def define_jobs_context(self, context):
get_diffeo2ddslearn_config().load('default')
estimators = DDSLDemo1.estimators
streams = DDSLDemo1.streams
children = iterate_context_names_pair(context, streams, estimators)
for c, stream, estimator in children:
c.subtask(DDSLLearnParallel, stream=stream, estimator=estimator,
max_displ=0.24)
def new_estimator(self):
""" Instances a new estimator. """
c = get_diffeo2ddslearn_config()
_, estimator = \
c.diffeoaction_estimators.instance_smarter(self.diffeo_action_estimator)
estimator.set_max_displ(self.max_displ)
return estimator
def new_estimator(self):
""" Instances a new estimator. """
c = get_diffeo2ddslearn_config()
_, estimator = \
c.diffeoaction_estimators.instance_smarter(self.diffeo_action_estimator)
estimator.set_max_displ(self.max_displ)
return estimator
def get_comptests():
# get testing configuration directory
from pkg_resources import resource_filename # @UnresolvedImport
dirname = resource_filename("diffeo2dds_sim", "configs")
tests = []
import bootstrapping_olympics
bootstrapping_olympics.get_boot_config().load(dirname)
tests.extend(bootstrapping_olympics.get_comptests())
import diffeo2dds_learn
diffeo2dds_learn.get_diffeo2ddslearn_config().load(dirname)
tests.extend(diffeo2dds_learn.get_comptests())
return tests
def get_comptests():
# get testing configuration directory
from pkg_resources import resource_filename # @UnresolvedImport
dirname = resource_filename("diffeo2dds_sim", "configs")
tests = []
import bootstrapping_olympics
bootstrapping_olympics.get_boot_config().load(dirname)
tests.extend(bootstrapping_olympics.get_comptests())
import diffeo2dds_learn
diffeo2dds_learn.get_diffeo2ddslearn_config().load(dirname)
tests.extend(diffeo2dds_learn.get_comptests())
return tests
def make_stream_report(id_stream, nsamples):
config = get_diffeo2ddslearn_config()
stream = config.streams.instance(id_stream)
r = Report(id_stream)
data = itertools.islice(stream.read_all(), nsamples)
for i, log_item in enumerate(data):
with r.subsection('log_item%d' % i) as sub:
log_item.display(sub)
return r
def make_stream_report(id_stream, nsamples):
config = get_diffeo2ddslearn_config()
stream = config.streams.instance(id_stream)
r = Report(id_stream)
data = itertools.islice(stream.read_all(), nsamples)
for i, log_item in enumerate(data):
with r.subsection('log_item%d' % i) as sub:
log_item.display(sub)
return r
def add_bootstream(boot_root, id_robot, limit=1000):
""" Creates a Stream that reads data from a robot """
bootstream = ['diffeo_agents.library.BootStream',
dict(id_robot=id_robot, shape=[128, 128],
boot_root=boot_root)]
spec = dict(id=id_robot, desc="",
code=['diffeo2dds_learn.library.LimitStream',
dict(n=limit, stream=bootstream)])
config = get_diffeo2ddslearn_config()
config.streams[spec['id']] = spec
def add_bootstream(boot_root, id_robot, limit=1000):
""" Creates a Stream that reads data from a robot """
bootstream = [
'diffeo_agents.library.BootStream',
dict(id_robot=id_robot, shape=[128, 128], boot_root=boot_root)
]
spec = dict(id=id_robot,
desc="",
code=[
'diffeo2dds_learn.library.LimitStream',
dict(n=limit, stream=bootstream)
])
config = get_diffeo2ddslearn_config()
config.streams[spec['id']] = spec
def define_jobs_context(self, context):
config = get_diffeo2ddslearn_config()
nsamples = self.options.nsamples
which = self.options.streams
todo = config.streams.expand_names(which)
self.info('given streams: %s' % which)
self.info('using streams: %s' % todo)
for c, id_stream in iterate_context_names(context, todo):
report = c.comp_config(make_stream_report,
id_stream, nsamples=nsamples)
c.add_report(report, 'stream_report',
id_stream=id_stream, nsamples=nsamples)
def learn_from_stream(stream, estimator, max_displ):
""" Returns the estimator instance at the end of the learning. """
ddsl_config = get_diffeo2ddslearn_config()
estimator = ddsl_config.diffeosystem_estimators.instance(estimator)
estimator.set_max_displ(max_displ)
assert isinstance(estimator, DiffeoSystemEstimatorInterface)
stream = ddsl_config.streams.instance(stream)
for log_item in stream.read_all():
y0 = log_item.y0
y1 = log_item.y1
u = log_item.u
try:
estimator.update(y0, u, y1)
except DiffeoSystemEstimatorInterface.LearningConverged:
break
return estimator
def learn_from_stream(stream, estimator, max_displ):
""" Returns the estimator instance at the end of the learning. """
ddsl_config = get_diffeo2ddslearn_config()
estimator = ddsl_config.diffeosystem_estimators.instance(estimator)
estimator.set_max_displ(max_displ)
assert isinstance(estimator, DiffeoSystemEstimatorInterface)
stream = ddsl_config.streams.instance(stream)
for log_item in stream.read_all():
y0 = log_item.y0
y1 = log_item.y1
u = log_item.u
try:
estimator.update(y0, u, y1)
except DiffeoSystemEstimatorInterface.LearningConverged:
break
return estimator
def init(self, boot_spec):
shape = boot_spec.get_observations().shape()
is_2D = len(shape) == 2
is_RGB = len(shape) == 3 and shape[2] == 3
if not(is_2D or is_RGB):
