nbrightandClaude Opus 5
609d9a6972
Reproduces the SUM Parts (CVPR 2025) face-labeling benchmark on a single consumer GPU, then applies it to drone-photogrammetry road survey meshes. Verified on RTX 3060 12GB / WSL2 Ubuntu 22.04 / CUDA 11.8 / torch 2.0.1: - CUDA extensions build (pointnet2_batch, pointops, chamfer_dist, emd, subsampling) - PointNet 100 epochs reaches mIoU 17.19, matching the paper's reported 15.1 - OBJ -> PLY conversion round-trips through the model and yields per-point predictions Four upstream source patches, all idempotent, originals preserved: - numpy aliases removed in 1.24 (np.long etc.) and collections ABCs moved in python 3.10 - the blind test split ships label = -1, which crashed ConfusionMatrix - mode=val referenced `epoch` before assignment Documents the traps that cost the most time, including VRAM overflow silently falling back to host RAM on WSL2 (25-100x slowdown, no OOM) and the colour scale mismatch between r/g/b float32 and red/green/blue uint8. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
99 lines
2.9 KiB
Python
99 lines
2.9 KiB
Python
#!/usr/bin/env python3
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"""Compare PLY point clouds against what the SUM Parts loader needs.
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Pass the converted file first and a reference SUM Parts tile second to see the
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two side by side.
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Usage:
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python check_ply.py mine.ply [reference.ply ...]
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"""
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from __future__ import annotations
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import sys
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from pathlib import Path
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import numpy as np
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from plyfile import PlyData
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# read_ply_with_plyfilelib tries red/green/blue first, then r/g/b
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RGB_SETS = (("red", "green", "blue"), ("r", "g", "b"))
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def report(path: Path) -> bool:
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ply = PlyData.read(str(path))
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v = ply["vertex"]
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props = [p.name for p in v.properties]
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print(f"=== {path.name} ===")
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print(f" format : {'binary' if not ply.text else 'ascii'}")
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print(f" points : {len(v):,}")
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print(f" properties: {props}")
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ok = True
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missing_xyz = [c for c in ("x", "y", "z") if c not in props]
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if missing_xyz:
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print(f" MISSING : {missing_xyz}")
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ok = False
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else:
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xyz = np.stack([v["x"], v["y"], v["z"]], axis=1)
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lo, hi = xyz.min(axis=0), xyz.max(axis=0)
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ext = hi - lo
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print(f" extent : {np.round(ext, 2).tolist()} m")
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print(f" origin : {np.round(lo, 2).tolist()}")
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if ext.max() < 0.5:
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print(" WARNING : extent < 0.5 -- degrees, not metres?")
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ok = False
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area = ext[0] * ext[1]
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if area > 0:
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print(f" density : {len(v) / area:,.1f} pts/m^2")
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rgb_set = next((s for s in RGB_SETS if all(c in props for c in s)), None)
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if rgb_set is None:
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print(f" MISSING : colour -- need {RGB_SETS[0]} or {RGB_SETS[1]}")
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ok = False
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else:
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rgb = np.stack([v[c] for c in rgb_set], axis=1)
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print(f" colour : {rgb_set} dtype={rgb.dtype} "
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f"range=[{rgb.min()}, {rgb.max()}] mean={rgb.mean(axis=0).round(1).tolist()}")
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grey = int((rgb == 128).all(axis=1).sum())
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if grey:
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print(f" WARNING : {grey:,} points are exactly grey (texture miss?)")
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if "label" not in props:
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print(" MISSING : label")
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ok = False
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else:
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lab = np.asarray(v["label"])
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u, c = np.unique(lab, return_counts=True)
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shown = list(zip(u.tolist(), c.tolist()))[:14]
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print(f" label : dtype={lab.dtype} uniq={u.tolist()[:20]}")
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print(f" label hist: {shown}")
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if u.tolist() == [0]:
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print(" note : all unclassified -- inference only, cannot train/score")
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print(f" verdict : {'OK' if ok else 'INCOMPATIBLE'}")
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print()
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return ok
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def main() -> None:
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if len(sys.argv) < 2:
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print(__doc__)
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raise SystemExit(2)
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all_ok = True
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for a in sys.argv[1:]:
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p = Path(a)
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if not p.exists():
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print(f"{a}: not found\n")
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all_ok = False
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continue
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all_ok &= report(p)
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raise SystemExit(0 if all_ok else 1)
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if __name__ == "__main__":
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main()
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