Сортировочная ячейка Isaac Sim: CV-пайплайн и меши товаров

Замкнутый контур "поток -> CV -> механика": товары идут по конвейеру с шагом 700 мм, класс определяется стереопайплайном во время движения, пушер и плуг реагируют физически. Состав: * control_test/ - ячейка и CV. run_sorting_cv.py + cv_worker.py (два процесса, потому что torch внутри Isaac роняет сцену), cell.py (физика лент, плуга, пушера), measure_plane.py (замер габаритов), README.md и .memory.md с замерами, проблемами и ловушками * robozon_sorter/ - модули симуляции, scripts/ - утилиты, scene/ - сцены * assets/ - меши товаров, плуг, объекты Objaverse Бейзлайн CV: DEFOM-Stereo vitl, вход 480, iters 24, кроп зоны осмотра, без сегментации. На потоке 700 мм - классы 8/9, габариты MAE 32.8 мм, 469 мс на товар при такте 700 мс. Веса моделей (4.5 ГБ) и пропсы конвейера NVIDIA (274 МБ) не включены - источники и команды скачивания в MODELS.md. Выход прогонов (captures/, runtime/) не включён: воспроизводится. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-08-01 13:07:24 +00:00
parent 6ce460378a
commit 0d32f32db0
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+#!/usr/bin/env python3
+"""Fetch the model weights the pipeline needs. They are far too big for git.
+
+    python scripts/fetch_models.py
+
+Downloads in small Range chunks with per-chunk retries: the CRE weights come off a slow
+mirror and a plain single-stream GET truncates silently, leaving an unloadable file.
+"""
+from __future__ import annotations
+
+import os
+import sys
+import time
+import urllib.request
+from pathlib import Path
+
+ROOT = Path(__file__).resolve().parent.parent
+MODELS = ROOT / "assets" / "models"
+CHUNK = 1 << 21
+TRIES = 12
+
+SOURCES = {
+    "FastSAM-s.pt":
+        "https://huggingface.co/Ultralytics/FastSAM/resolve/main/FastSAM-s.pt?download=true",
+    # CRE-Stereo ETH3D weights, from the megvii research release mirror
+    "crestereo_eth3d.pth":
+        "https://github.com/ibaiGorordo/CREStereo-Pytorch/releases/download/0.0.1/crestereo_eth3d.pth",
+}
+
+CRE_REPO = "https://github.com/ibaiGorordo/CREStereo-Pytorch"
+
+
+def content_length(url):
+    req = urllib.request.Request(url, method="HEAD")
+    with urllib.request.urlopen(req, timeout=60) as r:
+        if r.status in (301, 302) and r.headers.get("Location"):
+            return content_length(r.headers["Location"])
+        return int(r.headers.get("Content-Length", 0))
+
+
+def get_range(url, a, b):
+    req = urllib.request.Request(url)
+    req.add_header("Range", f"bytes={a}-{b}")
+    with urllib.request.urlopen(req, timeout=120) as r:
+        return r.read()
+
+
+def fetch(name, url, out_dir):
+    out = out_dir / name
+    total = content_length(url)
+    have = out.stat().st_size if out.exists() else 0
+    if total and have == total:
+        print(f"  {name}: already complete ({total/1e6:.1f} MB)")
+        return True
+    if not total:                                  # server refuses HEAD: plain download
+        print(f"  {name}: streaming (no content-length)")
+        urllib.request.urlretrieve(url, out)
+        return out.exists()
+    if have > total:
+        have = 0
+    mode = "r+b" if have else "wb"
+    pos = have
+    with open(out, mode) as fh:
+        fh.seek(pos)
+        t0 = time.time()
+        while pos < total:
+            end = min(pos + CHUNK - 1, total - 1)
+            for attempt in range(TRIES):
+                try:
+                    data = get_range(url, pos, end)
+                    if len(data) != end - pos + 1:
+                        raise IOError("short chunk")
+                    fh.write(data)
+                    fh.flush()
+                    pos += len(data)
+                    pct = 100.0 * pos / total
+                    rate = (pos - have) / max(time.time() - t0, 1e-6) / 1024
+                    print(f"\r  {name}: {pct:5.1f}%  {rate:6.0f} KB/s", end="", flush=True)
+                    break
+                except Exception as exc:
+                    if attempt == TRIES - 1:
+                        print(f"\n  {name}: FAILED at byte {pos}: {exc}")
+                        return False
+                    time.sleep(min(2 ** attempt, 20))
+    print()
+    ok = out.stat().st_size == total
+    print(f"  {name}: {'ok' if ok else 'SIZE MISMATCH'} ({out.stat().st_size/1e6:.1f} MB)")
+    return ok
+
+
+def main():
+    MODELS.mkdir(parents=True, exist_ok=True)
+    print(f"fetching model weights into {MODELS}")
+    results = {n: fetch(n, u, MODELS) for n, u in SOURCES.items()}
+
+    cre_dir = MODELS / "crestereo"
+    if not (cre_dir / "nets").exists():
+        print(f"\nCRE-Stereo network code is not vendored here. Clone it next to the weights:")
+        print(f"  git clone {CRE_REPO} {cre_dir}")
+        print("  (only the `nets` package is imported)")
+
+    missing = [n for n, ok in results.items() if not ok]
+    if missing:
+        print(f"\nincomplete: {missing} - re-run, downloads resume where they stopped")
+        return 1
+    print("\nall weights present")
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())