def dp_dist_fps(config, parser): #@UnusedVariable
""" Computation speed statistics for distances. """
parser.add_option("-d", "--distances", default='*',
help="Comma-separated list of distances. Can use *.")
parser.add_option("-t", "--testcases", default='*',
help="Comma-separated list of algorithms. Can use *.")
parser.add_option("-r", "--repeat", default=1, type='int',
help="Repeat many times.")
options = parser.parse_options()
distances = config.distances.expand_names(options.distances)
testcases = config.testcases.expand_names(options.testcases)
logger.info('Using distances: %s' % distances)
logger.info('Using testcases: %s' % testcases)
testcases = map(config.testcases.instance, testcases)
for id_distance in distances:
dist = config.distances.instance(id_distance)
fps = InAWhile()
for _ in range(options.repeat):
for tc in testcases:
fps.its_time()
result = dist.distance(tc.y0, tc.y1)
print('%s / %s: %s (%s)' % (id_distance, tc.id_tc, result, tc.true_plan))
print('frames per second: %s' % fps.fps())
def measure_fps():
n = 100
for id_data, id_algo in itertools.product(data, algos):
v = data[id_data]
algo = algos[id_algo]
count = InAWhile()
for _ in xrange(n):
algo(v)
count.its_time()
fps = count.fps()
print('%s-%15s: %s' % (id_data, id_algo, fps))
def diffeo_quick1(id_dd, dd): # @UnusedVariable
M, N = dd.shape[0], dd.shape[1]
template = np.random.rand(M, N)
fda = FastDiffeoApply(dd)
functions = dict(regular=lambda: diffeo_apply(dd, template),
fast=lambda: fda(template))
repeat = 100
for name, function in functions.items():
fps = InAWhile()
for _ in range(repeat):
fps.its_time()
_ = function()
print('%s: %s fps' % (name, fps.fps()))
def diffeo_quick1(id_dd, dd): # @UnusedVariable
M, N = dd.shape[0], dd.shape[1]
template = np.random.rand(M, N)
fda = FastDiffeoApply(dd)
functions = dict(regular=lambda: diffeo_apply(dd, template),
fast=lambda: fda(template))
repeat = 100
for name, function in functions.items():
fps = InAWhile()
for _ in range(repeat):
fps.its_time()
_ = function()
print('%s: %s fps' % (name, fps.fps()))
def dp_dist_fps(config, parser): #@UnusedVariable
""" Computation speed statistics for distances. """
parser.add_option("-d",
"--distances",
default='*',
help="Comma-separated list of distances. Can use *.")
parser.add_option("-t",
"--testcases",
default='*',
help="Comma-separated list of algorithms. Can use *.")
parser.add_option("-r",
"--repeat",
default=1,
type='int',
help="Repeat many times.")
options = parser.parse_options()
distances = config.distances.expand_names(options.distances)
testcases = config.testcases.expand_names(options.testcases)
logger.info('Using distances: %s' % distances)
logger.info('Using testcases: %s' % testcases)
testcases = map(config.testcases.instance, testcases)
for id_distance in distances:
dist = config.distances.instance(id_distance)
fps = InAWhile()
for _ in range(options.repeat):
for tc in testcases:
fps.its_time()
result = dist.distance(tc.y0, tc.y1)
print('%s / %s: %s (%s)' %
(id_distance, tc.id_tc, result, tc.true_plan))
print('frames per second: %s' % fps.fps())
def diffeo_learner_main():
usage = "usage: %prog -p outputpath -n ddsname -s [W,H] -a [rx,ry] bag1 bag2 .."
parser = OptionParser(usage=usage, version="%prog 1.0")
parser.add_option("-p", "--path", default=None,
help="Path to output files")
parser.add_option("-n", "--name", default='camdds',
help="Output dds system name")
parser.add_option("-s", "--size", default=None, # default='[160,120]',
help="Image size WxH")
parser.add_option("-a", "--area", default=None, #default='[6,6]',
help="Size of search area")
parser.add_option("-r", "--ratio", default=0.1, type='float',
help="Search area ratio.")
