def setUp(self):
self.log_dir = tempfile.mkdtemp()
# We use numpy.random to generate images. We seed to avoid non-determinism
# in this test.
numpy.random.seed(42)
# Create old-style image summaries for run "foo".
tf.reset_default_graph()
sess = tf.Session()
placeholder = tf.placeholder(tf.uint8)
tf.summary.image(name="baz", tensor=placeholder)
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):
writer.add_summary(sess.run(merged_summary_op,
feed_dict={
placeholder:
(numpy.random.rand(1, 16, 42, 3) *
255).astype(numpy.uint8)
}),
global_step=step)
writer.close()
# Create new-style image summaries for run bar.
tf.reset_default_graph()
sess = tf.Session()
placeholder = tf.placeholder(tf.uint8)
summary.op(name="quux",
images=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):
writer.add_summary(sess.run(merged_summary_op,
feed_dict={
placeholder:
(numpy.random.rand(1, 6, 8, 3) *
255).astype(numpy.uint8)
}),
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)
plugin = images_plugin.ImagesPlugin(context)
wsgi_app = application.TensorBoardWSGIApp(self.log_dir, [plugin],
multiplexer,
reload_interval=0)
self.server = werkzeug_test.Client(wsgi_app, wrappers.BaseResponse)
self.routes = plugin.get_plugin_apps()
def setUp(self):
self.log_dir = tempfile.mkdtemp()
# We use numpy.random to generate images. We seed to avoid non-determinism
# in this test.
numpy.random.seed(42)
# Create old-style image summaries for run "foo".
tf.reset_default_graph()
sess = tf.Session()
placeholder = tf.placeholder(tf.uint8)
tf.summary.image(name="baz", tensor=placeholder)
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):
writer.add_summary(sess.run(merged_summary_op, feed_dict={
placeholder: (numpy.random.rand(1, 16, 42, 3) * 255).astype(
numpy.uint8)
}), global_step=step)
writer.close()
# Create new-style image summaries for run bar.
tf.reset_default_graph()
sess = tf.Session()
placeholder = tf.placeholder(tf.uint8)
summary.op(name="quux", images=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):
writer.add_summary(sess.run(merged_summary_op, feed_dict={
placeholder: (numpy.random.rand(1, 6, 8, 3) * 255).astype(
numpy.uint8)
}), 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)
plugin = images_plugin.ImagesPlugin(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, [plugin], multiplexer, reload_interval=-1, path_prefix='')
self.server = werkzeug_test.Client(wsgi_app, wrappers.BaseResponse)
multiplexer.Reload()
self.routes = plugin.get_plugin_apps()
def setUp(self):
self.log_dir = tempfile.mkdtemp()
# We use numpy.random to generate images. We seed to avoid non-determinism
# in this test.
numpy.random.seed(42)
# Create old-style image summaries for run "foo".
tf.compat.v1.reset_default_graph()
sess = tf.compat.v1.Session()
placeholder = tf.compat.v1.placeholder(tf.uint8)
tf.compat.v1.summary.image(name="baz", tensor=placeholder)
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):
writer.add_summary(sess.run(merged_summary_op, feed_dict={
placeholder: (numpy.random.rand(1, 16, 42, 3) * 255).astype(
numpy.uint8)
}), global_step=step)
# Create new-style image summaries for run bar.
tf.compat.v1.reset_default_graph()
sess = tf.compat.v1.Session()
placeholder = tf.compat.v1.placeholder(tf.uint8)
summary.op(name="quux", images=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):
writer.add_summary(sess.run(merged_summary_op, feed_dict={
placeholder: (numpy.random.rand(1, 8, 6, 3) * 255).astype(
numpy.uint8)
}), 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)
plugin = images_plugin.ImagesPlugin(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, [plugin], multiplexer, reload_interval=-1, path_prefix='')
self.server = werkzeug_test.Client(wsgi_app, wrappers.BaseResponse)
multiplexer.Reload()
self.routes = plugin.get_plugin_apps()
def image(self, *args, **kwargs):
args = list(args)
# Force first argument to tf.uint8 since the V1 version requires this.
args[1] = tf.cast(tf.constant(args[1]), tf.uint8)
return summary_pb2.Summary.FromString(
summary.op(*args, **kwargs).numpy()
)
def test_new_style_image(self):
op = image_summary.op('mona_lisa',
tf.cast(tf.random_normal(shape=[1, 400, 200, 3]),
tf.uint8),
display_name='The Mona Lisa',
description='A renowned portrait by da Vinci.')
value = self._value_from_op(op)
assert value.HasField('tensor'), value
self._assert_noop(value)
def test_new_style_image(self):
op = image_summary.op('mona_lisa',
tf.cast(tf.random_normal(shape=[1, 400, 200, 3]),
tf.uint8),
display_name='The Mona Lisa',
description='A renowned portrait by da Vinci.')
value = self._value_from_op(op)
assert value.HasField('tensor'), value
self._assert_noop(value)
def test_new_style_image(self):
with tf.compat.v1.Graph().as_default():
op = image_summary.op(
'mona_lisa',
tf.image.convert_image_dtype(
tf.random.normal(shape=[1, 400, 200, 3]),
tf.uint8,
saturate=True),
display_name='The Mona Lisa',
description='A renowned portrait by da Vinci.')
value = self._value_from_op(op)
assert value.HasField('tensor'), value
self._assert_noop(value)
def compute_and_check_summary_pb(self,
name,
images,
max_outputs=3,
images_tensor=None,
feed_dict=None):
"""Use both `op` and `pb` to get a summary, asserting equality.
