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>
65 lines
2.0 KiB
Bash
65 lines
2.0 KiB
Bash
#!/usr/bin/env bash
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# SUM Parts - A/B the validation protocol on the same checkpoint
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#
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# Training reported val_miou 17.19 @E90, but a standalone mode=val gave 4.20.
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# The only difference is dataset.val.voxel_max:
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#
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# A) 64000 what training used after the memory mitigation (§3-2-1),
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# i.e. validation on a 64k-point crop, matching the training
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# input size
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# B) null the cfg's own default: the whole ~700k-point tile in one
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# forward pass
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#
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# PointNet pools a single global feature over whatever it is given, so input
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# size is not a neutral knob -- this measures how much it moved the number.
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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="$HOME/sum-parts/data/face_labeling/texsp_pcl"
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OUT="$HOME/sum-parts/runs/val_ab"
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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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CKPT="${CKPT:-$(find "$SEG/log/sumv2_triangle" -name '*_ckpt_best.pth' -printf '%T@ %p\n' \
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| sort -rn | head -1 | cut -d' ' -f2-)}"
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echo "checkpoint: $(basename "$CKPT")"
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echo
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run_val() {
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local tag="$1"; shift
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local log="$OUT/val_${tag}.log"
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python -u main.py \
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--cfg ../../cfgs/sumv2_triangle/pointnet.yaml \
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mode=val \
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--pretrained_path "$CKPT" \
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dataset.common.data_root="$DATA" \
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wandb.use_wandb=False \
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val_batch_size=1 \
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"$@" \
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> "$log" 2>&1
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local rc=$?
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if [ $rc -ne 0 ]; then
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echo " [$tag] FAILED rc=$rc"
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tail -6 "$log"
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return 1
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fi
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grep -aA1 'Best ckpt' "$log" | tail -2 | sed "s/^/ [$tag] /"
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}
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echo "=== A: voxel_max 64000 (what training measured) ==="
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run_val capped dataset.val.voxel_max=64000
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echo
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echo "=== B: voxel_max null (cfg default, whole tile) ==="
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run_val full
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echo
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echo "logs in $OUT"
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