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>
75 lines
2.3 KiB
Bash
75 lines
2.3 KiB
Bash
#!/usr/bin/env bash
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# SUM Parts - pipeline smoke test
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#
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# Goal is NOT accuracy. Goal is proving the whole chain survives:
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# ply load -> grid subsample -> cuda ops -> forward -> backward -> val
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# Uses the lightest model (pointnet) and 1 epoch. Expect garbage mIoU.
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#
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# Usage:
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# bash smoke_train.sh # pointnet, 1 epoch
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# bash smoke_train.sh pointnext-xl 2 # other cfg / epoch count
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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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CFG="${1:-pointnet}"
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EPOCHS="${2:-1}"
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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 CUDA_HOME="$CONDA_PREFIX"
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export PATH="$CUDA_HOME/bin:$PATH"
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# main.py imports wandb at module scope, so the package must exist even with
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# wandb.use_wandb=False. Disabled mode keeps it from asking for a login.
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export WANDB_MODE=disabled
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export WANDB_SILENT=true
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cd "$REPO/examples/segmentation"
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# Two constraints squeeze BS from both sides, and both fail confusingly:
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#
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# too large -> the loader drops the last partial batch, so a single tile with
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# the cfg's batch_size (6 for pointnet) yields ZERO batches. The confusion
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# matrix is then never populated and cm.all_metrics() dies on
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# AttributeError: 'int' object has no attribute 'diag'
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#
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# too small -> BatchNorm1d refuses a batch of 1 while training:
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# ValueError: Expected more than 1 value per channel when training,
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# got input size torch.Size([1, 512])
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#
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# So: BS=2 (the BatchNorm floor), and LOOP repeats the tile enough times to fill
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# whole batches.
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LOOP="${3:-4}"
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BS="${BS:-2}"
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LOG="${LOG:-/tmp/sumparts_smoke.log}"
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echo "=== smoke train: cfg=$CFG epochs=$EPOCHS loop=$LOOP bs=$BS ==="
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echo "log: $LOG"
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# Capture both streams to a file. Tracebacks were getting lost when this ran
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# under a wrapper that only kept stdout, which made crashes look silent.
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set +e
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python -u main.py \
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--cfg "../../cfgs/sumv2_triangle/${CFG}.yaml" \
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mode=train \
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epochs="$EPOCHS" \
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wandb.use_wandb=False \
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dataset.train.loop="$LOOP" \
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batch_size="$BS" \
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val_batch_size=1 \
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val_freq=1 \
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> "$LOG" 2>&1
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rc=$?
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set -e
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echo "=== exit=$rc | last 30 lines ==="
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tail -30 "$LOG"
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[ "$rc" -eq 0 ] && echo "SMOKE DONE" || echo "SMOKE FAILED (rc=$rc)"
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exit "$rc"
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