Returns:
a `Summary` protocol buffer
"""
if images_tensor is None:
images_tensor = tf.cast(tf.constant(images), tf.uint8)
op = summary.op(name, images_tensor, max_outputs=max_outputs)
pb = summary.pb(name, images, max_outputs=max_outputs)
pb_via_op = self.pb_via_op(op, feed_dict=feed_dict)
self.assertProtoEquals(pb, pb_via_op)
return pb
def testNewStyleImageSummary(self):
"""Verify processing of tensorboard.plugins.image.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.ones([10, 4, 4, 3], tf.uint8)
# This is an interesting example, because the old tf.image_summary op
# would throw an error here, because it would be tag reuse.
# Using the tf node name instead allows argument re-use to the image
# summary.
with tf.name_scope("1"):
image_summary.op("images", ipt, max_outputs=1)
with tf.name_scope("2"):
image_summary.op("images", ipt, max_outputs=2)
with tf.name_scope("3"):
image_summary.op("images", ipt, 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/images/image_summary",
"2/images/image_summary",
"3/images/image_summary",
]
self.assertTagsEqual(
accumulator.Tags(),
{
ea.TENSORS: tags,
ea.GRAPH: True,
ea.META_GRAPH: False,
},
)
self.assertItemsEqual(
accumulator.ActivePlugins(),
[image_metadata.PLUGIN_NAME, graph_metadata.PLUGIN_NAME],
)
def testNewStyleImageSummary(self):
"""Verify processing of tensorboard.plugins.image.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.ones([10, 4, 4, 3], tf.uint8)
# This is an interesting example, because the old tf.image_summary op
# would throw an error here, because it would be tag reuse.
# Using the tf node name instead allows argument re-use to the image
# summary.
with tf.name_scope('1'):
image_summary.op('images', ipt, max_outputs=1)
with tf.name_scope('2'):
image_summary.op('images', ipt, max_outputs=2)
with tf.name_scope('3'):
image_summary.op('images', ipt, 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/images/image_summary',
u'2/images/image_summary',
u'3/images/image_summary',
]
self.assertTagsEqual(accumulator.Tags(), {
ea.TENSORS: tags,
ea.GRAPH: True,
ea.META_GRAPH: False,
})
def testNewStyleImageSummary(self):
"""Verify processing of tensorboard.plugins.image.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.ones([10, 4, 4, 3], tf.uint8)
# This is an interesting example, because the old tf.image_summary op
# would throw an error here, because it would be tag reuse.
# Using the tf node name instead allows argument re-use to the image
# summary.
with tf.name_scope('1'):
image_summary.op('images', ipt, max_outputs=1)
with tf.name_scope('2'):
image_summary.op('images', ipt, max_outputs=2)
with tf.name_scope('3'):
image_summary.op('images', ipt, 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/images/image_summary',
u'2/images/image_summary',
u'3/images/image_summary',
]
self.assertTagsEqual(accumulator.Tags(), {
ea.TENSORS: tags,
ea.GRAPH: True,
ea.META_GRAPH: False,
})
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:
logger.info('--- Starting run: sobel')
tf.compat.v1.reset_default_graph()
tf.compat.v1.set_random_seed(0)
image = get_image(verbose=verbose)
kernel_radius = tf.compat.v1.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(a=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(tensor=[dx, dy],
axis=0,
name='gradient_magnitude')
with tf.name_scope('normalized_gradient'):
normalized_gradient = gradient_magnitude / tf.reduce_max(
input_tensor=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.compat.v1.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:
logger.info("--- sobel: step: %s" % step)
feed_dict = {kernel_radius: step}
run_options = tf.compat.v1.RunOptions(
trace_level=tf.compat.v1.RunOptions.FULL_TRACE)
run_metadata = tf.compat.v1.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:
logger.info('--- Starting run: box_to_gaussian')
tf.compat.v1.reset_default_graph()
tf.compat.v1.set_random_seed(0)
image = get_image(verbose=verbose)
blur_radius = tf.compat.v1.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(input=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.compat.v1.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:
logger.info('--- box_to_gaussian: step: %s' % step)
feed_dict = {blur_radius: step}
run_options = tf.compat.v1.RunOptions(
trace_level=tf.compat.v1.RunOptions.FULL_TRACE)
run_metadata = tf.compat.v1.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 _create_data(self):
"""Write test data to disk, returning `(logdir, multiplexer)`."""
self.log_dir = tempfile.mkdtemp()
# We use numpy.random to generate images. We seed to avoid non-determinism
# in this test.
numpy.random.seed(42)
# Create old-style image summaries for run "foo".
tf.compat.v1.reset_default_graph()
sess = tf.compat.v1.Session()
placeholder = tf.compat.v1.placeholder(tf.uint8)
tf.compat.v1.summary.image(name="baz", tensor=placeholder)
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):
writer.add_summary(
sess.run(
merged_summary_op,
feed_dict={
placeholder: (numpy.random.rand(1, 16, 42, 3) *
255).astype(numpy.uint8)
},
),
global_step=step,
)
# Create new-style image summaries for run bar.
tf.compat.v1.reset_default_graph()
sess = tf.compat.v1.Session()
placeholder = tf.compat.v1.placeholder(tf.uint8)
summary.op(
name="quux",
images=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):
writer.add_summary(
sess.run(
merged_summary_op,
feed_dict={
placeholder: (numpy.random.rand(1, 8, 6, 3) *
255).astype(numpy.uint8)
},
),
global_step=step,
)
# Start a server with the plugin.
multiplexer = event_multiplexer.EventMultiplexer({
"foo": foo_directory,
"bar": bar_directory,
})
multiplexer.Reload()
return (self.log_dir, multiplexer)
def test_requires_rank_4_in_op(self):
with six.assertRaisesRegex(self, ValueError, 'must have rank 4'):
summary.op('mona_lisa', tf.constant([[1, 2, 3], [4, 5, 6]]))