def define_jobs_context(self, context):
from pkg_resources import resource_filename # @UnresolvedImport
config_dir = resource_filename("yc1304.uzhturtle", "config")
GlobalConfig.global_load_dir(config_dir)
GlobalConfig.global_load_dir('${DATASET_UZHTURTLE}')
rm = context.get_report_manager()
rm.set_html_resources_prefix('uzh-turtle-stats')
data_central = self.get_data_central()
logs = list(self.get_explogs_by_tag('uzhturtle'))
agents = ['stats2', 'bdse_e1_ss']
robots = ['uzhturtle_un1_cf1', 'uzhturtle_un1_cf1_third']
for c, id_explog in iterate_context_explogs(context, logs):
explog = get_conftools_explogs().instance(id_explog)
print id_explog, explog
jobs_learn_parallel(context,
data_central=data_central,
explogs_learn=logs,
agents=agents, robots=robots,
episodes_per_tranche=2)
def define_jobs_context(self, context):
id_agent = 'exp10_bdser1'
id_robot = 'uA_b1_tw_cf'
id_adapter = 'uA_b1_tw_cf'
explogs_learn = good_logs_cf
explogs_test = ['unicornA_tran1_2013-04-12-23-34-08']
explogs_convert = explogs_learn + explogs_test
for c, id_explog in iterate_context_explogs(context, explogs_convert):
c.subtask(RS2BConvertOne,
boot_root=self.get_boot_root(),
id_explog=id_explog,
id_adapter=id_adapter,
id_robot=id_robot)
context.checkpoint('conversion')
context.subtask(LearnLog, agent=id_agent, robot=id_robot, interval_publish=5000)
context.checkpoint('learning')
context.subtask(PublishLearningResult, agent=id_agent, robot=id_robot)
test_episodes = explogs_test
for c, id_episode in iterate_context_episodes(context, test_episodes):
c.subtask(ServoField, id_robot=id_robot, id_agent=id_agent,
variation='default', id_episode=id_episode)
def define_jobs_context(self, context):
rm = context.get_report_manager()
rm.set_html_resources_prefix('jbds-nav')
logs = set(self.get_explogs_by_tag('navigation'))
logs = list(logs)
assert len(logs) >= 4
data_central = self.get_data_central()
recipe_episodeready_by_convert2(context, data_central.get_boot_root())
recipe_navigation_map1(context, data_central)
for c, id_explog in iterate_context_explogs(context, logs):
explog = get_conftools_explogs().instance(id_explog)
# Get the map that we need
annotation_navigation = explog.get_annotations()['navigation']
id_explog_map = annotation_navigation['map']
id_robot = annotation_navigation['robot']
# if not 'params' in annotation_navigation:
# self.error('incomplete %r' % id_explog)
# continue
navigation_params = annotation_navigation['params']
nmap = c.get_resource(RP_NAVIGATION_MAP,
id_episode=id_explog_map,
id_robot=id_robot)
out_base = os.path.join(c.get_output_dir(), '%s' % id_explog)
c.comp_config(reconstruct_servo_state, id_explog, id_robot, nmap,
out_base=out_base,
navigation_params=navigation_params,
job_id='reconstruct')
extra_dep = [c.get_resource(RM_EPISODE_READY, id_robot=id_robot,
id_episode=id_explog)]
nmap_dist = c.comp_config(nmap_distances, data_central,
id_episode=id_explog,
id_robot=id_robot, nmap=nmap,
extra_dep=extra_dep)
report_keys = dict(id_robot=id_robot, id_episode=id_explog,
id_episode_map=id_explog_map)
r = c.comp(report_nmap_distances, nmap, nmap_dist)
c.add_report(r, 'nmap_distances', **report_keys)
ep = c.get_resource(RM_EPISODE_READY,
id_episode=id_explog, id_robot=id_robot)
poses = c.comp_config(poses_from_episode, data_central=data_central,
id_robot=id_robot, id_episode=id_explog,
extra_dep=[ep])
ep0 = c.get_resource(RM_EPISODE_READY,
id_episode=id_explog_map, id_robot=id_robot)
poses0 = c.comp_config(poses_from_episode, data_central=data_central,
id_robot=id_robot, id_episode=id_explog_map,
extra_dep=[ep0])
r = c.comp(report_trajectory, poses, poses0)
c.add_report(r, 'trajectory', **report_keys)
def define_jobs_context(self, context):
config = self.get_rs2b_config()
explogs = list(config.explogs.keys())
for c, id_explog in iterate_context_explogs(context, explogs):