parser.add_option("-l", "--learner", default='No',
help="Reuse learner")
parser.add_option("-o", "--outlearner", default='No',
help="Save the learner")
parser.add_option("-r", "--refine", default=0,
help="Refine the learning by running through the logs r times")
options, args = parser.parse_args()
if options.path is None:
dirname = os.path.dirname(options.input)
logger.info('No path given; using %s' % dirname)
else:
dirname = options.path
name = options.name
if options.size or options.area:
msg = 'Using deprecated options. Start using --ratio.'
raise UserError(msg)
learner = options.learner
outlearner = options.outlearner
if learner == 'No':
ratios = (options.ratio, options.ratio)
learn = DiffeoLearner(True, {'max_displ': ratios, 'inference_method': 'sim'})
else:
logger.info('Loading diffeomorphism estimators')
if learner[:4] == 'http':
logger.info('from online source: %s' % learner)
ofile = urllib.urlopen(learner)
else:
logger.info('from local source: %s' % learner)
ofile = open(learner)
learn = pickle.load(ofile)
logger.debug('loaded')
in_a_while = InAWhile(30)
refine = int(options.refine)
for _ in range(refine):
for bagfile in args:
i = 0
for y0, u, y1 in read_bag(bagfile):
logger.info('Iteration number %d' % i)
learn.update(y0, u, y1)
i += 1
if in_a_while.its_time():
display_current_results(learn, name, dirname, i)
in_a_while.reset()
if i > 15:
break
if outlearner == 'No':
pass
else:
logger.info('Saving learning agent to %s' % outlearner)
pickle.dump(learn, open(outlearner, 'wb'))
logger.info('Commands: %s' % learn.command_list)
learn.summarize(prefix=name)
learn.analyze(prefix=name, folder='out/diffeo-analysis/')
learn.diffeo_dump(dirname, name)
def diffeo_learner_main():
usage = "usage: %prog -p outputpath -n ddsname -s [W,H] -a [rx,ry] bag1 bag2 .."
parser = OptionParser(usage=usage, version="%prog 1.0")
parser.add_option("-p",
"--path",
default=None,
help="Path to output files")
parser.add_option("-n",
"--name",
default='camdds',
help="Output dds system name")
parser.add_option(
"-s",
"--size",
default=None, # default='[160,120]',
help="Image size WxH")
parser.add_option(
"-a",
"--area",
default=None, #default='[6,6]',
help="Size of search area")
parser.add_option("-r",
"--ratio",
default=0.1,
type='float',
help="Search area ratio.")
parser.add_option("-l", "--learner", default='No', help="Reuse learner")
parser.add_option("-o",
"--outlearner",
default='No',
help="Save the learner")
parser.add_option(
"-r",
"--refine",
default=0,
help="Refine the learning by running through the logs r times")
options, args = parser.parse_args()
if options.path is None:
dirname = os.path.dirname(options.input)
logger.info('No path given; using %s' % dirname)
else:
dirname = options.path
name = options.name
if options.size or options.area:
msg = 'Using deprecated options. Start using --ratio.'
raise UserError(msg)
learner = options.learner
outlearner = options.outlearner
if learner == 'No':
ratios = (options.ratio, options.ratio)
learn = DiffeoLearner(True, {
'max_displ': ratios,
'inference_method': 'sim'
})
else:
logger.info('Loading diffeomorphism estimators')
if learner[:4] == 'http':
logger.info('from online source: %s' % learner)
ofile = urllib.urlopen(learner)
else:
logger.info('from local source: %s' % learner)
ofile = open(learner)
learn = pickle.load(ofile)
logger.debug('loaded')
in_a_while = InAWhile(30)
refine = int(options.refine)
for _ in range(refine):
for bagfile in args:
i = 0
for y0, u, y1 in read_bag(bagfile):
logger.info('Iteration number %d' % i)
learn.update(y0, u, y1)
i += 1
if in_a_while.its_time():
display_current_results(learn, name, dirname, i)
in_a_while.reset()
if i > 15:
break
if outlearner == 'No':
pass
else:
logger.info('Saving learning agent to %s' % outlearner)
pickle.dump(learn, open(outlearner, 'wb'))
logger.info('Commands: %s' % learn.command_list)
learn.summarize(prefix=name)
learn.analyze(prefix=name, folder='out/diffeo-analysis/')
learn.diffeo_dump(dirname, name)