def showgraspfrompickle(self, object_name,metric_name,onlynagative,onlypositive,openravechecker):
object = self.dataset[object_name]
labelgrasps,labelmetrics=self.readdatafrompickle(object_name)
vis.figure()
vis.mesh(object.mesh.trimesh ,style='surface')
config=self._get_config(None)
low =0.0
high = np.max([labelmetrics[i][metric_name] for i in range(len(labelmetrics))])
# print 'high',high,config['quality_scale']
if low == high:
q_to_c = lambda quality: config['quality_scale']
else:
q_to_c = lambda quality: config['quality_scale'] * (quality - low) / (high - low)
recalculate_grasp=[]
for i in range(len(labelgrasps)):
if labelmetrics[i][metric_name]==0 :
if not onlypositive:
vis.graspwithapproachvectorusingcenter_point(labelgrasps[i] ,approaching_color=(1,0,0), grasp_axis_color=(1,0,0) )
elif labelmetrics[i][metric_name]==-1 :
if not onlypositive:
vis.shownormals(labelgrasps[i][1],labelgrasps[i][0],color=(1,0,0) )
recalculate_grasp.append(labelgrasps[i])
elif labelmetrics[i][metric_name]>0 :
if not onlynagative:
color = plt.get_cmap('hsv')(q_to_c(labelmetrics[i][metric_name]))[:-1]
vis.graspwithapproachvectorusingcenter_point(labelgrasps[i] ,approaching_color=color, grasp_axis_color=color )
recalculate_grasp.append(labelgrasps[i])
print i # ,high
vis.pose(RigidTransform(), alpha=0.1)
vis.show(animate=False)
def display_stable_poses(self, object_name, config=None):
"""Display an object's stable poses
Parameters
----------
object_name : :obj:`str`
Object to display.
config : :obj:`dict`
Configuration dict for visualization.
Parameters are in Other parameters. Values from self.default_config are used for keys not provided.
Other Parameters
----------------
animate
Whether or not to animate the displayed object
Raises
------
ValueError
invalid object name
RuntimeError
Database or dataset not opened.
"""
self._check_opens()
config = self._get_config(config)
if object_name not in self.dataset.object_keys:
raise ValueError(
"{} is not a valid object name".format(object_name))
logger.info('Displaying stable poses for'.format(object_name))
obj = self.dataset[object_name]
stable_poses = self.dataset.stable_poses(object_name)
for stable_pose in stable_poses:
print 'Stable pose %s with p=%.3f' % (stable_pose.id,
stable_pose.p)
vis.figure()
vis.mesh_stable_pose(obj.mesh,
stable_pose,
color=(0.5, 0.5, 0.5),
style='surface')
vis.pose(RigidTransform(), alpha=0.15)
vis.show(animate=config['animate'])
def benchmark_bin_picking_policy(policy,
# input_dataset_path,
# heap_ids,
# timesteps,
# output_dataset_path,
config,
# excluded_heaps_file):
):
""" Benchmark a bin picking policy.
