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>
23 lines
631 B
Python
23 lines
631 B
Python
#!/usr/bin/env python3
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"""Report the download sizes of the SUM Parts archives on Hugging Face."""
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from huggingface_hub import HfApi
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REPO = "gwxgrxhyz/SUM-Parts"
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api = HfApi()
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info = api.repo_info(REPO, repo_type="dataset", files_metadata=True)
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rows = []
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for s in info.siblings:
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size = s.size or (s.lfs.size if getattr(s, "lfs", None) else None)
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if size and size > 1_000_000:
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rows.append((s.rfilename, size))
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rows.sort(key=lambda r: -r[1])
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total = 0
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for name, size in rows:
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print(f"{size / 1e9:>8.2f} GB {name}")
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total += size
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print(f"{'-' * 30}")
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print(f"{total / 1e9:>8.2f} GB total (files > 1 MB)")
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