def define_jobs_context(self, context):
boot_root = self.get_boot_root()
data_central = self.get_data_central()
GlobalConfig.global_load_dir('default')
recipe_agentlearn_by_parallel_concurrent(context, data_central,
Exp23.explogs_learn, n=8, only_agents=['exp23_diffeof', 'exp23_diffeo_fast'])
recipe_agentlearn_by_parallel(context, data_central,
Exp23.explogs_learn,
only_agents=['stats2'])
from diffeo2dds_learn.programs.devel.save_video import video_visualize_diffeo_stream1_robot
for c, id_robot in iterate_context_names(context, Exp23.robots):
out = os.path.join(context.get_output_dir(),
'videos', '%s-diffeo_stream1.mp4' % id_robot)
c.comp_config(video_visualize_diffeo_stream1_robot,
id_robot=id_robot, boot_root=boot_root,
out=out)
for id_robot in Exp23.robots:
recipe_episodeready_by_convert2(context, boot_root, id_robot)
jobs_publish_learning_agents_robots(context, boot_root,
Exp23.agents, Exp23.robots)
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 jobs_learn_real(context, data_central, real_robots, explogs_learn):
for id_robot in real_robots:
recipe_agentlearn_by_parallel(context, data_central,
only_robots=[id_robot],
episodes=explogs_learn)
boot_root = data_central.get_boot_root()
for id_robot in real_robots:
recipe_episodeready_by_convert2(context, boot_root, id_robot)
def define_jobs_context(self, context):
boot_config = get_boot_config()
boot_config.agents.instance(Exp20.agents[0])
boot_root = self.get_boot_root()
data_central = self.get_data_central()
recipe_episodeready_by_convert2(context, boot_root, id_robot=Exp20.id_robot)
recipe_agentlearn_by_parallel(context, data_central, Exp20.explogs_learn)
jobs_publish_learning(context, Exp20.agents, id_robot=Exp20.id_robot)
def define_jobs_context(self, context):
boot_root = self.get_boot_root()
data_central = self.get_data_central()
recipe_agentlearn_by_parallel(context, data_central, Exp21.explogs_learn)
for id_robot in Exp21.robots:
recipe_episodeready_by_convert2(context, boot_root, id_robot)
jobs_publish_learning_agents_robots(context, boot_root,
Exp21.agents, Exp21.robots)
def define_jobs_context(self, context):
boot_root = self.get_boot_root()
data_central = self.get_data_central()
GlobalConfig.global_load_dir('default')
recipe_agentlearn_by_parallel_concurrent_reps(context, data_central,
Exp32.explogs_learn, n=8, max_reps=10)
for id_robot in Exp32.robots:
recipe_episodeready_by_convert2(context, boot_root, id_robot)
jobs_publish_learning_agents_robots(context, boot_root,
Exp32.agents, Exp32.robots)
def define_jobs_context(self, context):
boot_root = self.get_boot_root()
data_central = self.get_data_central()
GlobalConfig.global_load_dir("default")
recipe_agentlearn_by_parallel_concurrent_reps(
context, data_central, Exp27.explogs_learn, n=8, max_reps=20, only_agents=["exp23_diffeof"]
)
recipe_agentlearn_by_parallel(context, data_central, Exp27.explogs_learn, only_agents=["stats2", "cmdstats"])
for id_robot in Exp27.robots:
recipe_episodeready_by_convert2(context, boot_root, id_robot)
jobs_publish_learning_agents_robots(context, boot_root, Exp27.agents, Exp27.robots)
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):
boot_root = self.get_boot_root()
data_central = self.get_data_central()
GlobalConfig.global_load_dir('default')
recipe_agentlearn_by_parallel(context, data_central, Exp29.explogs_learn)
for id_robot in Exp29.robots:
recipe_episodeready_by_convert2(context, boot_root, id_robot)
combinations = iterate_context_names_pair(context, Exp29.nmaps, Exp29.robots)
for c, id_episode, id_robot in combinations:
jobs_navigation_map(c,
outdir=context.get_output_dir(),
data_central=data_central,
id_robot=id_robot,
id_episode=id_episode)
def define_jobs_context(self, context):
boot_root = self.get_boot_root()
data_central = self.get_data_central()
agents = Exp31.agents
robots = Exp31.robots
explogs_learn = Exp31.explogs_learn
explogs_test = Exp31.explogs_test
recipe_agentlearn_by_parallel(context, data_central, explogs_learn)
for id_robot in robots:
recipe_episodeready_by_convert2(context, boot_root, id_robot)
jobs_servo_field_agents(context, id_robot=id_robot,
agents=agents, episodes=explogs_test)
jobs_publish_learning_agents_robots(context, boot_root, agents, robots)
def define_jobs_context(self, context):
boot_root = self.get_boot_root()
data_central = self.get_data_central()
GlobalConfig.global_load_dir('default')
recipe_agentlearn_by_parallel_concurrent(context, data_central, \
Exp24.explogs_learn, n=8, only_agents=['exp23_diffeof', 'exp23_diffeo_fast'])
recipe_agentlearn_by_parallel(context, data_central,
Exp24.explogs_learn,
only_agents=['stats2'])
for id_robot in Exp24.robots:
recipe_episodeready_by_convert2(context, boot_root, id_robot)
jobs_publish_learning_agents_robots(context, boot_root,
Exp24.agents, Exp24.robots)
def define_jobs_context(self, context):
boot_root = self.get_boot_root()
data_central = self.get_data_central()
# for vehicles
GlobalConfig.global_load_dir('${B11_SRC}/bvapps/bdse1')
recipe_episodeready_by_convert2(context, boot_root)
recipe_episodeready_by_simulation_tranches(context, data_central,
explorer=Exp42.explorer,
episodes=Exp42.simulated_episodes,
max_episode_len=30,
episodes_per_tranche=50)
for c in Exp42.combinations:
recipe_agentlearn_by_parallel(context, data_central, c['episodes'],
only_robots=[c['id_robot']],
intermediate_reports=False,
episodes_per_tranche=50)
jobs_publish_learning_agents_robots(context, boot_root,
Exp42.agents, Exp42.robots)
