def __init__(self, bidirectional,
metric_goal,
metric_collapse, metric_collapse_threshold,
max_depth=10000, max_iterations=10000, max_time=120,
max_memory_MB=25):
'''
:param bidirectional:
:param metric_goal:
:param metric_collapse:
:param metric_collapse_threshold:
:param max_depth:
:param max_iterations:
:param max_time:
:param max_memory_MB:
'''
super(GenericGraphPlanner, self).__init__()
self.bidirectional = bidirectional
distances = get_conftools_uncertain_image_distances()
self.metric_goal = distances.instance(metric_goal)
self.metric_collapse = distances.instance(metric_collapse)
self.metric_collapse_threshold = metric_collapse_threshold
self.max_iterations = max_iterations
self.max_depth = max_depth
self.max_time = max_time
self.max_memory_MB = max_memory_MB
def __init__(self,
bidirectional,
metric_goal,
metric_collapse,
metric_collapse_threshold,
max_depth=10000,
max_iterations=10000,
max_time=120,
max_memory_MB=25):
'''
:param bidirectional:
:param metric_goal:
:param metric_collapse:
:param metric_collapse_threshold:
:param max_depth:
:param max_iterations:
:param max_time:
:param max_memory_MB:
'''
super(GenericGraphPlanner, self).__init__()
self.bidirectional = bidirectional
distances = get_conftools_uncertain_image_distances()
self.metric_goal = distances.instance(metric_goal)
self.metric_collapse = distances.instance(metric_collapse)
self.metric_collapse_threshold = metric_collapse_threshold
self.max_iterations = max_iterations
self.max_depth = max_depth
self.max_time = max_time
self.max_memory_MB = max_memory_MB
def __init__(self, top, right, bottom, left, distance):
self.top = top
self.right = right
self.bottom = bottom
self.left = left
self.id_distance = distance
l = get_conftools_uncertain_image_distances()
self.other = l.instance(self.id_distance)
self.other2 = l.instance(self.id_distance)
def __init__(self, top, right, bottom, left, distance):
self.top = top
self.right = right
self.bottom = bottom
self.left = left
self.id_distance = distance
l = get_conftools_uncertain_image_distances()
self.other = l.instance(self.id_distance)
self.other2 = l.instance(self.id_distance)
def define_jobs_context(self, context):
distances_library = get_conftools_uncertain_image_distances()
distances = distances_library.expand_names(self.options.distances)
distances = natsorted(distances)
streams_library = get_conftools_streams()
streams = streams_library.expand_names(self.options.streams)
streams = natsorted(streams)
# id_comb = ','.join(streams) + '-' + ','.join(distances)
create_diststats_jobs(context, distances=distances, streams=streams,
maxd=self.options.maxd)
def benchmark_distance(id_distance, testcases, repeat):
library_testcases = get_conftools_testcases()
testcases = map(library_testcases.instance, testcases)
d = get_conftools_uncertain_image_distances().instance(id_distance)
fps = InAWhile()
for _ in range(repeat):
for tc in testcases:
fps.its_time()
result = d.distance(tc.y0, tc.y1)
print('%s / %s: %s (%s)' % (id_distance, tc.id_tc, result, tc.true_plan))
print('%s: frames per second: %s' % (id_distance, fps.fps()))
def define_jobs_context(self, context):
library = get_conftools_uncertain_image_distances()
distances = library.expand_names(self.options.distances)
library = get_conftools_testcases()
testcases = library.expand_names(self.options.testcases)
self.info('Using distances: %s' % distances)
self.info('Using testcases: %s' % testcases)
for c, id_distance in iterate_context_names(context, distances):
c.comp_config(benchmark_distance, id_distance, testcases,
repeat=self.options.repeat)
def define_jobs_context(self, context):
distances_library = get_conftools_uncertain_image_distances()
distances = distances_library.expand_names(self.options.distances)
streams_library = get_conftools_streams()
streams = streams_library.expand_names(self.options.streams)
discdds_library = get_conftools_discdds()
discdds = discdds_library.expand_names(self.options.dds)
for c, id_discdds in iterate_context_names(context, discdds):
create_predstats_jobs(context=c, distances=distances,
id_discdds=id_discdds,
streams=streams, maxd=10)
def benchmark_distance(id_distance, testcases, repeat):
library_testcases = get_conftools_testcases()
testcases = map(library_testcases.instance, testcases)
d = get_conftools_uncertain_image_distances().instance(id_distance)
fps = InAWhile()
for _ in range(repeat):
for tc in testcases:
fps.its_time()
result = d.distance(tc.y0, tc.y1)
print('%s / %s: %s (%s)' %
(id_distance, tc.id_tc, result, tc.true_plan))
print('%s: frames per second: %s' % (id_distance, fps.fps()))
def define_jobs_context(self, context):
