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>
69 lines
2.3 KiB
Bash
69 lines
2.3 KiB
Bash
#!/usr/bin/env bash
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# SUM Parts - PointNeXt_bundle dependencies + CUDA extension build
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#
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# The upstream install.sh assumes python 3.7 / torch 1.12.1 / cu113 and pins
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# requirements.txt to versions that no longer resolve on python 3.10.
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# This script keeps the same package set but relaxes the pins, and drops
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# packages that are not needed for the sumv2 segmentation task:
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# - deepspeed (not used by the sumv2 training path, heavy build)
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# - mkdocs-* (docs only)
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# It also adds two packages the code needs but requirements.txt omits:
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# - plyfile : imported by both sumv2 dataset loaders
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# - wandb : main.py imports it at module scope, so it must exist even when
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# wandb.use_wandb=False. Run with WANDB_MODE=disabled.
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# NOTE: the chamfer_dist / emd extensions look reconstruction-only, but
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# openpoints/models/__init__.py imports .reconstruction unconditionally, so
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# segmentation runs need them too. build_ext.sh builds them.
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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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source "$CONDA_ROOT/etc/profile.d/conda.sh"
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conda activate "$ENV_NAME"
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# RTX 3060 == compute capability 8.6. Pin it so nvcc does not build every arch.
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export TORCH_CUDA_ARCH_LIST="8.6"
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export CUDA_HOME="$CONDA_PREFIX"
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export PATH="$CUDA_HOME/bin:$PATH"
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echo "=== [1/4] python deps ==="
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# setuptools 69.5.1 : subsampling/setup.py imports numpy.distutils, which needs
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# distutils.msvccompiler -- removed in setuptools 74.0. The extensions also
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# still use `python setup.py install`, dropped in setuptools 80.
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pip install --no-cache-dir "setuptools==69.5.1" wheel
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pip install --no-cache-dir \
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plyfile \
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scikit-learn \
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ninja \
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easydict \
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PyYAML \
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tensorboard \
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termcolor \
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tqdm \
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multimethod \
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h5py \
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matplotlib \
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pandas \
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shortuuid \
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gdown \
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Cython \
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pyvista \
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wandb \
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trimesh \
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pillow \
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"numpy<2"
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echo "=== [2/4] torch-scatter (matched to torch 2.0.1+cu118) ==="
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pip install --no-cache-dir torch-scatter \
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-f https://data.pyg.org/whl/torch-2.0.1+cu118.html
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echo "=== [3/4] build CUDA extensions ==="
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# Delegated to build_ext.sh so a failed compile can be retried on its own.
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# That script also pins ninja==1.11.1.1 (torch 2.0 + ninja>=1.12 dies on SIGPIPE).
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bash "$(dirname "$0")/build_ext.sh"
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echo "POINTNEXT DONE"
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