsRealistically(self):
"""Test accumulator by writing values and then reading them."""
def FakeScalarSummary(tag, value):
value = tf.Summary.Value(tag=tag, simple_value=value)
summary = tf.Summary(value=[value])
return summary
directory = os.path.join(self.get_temp_dir(), 'values_dir')
if tf.gfile.IsDirectory(directory):
tf.gfile.DeleteRecursively(directory)
tf.gfile.MkDir(directory)
writer = tf.summary.FileWriter(directory, max_queue=100)
with tf.Graph().as_default() as graph:
_ = tf.constant([2.0, 1.0])
# Add a graph to the summary writer.
writer.add_graph(graph)
meta_graph_def = tf.train.export_meta_graph(
graph_def=graph.as_graph_def(add_shapes=True))
writer.add_meta_graph(meta_graph_def)
run_metadata = tf.RunMetadata()
device_stats = run_metadata.step_stats.dev_stats.add()
device_stats.device = 'test device'
writer.add_run_metadata(run_metadata, 'test run')
# Write a bunch of events using the writer.
for i in xrange(30):
summ_id = FakeScalarSummary('id', i)
summ_sq = FakeScalarSummary('sq', i * i)
writer.add_summary(summ_id, i * 5)
writer.add_summary(summ_sq, i * 5)
writer.flush()
# Verify that we can load those events properly
acc = ea.EventAccumulator(directory)
acc.Reload()
self.assertTagsEqual(
acc.Tags(), {
ea.SCALARS: ['id', 'sq'],
ea.GRAPH: True,
ea.META_GRAPH: True,
ea.RUN_METADATA: ['test run'],
})
id_events = acc.Scalars('id')
sq_events = acc.Scalars('sq')
self.assertEqual(30, len(id_events))
self.assertEqual(30, len(sq_events))
for i in xrange(30):
self.assertEqual(i * 5, id_events[i].step)
self.assertEqual(i * 5, sq_events[i].step)
self.assertEqual(i, id_events[i].value)
self.assertEqual(i * i, sq_events[i].value)
# Write a few more events to test incremental reloading
for i in xrange(30, 40):
summ_id = FakeScalarSummary('id', i)
summ_sq = FakeScalarSummary('sq', i * i)
writer.add_summary(summ_id, i * 5)
writer.add_summary(summ_sq, i * 5)
writer.flush()
# Verify we can now see all of the data
acc.Reload()
id_events = acc.Scalars('id')
sq_events = acc.Scalars('sq')
self.assertEqual(40, len(id_events))
self.assertEqual(40, len(sq_events))
for i in xrange(40):
self.assertEqual(i * 5, id_events[i].step)
self.assertEqual(i * 5, sq_events[i].step)
self.assertEqual(i, id_events[i].value)
self.assertEqual(i * i, sq_events[i].value)
self.assertProtoEquals(graph.as_graph_def(add_shapes=True),
acc.Graph())
self.assertProtoEquals(meta_graph_def, acc.MetaGraph())