nbrightandClaude Opus 5
f39b093106
Adds STATUS.md as the handoff document: benchmark numbers, the bare-earth
metrics that actually matter for this project, the Korean-data domain gap that
retraining will not fix, and what to do on the 24 GB machine.
The memory problem was in how a prediction's colours were turned back into
class indices. Every consumer built an (N, 13, 3) float64 temporary:
d = ((rgb[:, None, :] - COLOR_MAP[None, :, :]) ** 2).sum(axis=2)
That is ~250 MB of intermediates per 800k-point tile, several live at once, and
a full 4.7M-point block pushes it into gigabytes. main.py writes exact palette
entries, so an exact hash lookup resolves nearly every point with no large
temporary; only leftovers fall back to a chunked distance search. Peak RSS on a
470k-point tile drops to 61 MB. Extracted to sumparts_palette.py and shared by
coarse_eval.py and split_by_class.py.
Also from this round:
- patch_cm_mutation.sh: ConfusionMatrix.update() rewrote the caller's pred
tensor in place, folding every ignore_index point into class num_classes-1.
test() saves its visualization from that same tensor afterwards, so an
unlabelled tile came out 100% wall and the model looked degenerate when it
was not.
- patch_class_mask.sh: SUMPARTS_MASK_CLASSES drops known-absent classes from
the argmax. Measured on Seosan and it does not help - the runner-up for
"water" is "wall", not "terrain" - but the experiment is worth keeping.
- split_by_class.py now writes .ply alongside .obj. A vertex-only OBJ has zero
faces and most viewers render nothing, which is why the first export looked
broken.
- verify_outputs.sh reads exported files back with a parser, so "here are your
files" can be checked rather than asserted.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
28 lines
903 B
Bash
28 lines
903 B
Bash
#!/usr/bin/env bash
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# SUM Parts - re-score existing predictions without re-running inference
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#
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# The prediction plys are already on disk; only the scoring changed. No GPU,
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# takes seconds.
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set -uo pipefail
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SCRIPTS="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
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DATA="$HOME/sum-parts/data/face_labeling/texsp_pcl"
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LOGROOT="$HOME/sum-parts/semantic_segmentation/PointNeXt_bundle/examples/segmentation/log/sumv2_triangle"
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OUT="$HOME/sum-parts/runs/coarse_eval"
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source "$HOME/miniconda3/etc/profile.d/conda.sh"
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conda activate sumparts
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mkdir -p "$OUT"
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VIS=$(find "$LOGROOT" -type d -name visualization -printf '%T@ %p\n' \
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| sort -rn | head -1 | cut -d' ' -f2-)
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[ -n "$VIS" ] || { echo "no visualization directory found"; exit 1; }
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echo "predictions: $VIS"
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echo "ground truth: $DATA/val"
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echo
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python "$SCRIPTS/coarse_eval.py" --pred-dir "$VIS" --gt-dir "$DATA/val" \
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| tee "$OUT/coarse.txt"
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