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>
30 lines
889 B
Bash
30 lines
889 B
Bash
#!/usr/bin/env bash
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# SUM Parts - benchmark VRAM and speed of each sumv2_triangle model
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#
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# Uses whatever tiles are in data/sumv2_tri_texpcl. With only the demo tile
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# present the per-iteration cost is still representative: voxel_max caps how
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# many points reach the network per sample, so timing does not depend on how
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# many tiles exist.
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set -euo pipefail
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CONDA_ROOT="$HOME/miniconda3"
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ENV_NAME="sumparts"
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SEG="$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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LOG="${LOG:-/tmp/sumparts_bench.log}"
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source "$CONDA_ROOT/etc/profile.d/conda.sh"
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conda activate "$ENV_NAME"
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export WANDB_MODE=disabled WANDB_SILENT=true CUDA_HOME="$CONDA_PREFIX"
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cd "$SEG"
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set +e
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python -u "$SCRIPTS/bench_models.py" "$@" > "$LOG" 2>&1
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rc=$?
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set -e
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grep -v "it/s\]" "$LOG" | tail -40
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echo "(full log: $LOG)"
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exit "$rc"
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