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>
72 lines
2.6 KiB
Bash
72 lines
2.6 KiB
Bash
#!/usr/bin/env bash
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# SUM Parts - score the trained model on the classes this project actually needs
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#
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# Runs the sliding-window test() path over the VAL split (which has real labels,
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# unlike the blind test split), then re-scores the predictions after merging
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# SUM's 13 fine classes down to building / vegetation / vehicle / ground.
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set -uo pipefail
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CONDA_ROOT="$HOME/miniconda3"
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SEG="$HOME/sum-parts/semantic_segmentation/PointNeXt_bundle/examples/segmentation"
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DATA="$HOME/sum-parts/data/face_labeling/texsp_pcl"
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SCRIPTS="/mnt/d/MYCLAUDE_PROJECT/sum-parts-test/scripts"
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OUT="$HOME/sum-parts/runs/coarse_eval"
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source "$CONDA_ROOT/etc/profile.d/conda.sh"
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conda activate sumparts
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export WANDB_MODE=disabled WANDB_SILENT=true CUDA_HOME="$CONDA_PREFIX"
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export PYTORCH_CUDA_ALLOC_CONF="garbage_collection_threshold:0.7,max_split_size_mb:128"
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mkdir -p "$OUT"
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cd "$SEG"
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# The cfg has to match the architecture that wrote the checkpoint. Hardcoding
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# pointnet.yaml here meant a PointVector checkpoint loaded into the wrong model:
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# RuntimeError: Error(s) in loading state_dict for BaseSeg
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source "$SCRIPTS/resolve_ckpt.sh"
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# test() writes prediction plys and slides over whole tiles; point it at val so
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# there is ground truth to score against.
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#
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# TEST_VOXEL_MAX matters more than it looks. PointNet max-pools ONE global
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# feature over whatever it is handed, so the number of points per forward pass
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# is part of the model's operating point, not a neutral batching knob. Training
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# used 64000-point crops; the cfg's test default (null) feeds ~350k-point chunks
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# and the predictions collapse toward the majority class. Set it to 64000 to
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# make inference match training.
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TEST_VOXEL_MAX="${TEST_VOXEL_MAX:-}"
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VM_ARG=()
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[ -n "$TEST_VOXEL_MAX" ] && VM_ARG=(dataset.test.voxel_max="$TEST_VOXEL_MAX")
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echo "=== inference over val split (sliding window) ==="
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echo " test voxel_max: ${TEST_VOXEL_MAX:-null (cfg default)}"
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python -u main.py \
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--cfg "../../cfgs/sumv2_triangle/${CKPT_CFG}.yaml" \
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mode=test \
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--pretrained_path "$CKPT" \
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dataset.common.data_root="$DATA" \
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dataset.test.split=val \
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"${VM_ARG[@]}" \
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wandb.use_wandb=False \
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> "$OUT/infer.log" 2>&1
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rc=$?
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if [ $rc -ne 0 ]; then
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echo " FAILED rc=$rc"
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tail -15 "$OUT/infer.log"
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exit $rc
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fi
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grep -aE 'test_oa|iou per cls' "$OUT/infer.log" | tail -4
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VIS=$(dirname "$(dirname "$CKPT")")/visualization
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echo
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echo "=== predictions in $VIS ==="
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ls -1 "$VIS" | grep -c '_pred.ply' || true
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echo
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echo "=== re-scoring at project granularity ==="
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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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echo
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echo "logs in $OUT"
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