Parameters
----------
policy : :obj:`Policy`
policy to roll out
input_dataset_path : str
path to the input dataset
heap_ids : list
integer identifiers for the heaps to re-run
timesteps : list
integer timesteps to seed the simulation from
output_dataset_path : str
path to store the results
config : dict
dictionary-like objects containing parameters of the simulator and visualization
"""
# read subconfigs
vis_config = config['vis']
dataset_config = config['dataset']
# read parameters
fully_observed = config['fully_observed']
steps_per_test_case = config['steps_per_test_case']
rollouts_per_garbage_collect = config['rollouts_per_garbage_collect']
debug = config['debug']
im_height = config['state_space']['camera']['im_height']
im_width = config['state_space']['camera']['im_width']
max_obj_per_pile = config['state_space']['object']['max_obj_per_pile']
if debug:
random.seed(SEED)
np.random.seed(SEED)
# read ids
# if len(heap_ids) != len(timesteps):
# raise ValueError('Must provide same number of heap ids and timesteps')
# num_rollouts = len(heap_ids)
num_rollouts = 1
# set dataset params
tensor_config = dataset_config['tensors']
fields_config = tensor_config['fields']
# fields_config['color_ims']['height'] = im_height
# fields_config['color_ims']['width'] = im_width
# fields_config['depth_ims']['height'] = im_height
# fields_config['depth_ims']['width'] = im_width
fields_config['obj_poses']['height'] = POSE_DIM * max_obj_per_pile
fields_config['obj_coms']['height'] = POINT_DIM * max_obj_per_pile
fields_config['obj_ids']['height'] = max_obj_per_pile
fields_config['bin_distances']['height'] = max_obj_per_pile
# matrix has (n choose 2) elements in it
max_distance_matrix_length = int(comb(max_obj_per_pile, 2))
fields_config['distance_matrix']['height'] = max_distance_matrix_length
# sample a process id
proc_id = utils.gen_experiment_id()
# if not os.path.exists(output_dataset_path):
# try:
# os.mkdir(output_dataset_path)
# except:
# logging.warning('Failed to create %s. The dataset path may have been created simultaneously by another process' %(dataset_path))
# proc_id = 'clustering_2'
# output_dataset_path = os.path.join(output_dataset_path, 'dataset_%s' %(proc_id))
# open input dataset
# logging.info('Opening input dataset: %s' % input_dataset_path)
# input_dataset = TensorDataset.open(input_dataset_path)
# open output_dataset
# logging.info('Opening output dataset: %s' % output_dataset_path)
# dataset = TensorDataset(output_dataset_path, tensor_config)
# datapoint = dataset.datapoint_template
# setup logging
# experiment_log_filename = os.path.join(output_dataset_path, 'dataset_generation.log')
# formatter = logging.Formatter('%(asctime)s %(levelname)s: %(message)s')
# hdlr = logging.FileHandler(experiment_log_filename)
# hdlr.setFormatter(formatter)
# logging.getLogger().addHandler(hdlr)
# config.save(os.path.join(output_dataset_path, 'dataset_generation_params.yaml'))
# key mappings
# we add the empty string as a mapping because if you don't evaluate dexnet on the 'before' state of the push
obj_id = 1
obj_ids = {'': 0}
action_ids = {
'ParallelJawGraspAction': 0,
'SuctionGraspAction': 1,
'LinearPushAction': 2
}
# add action ids
reverse_action_ids = utils.reverse_dictionary(action_ids)
# dataset.add_metadata('action_ids', reverse_action_ids)
# perform rollouts
n = 0
rollout_start = time.time()
current_heap_id = None
while n < num_rollouts:
# create env
create_start = time.time()
bin_picking_env = GraspingEnv(config, vis_config)
create_stop = time.time()
logging.info('Creating env took %.3f sec' %(create_stop-create_start))
# perform rollouts
rollouts_remaining = num_rollouts - n
for i in range(min(rollouts_per_garbage_collect, rollouts_remaining)):
# log current rollout
logging.info('\n')
if n % vis_config['log_rate'] == 0:
logging.info('Rollout: %03d' %(n))
try:
# mark rollout status
data_saved = False
