Add SUM Parts reproduction and Seosan Myeongcheon application pipeline
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>
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#!/usr/bin/env python3
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"""Measure peak VRAM and per-iteration time for each sumv2_triangle model.
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Answers "will full training fit on this GPU, and how long would it take" with
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numbers off the actual card rather than a guess. Builds the model and the real
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train loader from each cfg, runs a handful of train steps at the cfg's own
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batch_size, and reports peak allocated memory plus iterations/second.
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Run from PointNeXt_bundle/examples/segmentation.
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Usage:
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python bench_models.py [--iters 8] [--cfgs pointnet pointnext-xl ...]
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"""
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from __future__ import annotations
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import argparse
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import sys
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import time
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from pathlib import Path
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import torch
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sys.path.append(str(Path(__file__).resolve().parent))
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sys.path.append("../../")
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from openpoints.utils import EasyConfig # noqa: E402
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from openpoints.dataset import build_dataloader_from_cfg, get_features_by_keys # noqa: E402
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from openpoints.models import build_model_from_cfg # noqa: E402
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from openpoints.loss import build_criterion_from_cfg # noqa: E402
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from openpoints.optim import build_optimizer_from_cfg # noqa: E402
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ALL_CFGS = ["pointnet", "pointnet++msg", "pointnext-xl", "pointvector-xl"]
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def bench(cfg_name: str, iters: int, voxel_max: int | None = None,
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batch_size: int | None = None) -> dict:
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cfg = EasyConfig()
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cfg.load(f"../../cfgs/sumv2_triangle/{cfg_name}.yaml", recursive=True)
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cfg.rank, cfg.distributed, cfg.mp = 0, False, False
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if voxel_max is not None:
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cfg.dataset.train.voxel_max = voxel_max
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if batch_size is not None:
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cfg.batch_size = batch_size
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model = build_model_from_cfg(cfg.model).cuda()
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n_params = sum(p.numel() for p in model.parameters())
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cfg.criterion_args.weight = None
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criterion = build_criterion_from_cfg(cfg.criterion_args).cuda()
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optimizer = build_optimizer_from_cfg(model, lr=cfg.lr, **cfg.optimizer)
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train_loader = build_dataloader_from_cfg(
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cfg.batch_size, cfg.dataset, cfg.dataloader,
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datatransforms_cfg=cfg.datatransforms, split="train", distributed=False,
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)
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torch.cuda.empty_cache()
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torch.cuda.reset_peak_memory_stats()
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model.train()
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times: list[float] = []
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it = iter(train_loader)
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n_points = None
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for i in range(iters):
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try:
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data = next(it)
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except StopIteration:
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it = iter(train_loader)
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data = next(it)
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for k in data:
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data[k] = data[k].cuda(non_blocking=True)
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target = data["y"].squeeze(-1)
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data["x"] = get_features_by_keys(data, cfg.feature_keys)
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if n_points is None:
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n_points = int(data["pos"].shape[0] * data["pos"].shape[1]) \
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if data["pos"].dim() == 3 else int(data["pos"].shape[0])
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torch.cuda.synchronize()
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t0 = time.perf_counter()
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logits = model(data)
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loss = criterion(logits, target)
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loss.backward()
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optimizer.step()
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optimizer.zero_grad()
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torch.cuda.synchronize()
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dt = time.perf_counter() - t0
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if i >= 2: # skip warm-up iterations
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times.append(dt)
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peak = torch.cuda.max_memory_allocated() / 1024**3
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reserved = torch.cuda.max_memory_reserved() / 1024**3
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avg = sum(times) / len(times) if times else float("nan")
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del model, optimizer, criterion, train_loader
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torch.cuda.empty_cache()
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return {
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"cfg": cfg_name, "params_m": n_params / 1e6, "batch_size": cfg.batch_size,
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"voxel_max": cfg.dataset.train.voxel_max, "points_per_batch": n_points,
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"peak_gb": peak, "reserved_gb": reserved, "sec_per_iter": avg,
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}
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def main() -> None:
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ap = argparse.ArgumentParser()
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ap.add_argument("--iters", type=int, default=8)
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ap.add_argument("--cfgs", nargs="*", default=ALL_CFGS)
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ap.add_argument("--voxel-max", type=int, default=None,
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help="override dataset.train.voxel_max (cfg default: 64000)")
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ap.add_argument("--batch-size", type=int, default=None)
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args = ap.parse_args()
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total_gb = torch.cuda.get_device_properties(0).total_memory / 1024**3
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print(f"GPU: {torch.cuda.get_device_name(0)} {total_gb:.1f} GB")
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if args.voxel_max or args.batch_size:
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print(f"overrides: voxel_max={args.voxel_max} batch_size={args.batch_size}")
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print("NOTE: on WSL2 the NVIDIA driver spills past VRAM into host RAM instead")
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print(" of raising OOM. A peak above the card's capacity means the run")
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print(" was paging over PCIe -- it completes, but uselessly slowly.\n")
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rows = []
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for name in args.cfgs:
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print(f"--- benchmarking {name} ---", flush=True)
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try:
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rows.append(bench(name, args.iters, args.voxel_max, args.batch_size))
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print(f" ok\n", flush=True)
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except torch.cuda.OutOfMemoryError as e:
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print(f" OOM: {str(e)[:120]}\n", flush=True)
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rows.append({"cfg": name, "oom": True})
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torch.cuda.empty_cache()
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except Exception as e: # noqa: BLE001
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print(f" FAILED {type(e).__name__}: {str(e)[:200]}\n", flush=True)
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rows.append({"cfg": name, "error": f"{type(e).__name__}: {e}"})
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torch.cuda.empty_cache()
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print()
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print(f"{'cfg':<16}{'params':>9}{'bs':>4}{'pts/batch':>12}"
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f"{'peak VRAM':>11}{'s/iter':>9} fits?")
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print("-" * 70)
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for r in rows:
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if r.get("oom"):
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print(f"{r['cfg']:<16}{'':>9}{'':>4}{'':>12}{'OOM':>11}{'':>9}")
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elif r.get("error"):
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print(f"{r['cfg']:<16} {r['error'][:44]}")
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else:
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fits = "yes" if r["peak_gb"] < total_gb * 0.95 else "NO (spilling)"
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print(f"{r['cfg']:<16}{r['params_m']:>8.1f}M{r['batch_size']:>4}"
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f"{r['points_per_batch']:>12,}{r['peak_gb']:>10.2f}G"
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f"{r['sec_per_iter']:>9.3f} {fits}")
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if __name__ == "__main__":
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main()
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