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>
55 lines
1.9 KiB
Bash
55 lines
1.9 KiB
Bash
#!/usr/bin/env bash
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# SUM Parts - find the largest voxel_max that still fits in VRAM
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#
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# Why this matters: on WSL2 the NVIDIA driver spills past VRAM into host RAM
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# instead of raising OOM, so an oversized config still "works" -- at 47 s/iter
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# instead of 0.43. Dropping voxel_max until the peak fits gave a 108x speedup
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# on pointnext-xl. But voxel_max is also the cfg value the paper trained with
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# (64000), so anything lower is a deviation worth naming. This sweep finds how
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# close to 64000 the card can actually get.
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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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SCRIPTS="/mnt/d/MYCLAUDE_PROJECT/sum-parts-test/scripts"
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CFG="${CFG:-pointnext-xl}"
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ITERS="${ITERS:-5}"
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VALUES="${*:-24000 32000 40000 48000 64000}"
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source "$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="$CONDA_PREFIX"
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cd "$SEG"
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echo "sweeping $CFG over voxel_max: $VALUES"
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echo
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printf '%-10s %-12s %-11s %-10s %s\n' "voxel_max" "pts/batch" "peak VRAM" "s/iter" "fits?"
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printf -- '---------------------------------------------------------\n'
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for vm in $VALUES; do
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log="/tmp/sweep_${CFG}_${vm}.log"
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python -u "$SCRIPTS/bench_models.py" \
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--iters "$ITERS" --voxel-max "$vm" --cfgs "$CFG" > "$log" 2>&1
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rc=$?
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if [ $rc -ne 0 ]; then
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printf '%-10s %s\n' "$vm" "FAILED (rc=$rc, see $log)"
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continue
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fi
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line=$(grep -E "^${CFG} +[0-9]" "$log" | tail -1)
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if [ -z "$line" ]; then
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printf '%-10s %s\n' "$vm" "no result (see $log)"
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continue
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fi
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# cfg params bs pts peak s/iter fits...
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pts=$(echo "$line" | awk '{print $4}')
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peak=$(echo "$line" | awk '{print $5}')
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sec=$(echo "$line" | awk '{print $6}')
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fits=$(echo "$line" | cut -d' ' -f7- | sed 's/^ *//')
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printf '%-10s %-12s %-11s %-10s %s\n' "$vm" "$pts" "$peak" "$sec" "$fits"
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done
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echo
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echo "SWEEP DONE"
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