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>
80 lines
2.7 KiB
Bash
80 lines
2.7 KiB
Bash
#!/usr/bin/env bash
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# SUM Parts - repair packages truncated by the WSL VM crash
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#
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# The WSL instance died (Wsl/Service/CreateInstance/E_FAIL) partway through the
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# dependency install. Everything pip had open at that moment landed on disk as
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# 0-byte files. The failure mode is nasty because import still succeeds -- the
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# module is simply empty, so you get things like
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#
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# from torch_scatter import scatter
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# TypeError: 'module' object is not callable
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#
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# pointing at the call site rather than at the broken install.
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#
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# This reinstalls the batch that was in flight. Zero-byte __init__.py files are
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# normal (namespace markers), so the scan below ignores those.
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set -euo pipefail
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CONDA_ROOT="$HOME/miniconda3"
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ENV_NAME="sumparts"
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source "$CONDA_ROOT/etc/profile.d/conda.sh"
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conda activate "$ENV_NAME"
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SITE=$(python -c "import site; print(site.getsitepackages()[0])")
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scan() {
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find "$SITE" -type f \( -name '*.py' -o -name '*.so' \) -size 0 \
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! -name '__init__.py' | wc -l
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}
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echo "=== suspicious 0-byte files before: $(scan) ==="
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# The dependency batch that was installing when the VM went down.
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PKGS=(
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plyfile scikit-learn easydict PyYAML tensorboard termcolor tqdm
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multimethod h5py matplotlib pandas shortuuid gdown Cython pyvista
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wandb trimesh pillow
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)
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echo "=== force-reinstalling ${#PKGS[@]} packages ==="
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pip install --no-cache-dir --force-reinstall "${PKGS[@]}"
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# Pins that other steps depend on; --force-reinstall above can pull them up.
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echo "=== restoring pins ==="
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pip install --no-cache-dir "numpy<2" "setuptools==69.5.1" "ninja==1.11.1.1"
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# torch-scatter must match the exact torch build, so it needs its own index.
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python -c "import torch_scatter, inspect; assert callable(torch_scatter.scatter)" 2>/dev/null || {
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echo "=== reinstalling torch-scatter ==="
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pip install --no-cache-dir --force-reinstall torch-scatter \
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-f https://data.pyg.org/whl/torch-2.0.1+cu118.html
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}
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echo
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echo "=== suspicious 0-byte files after: $(scan) ==="
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find "$SITE" -type f \( -name '*.py' -o -name '*.so' \) -size 0 \
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! -name '__init__.py' | head -20
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echo
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echo "=== import check ==="
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python - <<'PY'
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import importlib
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mods = ["numpy", "torch", "torch_scatter", "plyfile", "sklearn", "matplotlib",
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"pandas", "h5py", "yaml", "easydict", "tqdm", "termcolor", "multimethod",
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"wandb", "trimesh", "PIL", "gdown"]
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bad = []
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for m in mods:
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try:
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importlib.import_module(m)
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print(f"{m:14s} OK")
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except Exception as e:
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bad.append(m)
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print(f"{m:14s} FAIL {type(e).__name__}: {e}")
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import torch_scatter
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print("torch_scatter.scatter callable:", callable(torch_scatter.scatter))
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raise SystemExit(1 if bad or not callable(torch_scatter.scatter) else 0)
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PY
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echo "REPAIR DONE"
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