def get_diagnostics(self, paths, prefix=''):
statistics = OrderedDict()
# for stat_name in [
# 'touch_distance',
# 'hand_success',
# 'obj_success',
# 'hand_and_obj_success',
# 'touch_success',
# 'hand_distance',
# 'obj_distance',
# 'hand_and_obj_distance',
# 'total_pickups',
# ]: # these are copied from saywer_pickup_and_place.py
for stat_name in [
'ag_state',
'g_state',
'image_dist',
'image_success',
]:
stat_name = stat_name
stat = get_stat_in_paths(paths, 'env_infos', stat_name)
statistics.update(
create_stats_ordered_dict(
'%s%s' % (prefix, stat_name),
stat,
always_show_all_stats=True,
))
statistics.update(
create_stats_ordered_dict(
'Final %s%s' % (prefix, stat_name),
[s[-1] for s in stat],
always_show_all_stats=True,
))
return statistics
def goal_conditioned_diagnostics(self, paths, goals):
statistics = OrderedDict()
stat_to_lists = defaultdict(list)
for path, goal in zip(paths, goals):
difference = path['observations'] - goal
for i in range(len(self._all_objects)):
distance = np.linalg.norm(difference[:, 2 * i:2 * i + 2],
axis=-1)
distance_key = 'distance_to_target_obj_{}'.format(i)
stat_to_lists[distance_key].append(distance)
success_key = 'success_obj_{}'.format(i)
stat_to_lists[success_key].append(
distance < self.success_threshold)
for stat_name, stat_list in stat_to_lists.items():
statistics.update(
create_stats_ordered_dict(
stat_name,
stat_list,
always_show_all_stats=True,
))
statistics.update(
create_stats_ordered_dict(
'{}/final'.format(stat_name),
[s[-1:] for s in stat_list],
always_show_all_stats=True,
exclude_max_min=True,
))
statistics.update(
create_stats_ordered_dict(
'{}/initial'.format(stat_name),
[s[:1] for s in stat_list],
always_show_all_stats=True,
exclude_max_min=True,
))
return statistics
def get_diagnostics(self, paths, prefix=''):
statistics = OrderedDict()
for stat_name in [
'touch_distance',
'hand_success',
'obj_success',
'hand_and_obj_success',
'touch_success',
'hand_distance',
'obj_distance',
'hand_and_obj_distance',
'total_pickups',
]:
stat_name = stat_name
stat = get_stat_in_paths(paths, 'env_infos', stat_name)
statistics.update(create_stats_ordered_dict(
'%s%s' % (prefix, stat_name),
stat,
always_show_all_stats=True,
))
statistics.update(create_stats_ordered_dict(
'Final %s%s' % (prefix, stat_name),
[s[-1] for s in stat],
always_show_all_stats=True,
))
return statistics
def get_diagnostics(self, paths, prefix=''):
statistics = OrderedDict()
for stat_name in [
'radius',
'target_position',
'distance_to_target',
'velocity',
'speed',
'is_success',
]:
stat_name = stat_name
stat = get_stat_in_paths(paths, 'env_infos', stat_name)
statistics.update(
create_stats_ordered_dict(
'%s%s' % (prefix, stat_name),
stat,
always_show_all_stats=True,
))
statistics.update(
create_stats_ordered_dict(
'Final %s%s' % (prefix, stat_name),
[s[-1] for s in stat],
always_show_all_stats=True,
))
return statistics
def get_diagnostics(self, paths, prefix=''):
statistics = OrderedDict()
list_of_stat_names = [
'state_diff',
'pos_diff',
'angle_diff',
'pos_angle_diff',
'pos_success',
'angle_success',
]
if self.vel_in_state:
list_of_stat_names.append('velocity_diff')
list_of_stat_names.append('angular_velocity_diff')
for stat_name in list_of_stat_names:
stat_name = stat_name
stat = get_stat_in_paths(paths, 'env_infos', stat_name)
statistics.update(
create_stats_ordered_dict(
'%s%s' % (prefix, stat_name),
stat,
always_show_all_stats=True,
))
statistics.update(
create_stats_ordered_dict(
'Final %s%s' % (prefix, stat_name),
[s[-1] for s in stat],
always_show_all_stats=True,
))
return statistics
def log_diagnostics(self, paths, logger=None, prefix=""):
if logger is None:
return
statistics = OrderedDict()
for stat_name in [
'hand_distance',
'puck_distance',
'touch_distance',
'success',
]:
stat_name = stat_name
stat = get_stat_in_paths(paths, 'env_infos', stat_name)
statistics.update(
create_stats_ordered_dict(
'%s %s' % (prefix, stat_name),
stat,
always_show_all_stats=True,
))
statistics.update(
create_stats_ordered_dict(
'Final %s %s' % (prefix, stat_name),
[s[-1] for s in stat],
always_show_all_stats=True,
))
for key, value in statistics.items():
logger.record_tabular(key, value)
def get_diagnostics(self, paths, prefix=''):
statistics = OrderedDict()