num_steps = 0
# read heap id
# heap_id = heap_ids[n]
# timestep = timesteps[n]
# while heap_id == current_heap_id:# or heap_id < 81:#[226, 287, 325, 453, 469, 577, 601, 894, 921]: 26
# n += 1
# heap_id = heap_ids[n]
# timestep = timesteps[n]
push_logger = logging.getLogger('push')
# push_logger.info('~')
# push_logger.info('Heap ID %d' % heap_id)
# current_heap_id = heap_id
# reset env
reset_start = time.time()
# bin_picking_env.reset_from_dataset(input_dataset,
# heap_id,
# timestep)
bin_picking_env.reset()
state = bin_picking_env.state
environment = bin_picking_env.environment
if fully_observed:
observation = None
else:
observation = bin_picking_env.observation
policy.set_environment(environment)
reset_stop = time.time()
# add objects to mapping
for obj_key in state.obj_keys:
if obj_key not in obj_ids.keys():
obj_ids[obj_key] = obj_id
obj_id += 1
push_logger.info(obj_key)
# save id mappings
reverse_obj_ids = utils.reverse_dictionary(obj_ids)
# dataset.add_metadata('obj_ids', reverse_obj_ids)
# store datapoint env params
# datapoint['heap_ids'] = current_heap_id
# datapoint['camera_poses'] = environment.camera.T_camera_world.vec
# datapoint['camera_intrs'] = environment.camera.intrinsics.vec
# datapoint['robot_poses'] = environment.robot.T_robot_world.vec
# render
if vis_config['initial_state']:
vis3d.figure()
bin_picking_env.render_3d_scene()
vis3d.pose(environment.robot.T_robot_world)
vis3d.show(starting_camera_pose=CAMERA_POSE)
# observe
if vis_config['initial_obs']:
vis2d.figure()
vis2d.imshow(observation, auto_subplot=True)
vis2d.show()
# rollout on current satte
done = False
failed = False
# if isinstance(policy, SingulationFullRolloutPolicy):
# policy.reset_num_failed_grasps()
while not done:
if vis_config['step_stats']:
logging.info('Heap ID: %s' % heap_id)
logging.info('Timestep: %s' % bin_picking_env.timestep)
# get action
policy_start = time.time()
if fully_observed:
action = policy.action(state)
else:
action = policy.action(observation)
policy_stop = time.time()
logging.info('Composite Policy took %.3f sec' %(policy_stop-policy_start))
# render scene before
if vis_config['action']:
#gripper = bin_picking_env.gripper(action)
vis3d.figure()
# GRASPINGENV
# bin_picking_env.render_3d_scene(render_camera=False, workspace_objs_wireframe=False)
bin_picking_env.render_3d_scene()
if isinstance(action, GraspAction):
vis3d.gripper(gripper, action.grasp(gripper))
#if isinstance(action, LinearPushAction):
else:
# # T_start_world = action.T_begin_world * gripper.T_mesh_grasp
# # T_end_world = action.T_end_world * gripper.T_mesh_grasp
# #start_point = action.T_begin_world.translation
# start_point = action['start']
# #end_point = action.T_end_world.translation
# end_point = action['end']
# vec = (end_point - start_point) / np.linalg.norm(end_point-start_point) if np.linalg.norm(end_point-start_point) > 0 else end_point-start_point
# #h1 = np.array([[0.7071,-0.7071,0],[0.7071,0.7071,0],[0,0,1]]).dot(vec)
# #h2 = np.array([[0.7071,0.7071,0],[-0.7071,0.7071,0],[0,0,1]]).dot(vec)
# arrow_len = np.linalg.norm(start_point - end_point)
# h1 = (end_point - start_point + np.array([0,0,arrow_len])) / (arrow_len*math.sqrt(2))
# h2 = (end_point - start_point - np.array([0,0,arrow_len])) / (arrow_len*math.sqrt(2))
# shaft_points = [start_point, end_point]
# head_points = [end_point - 0.03*h2, end_point, end_point - 0.03*h1]
# #vis3d.plot3d(shaft_points, color=[0,0,1])
# #vis3d.plot3d(head_points, color=[0,0,1])
# Displaying all potential topple points
for vertex, prob in zip(action['vertices'], action['probabilities']):
color = np.array([min(1, 2*(1-prob)), min(2*prob, 1), 0])
vis3d.points(Point(vertex, 'world'), scale=.0005, color=color)
for vertex in action['bottom_points']:
color = np.array([0,0,1])
vis3d.points(Point(vertex, 'world'), scale=.0005, color=color)