distances_library = get_conftools_uncertain_image_distances()
distances = distances_library.expand_names(self.options.distances)
distances = natsorted(distances)
streams_library = get_conftools_streams()
streams = streams_library.expand_names(self.options.streams)
streams = natsorted(streams)
# id_comb = ','.join(streams) + '-' + ','.join(distances)
create_diststats_jobs(context,
distances=distances,
streams=streams,
maxd=self.options.maxd)
def compute_dist_stats(id_distance, id_stream, delta):
distances_library = get_conftools_uncertain_image_distances()
distance = distances_library.instance(id_distance)
stream = get_conftools_streams().instance(id_stream)
it = stream.read_all()
results = []
for logitem in iterate_testcases(it, delta):
assert_allclose(len(logitem.u), delta)
y0 = UncertainImage(logitem.y0)
y1 = UncertainImage(logitem.y1)
d = distance.distance(y0, y1)
results.append(d)
logger.info('%s: found %d of %d steps in %s' %
(id_distance, len(results), delta, id_stream))
return results
def define_jobs_context(self, context):
library = get_conftools_uncertain_image_distances()
distances = library.expand_names(self.options.distances)
library = get_conftools_testcases()
testcases = library.expand_names(self.options.testcases)
self.info('Using distances: %s' % distances)
self.info('Using testcases: %s' % testcases)
for c, id_distance in iterate_context_names(context, distances):
c.comp_config(benchmark_distance,
id_distance,
testcases,
repeat=self.options.repeat)
def define_jobs_context(self, context):
distances_library = get_conftools_uncertain_image_distances()
distances = distances_library.expand_names(self.options.distances)
streams_library = get_conftools_streams()
streams = streams_library.expand_names(self.options.streams)
discdds_library = get_conftools_discdds()
discdds = discdds_library.expand_names(self.options.dds)
for c, id_discdds in iterate_context_names(context, discdds):
create_predstats_jobs(context=c,
distances=distances,
id_discdds=id_discdds,
streams=streams,
maxd=10)
def compute_dist_stats(id_distance, id_stream, delta):
distances_library = get_conftools_uncertain_image_distances()
distance = distances_library.instance(id_distance)
stream = get_conftools_streams().instance(id_stream)
it = stream.read_all()
results = []
for logitem in iterate_testcases(it, delta):
assert_allclose(len(logitem.u), delta)
y0 = UncertainImage(logitem.y0)
y1 = UncertainImage(logitem.y1)
d = distance.distance(y0, y1)
results.append(d)
logger.info('%s: found %d of %d steps in %s' %
(id_distance, len(results), delta, id_stream))
return results
def compute_predstats(id_discdds, id_stream, delta, id_distances):
dds = get_conftools_discdds().instance(id_discdds)
stream = get_conftools_streams().instance(id_stream)
distances_library = get_conftools_uncertain_image_distances()
distances = dict(map(lambda x: (x, distances_library.instance(x)), id_distances))
dtype = [(x, 'float32') for x in id_distances]
results = []
for logitem in iterate_testcases(stream.read_all(), delta):
assert_allclose(len(logitem.u), delta)
y0 = UncertainImage(logitem.y0)
y1 = UncertainImage(logitem.y1)
py0 = dds.predict(y0, dds.commands_to_indices(logitem.u))
ds = []
for name in id_distances:
d = distances[name].distance(y1, py0)
# d0 = distances[name].distance(y1, y0)
ds.append(d)
a = np.array(tuple(ds), dtype=dtype)
results.append(a)
return results
def compute_predstats(id_discdds, id_stream, delta, id_distances):
dds = get_conftools_discdds().instance(id_discdds)
stream = get_conftools_streams().instance(id_stream)
distances_library = get_conftools_uncertain_image_distances()
distances = dict(
map(lambda x: (x, distances_library.instance(x)), id_distances))
dtype = [(x, 'float32') for x in id_distances]
results = []
for logitem in iterate_testcases(stream.read_all(), delta):
assert_allclose(len(logitem.u), delta)
y0 = UncertainImage(logitem.y0)
y1 = UncertainImage(logitem.y1)
py0 = dds.predict(y0, dds.commands_to_indices(logitem.u))
ds = []
for name in id_distances:
d = distances[name].distance(y1, py0)
# d0 = distances[name].distance(y1, y0)
ds.append(d)
a = np.array(tuple(ds), dtype=dtype)
results.append(a)
return results
def __init__(self, metric_attractor, **kwargs):
distances = get_conftools_uncertain_image_distances()
self.metric_attractor = distances.instance(metric_attractor)
super(InformedPlannerGreedy, self).__init__(**kwargs)
def __init__(self, metric_attractor, **kwargs):
distances = get_conftools_uncertain_image_distances()
self.metric_attractor = distances.instance(metric_attractor)
super(InformedPlannerGreedyTree, self).__init__(**kwargs)
