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7f97480 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 | 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)
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