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>
50 lines
1.7 KiB
Bash
50 lines
1.7 KiB
Bash
#!/usr/bin/env bash
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# SUM Parts - WSL2 conda env setup (no sudo required)
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#
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# NOTE: Anaconda's "defaults" channels (repo.anaconda.com/pkgs/*) require accepting
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# a Terms of Service that carries commercial-license obligations for larger orgs.
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# This script deliberately avoids them entirely: conda-forge for packages,
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# nvidia channel for the CUDA toolkit, both with --override-channels.
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set -euo pipefail
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CONDA_ROOT="$HOME/miniconda3"
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ENV_NAME="sumparts"
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export PATH="$CONDA_ROOT/bin:$PATH"
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source "$CONDA_ROOT/etc/profile.d/conda.sh"
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echo "=== [0/5] pin channels to conda-forge (drop anaconda defaults) ==="
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conda config --remove channels defaults 2>/dev/null || true
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conda config --add channels conda-forge
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conda config --set channel_priority strict
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echo "=== [1/5] create env: $ENV_NAME (python 3.10) ==="
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conda create -n "$ENV_NAME" -y --override-channels -c conda-forge python=3.10
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conda activate "$ENV_NAME"
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echo "=== [2/5] CUDA Toolkit 11.8 (nvidia channel, no sudo) ==="
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conda install -y --override-channels -c "nvidia/label/cuda-11.8.0" cuda-toolkit
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echo "=== [3/5] PyTorch 2.0.1 + cu118 (pip wheels) ==="
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pip install --no-cache-dir \
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torch==2.0.1+cu118 torchvision==0.15.2+cu118 \
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--index-url https://download.pytorch.org/whl/cu118
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echo "=== [4/5] pin numpy<2 (torch 2.0.x is ABI-incompatible with numpy 2) ==="
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pip install --no-cache-dir "numpy<2"
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echo "=== [5/5] verify ==="
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which nvcc
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nvcc --version | tail -2
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python - <<'PY'
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import torch, numpy
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print("torch :", torch.__version__)
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print("cuda :", torch.version.cuda)
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print("avail :", torch.cuda.is_available())
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print("device :", torch.cuda.get_device_name(0) if torch.cuda.is_available() else None)
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print("numpy :", numpy.__version__)
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PY
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echo "ENV DONE"
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