msg = 'This agent can only work with image-like signals. '
msg = 'Found shape: %r' % str(shape)
raise UnsupportedSpec(msg)
estimators = get_diffeo2ddslearn_config().diffeosystem_estimators
_, self.diffeosystem_estimator = estimators.instance_smarter(self.estimator_spec)
self.log_add_child('dds_est', self.diffeosystem_estimator)
self.diffeosystem_estimator.set_max_displ(self.max_displ)
# initialize explorer
agents = get_boot_config().agents
_, self.explorer = agents.instance_smarter(self.explorer_spec)
self.explorer.init(boot_spec)
def init(self, boot_spec):
shape = boot_spec.get_observations().shape()
is_2D = len(shape) == 2
is_RGB = len(shape) == 3 and shape[2] == 3
if not (is_2D or is_RGB):
msg = 'This agent can only work with image-like signals. '
msg = 'Found shape: %r' % str(shape)
raise UnsupportedSpec(msg)
estimators = get_diffeo2ddslearn_config().diffeosystem_estimators
_, self.diffeosystem_estimator = estimators.instance_smarter(
self.estimator_spec)
self.log_add_child('dds_est', self.diffeosystem_estimator)
self.diffeosystem_estimator.set_max_displ(self.max_displ)
# initialize explorer
agents = get_boot_config().agents
_, self.explorer = agents.instance_smarter(self.explorer_spec)
self.explorer.init(boot_spec)
def learn_from_stream_parallel(stream, estimator, max_displ, i, n):
""" This version also gives the parallel hints. """
ddsl_config = get_diffeo2ddslearn_config()
stream = ddsl_config.streams.instance(stream)
estimator = ddsl_config.diffeosystem_estimators.instance(estimator)
assert isinstance(estimator, DiffeoSystemEstimatorInterface)
estimator.set_max_displ(max_displ)
# here we hint that you are one of many
estimator.parallel_process_hint(i, n)
for log_item in stream.read_all():
y0 = log_item.y0
y1 = log_item.y1
u = log_item.u
estimator.update(y0, u, y1)
return estimator
def define_jobs_context(self, context):
config = get_diffeo2ddslearn_config()
nsamples = self.options.nsamples
which = self.options.streams
todo = config.streams.expand_names(which)
self.info('given streams: %s' % which)
self.info('using streams: %s' % todo)
for c, id_stream in iterate_context_names(context, todo):
report = c.comp_config(make_stream_report,
id_stream,
nsamples=nsamples)
c.add_report(report,
'stream_report',
id_stream=id_stream,
nsamples=nsamples)
def learn_from_stream_parallel(stream, estimator, max_displ, i, n):
""" This version also gives the parallel hints. """
ddsl_config = get_diffeo2ddslearn_config()
stream = ddsl_config.streams.instance(stream)
estimator = ddsl_config.diffeosystem_estimators.instance(estimator)
assert isinstance(estimator, DiffeoSystemEstimatorInterface)
estimator.set_max_displ(max_displ)
# here we hint that you are one of many
estimator.parallel_process_hint(i, n)
for log_item in stream.read_all():
y0 = log_item.y0
y1 = log_item.y1
u = log_item.u
estimator.update(y0, u, y1)
return estimator
def define_jobs_context(self, context):
boot_root = "${YC2013WS}/out/"
boot_root = '/Users/andrea/scm/boot12env/ws-yc1304-grace/out/boot-root'
id_stream = Dev01.id_stream
id_robot = Dev01.id_robot
estimator = Dev01.estimator
bootstream = ['diffeo_agents.library.BootStream',
dict(id_robot=id_robot, shape=[64, 64],
boot_root=boot_root)]
spec = dict(id=id_stream, desc="",
code=['diffeo2dds_learn.library.LimitStream',
dict(n=1000, stream=bootstream)])
config = get_diffeo2ddslearn_config()
config.streams[spec['id']] = spec
id_stream = 'test_gauss_drx1_30_300'
context.subtask(DDSLLearn, stream=id_stream, estimator=estimator)
def __init__(self, image_stream, discdds):
'''
:param discdds: Which DDS dynamics to use.
:param image_stream: What ImageStream to use.
Each image is used to start a new episode.
'''
diffeo2ddslearn_config = get_diffeo2ddslearn_config()
self.id_image_stream, self.image_stream = \
diffeo2ddslearn_config.image_streams.instance_smarter(image_stream)
if self.id_image_stream is None:
self.id_image_stream = 'image_stream'
diffeo2dds_config = get_diffeo2dds_config()
self.id_discdds, self.discdds = \
diffeo2dds_config.discdds.instance_smarter(discdds)
if self.id_discdds is None:
self.id_discdds = 'discdds'
self.shape = None
def define_jobs_context(self, context):
boot_root = "${YC2013WS}/out/"
boot_root = '/Users/andrea/scm/boot12env/ws-yc1304-grace/out/boot-root'
id_stream = Dev01.id_stream
id_robot = Dev01.id_robot
estimator = Dev01.estimator
bootstream = [
'diffeo_agents.library.BootStream',
dict(id_robot=id_robot, shape=[64, 64], boot_root=boot_root)
]
spec = dict(id=id_stream,
desc="",
code=[
'diffeo2dds_learn.library.LimitStream',
dict(n=1000, stream=bootstream)
])
config = get_diffeo2ddslearn_config()
config.streams[spec['id']] = spec
id_stream = 'test_gauss_drx1_30_300'
context.subtask(DDSLLearn, stream=id_stream, estimator=estimator)
def __init__(self, stream, n):
config = get_diffeo2ddslearn_config()
_, self.stream = config.streams.instance_smarter(stream)
self.n = n
def __init__(self, stream, n):
config = get_diffeo2ddslearn_config()
_, self.stream = config.streams.instance_smarter(stream)
self.n = n