vis3d.points(Point(action['com'], 'world'), scale=.005, color=np.array([0,0,1]))
vis3d.points(Point(np.array([0,0,0]), 'world'), scale=.005, color=np.array([0,1,0]))
#set_of_lines = action['set_of_lines']
#for i, line in enumerate(set_of_lines):
# color = str(bin(i+1))[2:].zfill(3)
# color = np.array([color[2], color[1], color[0]])
# vis3d.plot3d(line, color=color)
vis3d.show(starting_camera_pose=CAMERA_POSE)
# Show
vis3d.figure()
bin_picking_env.render_3d_scene()
final_pose_ind = action['final_pose_ind'] / np.amax(action['final_pose_ind'])
for vertex, final_pose_ind in zip(action['vertices'], final_pose_ind):
color = np.array([0, min(1, 2*(1-prob)), min(2*prob, 1)])
vis3d.points(Point(vertex, 'world'), scale=.0005, color=color)
vis3d.show(starting_camera_pose=CAMERA_POSE)
color=np.array([0,0,1])
original_pose = state.obj.T_obj_world
pose_num = 0
for pose, edge_point1, edge_point2 in zip(action['final_poses'], action['bottom_points'], np.roll(action['bottom_points'],-1,axis=0)):
print 'Pose:', pose_num
pose_num += 1
pose = pose.T_obj_table
vis3d.figure()
state.obj.T_obj_world = original_pose
bin_picking_env.render_3d_scene()
vis3d.points(Point(edge_point1, 'world'), scale=.0005, color=color)
vis3d.points(Point(edge_point2, 'world'), scale=.0005, color=color)
vis3d.show(starting_camera_pose=CAMERA_POSE)
vis3d.figure()
state.obj.T_obj_world = pose
bin_picking_env.render_3d_scene()
vis3d.points(Point(edge_point1, 'world'), scale=.0005, color=color)
vis3d.points(Point(edge_point2, 'world'), scale=.0005, color=color)
vis3d.show(starting_camera_pose=CAMERA_POSE)
#vis3d.save('/home/mjd3/Pictures/weird_pics/%d_%d_before.png' % (heap_id, bin_picking_env.timestep), starting_camera_pose=CAMERA_POSE)
# store datapoint pre-step data
j = 0
obj_poses = np.zeros(fields_config['obj_poses']['height'])
obj_coms = np.zeros(fields_config['obj_coms']['height'])
obj_ids_vec = np.iinfo(np.uint32).max * np.ones(fields_config['obj_ids']['height'])
for obj_state in state.obj_states:
obj_poses[j*POSE_DIM:(j+1)*POSE_DIM] = obj_state.T_obj_world.vec
obj_coms[j*POINT_DIM:(j+1)*POINT_DIM] = obj_state.center_of_mass
obj_ids_vec[j] = obj_ids[obj_state.key]
j += 1
action_poses = np.zeros(fields_config['action_poses']['height'])
#if isinstance(action, GraspAction):
# action_poses[:7] = action.T_grasp_world.vec
#else:
# action_poses[:7] = action.T_begin_world.vec
# action_poses[7:] = action.T_end_world.vec
# if isinstance(policy, SingulationMetricsCompositePolicy):
# actual_distance_matrix_length = int(comb(len(state.objs), 2))
# bin_distances = np.append(action.metadata['bin_distances'],
# np.zeros(max_obj_per_pile-len(state.objs))
# )
# distance_matrix = np.append(action.metadata['distance_matrix'],
# np.zeros(max_distance_matrix_length - actual_distance_matrix_length)
# )
# datapoint['bin_distances'] = bin_distances
# datapoint['distance_matrix'] = distance_matrix
# datapoint['T_begin_world'] = action.T_begin_world.matrix
# datapoint['T_end_world'] = action.T_end_world.matrix
# datapoint['parallel_jaw_best_q_value'] = action.metadata['parallel_jaw_best_q_value']
# # datapoint['parallel_jaw_mean_q_value'] = action.metadata['parallel_jaw_mean_q_value']
# # datapoint['parallel_jaw_num_grasps'] = action.metadata['parallel_jaw_num_grasps']
# datapoint['suction_best_q_value'] = action.metadata['suction_best_q_value']
# # datapoint['suction_mean_q_value'] = action.metadata['suction_mean_q_value']
# # datapoint['suction_num_grasps'] = action.metadata['suction_num_grasps']
# # logging.info('Suction Q: %f, PJ Q: %f' % (action.metadata['suction_q_value'], action.metadata['parallel_jaw_q_value']))
# # datapoint['obj_index'] = action.metadata['obj_index']
# # datapoint['parallel_jaw_best_q_value_single'] = action.metadata['parallel_jaw_best_q_value_single']
# # datapoint['suction_best_q_value_single'] = action.metadata['suction_best_q_value_single']
