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e4eb88a | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 | #!/usr/bin/env python3
"""
Reprocess LIBERO LeRobot dataset to mesh-only tracks using simulator vertices.
This script keeps core fields (image, wrist_image, state, actions, task) and writes
only mesh-point tracks for both views, using per-task BDDL dynamic gripper selection.
"""
from __future__ import annotations
import openpi.shared.local_cache_bootstrap # noqa: F401
import argparse
import os
import shutil
import sys
from pathlib import Path
import numpy as np
from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
import lerobot_preprocess_cotracker as cot
def _build_features() -> dict[str, dict]:
return {
"image": {"dtype": "image", "shape": (256, 256, 3), "names": ["height", "width", "channel"]},
"wrist_image": {"dtype": "image", "shape": (256, 256, 3), "names": ["height", "width", "channel"]},
"state": {"dtype": "float32", "shape": (8,), "names": ["state"]},
"actions": {"dtype": "float32", "shape": (7,), "names": ["actions"]},
"agentview_tracks": {"dtype": "float32", "shape": (7, 2), "names": ["points", "xy"]},
"agentview_vis": {"dtype": "float32", "shape": (7,), "names": ["points"]},
"wrist_tracks": {"dtype": "float32", "shape": (7, 2), "names": ["points", "xy"]},
"wrist_vis": {"dtype": "float32", "shape": (7,), "names": ["points"]},
"agentview_mesh_vertices_2d": {"dtype": "float32", "shape": (7, 2), "names": ["points", "xy"]},
"wrist_mesh_vertices_2d": {"dtype": "float32", "shape": (7, 2), "names": ["points", "xy"]},
"has_track_mesh": {"dtype": "float32", "shape": (1,), "names": ["flag"]},
}
def process_episode(ds: LeRobotDataset, ep_idx: int):
bnds = cot._episode_bounds(ds, ep_idx)
scene = cot._scene_from_task(bnds.task)
frames = []
for i in range(bnds.start, bnds.end):
row = ds[i]
frames.append(
(
cot._to_hwc_uint8(np.asarray(row["image"])),
cot._to_hwc_uint8(np.asarray(row["wrist_image"])),
np.asarray(row["state"], dtype=np.float32),
np.asarray(row["actions"], dtype=np.float32),
row["task"],
)
)
images = np.stack([f[0] for f in frames], axis=0)
wrist_images = np.stack([f[1] for f in frames], axis=0)
states = np.stack([f[2] for f in frames], axis=0)
actions = np.stack([f[3] for f in frames], axis=0)
task = frames[0][4]
agent_mesh_seq, wrist_mesh_seq = cot._get_mesh_sequence_from_sim(
scene,
states[0],
actions,
task_name=task,
img_hw=(images.shape[1], images.shape[2]),
)
T = min(images.shape[0], agent_mesh_seq.shape[0], wrist_mesh_seq.shape[0])
for t in range(T):
yield {
"image": images[t],
"wrist_image": wrist_images[t],
"state": states[t],
"actions": actions[t],
"task": task,
"agentview_tracks": agent_mesh_seq[t],
"agentview_vis": np.ones((7,), dtype=np.float32),
"wrist_tracks": wrist_mesh_seq[t],
"wrist_vis": np.ones((7,), dtype=np.float32),
"agentview_mesh_vertices_2d": agent_mesh_seq[t],
"wrist_mesh_vertices_2d": wrist_mesh_seq[t],
"has_track_mesh": np.asarray([1.0], dtype=np.float32),
}
def main():
p = argparse.ArgumentParser(description="Reprocess LIBERO to mesh-only (both views, dynamic BDDL).")
p.add_argument("--source-repo-id", default="/mnt/kevin/data/physical-intelligence/libero")
p.add_argument("--target-repo-id", default="/mnt/kevin/data/physical-intelligence/libero_mesh_only_dynamic")
p.add_argument("--overwrite", action="store_true")
p.add_argument("--max-episodes", type=int, default=None)
p.add_argument("--start-episode", type=int, default=0)
p.add_argument("--end-episode", type=int, default=None)
p.add_argument("--libero-root", type=str, default=None)
p.add_argument("--libero-data", type=str, default=None)
p.add_argument(
"--extra-libero-path",
type=str,
default="/mnt/kevin/code/wmrl/howard-branch/openpi/third_party/libero/libero",
)
args = p.parse_args()
cot.EXTRA_LIBERO_PATH = args.extra_libero_path
if args.libero_root and (Path(args.libero_root) / "libero" / "envs" / "mesh_vertex_wrapper.py").exists():
cot.EXTRA_LIBERO_PATH = args.libero_root
for pth in [args.libero_root, args.extra_libero_path]:
if not pth:
continue
candidates = [Path(pth), Path(pth) / "libero"]
for cand in candidates:
s = str(cand)
if cand.exists() and s not in sys.path:
sys.path.insert(0, s)
if args.libero_data:
os.environ.setdefault("LIBERO_PATH", args.libero_data)
os.environ.setdefault("MUJOCO_GL", "egl")
os.environ.setdefault("PYOPENGL_PLATFORM", "egl")
src = LeRobotDataset(args.source_repo_id)
total_eps = len(src.meta.episodes)
start_ep = max(0, int(args.start_episode))
end_ep = total_eps if args.end_episode is None else min(int(args.end_episode), total_eps)
if args.max_episodes is not None:
end_ep = min(end_ep, start_ep + int(args.max_episodes))
if end_ep <= start_ep:
raise ValueError(f"Invalid episode range [{start_ep}, {end_ep})")
target_path = Path(args.target_repo_id)
if target_path.exists() and args.overwrite:
shutil.rmtree(target_path)
dst = LeRobotDataset.create(
repo_id=str(target_path),
robot_type="panda",
fps=src.fps,
features=_build_features(),
image_writer_threads=10,
image_writer_processes=5,
)
for ep_idx in range(start_ep, end_ep):
print(f"[ep {ep_idx-start_ep}/{end_ep-start_ep}] abs={ep_idx}")
for frame in process_episode(src, ep_idx):
dst.add_frame(frame)
dst.save_episode()
print(f"Done. Wrote {end_ep-start_ep} episodes ({start_ep}:{end_ep}) to {target_path}")
if __name__ == "__main__":
main()
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