Datasets:
File size: 2,690 Bytes
9cbc687 | 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 | """FailBench v2 quickstart — download a repo, load a trial, rebuild its contact target.
pip install -r requirements.txt
python -c "from huggingface_hub import snapshot_download as s; \
s('aaronngx/failbench-robocasa-v2', repo_type='dataset', local_dir='failbench-robocasa-v2')"
python examples/quickstart.py --data_root failbench-robocasa-v2
Everything here uses only the standalone `load_failbench.py` (pure h5py+numpy+scipy) — no
FailBench package, no MuJoCo. For training, see the `unet`/`mlp` command in the README.
"""
import argparse
import sys
from pathlib import Path
# make load_failbench importable whether you run from repo root or examples/
sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
import load_failbench as fb # noqa: E402
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--data_root", required=True, help="downloaded repo root (contains v2/)")
ap.add_argument("--cache_root", default=None,
help="optional target_cache/ dir (defaults to <data_root>/target_cache)")
args = ap.parse_args()
cache_root = args.cache_root
if cache_root is None and (Path(args.data_root) / "target_cache").exists():
cache_root = str(Path(args.data_root) / "target_cache")
df = fb.load_manifest(args.data_root)
print(f"{len(df)} trials, {df['task'].nunique()} tasks, splits={sorted(df['split'].unique())}")
row = (df[df["n_contacts"] > 0] if "n_contacts" in df else df).iloc[0]
split, task, tid = row["split"], row["task"], row["trial_id"]
trial = fb.read_trial(fb.resolve_h5(args.data_root, split, task), tid)
print(f"\ntrial {split}/{task}/{tid}")
print(f" pre_rgb {trial['pre_rgb'].shape} (H,W,3 uint8)")
print(f" window_agentview {trial['window_agentview_rgb'].shape} (T=8,H,W,3)")
print(f" contacts {trial['contact_positions'].shape} world-frame xyz")
print(f" failure {trial.get('failure_mode')} @ progress {trial.get('traj_progress')}")
# contact-prediction TARGET: (240,320) heatmap, the model's supervision signal
if cache_root:
target = fb.heatmap_from_projection(cache_root, split, task, tid) # filtered (training target)
print(f"\ncached target heatmap (failure-induced, filtered): {target.shape} sum={target.sum():.1f}")
target_live = fb.heatmap_from_contacts(trial) # rebuilt from raw contacts, no cache/sim
print(f"on-the-fly heatmap (all contacts): {target_live.shape} sum={target_live.sum():.1f}")
print("\nDone. `target` (240,320) is what the contact-prediction model regresses.")
if __name__ == "__main__":
main()
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