# datapoint['singulated_obj_index'] = action.metadata['singulated_obj_index']
# datapoint['parallel_jaw_grasped_obj_index'] = obj_ids[action.metadata['parallel_jaw_grasped_obj_key']]
# datapoint['suction_grasped_obj_index'] = obj_ids[action.metadata['suction_grasped_obj_key']]
# else:
# datapoint['bin_distances'] = np.zeros(max_obj_per_pile)
# datapoint['distance_matrix'] = np.zeros(max_distance_matrix_length)
# datapoint['T_begin_world'] = np.zeros((4,4))
# datapoint['T_end_world'] = np.zeros((4,4))
# datapoint['parallel_jaw_best_q_value'] = -1
# datapoint['suction_best_q_value'] = -1
# datapoint['singulated_obj_index'] = -1
# datapoint['parallel_jaw_grasped_obj_index'] = -1
# datapoint['suction_grasped_obj_index'] = -1
# policy_id = 0
# if 'policy_id' in action.metadata.keys():
# policy_id = action.metadata['policy_id']
# greedy_q_value = 0
# if 'greedy_q_value' in action.metadata.keys():
# greedy_q_value = action.metadata['greedy_q_value']
# datapoint['timesteps'] = bin_picking_env.timestep
# datapoint['obj_poses'] = obj_poses
# datapoint['obj_coms'] = obj_coms
# datapoint['obj_ids'] = obj_ids_vec
# # if bin_picking_env.render_mode == RenderMode.RGBD:
# # color_data = observation.color.raw_data
# # depth_data = observation.depth.raw_data
# # elif bin_picking_env.render_mode == RenderMode.DEPTH:
# # color_data = np.zeros(observation.shape).astype(np.uint8)
# # depth_data = observation.raw_data
# # elif bin_picking_env.render_mode == RenderMode.COLOR:
# # color_data = observation.raw_data
# # depth_data = np.zeros(observation.shape)
# # datapoint['color_ims'] = color_data
# # datapoint['depth_ims'] = depth_data
# datapoint['action_ids'] = action_ids[type(action).__name__]
# datapoint['action_poses'] = action_poses
# datapoint['policy_ids'] = policy_id
# datapoint['greedy_q_values'] = greedy_q_value
# datapoint['pred_q_values'] = action.q_value
# step the policy
#observation, reward, done, info = bin_picking_env.step(action)
#state = bin_picking_env.state
state.objs[0].T_obj_world = action['final_state']
# if isinstance(policy, SingulationFullRolloutPolicy):
# policy.grasp_succeeds(info['grasp_succeeds'])
# debugging info
if vis_config['step_stats']:
logging.info('Action type: %s' %(type(action).__name__))
logging.info('Action Q-value: %.3f' %(action.q_value))
logging.info('Reward: %d' %(reward))
logging.info('Policy took %.3f sec' %(policy_stop-policy_start))
logging.info('Num objects remaining: %d' %(bin_picking_env.num_objects))
if info['cleared_pile']:
logging.info('Cleared pile!')
# # store datapoint post-step data
# datapoint['rewards'] = reward
# datapoint['grasp_metrics'] = info['grasp_metric']
# datapoint['collisions'] = 1 * info['collides']
# datapoint['collisions_with_env'] = 1 * info['collides_with_static_obstacles']
# datapoint['grasped_obj_ids'] = obj_ids[info['grasped_obj_key']]
# datapoint['cleared_pile'] = 1 * info['cleared_pile']
# # store datapoint
# # dataset.add(datapoint)
# data_saved = True
# render observation
if vis_config['obs']:
vis2d.figure()
vis2d.imshow(observation, auto_subplot=True)
vis2d.show()
# render scene after
if vis_config['state']:
vis3d.figure()
bin_picking_env.render_3d_scene(render_camera=False)
vis3d.show(starting_camera_pose=CAMERA_POSE)
# vis3d.save('/home/mjd3/Pictures/weird_pics/%d_%d_after.png' % (heap_id, bin_picking_env.timestep), starting_camera_pose=CAMERA_POSE)
state.objs[0].T_obj_world = action['tmpR']
vis3d.figure()
bin_picking_env.render_3d_scene(render_camera=False)
vis3d.show(starting_camera_pose=CAMERA_POSE)
state.objs[0].T_obj_world = action['final_state']
# increment the number of steps
num_steps += 1
if num_steps >= steps_per_test_case:
done = True
except NoActionFoundException as e:
logging.warning('The policy failed to plan an action!')
done = True
except Exception as e:
# log an error
logging.warning('Rollout failed!')