stat_names = [
'hand_success',
'obj_success',
'hand_and_obj_success',
'hand_distance',
'obj_distance',
'obj_distance0',
'hand_and_obj_distance',
'total_pickups',
'obj_x0', 'obj_y0', 'obj_z0',
'mujoco_exception_raised', 'flying_object',
]
if self.two_obj:
stat_names.append('obj_distance1')
stat_names.append('obj_x1')
stat_names.append('obj_y1')
stat_names.append('obj_z1')
for stat_name in stat_names:
stat_name = stat_name
stat = get_stat_in_paths(paths, 'env_infos', stat_name)
statistics.update(create_stats_ordered_dict(
'%s%s' % (prefix, stat_name),
stat,
always_show_all_stats=True,
))
statistics.update(create_stats_ordered_dict(
'Final %s%s' % (prefix, stat_name),
[s[-1] for s in stat],
always_show_all_stats=True,
))
return statistics
def goal_conditioned_diagnostics(self, paths, goals):
statistics = OrderedDict()
distance_to_target_list = []
is_success_list = []
for path, goal in zip(paths, goals):
distance_to_target = np.linalg.norm(path['observations'] - goal,
axis=1)
is_success = distance_to_target < self.target_radius
distance_to_target_list.append(distance_to_target)
is_success_list.append(is_success)
for stat_name, stat_list in [
('distance_to_target', distance_to_target_list),
('is_success', is_success_list),
]:
statistics.update(
create_stats_ordered_dict(
stat_name,
stat_list,
always_show_all_stats=True,
))
statistics.update(
create_stats_ordered_dict(
'{}/final'.format(stat_name),
[s[-1:] for s in stat_list],
always_show_all_stats=True,
exclude_max_min=True,
))
statistics.update(
create_stats_ordered_dict(
'{}/initial'.format(stat_name),
[s[:1] for s in stat_list],
always_show_all_stats=True,
exclude_max_min=True,
))
return statistics
def get_contextual_diagnostics(self, paths, contexts):
diagnostics = OrderedDict()
state_key = "state_observation"
goal_key = "state_desired_goal"
values = []
for i in range(len(paths)):
state = paths[i]["observations"][-1][state_key]
goal = contexts[i][goal_key]
distance = np.linalg.norm(state - goal)
values.append(distance)
diagnostics_key = goal_key + "/final/distance"
diagnostics.update(create_stats_ordered_dict(
diagnostics_key,
values,
))
values = []
for i in range(len(paths)):
for j in range(len(paths[i]["observations"])):
state = paths[i]["observations"][j][state_key]
goal = contexts[i][goal_key]
distance = np.linalg.norm(state - goal)
values.append(distance)
diagnostics_key = goal_key + "/distance"
diagnostics.update(create_stats_ordered_dict(
diagnostics_key,
values,
))
return diagnostics
def get_diagnostics(self, paths, **kwargs):
statistics = self.wrapped_env.get_diagnostics(paths, **kwargs)
for stat_name_in_paths in ["image_dist", "image_success"]:
stats = get_stat_in_paths(paths, 'env_infos', stat_name_in_paths)
statistics.update(create_stats_ordered_dict(
stat_name_in_paths,
stats,
always_show_all_stats=True,
))
final_stats = [s[-1] for s in stats]
statistics.update(create_stats_ordered_dict(
"Final " + stat_name_in_paths,
final_stats,
always_show_all_stats=True,
))
return statistics
def get_diagnostics(self, trajectories, desired_goal_states):
"""
Logs things
Args:
trajectories: Numpy Array [# Trajectories x Max Path Length x State Dim]
desired_goal_states: Numpy Array [# Trajectories x State Dim]
"""
endeff_distances = np.array([self.endeff_distance(trajectories[i], np.tile(desired_goal_states[i], (trajectories.shape[1],1))) for i in range(trajectories.shape[0])])
puck_distances = np.array([self.puck_distance(trajectories[i], np.tile(desired_goal_states[i], (trajectories.shape[1],1))) for i in range(trajectories.shape[0])])
endeff_movement = self.endeff_distance(trajectories[:,0], trajectories[:, -1])
puck_movement = self.puck_distance(trajectories[:,0], trajectories[:, -1])
statistics = OrderedDict()
for stat_name, stat in [
('final puck distance', puck_distances[:,-1]),
('final endeff distance', endeff_distances[:,-1]),
('puck movement', puck_movement),
('endeff movement', endeff_movement),
]:
statistics.update(create_stats_ordered_dict(
stat_name,
stat,
always_show_all_stats=True,
))
return statistics
def get_diagnostics(self, paths, prefix=''):
statistics = OrderedDict()
stat_names = [
'opt_func_loss',
'opt_grad_norm',
'opt_v_value',
'opt_v_value_min',
'opt_v_value_sum',
