Download code/scripts/run_oven_pregrasp_batch.py from lsnu/VLAdaptorBench: direct link, hf CLI and curl.
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https://huggingface.co/lsnu/VLAdaptorBench/resolve/cd265468967fd6bb40591e7532e98dcdc4e2baec/code/scripts/run_oven_pregrasp_batch.py
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curl -L -o run_oven_pregrasp_batch.py https://huggingface.co/lsnu/VLAdaptorBench/resolve/cd265468967fd6bb40591e7532e98dcdc4e2baec/code/scripts/run_oven_pregrasp_batch.py
3.23 kB
| from pathlib import Path | |
| import argparse | |
| import json | |
| import pickle | |
| import sys | |
| import numpy as np | |
| PROJECT_ROOT = Path(__file__).resolve().parents[1] | |
| if str(PROJECT_ROOT) not in sys.path: | |
| sys.path.insert(0, str(PROJECT_ROOT)) | |
| from rr_label_study.oven_study import ( | |
| BimanualTakeTrayOutOfOven, | |
| ReplayCache, | |
| _launch_replay_env, | |
| _load_demo, | |
| _pregrasp_progress_and_distance, | |
| _pregrasp_score_and_success, | |
| ) | |
| def main() -> int: | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument("--episode-dir", required=True) | |
| parser.add_argument("--templates-pkl", required=True) | |
| parser.add_argument("--frame-indices", nargs="+", type=int, required=True) | |
| parser.add_argument("--checkpoint-stride", type=int, default=16) | |
| parser.add_argument("--output-dir", required=True) | |
| args = parser.parse_args() | |
| output_dir = Path(args.output_dir) | |
| output_dir.mkdir(parents=True, exist_ok=True) | |
| frame_indices = sorted(set(args.frame_indices)) | |
| pending_frame_indices = [ | |
| frame_index | |
| for frame_index in frame_indices | |
| if not output_dir.joinpath(f"frame_{frame_index:04d}.json").exists() | |
| ] | |
| if not pending_frame_indices: | |
| return 0 | |
| episode_dir = Path(args.episode_dir) | |
| with Path(args.templates_pkl).open("rb") as handle: | |
| templates = pickle.load(handle) | |
| demo = _load_demo(episode_dir) | |
| env = _launch_replay_env() | |
| try: | |
| task = env.get_task(BimanualTakeTrayOutOfOven) | |
| cache = ReplayCache(task, demo, checkpoint_stride=args.checkpoint_stride) | |
| cache.reset() | |
| total = len(pending_frame_indices) | |
| for completed, frame_index in enumerate(pending_frame_indices, start=1): | |
| cache.step_to(frame_index) | |
| state = cache.current_state() | |
| pregrasp_progress, pregrasp_distance = _pregrasp_progress_and_distance( | |
| np.asarray(state.left_gripper_pose, dtype=np.float64), | |
| np.asarray(state.tray_pose, dtype=np.float64), | |
| templates, | |
| ) | |
| p_pre, y_pre = _pregrasp_score_and_success(task, templates) | |
| row = { | |
| "frame_index": int(frame_index), | |
| "pregrasp_progress": float(pregrasp_progress), | |
| "pregrasp_distance": float(pregrasp_distance), | |
| "p_pre": float(p_pre), | |
| "y_pre_raw": float(bool(y_pre)), | |
| "y_pre": float(bool(y_pre)), | |
| } | |
| row_path = output_dir.joinpath(f"frame_{frame_index:04d}.json") | |
| tmp_path = row_path.with_suffix(".json.tmp") | |
| with tmp_path.open("w", encoding="utf-8") as handle: | |
| json.dump(row, handle) | |
| tmp_path.replace(row_path) | |
| if completed == total or completed % 8 == 0: | |
| print( | |
| json.dumps( | |
| { | |
| "done": completed, | |
| "total": total, | |
| "frame_index": int(frame_index), | |
| } | |
| ), | |
| flush=True, | |
| ) | |
| finally: | |
| env.shutdown() | |
| return 0 | |
| if __name__ == "__main__": | |
| raise SystemExit(main()) | |