db50bca7fd
* 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>
75 lines
2.8 KiB
Python
75 lines
2.8 KiB
Python
import argparse
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import pickle
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import json
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import os
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import numpy as np
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from scenarionet_training.scripts.train_nuplan import config
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from scenarionet_training.train_utils.callbacks import DrivingCallbacks
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from scenarionet_training.train_utils.multi_worker_PPO import MultiWorkerPPO
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from scenarionet_training.train_utils.utils import initialize_ray
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class NumpyEncoder(json.JSONEncoder):
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def default(self, obj):
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if isinstance(obj, np.ndarray):
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return obj.tolist()
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elif isinstance(obj, np.int32):
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return int(obj)
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elif isinstance(obj, np.int64):
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return int(obj)
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return json.JSONEncoder.default(self, obj)
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument("--start_index", type=int, default=0)
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parser.add_argument("--ckpt_path", type=str, required=True)
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parser.add_argument("--database_path", type=str, required=True)
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parser.add_argument("--id", type=str, default="")
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parser.add_argument("--num_scenarios", type=int, default=5000)
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parser.add_argument("--num_workers", type=int, default=10)
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parser.add_argument("--horizon", type=int, default=600)
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parser.add_argument("--allowed_more_steps", type=int, default=50)
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parser.add_argument("--max_lateral_dist", type=int, default=2.5)
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parser.add_argument("--overwrite", action="store_true")
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args = parser.parse_args()
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file = "eval_{}_{}_{}".format(args.id, os.path.basename(args.ckpt_path), os.path.basename(args.database_path))
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if os.path.exists(file) and not args.overwrite:
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raise FileExistsError("Please remove {} or set --overwrite".format(file))
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initialize_ray(test_mode=True, num_gpus=1)
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config["callbacks"] = DrivingCallbacks
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config["evaluation_num_workers"] = args.num_workers
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config["evaluation_num_episodes"] = args.num_scenarios
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config["metrics_smoothing_episodes"] = args.num_scenarios
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config["custom_eval_function"] = None
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config["num_workers"] = 0
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config["evaluation_config"]["env_config"].update(dict(
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start_scenario_index=args.start_index,
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num_scenarios=args.num_scenarios,
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sequential_seed=True,
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store_map=False,
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store_data=False,
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allowed_more_steps=args.allowed_more_steps,
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# no_map=True,
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max_lateral_dist=args.max_lateral_dist,
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curriculum_level=1, # disable curriculum
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target_success_rate=1,
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horizon=args.horizon,
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episodes_to_evaluate_curriculum=args.num_scenarios,
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data_directory=args.database_path,
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use_render=False))
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trainer = MultiWorkerPPO(config)
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trainer.restore(args.ckpt_path)
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ret = trainer._evaluate()["evaluation"]
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with open(file + ".json", "w") as f:
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json.dump(ret, f, cls=NumpyEncoder)
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with open(file + ".pkl", "wb+") as f:
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pickle.dump(ret, f)
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