def test_scene(dump_file, test_cnt):
config = Configuration()
scene = os.path.basename(dump_file).split('.')[0]
if scene not in TASK_LIST:
env = THORDiscreteEnvironment({
'h5_file_path': dump_file,
})
task_list = np.random.choice(list(range(env.n_locations)), test_cnt)
else:
task_list = TASK_LIST[scene]
logging.info(
"testing scene %(scene)s task_list=%(task_list)s from dump file %(dump_file)s"
% locals())
for t in task_list:
target = int(t)
env = THORDiscreteEnvironment({
'h5_file_path': dump_file,
'terminal_state_id': target,
})
start = time.time()
expert = Expert(env)
logging.debug("building policy takes %f s" % (time.time() - start))
assert expert.verify_distance_matrix()
for _ in range(test_cnt):
env.reset()
logging.debug("scene=%s target=%d source=%d" %
(scene, target, env.current_state_id))
steps = []
orig_state = env.current_state_id
while not env.terminal:
a = expert.get_next_action()
logging.debug("state=%d action=%d" % (env.current_state_id, a))
env.step(a)
steps.append((env.current_state_id, a))
assert len(
steps
) < config.max_steps_per_e, "current steps is beyond max_steps_per_e"
logging.debug(
str(orig_state) +
''.join([expert.get_a_str(a) + str(s) for (s, a) in steps]))
assert len(
steps) == env.shortest_path_distances[orig_state][target]
viewer.imshow(env.observation)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument("-s",
"--scene_dump",
type=str,
default="./data/bedroom_04.h5",
help="path to a hdf5 scene dump file")
args = parser.parse_args()
print("Loading scene dump {}".format(args.scene_dump))
env = THORDiscreteEnvironment({'h5_file_path': args.scene_dump})
# manually disable terminal states
env.terminals = np.zeros_like(env.terminals)
env.terminal_states, = np.where(env.terminals)
env.reset()
human_agent_action = None
human_wants_restart = False
stop_requested = False
viewer = SimpleImageViewer()
viewer.imshow(env.observation)
viewer.window.on_key_press = key_press
print("Use arrow keys to move the agent.")
parser.add_argument(
"-a",
"--action",
type=str,
help="specify a sequence of action the agent should take")
parser.add_argument("--start",
type=int,
help="initial location of the agent")
parser.add_argument("--save-dir",
type=str,
help="directory to which image at each state is saved")
args = parser.parse_args()
print("Loading scene dump {}".format(args.scene_dump))
env = THORDiscreteEnvironment({
'h5_file_path': args.scene_dump,
'initial_state': args.start,
})
# manually disable terminal states
env.terminals = np.zeros_like(env.terminals)
env.terminal_states, = np.where(env.terminals)
env.reset()
human_agent_action = None
human_wants_restart = False
stop_requested = False
action_list = [int(i)
for i in args.action.split(',')] if args.action else None
action_idx = 0
save_img = None
help="path to a hdf5 scene dump file")
args = parser.parse_args()
print("Loading scene dump {}".format(args.scene_dump))
# env = THORDiscreteEnvironment({
# 'h5_file_path': args.scene_dump
# })
#
# # manually disable terminal states
# env.terminals = np.zeros_like(env.terminals)
# env.terminal_states, = np.where(env.terminals)
# env.reset()
env = THORDiscreteEnvironment({
'scene_name': 'living_room_08',
'terminal_state_id': 26
})
env.reset()
human_agent_action = None
human_wants_restart = False
stop_requested = False
viewer = SimpleImageViewer()
viewer.imshow(env.observation)
viewer.window.on_key_press = key_press
print("Use arrow keys to move the agent.")
print("Press R to reset agent\'s location.")
print("Press Q to quit.")