def replay_on_holdout(args, action_selection, reg_and_traj_transferer, lfd_env, sim):
loadresultfile = h5py.File(args.replay.loadresultfile, 'r')
loadresult_items = eval_util.get_indexed_items(loadresultfile, task_list=args.tasks, task_file=args.taskfile, i_start=args.i_start, i_end=args.i_end)
num_successes = 0
num_total = 0
for i_task, task_info in loadresult_items:
redprint("task %s" % i_task)
for i_step in range(len(task_info)):
redprint("task %s step %i" % (i_task, i_step))
replay_results = eval_util.load_task_results_step(args.replay.loadresultfile, i_task, i_step)
sim_state = replay_results['sim_state']
if i_step > 0: # sanity check for reproducibility
sim_util.reset_arms_to_side(sim)
if sim.simulation_state_equal(sim_state, sim.get_state()):
yellowprint("Reproducible results OK")
else:
yellowprint("The replayed simulation state doesn't match the one from the result file")
sim.set_state(sim_state)
if args.replay.simulate_traj_steps is not None and i_step not in args.replay.simulate_traj_steps:
continue
if i_step in args.replay.compute_traj_steps: # compute the trajectory in this step
best_root_action = replay_results['best_action']
scene_state = replay_results['scene_state']
# plot cloud of the test scene
handles = []
if args.plotting:
handles.append(sim.env.plot3(scene_state.cloud[:,:3], 2, scene_state.color if scene_state.color is not None else (0,0,1)))
sim.viewer.Step()
test_aug_traj = reg_and_traj_transferer.transfer(GlobalVars.demos[best_root_action], scene_state, plotting=args.plotting)
else:
test_aug_traj = replay_results['aug_traj']
feasible, misgrasp = lfd_env.execute_augmented_trajectory(test_aug_traj, step_viewer=args.animation, interactive=args.interactive, check_feasible=args.eval.check_feasible)
if replay_results['knot']:
num_successes += 1
num_total += 1
redprint('REPLAY Successes / Total: ' + str(num_successes) + '/' + str(num_total))
def estimate_performance(fname):
results_file = h5py.File(fname, 'r')
loadresult_items = eval_util.get_indexed_items(results_file)
num_knots = 0
num_misgrasps = 0
num_infeasible = 0
action_time = 0
exec_time = 0
for i_task, task_info in loadresult_items:
knot_exists = False
infeasible = False
misgrasp = False
for i_step in range(len(task_info)):
results = eval_util.load_task_results_step(fname, i_task, i_step)
eval_stats = results['eval_stats']
misgrasp |= eval_stats.misgrasp
infeasible |= not eval_stats.feasible
action_time += eval_stats.action_elapsed_time
exec_time += eval_stats.exec_elapsed_time
if results['knot']:
knot_exists = True
elif i_step == len(task_info)-1:
print i_task
if infeasible:
num_infeasible += 1
if misgrasp:
num_misgrasps += 1
if knot_exists:
num_knots += 1
num_tasks = len(loadresult_items)
print "# Misgrasps:", num_misgrasps
print "# Infeasible:", num_infeasible
print "Time taken to choose demo:", action_time, "seconds"
print "Time taken to warp and execute demo:", exec_time, "seconds"
return num_knots, num_tasks
def estimate_performance(fname):
results_file = h5py.File(fname, 'r')
loadresult_items = eval_util.get_indexed_items(results_file)
num_knots = 0
num_misgrasps = 0
num_infeasible = 0
action_time = 0
exec_time = 0
for i_task, task_info in loadresult_items:
knot_exists = False
infeasible = False
misgrasp = False
for i_step in range(len(task_info)):
results = eval_util.load_task_results_step(fname, i_task, i_step)
eval_stats = results['eval_stats']
misgrasp |= eval_stats.misgrasp
infeasible |= not eval_stats.feasible
action_time += eval_stats.action_elapsed_time
exec_time += eval_stats.exec_elapsed_time
if results['knot']:
knot_exists = True
elif i_step == len(task_info) - 1:
print i_task
if infeasible:
num_infeasible += 1
if misgrasp:
num_misgrasps += 1
if knot_exists:
num_knots += 1
num_tasks = len(loadresult_items)
print "# Misgrasps:", num_misgrasps
print "# Infeasible:", num_infeasible
print "Time taken to choose demo:", action_time, "seconds"
print "Time taken to warp and execute demo:", exec_time, "seconds"
return num_knots, num_tasks
