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>
90 lines
2.8 KiB
Bash
90 lines
2.8 KiB
Bash
#!/usr/bin/env bash
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# SUM Parts - dataset download from Hugging Face
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#
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# PREREQUISITE: the dataset is GATED (gated: auto). Before this works you must,
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# once, in a browser:
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# 1. open https://huggingface.co/datasets/gwxgrxhyz/SUM-Parts
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# 2. log in and accept the CC BY-NC 4.0 terms on the gate form
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# Without that step every download returns HTTP 403.
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#
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# Usage:
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# bash download_data.sh # demo only (smoke test, small)
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# bash download_data.sh all # demo + mesh + pcl (large)
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set -euo pipefail
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CONDA_ROOT="$HOME/miniconda3"
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ENV_NAME="sumparts"
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REPO_ID="gwxgrxhyz/SUM-Parts"
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# cfgs/sumv2_triangle/default.yaml uses data_root: ../../data/... relative to
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# PointNeXt_bundle, which resolves to <repo>/data/
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DATA_DIR="$HOME/sum-parts/data"
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DL_DIR="$DATA_DIR/_archives"
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source "$CONDA_ROOT/etc/profile.d/conda.sh"
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conda activate "$ENV_NAME"
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python -c "import huggingface_hub" 2>/dev/null || pip install --no-cache-dir "huggingface_hub[cli]"
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# Reuse the token already cached on the Windows side if WSL has none.
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if [ ! -f "$HOME/.cache/huggingface/token" ] && [ -f /mnt/c/Users/"$USER"/.cache/huggingface/token ]; then
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mkdir -p "$HOME/.cache/huggingface"
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cp /mnt/c/Users/"$USER"/.cache/huggingface/token "$HOME/.cache/huggingface/token"
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chmod 600 "$HOME/.cache/huggingface/token"
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echo "copied HF token from Windows profile"
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fi
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if [ "${1:-demo}" = "all" ]; then
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FILES=(demo.zip mesh.zip pcl.zip)
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else
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FILES=(demo.zip)
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fi
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mkdir -p "$DL_DIR"
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for f in "${FILES[@]}"; do
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echo "=== downloading $f ==="
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python - "$REPO_ID" "$f" "$DL_DIR" <<'PY'
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import sys
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from huggingface_hub import hf_hub_download
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repo_id, filename, out_dir = sys.argv[1:4]
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p = hf_hub_download(
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repo_id=repo_id,
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filename=filename,
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repo_type="dataset",
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local_dir=out_dir,
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)
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print("saved:", p)
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PY
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done
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# Extract with python's zipfile rather than `unzip`: the distro has no unzip
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# installed and sudo needs a password here, so apt is not an option.
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for f in "${FILES[@]}"; do
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echo "=== extracting $f ==="
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python - "$DL_DIR/$f" "$DATA_DIR" <<'PY'
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import sys, zipfile
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src, dest = sys.argv[1:3]
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with zipfile.ZipFile(src) as z:
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names = z.namelist()
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print(f" {len(names)} entries")
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z.extractall(dest)
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print(" ->", dest)
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PY
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done
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# NOTE: no `find ... | head` here. Under `set -euo pipefail`, head closing the
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# pipe sends SIGPIPE to find and the script exits 141 -- reported as a failed
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# download even though everything extracted fine.
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echo "=== resulting layout (depth 2) ==="
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find "$DATA_DIR" -maxdepth 2 -not -path '*/_archives/*' -type d | sort
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echo
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echo "=== ply counts ==="
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for d in "$DATA_DIR"/*/; do
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[ "$(basename "$d")" = "_archives" ] && continue
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n=$(find "$d" -name '*.ply' 2>/dev/null | wc -l)
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[ "$n" -gt 0 ] && printf '%-28s %5s ply\n' "$(basename "$d")/" "$n"
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done
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echo "DATA DONE"
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