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>
101 lines
2.9 KiB
Python
101 lines
2.9 KiB
Python
#!/usr/bin/env python3
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"""Check that every compiled extension and import the sumv2 training path needs
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is actually present.
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Extension import names do not match their directory names, which is an easy way
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to waste an hour chasing a build that already succeeded:
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openpoints/cpp/pointnet2_batch -> pointnet2_batch_cuda
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openpoints/cpp/pointops -> pointops_cuda
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openpoints/cpp/chamfer_dist -> chamfer
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openpoints/cpp/emd -> emd_cuda (package name: emd_ext)
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openpoints/cpp/subsampling -> openpoints.cpp.subsampling.grid_subsampling
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Run from PointNeXt_bundle/ (or anywhere, if openpoints is importable).
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"""
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import os
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import sys
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from pathlib import Path
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def add_openpoints_to_path() -> Path | None:
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"""Put PointNeXt_bundle on sys.path.
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main.py does this itself with a hardcoded '../../', but this script lives
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outside the repo, so walk up from cwd (then from the default clone path)
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until a directory containing openpoints/ turns up.
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"""
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candidates = [Path.cwd(), *Path.cwd().parents,
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Path.home() / "sum-parts/semantic_segmentation/PointNeXt_bundle"]
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for c in candidates:
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if (c / "openpoints" / "__init__.py").exists():
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sys.path.insert(0, str(c))
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return c
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return None
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MODULES = [
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"torch",
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"numpy",
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"pointnet2_batch_cuda",
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"pointops_cuda",
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"chamfer",
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"emd_cuda",
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"torch_scatter",
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"plyfile",
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"wandb",
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"trimesh",
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]
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FROM_IMPORTS = [
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("openpoints.cpp.subsampling", "grid_subsampling"),
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("openpoints.models", "build_model_from_cfg"),
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("openpoints.dataset", "build_dataloader_from_cfg"),
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]
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def main() -> int:
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ok = True
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root = add_openpoints_to_path()
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print(f"openpoints root: {root or 'NOT FOUND'}")
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if root is None:
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ok = False
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try:
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import torch
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print(f"torch {torch.__version__} | cuda {torch.version.cuda} | "
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f"available {torch.cuda.is_available()}")
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if torch.cuda.is_available():
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print(f"device: {torch.cuda.get_device_name(0)}")
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except Exception as e: # noqa: BLE001
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print(f"torch import failed: {e}")
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return 1
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print()
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for name in MODULES:
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try:
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__import__(name)
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print(f"{name:32s} OK")
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except Exception as e: # noqa: BLE001
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ok = False
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print(f"{name:32s} FAIL {type(e).__name__}: {e}")
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print()
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for mod, attr in FROM_IMPORTS:
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try:
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m = __import__(mod, fromlist=[attr])
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getattr(m, attr)
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print(f"{mod + '.' + attr:32s} OK")
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except Exception as e: # noqa: BLE001
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ok = False
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print(f"{mod + '.' + attr:32s} FAIL {type(e).__name__}: {e}")
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print()
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print("ALL OK" if ok else "SOME CHECKS FAILED")
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return 0 if ok else 1
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if __name__ == "__main__":
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sys.exit(main())
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