explog = config.explogs.instance(id_explog)
if not isinstance(explog, ExpLogFromYaml):
continue # XX
if explog.get_outside_movie() is None:
continue
c.subtask(MakeVideoSFX, id_explog=id_explog, add_job_prefix='', add_outdir='')
def define_jobs_context(self, context):
from pkg_resources import resource_filename # @UnresolvedImport
config_dir = resource_filename("yc1304.uzhturtle", "config")
GlobalConfig.global_load_dir(config_dir)
GlobalConfig.global_load_dir("${DATASET_UZHTURTLE}")
rm = context.get_report_manager()
rm.set_html_resources_prefix("uzh-turtle-plots")
data_central = self.get_data_central()
id_robot = "uzhturtle_un1_cf1_third"
recipe_episodeready_by_convert2(context, boot_root=self.get_boot_root())
logs = list(self.get_explogs_by_tag("uzhturtle"))
for c, id_explog in iterate_context_explogs(context, logs):
jobs_turtleplot(c, data_central, id_robot, id_episode=id_explog)
def define_jobs_context(self, context):
id_agent = 'exp10_bdser1'
id_robot = 'exp12_uA_b1_tw_cf'
id_adapter = 'uA_b1_tw_cf'
explogs_learn = good_logs_cf
explogs_test = ['unicornA_tran1_2013-04-12-23-34-08']
explogs_convert = explogs_learn + explogs_test
agent = None
for c, id_explog in iterate_context_explogs(context, explogs_convert):
episodes = c.subtask(RS2BConvertOne,
boot_root=self.get_boot_root(),
id_explog=id_explog,
id_adapter=id_adapter,
id_robot=id_robot)
if id_explog in explogs_learn:
agent_i = c.subtask(LearnLogNoSave, agent=id_agent, robot=id_robot,
episodes=episodes)
if agent is None:
agent = agent_i
else:
agent = context.comp(merge_agents, agent, agent_i)
id_episodes = explogs_learn
data_central = self.get_data_central()
context.comp(save_state, data_central, id_agent, id_robot, agent, id_episodes)
context.checkpoint('learning')
context.subtask(PublishLearningResult, agent=id_agent, robot=id_robot)
test_episodes = [
'unicornA_tran1_2013-04-12-23-34-08'
]
for c, id_episode in iterate_context_episodes(context, test_episodes):
c.subtask(ServoField, id_robot=id_robot, id_agent=id_agent,
variation='default', id_episode=id_episode)
def define_jobs_context(self, context):
logs = (set(self.get_explogs_by_tag('servo')) &
set(self.get_explogs_by_tag('fieldsampler')))
assert len(logs) >= 4
for c, id_explog in iterate_context_explogs(context, logs):
explog = get_conftools_explogs().instance(id_explog)
# jobs_video_servo_multi(c, id_explog)
annotation_servo = explog.get_annotations()['servo']
id_robot = annotation_servo['robot']
goal_at = annotation_servo['goal']
out_base = os.path.join(c.get_output_dir(), '%s' % id_explog)
c.comp_config(reconstruct_servo_state_nomap,
id_explog, id_robot, out_base, goal_at=goal_at)
def define_jobs_context(self, cc):
id_agent = 'exp13_bdser1'
id_robot = 'exp13_uA_b1_tw_cf'
id_adapter = 'uA_b1_tw_cf_strip'
explogs_learn = good_logs_cf
explogs_test = ['unicornA_tran1_2013-04-12-23-34-08']
explogs_convert = explogs_learn + explogs_test
for c, id_explog in iterate_context_explogs(cc, explogs_convert):
c.subtask(RS2BConvertOne,
boot_root=self.get_boot_root(),
id_explog=id_explog,
id_adapter=id_adapter,
id_robot=id_robot)
cc.checkpoint('conversion')
cc.subtask(LearnLog, agent=id_agent, robot=id_robot, interval_publish=5000)
cc.checkpoint('learning')
cc.subtask(PublishLearningResult, agent=id_agent, robot=id_robot)
test_episodes = explogs_test
for c, id_episode in iterate_context_episodes(cc, test_episodes):
c.subtask(ServoField, id_robot=id_robot, id_agent=id_agent,
variation='default', id_episode=id_episode)
data_central = self.get_data_central()
cc.add_report(cc.comp(report_prediction,
data_central=data_central,
id_agent=id_agent,
id_robot=id_robot),
'prediction', id_agent=id_agent, id_robot=id_robot)
cc.add_report(cc.comp(report_prediction2,
data_central=data_central,
id_agent=id_agent,
id_robot=id_robot),
'prediction2', id_agent=id_agent, id_robot=id_robot)
