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42afafb | 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 | #!/usr/bin/env python
"""DTW score: re-simulated trajectories vs baked ground truth, per active object.
Usage:
python score_dtw.py --world work/B_03/world --sims work/B_03/out_p1 work/B_03/out_p2
"""
import argparse
import json
from pathlib import Path
import numpy as np
def dtw(a, b):
"""Mean-per-step DTW cost between two (F,3) trajectories."""
n, m = len(a), len(b)
D = np.full((n + 1, m + 1), np.inf)
D[0, 0] = 0.0
for i in range(1, n + 1):
for j in range(1, m + 1):
c = float(np.linalg.norm(a[i - 1] - b[j - 1]))
D[i, j] = c + min(D[i - 1, j - 1], D[i, j - 1], D[i - 1, j])
return D[n, m] / (n + m)
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--world", required=True)
ap.add_argument("--sims", nargs="+", required=True)
ap.add_argument("--out", default=None, help="write JSON here too")
args = ap.parse_args()
world = Path(args.world)
names = [p.stem for p in (world / "dyn").glob("*.npz")]
report = {}
for sim in args.sims:
traj = np.load(Path(sim) / "simtraj.npz")
sim_names = [k for k in traj.files if k in names and k != "Ball"] # Ball = kinematic replay == truth
per = {}
for n in sorted(sim_names):
truth = np.load(world / "dyn" / f"{n}.npz")["pos"].astype(np.float64).mean(axis=1)
sim_t = traj[n].astype(np.float64)
per[n] = round(dtw(truth, sim_t), 5)
report[sim] = {
"per_object": per,
"mean": round(float(np.mean(list(per.values()))) if per else None, 5),
}
print(json.dumps({sim: report[sim]}, indent=1))
if args.out:
Path(args.out).write_text(json.dumps(report, indent=1))
if __name__ == "__main__":
main()
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