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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 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 | #!/usr/bin/env python
"""Score the 2x2 ablation arms for the B_07 cloth case, 4DCodeBench-style.
Metrics (all computed against the baked ground-truth cloth trajectory):
chamfer_t mean per-frame symmetric chamfer between sim and GT cloth point
clouds, subsampled, averaged over frames (meters, lower better)
dtw_com DTW between the cloth center-of-mass trajectories (meters)
bbox_iou_t mean 3D IoU of the axis-aligned cloth bounding box per frame
final_cham chamfer at the last frame (did it end in the same place?)
Usage:
python score_ablation.py --world work/B_07/world --runs work/B_07/ablation \
--out work/B_07/ablation/metrics.json [--csv work/B_07/ablation/metrics.csv]
"""
import argparse
import csv
import json
from pathlib import Path
import numpy as np
def chamfer(a, b, n=2000, seed=0):
rng = np.random.default_rng(seed)
ai = rng.choice(len(a), min(n, len(a)), replace=False)
bi = rng.choice(len(b), min(n, len(b)), replace=False)
A, B = a[ai].astype(np.float64), b[bi].astype(np.float64)
d_ab = ((A[:, None, :] - B[None, :, :]) ** 2).sum(-1)
d1 = np.sqrt(d_ab.min(1))
d2 = np.sqrt(((B[:, None, :] - A[None, :, :]) ** 2).sum(-1)).min(1)
return float(0.5 * (d1.mean() + d2.mean()))
def dtw(a, b):
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 float(D[n, m] / (n + m))
def bbox_iou(a, b):
amin, amax = a.min(0), a.max(0)
bmin, bmax = b.min(0), b.max(0)
inter_min = np.maximum(amin, bmin)
inter_max = np.minimum(amax, bmax)
inter = np.clip(inter_max - inter_min, 0, None).prod()
va = (amax - amin).prod()
vb = (bmax - bmin).prod()
union = va + vb - inter
return float(inter / union) if union > 0 else 0.0
def score(world_dir, traj_dir, frame_stride=3):
gt = np.load(Path(world_dir) / "dyn" / "Cloth.npz")["pos"].astype(np.float32)
sim = np.load(Path(traj_dir) / "clothtraj.npz")["pos"].astype(np.float32)
assert sim.shape == gt.shape, f"shape mismatch {sim.shape} vs {gt.shape}"
F = gt.shape[0]
frames = list(range(0, F, frame_stride))
if frames[-1] != F - 1:
frames.append(F - 1)
ch = [chamfer(sim[i], gt[i], seed=i) for i in frames]
gt_com = gt.mean(1)
sim_com = sim.mean(1)
iou = [bbox_iou(sim[i], gt[i]) for i in frames]
return {
"chamfer_t_m": round(float(np.mean(ch)), 4),
"final_chamfer_m": round(ch[-1], 4),
"dtw_com_m": round(dtw(sim_com, gt_com), 4),
"bbox_iou_t": round(float(np.mean(iou)), 4),
}
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--world", required=True)
ap.add_argument("--runs", required=True, nargs="+")
ap.add_argument("--names", required=True, nargs="+")
ap.add_argument("--out", required=True)
ap.add_argument("--csv", default=None)
args = ap.parse_args()
report = {}
for name, run in zip(args.names, args.runs):
try:
report[name] = score(args.world, run)
except Exception as e:
report[name] = {"error": str(e)}
print(name, json.dumps(report[name]))
Path(args.out).write_text(json.dumps(report, indent=1))
print(f"wrote {args.out}")
if args.csv:
keys = ["chamfer_t_m", "final_chamfer_m", "dtw_com_m", "bbox_iou_t"]
with open(args.csv, "w", newline="") as f:
w = csv.writer(f)
w.writerow(["arm"] + keys)
for name in args.names:
r = report[name]
w.writerow([name] + [r.get(k, "") for k in keys])
print(f"wrote {args.csv}")
if __name__ == "__main__":
main()
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