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>
59 lines
1.9 KiB
Bash
59 lines
1.9 KiB
Bash
#!/usr/bin/env bash
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# SUM Parts - run inference on the converted Korean tile
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#
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# This answers one question: does a SUM Parts model accept our data and emit
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# per-point predictions? It does NOT measure anything. The checkpoint is the
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# 1-epoch smoke-test model and the tile carries label=0 everywhere, so the
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# reported mIoU/OA are meaningless by construction. Look at whether it runs and
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# what the predicted class histogram looks like, nothing else.
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set -euo pipefail
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CONDA_ROOT="$HOME/miniconda3"
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ENV_NAME="sumparts"
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REPO="$HOME/sum-parts/semantic_segmentation/PointNeXt_bundle"
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SEG="$REPO/examples/segmentation"
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TILE="$HOME/sum-parts/data/korea_poc/seosan_BlockYBA_tile0.ply"
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TRACK="$HOME/sum-parts/data/korea_poc_track"
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LOG="${LOG:-/tmp/sumparts_poc_infer.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
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export CUDA_HOME="$CONDA_PREFIX"
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[ -f "$TILE" ] || { echo "error: $TILE missing -- run poc_korea.sh first" >&2; exit 1; }
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# The dataset class globs {train,val,test}/*.ply and errors on a missing split,
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# so all three have to exist even for a test-only run.
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for split in train val test; do
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mkdir -p "$TRACK/$split"
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ln -f "$TILE" "$TRACK/$split/$(basename "$TILE")"
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done
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rm -rf "$TRACK/processed"
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CKPT=$(find "$SEG/log/sumv2_triangle" -name '*_ckpt_best.pth' -printf '%T@ %p\n' \
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| sort -rn | head -1 | cut -d' ' -f2-)
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[ -n "$CKPT" ] || { echo "error: no checkpoint found -- run smoke_train.sh first" >&2; exit 1; }
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echo "checkpoint: $CKPT"
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cd "$SEG"
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set +e
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python -u main.py \
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--cfg ../../cfgs/sumv2_triangle/pointnet.yaml \
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mode=test \
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--pretrained_path "$CKPT" \
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dataset.common.data_root="$TRACK" \
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wandb.use_wandb=False \
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batch_size=2 \
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val_batch_size=1 \
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> "$LOG" 2>&1
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rc=$?
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set -e
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echo "=== exit=$rc | last 25 lines ==="
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tail -25 "$LOG"
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[ "$rc" -eq 0 ] && echo "POC INFER DONE" || echo "POC INFER FAILED (rc=$rc)"
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exit "$rc"
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