import os import json import argparse from VLABench.evaluation.evaluator import Evaluator from VLABench.evaluation.model.policy.openvla import OpenVLA from VLABench.evaluation.model.policy.base import RandomPolicy from VLABench.tasks import * from VLABench.robots import * os.environ["MUJOCO_GL"]= "egl" def get_args(): parser = argparse.ArgumentParser() parser.add_argument('--tasks', nargs='+', default=None, help="Specific tasks to run, work when eval-track is None") parser.add_argument( '--eval-track', default=None, type=str, choices=[ "track_1_in_distribution", "track_sem_all_safe1", "track_sem_all_safe2", "track_sem_all_unsafe", "custom", ], help="The evaluation track to run" ) parser.add_argument('--n-episode', default=1, type=int, help="The number of episodes to evaluate for a task") parser.add_argument('--policy', default="openvla", help="The policy to evaluate") parser.add_argument('--model_ckpt', default="/remote-home1/sdzhang/huggingface/openvla-7b", help="The base model checkpoint path") parser.add_argument('--lora_ckpt', default="/remote-home1/pjliu/openvla/weights/vlabench/select_fruit+CSv1+lora/", help="The lora checkpoint path") parser.add_argument('--save-dir', default="logs", help="The directory to save the evaluation results") parser.add_argument('--visulization', action="store_true", default=False, help="Whether to visualize the episodes") parser.add_argument('--metrics', nargs='+', default=["success_rate"], choices=["success_rate", "intention_score", "progress_score"], help="The metrics to evaluate") parser.add_argument('--host', default="localhost", type=str, help="The host to the remote server") parser.add_argument('--port', default=5555, type=int, help="The port to the remote server") parser.add_argument('--replanstep', default=5, type=int, help="The step to replan") parser.add_argument('--seed-base', default=42, type=int, help="Base seed for episode sampling (seed + episode_id)") # VLA-Adapter specific parser.add_argument('--vla_ckpt', default=None, type=str, help="Path to VLA-Adapter checkpoint directory") parser.add_argument('--vla_num_images', default=2, type=int, help="Number of images used as input (1 or 2)") parser.add_argument('--vla_no_proprio', action="store_true", help="Disable proprio input for VLA-Adapter") parser.add_argument('--vla_open_loop_steps', default=1, type=int, help="Number of open-loop steps (first executed)") parser.add_argument('--vla_skip_center_crop', action="store_true", help="Skip center crop") parser.add_argument('--vla_use_minivlm', action="store_true", help="Use mini VLM prompting") parser.add_argument('--vla_use_film', action="store_true", help="Enable FiLM in VLA-Adapter vision backbone") parser.add_argument('--vla_no_pro_version', action="store_true", help="Disable Pro version head logic") parser.add_argument('--vla_camera_index', default=None, type=int, help="Optional camera index override") parser.add_argument('--vla_wrist_index', default=None, type=int, help="Optional wrist camera index override") parser.add_argument('--action_absolute', action="store_true", help="Treat model outputs as absolute target pose") # Nora specific parser.add_argument('--nora_camera_index', default=None, type=int, help="Optional camera index override for Nora") parser.add_argument('--nora_time_horizon', default=1, type=int, help="Nora action time horizon used by tokenizer") args = parser.parse_args() return args def evaluate(args): episode_config = None if args.eval_track is not None: args.save_dir = os.path.join(args.save_dir, args.eval_track) with open(os.path.join(os.getenv("VLABENCH_ROOT"), "configs/evaluation/tracks", f"{args.eval_track}.json"), "r") as f: episode_config = json.load(f) tasks = list(episode_config.keys()) if args.tasks is not None: tasks = args.tasks assert isinstance(tasks, list) evaluator = Evaluator( tasks=tasks, n_episodes=args.n_episode, episode_config=episode_config, max_substeps=1, # repeat step in simulation save_dir=args.save_dir, visulization=args.visulization, metrics=args.metrics, seed_base=args.seed_base ) if args.policy.lower() == "openvla": policy = OpenVLA( model_ckpt=args.model_ckpt, lora_ckpt=args.lora_ckpt, debug_actions=True, norm_config_file=os.path.join(os.getenv("VLABENCH_ROOT"), "configs/model/openvla_config.json"), # TODO: re-compuate the norm state by your own dataset action_is_absolute=args.action_absolute ) elif args.policy.lower() == "nora": from VLABench.evaluation.model.policy.nora import NoraPolicy policy = NoraPolicy( model_ckpt=args.model_ckpt, device="cuda", camera_index=args.nora_camera_index if args.nora_camera_index is not None else 2, action_mode="absolute" if args.action_absolute else "delta", replan_steps=args.replanstep, time_horizon=max(1, args.nora_time_horizon), ) elif args.policy.lower() == "vla_adapter": from VLABench.evaluation.model.policy.vla_adapter import VLAAdapterPolicy if args.vla_ckpt is None: raise ValueError("Please provide --vla_ckpt for VLA-Adapter policy") policy = VLAAdapterPolicy( checkpoint_path=args.vla_ckpt, task_unnorm_key=args.unnorm_key if hasattr(args, "unnorm_key") else "primitive", num_images_in_input=args.vla_num_images, use_proprio=not args.vla_no_proprio, use_film=args.vla_use_film, num_open_loop_steps=args.vla_open_loop_steps, center_crop=not args.vla_skip_center_crop, use_minivlm=args.vla_use_minivlm, use_pro_version=not args.vla_no_pro_version, device="cuda", camera_index=args.vla_camera_index, wrist_index=args.vla_wrist_index, action_is_absolute=args.action_absolute, ) elif args.policy.lower() == "gr00t": from VLABench.evaluation.model.policy.gr00t import Gr00tPolicy policy = Gr00tPolicy(host=args.host, port=args.port, replan_steps=args.replanstep) elif args.policy.lower() == "openpi": from VLABench.evaluation.model.policy.openpi import OpenPiPolicy policy = OpenPiPolicy(host=args.host, port=args.port, replan_steps=args.replanstep) else: policy = RandomPolicy(None) result = evaluator.evaluate(policy) track = args.eval_track or "custom" os.makedirs(os.path.join(args.save_dir, args.policy, track), exist_ok=True) with open(os.path.join(args.save_dir, args.policy, track, "evaluation_result.json"), "w") as f: json.dump(result, f) if __name__ == "__main__": args = get_args() evaluate(args)