'opt_realistic_subgoal_rew',
'init_opt_func_loss',
'init_opt_grad_norm',
'init_opt_v_value',
'init_opt_v_value_min',
'init_opt_v_value_sum',
'init_opt_realistic_subgoal_rew',
'opt_func_calls',
'opt_num_iters',
'opt_stopped',
]
for stat_name in stat_names:
stat_name = stat_name
stat = get_stat_in_paths(paths, 'agent_infos', stat_name)
statistics.update(create_stats_ordered_dict(
'%s%s' % (prefix, stat_name),
stat,
always_show_all_stats=True,
))
return statistics
def get_diagnostics(self, trajectories, desired_goal_states):
"""
Logs things
Args:
trajectories: Numpy Array [# Trajectories x Max Path Length x State Dim]
desired_goal_states: Numpy Array [# Trajectories x State Dim]
"""
puck_distances = np.array([
self.door_distance(
trajectories[i],
np.tile(desired_goal_states[i], (trajectories.shape[1], 1)))
for i in range(trajectories.shape[0])
])
statistics = OrderedDict()
for stat_name, stat in [
('final door distance', puck_distances[:, -1]),
('min door distance', np.min(puck_distances, axis=-1)),
]:
statistics.update(
create_stats_ordered_dict(
stat_name,
stat,
always_show_all_stats=True,
))
return statistics
def get_diagnostics(self, paths, prefix=''):
statistics = OrderedDict()
for stat_name in ['angle_difference', 'angle_success']:
stat_name = stat_name
stat = get_stat_in_paths(paths, 'env_infos', stat_name)
statistics.update(
create_stats_ordered_dict(
'%s%s' % (prefix, stat_name),
stat,
always_show_all_stats=True,
))
statistics.update(
create_stats_ordered_dict(
'Final %s%s' % (prefix, stat_name),
[s[-1] for s in stat],
always_show_all_stats=True,
))
return statistics
def get_diagnostics(self, paths, prefix=''):
statistics = OrderedDict()
for stat_name in [
obj_name + '_distance' for obj_name in self._object_names
]:
stat_name = stat_name
stat = get_stat_in_paths(paths, 'env_infos', stat_name)
statistics.update(
create_stats_ordered_dict(
'%s%s' % (prefix, stat_name),
stat,
always_show_all_stats=True,
))
statistics.update(
create_stats_ordered_dict(
'Final %s%s' % (prefix, stat_name),
[s[-1] for s in stat],
always_show_all_stats=True,
))
return statistics
def get_contextual_diagnostics(self, paths, contexts):
diagnostics = OrderedDict()
state_key = "state_observation"
goal_key = "state_desired_goal"
for idx, name in [
(slice(0, 2), "hand_goal"),
(slice(2, 4), "puck_goal"),
]:
values = []
for i in range(len(paths)):
state = paths[i]["observations"][-1][state_key][idx]
goal = contexts[i][goal_key][idx]
distance = np.linalg.norm(state - goal)
values.append(distance)
diagnostics_key = name + "/final/distance"
diagnostics.update(
create_stats_ordered_dict(
diagnostics_key,
values,
))
values = []
for i in range(len(paths)):
for j in range(len(paths[i]["observations"])):
state = paths[i]["observations"][j][state_key][idx]
goal = contexts[i][goal_key][idx]
distance = np.linalg.norm(state - goal)
values.append(distance)
diagnostics_key = name + "/distance"
diagnostics.update(
create_stats_ordered_dict(
diagnostics_key,
values,
))
return diagnostics
def get_diagnostics(self, paths, prefix=""):
statistics = collections.OrderedDict()
stats = ['hand_distance']
for i in range(self.puck_count):
stats += [
'puck{}_distance'.format(i),
'touch{}_distance'.format(i),
]
for stat_name in stats:
stat_name = stat_name
stat = get_stat_in_paths(paths, 'env_infos', stat_name)
statistics.update(
create_stats_ordered_dict(
'%s%s' % (prefix, stat_name),
stat,
always_show_all_stats=True,
))
statistics.update(
create_stats_ordered_dict(
'Final %s%s' % (prefix, stat_name),
[s[-1] for s in stat],
always_show_all_stats=True,
))
return statistics
def get_diagnostics(self, paths, prefix=''):
statistics = OrderedDict()
for stat_name in [
'distance_to_target_obj_{}'.format(i)
for i in range(len(self.objects))
] + ['success_obj_{}'.format(i) for i in range(len(self.objects))] + [
'distance_to_target_cursor',
'success_cursor',
]:
stat_name = stat_name
stat = get_stat_in_paths(paths, 'env_infos', stat_name)
statistics.update(
create_stats_ordered_dict(
'%s%s' % (prefix, stat_name),
stat,
always_show_all_stats=True,
))
statistics.update(
create_stats_ordered_dict(
'Final %s%s' % (prefix, stat_name),
[s[-1] for s in stat],
always_show_all_stats=True,
))
return statistics
