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>
74 lines
2.8 KiB
Bash
74 lines
2.8 KiB
Bash
#!/usr/bin/env bash
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# SUM Parts - let test() run on the withheld (unlabeled) test split
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#
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# The 8 test tiles ship with label = -1 for every point. That is deliberate:
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# the test set is blind, and the README says to email predictions to the authors
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# for scoring. Training and validation labels are normal (0..12).
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#
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# main.py only checks `if label is not None`, so the -1 placeholder flows into
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# ConfusionMatrix.update, where
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# unique_mapping = true * num_classes + pred
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# goes negative and torch.bincount rejects it:
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# RuntimeError: bincount only supports 1-d non-negative integral inputs
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#
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# The fix treats an all-negative label array as "no ground truth": predictions
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# are still produced and written, metrics are simply skipped for that tile.
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# This changes no scoring behaviour on labeled data.
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#
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# Idempotent -- re-running detects the patch is already applied.
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set -euo pipefail
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source "$HOME/miniconda3/etc/profile.d/conda.sh"
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conda activate sumparts
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REPO="${1:-$HOME/sum-parts/semantic_segmentation/PointNeXt_bundle}"
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MAIN="$REPO/examples/segmentation/main.py"
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[ -f "$MAIN" ] || { echo "error: $MAIN not found" >&2; exit 1; }
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if grep -q 'SUMPARTS-UNLABELED-TEST' "$MAIN"; then
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echo "already patched"
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exit 0
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fi
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cp -n "$MAIN" "$MAIN.orig" 2>/dev/null || true
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python - "$MAIN" <<'PY'
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import sys
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from pathlib import Path
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p = Path(sys.argv[1])
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src = p.read_text(encoding="utf-8")
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old = """ coord, feat, label, idx_points, voxel_idx, reverse_idx_part, reverse_idx = load_data(data_path, cfg)
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if label is not None:
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label = torch.from_numpy(label.astype(int).squeeze()).cuda(non_blocking=True)
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"""
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new = """ coord, feat, label, idx_points, voxel_idx, reverse_idx_part, reverse_idx = load_data(data_path, cfg)
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# SUMPARTS-UNLABELED-TEST: the shipped test split carries label = -1 for
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# every point (blind test set; predictions are emailed to the authors for
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# scoring). Passing that to ConfusionMatrix makes true*num_classes+pred
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# negative and torch.bincount raises. Treat it as "no ground truth" so
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# predictions are still produced. Labeled splits are unaffected.
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if label is not None and (label < 0).all():
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logging.info(f' no ground truth in {os.path.basename(data_path)} '
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f'(all labels are -1) -- predicting without scoring')
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label = None
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if label is not None:
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label = torch.from_numpy(label.astype(int).squeeze()).cuda(non_blocking=True)
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"""
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if old not in src:
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print("PATTERN NOT FOUND -- main.py differs from what this patch expects", file=sys.stderr)
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raise SystemExit(1)
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p.write_text(src.replace(old, new), encoding="utf-8")
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print("patched:", p)
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PY
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python -c "import ast,sys; ast.parse(open(sys.argv[1], encoding='utf-8').read())" "$MAIN" \
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&& echo "syntax OK"
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echo "PATCH DONE (original kept at $MAIN.orig)"
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