37d4e72db5
* update parameters for scripts * update write function * modify waymo script * use exist ok instead of overwrite * remove TODO * rename to comvine_dataset * use exist_ok and force_overwrite together * format * test * creat env for each thread * restore * fix bug * fix pg bug * fix * fix bug * add assert * don't return done info * to dict * add test * only compare sdc * no store mao * release memory * add start index to argumen * test * format some settings/flags * add tmp path * add tmp dir * test all scripts * suppress warning * suppress warning * format * test memory leak * fix memory leak * remove useless functions * imap * thread-1 process for avoiding memory leak * add list() * rename * verify existence * verify completeness * test * add test * add default value * add limit * use script * add anotation * test script * fix bug * fix bug * add author4 * add overwrite * fix bug * fix * combine overwrite * fix bug * gpu007 * add result save dir * adjust sequence * fix test bug * disable bash scri[t * add episode length limit * move scripts to root dir * format * fix test
53 lines
2.2 KiB
Python
53 lines
2.2 KiB
Python
import pkg_resources # for suppress warning
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import argparse
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import os
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from metadrive.envs.scenario_env import ScenarioEnv
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from metadrive.policy.replay_policy import ReplayEgoCarPolicy
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from metadrive.scenario.utils import get_number_of_scenarios
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument("--dataset_path", "-d", required=True, help="The path of the dataset")
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parser.add_argument("--render", action="store_true", help="Enable 3D rendering")
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parser.add_argument("--scenario_index", default=None, type=int, help="Specifying a scenario to run")
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args = parser.parse_args()
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dataset_path = os.path.abspath(args.dataset_path)
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num_scenario = get_number_of_scenarios(dataset_path)
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if args.scenario_index is not None:
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assert args.scenario_index < num_scenario, \
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"The specified scenario index exceeds the scenario range: {}!".format(num_scenario)
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env = ScenarioEnv(
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{
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"use_render": args.render,
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"agent_policy": ReplayEgoCarPolicy,
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"manual_control": False,
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"show_interface": True,
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"show_logo": False,
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"show_fps": False,
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"num_scenarios": num_scenario,
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"horizon": 1000,
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"vehicle_config": dict(
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show_navi_mark=False,
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no_wheel_friction=True,
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lidar=dict(num_lasers=120, distance=50, num_others=4),
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lane_line_detector=dict(num_lasers=12, distance=50),
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side_detector=dict(num_lasers=160, distance=50)
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),
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"data_directory": dataset_path,
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}
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)
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for seed in range(num_scenario if args.scenario_index is not None else 1000000):
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env.reset(force_seed=seed if args.scenario_index is None else args.scenario_index)
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for t in range(10000):
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o, r, d, info = env.step([0, 0])
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if env.config["use_render"]:
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env.render(text={
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"seed": env.engine.global_seed + env.config["start_scenario_index"],
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})
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if d and info["arrive_dest"]:
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print("scenario:{}, success".format(env.engine.global_random_seed))
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break
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