logging.warning('%s' %(str(e)))
logging.warning(traceback.print_exc())
# if debug:
# raise
# reset env
del bin_picking_env
gc.collect()
bin_picking_env = BinPickingEnv(config, vis_config)
# terminate current rollout
failed = True
done = True
# update test case id
n += 1
# dataset.flush()
# logging.info("\n\nflushing")
# logging.info("exiting")
# sys.exit()
# garbage collect
del bin_picking_env
gc.collect()
# return the dataset
# dataset.flush()
# log time
rollout_stop = time.time()
logging.info('Rollouts took %.3f sec' %(rollout_stop-rollout_start))
return dataset
def showgraspfrompicklewithcandidate(self, object_name,metric_name,onlynagative,onlypositive,openravechecker):
object = self.dataset[object_name]
labelgrasps,labelmetrics,candidate_labelgrasps,candidate_labelmetrics=self.readdatafrompicklewithcandidate(object_name)
config=self._get_config(None)
low =0.0 #np.min(metrics)
high = np.max([labelmetrics[i][metric_name] for i in range(len(labelmetrics))])
# print 'high',high,config['quality_scale']
if low == high:
q_to_c = lambda quality: config['quality_scale']
else:
q_to_c = lambda quality: config['quality_scale'] * (quality - low) / (high - low)
recalculate_grasp=[]
vis.figure()
vis.mesh(object.mesh.trimesh ,style='surface')
for i in range(len(labelgrasps)):
if labelmetrics[i][metric_name]==0 :
if not onlypositive:
vis.figure()
vis.mesh(object.mesh.trimesh ,style='surface')
color = plt.get_cmap('hsv')(q_to_c(labelmetrics[i][metric_name]))[:-1]
vis.graspwithapproachvectorusingcenter_point(labelgrasps[i] ,approaching_color=(1,0,0), grasp_axis_color=(1,0,0) )
for kk in range(len(candidate_labelgrasps[i] )):
print 'candidate_labelgrasps[i][kk]',candidate_labelgrasps[i][kk]
color = plt.get_cmap('hsv')(q_to_c(candidate_labelmetrics[i][candidate_labelgrasps[i][kk].id][metric_name]))[:-1]
vis.graspwithapproachvectorusingcenter_point(candidate_labelgrasps[i][kk] ,approaching_color=color, grasp_axis_color=color )
recalculate_grasp.append(labelgrasps[i])
print 'maxtrics',i,labelmetrics[i][metric_name] # ,high
vis.pose(RigidTransform(), alpha=0.1)
vis.show(animate=False)
elif labelmetrics[i][metric_name]==-1 :
if not onlypositive:
vis.shownormals(labelgrasps[i][1],labelgrasps[i][0],color=(0,0,1) )
else:
if not onlynagative:
vis.figure()
vis.mesh(object.mesh.trimesh ,style='surface')
color = plt.get_cmap('hsv')(q_to_c(labelmetrics[i][metric_name]))[:-1]
if len(candidate_labelgrasps[i])>0:
A=np.array(labelgrasps[i].rotated_full_axis[:,0] )
B=np.array(candidate_labelgrasps[i][0].rotated_full_axis[:,0] )
num = np.dot(A.T, B)
print num
if num<0.99:
labelgrasps[i].approach_angle_= labelgrasps[i]._angle_aligned_with_table( -candidate_labelgrasps[i][0].rotated_full_axis[:,0])
vis.graspwithapproachvectorusingcenter_point(labelgrasps[i] ,approaching_color=color, grasp_axis_color=(0,0,1) )
print 'length',len(candidate_labelgrasps[i] )
for kk in range(len(candidate_labelgrasps[i] )):
color = plt.get_cmap('hsv')(q_to_c(candidate_labelmetrics[i][candidate_labelgrasps[i][kk].id][metric_name]))[:-1]
vis.graspwithapproachvectorusingcenter_point(candidate_labelgrasps[i][kk] ,approaching_color=(0,1,1), grasp_axis_color=color )
recalculate_grasp.append(labelgrasps[i])
print 'maxtrics',i,labelmetrics[i][metric_name] # ,high
vis.pose(RigidTransform(), alpha=0.1)
vis.show(animate=False)
