Add come updates for Neurips paper (#4)
* scenarionet training * wandb * train utils * fix callback * run PPO * use pg test * save path * use torch * add dependency * update ignore * update training * large model * use curriculum training * add time to exp name * storage_path * restore * update training * use my key * add log message * check seed * restore callback * restore call bacl * add log message * add logging message * restore ray1.4 * length 500 * ray 100 * wandb * use tf * more levels * add callback * 10 worker * show level * no env horizon * callback result level * more call back * add diffuculty * add mroen stat * mroe stat * show levels * add callback * new * ep len 600 * fix setup * fix stepup * fix to 3.8 * update setup * parallel worker! * new exp * add callback * lateral dist * pg dataset * evaluate * modify config * align config * train single RL * update training script * 100w eval * less eval to reveal * 2000 env eval * new trianing * eval 1000 * update eval * more workers * more worker * 20 worker * dataset to database * split tool! * split dataset * try fix * train 003 * fix mapping * fix test * add waymo tqdm * utils * fix bug * fix bug * waymo * int type * 8 worker read * disable * read file * add log message * check existence * dist 0 * int * check num * suprass warning * add filter API * filter * store map false * new * ablation * filter * fix * update filyter * reanme to from * random select * add overlapping checj * fix * new training sceheme * new reward * add waymo train script * waymo different config * copy raw data * fix bug * add tqdm * update readme * waymo * pg * max lateral dist 3 * pg * crash_done instead of penalty * no crash done * gpu * update eval script * steering range penalty * evaluate * finish pg * update setup * fix bug * test * fix * add on line * train nuplan * generate sensor * udpate training * static obj * multi worker eval * filx bug * use ray for testing * eval! * filter senario * id filter * fox bug * dist = 2 * filter * eval * eval ret * ok * update training pg * test before use * store data=False * collect figures * capture pic --------- Co-authored-by: Quanyi Li <quanyi@bolei-gpu02.cs.ucla.edu>
This commit is contained in:
Quanyi Li
97
scenarionet/tests/script/generate_sensor.py
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97
scenarionet/tests/script/generate_sensor.py
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import time
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import pygame
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from metadrive.engine.asset_loader import AssetLoader
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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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NuScenesEnv = ScenarioEnv
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if __name__ == "__main__":
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env = NuScenesEnv(
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{
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"use_render": True,
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"agent_policy": ReplayEgoCarPolicy,
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"show_interface": False,
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# "need_lane_localization": False,
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"show_logo": False,
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"no_traffic": False,
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"sequential_seed": True,
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"reactive_traffic": False,
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"show_fps": False,
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# "debug": True,
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# "render_pipeline": True,
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"daytime": "11:01",
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"window_size": (1600, 900),
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"camera_dist": 0.8,
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"camera_height": 1.5,
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"camera_pitch": None,
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"camera_fov": 60,
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"start_scenario_index": 0,
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"num_scenarios": 10,
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# "force_reuse_object_name": True,
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# "data_directory": "/home/shady/Downloads/test_processed",
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"horizon": 1000,
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# "no_static_vehicles": True,
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# "show_policy_mark": True,
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# "show_coordinates": True,
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# "force_destroy": True,
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# "default_vehicle_in_traffic": True,
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"vehicle_config": dict(
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# light=True,
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# random_color=True,
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show_navi_mark=False,
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use_special_color=False,
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image_source="depth_camera",
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rgb_camera=(1600, 900),
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depth_camera=(1600, 900, True),
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# no_wheel_friction=True,
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lidar=dict(num_lasers=120, distance=50),
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lane_line_detector=dict(num_lasers=0, distance=50),
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side_detector=dict(num_lasers=12, distance=50)
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),
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"data_directory": AssetLoader.file_path("nuscenes", return_raw_style=False),
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"image_observation": True,
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}
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)
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# 0,1,3,4,5,6
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success = []
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reset_num = 0
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start = time.time()
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reset_used_time = 0
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s = 0
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while True:
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# for i in range(10):
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start_reset = time.time()
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env.reset(force_seed=0)
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reset_used_time += time.time() - start_reset
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reset_num += 1
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for t in range(10000):
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if t==30:
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# env.capture("camera_deluxe.jpg")
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# ret = env.render(mode="topdown", screen_size=(1600, 900), film_size=(5000, 5000), track_target_vehicle=True)
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# pygame.image.save(ret, "top_down.png")
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env.vehicle.get_camera("depth_camera").save_image(env.vehicle, "camera.jpg")
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o, r, d, info = env.step([1, 0.88])
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assert env.observation_space.contains(o)
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s += 1
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# if env.config["use_render"]:
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# env.render(text={"seed": env.current_seed,
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# # "num_map": info["num_stored_maps"],
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# "data_coverage": info["data_coverage"],
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# "reward": r,
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# "heading_r": info["step_reward_heading"],
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# "lateral_r": info["step_reward_lateral"],
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# "smooth_action_r": info["step_reward_action_smooth"]})
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if d:
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print(
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"Time elapse: {:.4f}. Average FPS: {:.4f}, AVG_Reset_time: {:.4f}".format(
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time.time() - start, s / (time.time() - start - reset_used_time),
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reset_used_time / reset_num
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)
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)
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print("seed:{}, success".format(env.engine.global_random_seed))
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print(list(env.engine.curriculum_manager.recent_success.dict.values()))
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break
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