Initial public release

scripts/train_experimental_yolo_candidate.py

@@ -0,0 +1,132 @@
+#!/usr/bin/env python3
+"""CUDA-train an isolated, non-promotable YOLO candidate."""
+
+from __future__ import annotations
+
+import argparse
+import hashlib
+import json
+import shutil
+from pathlib import Path
+
+EXPERIMENTAL_ROOT = Path("/app/storage/training/experimental")
+
+
+def sha256(path: Path) -> str:
+    digest = hashlib.sha256()
+    with path.open("rb") as handle:
+        for chunk in iter(lambda: handle.read(1024 * 1024), b""):
+            digest.update(chunk)
+    return digest.hexdigest()
+
+
+def _inside(path: Path, root: Path) -> bool:
+    try:
+        path.resolve().relative_to(root.resolve())
+        return True
+    except ValueError:
+        return False
+
+
+def validate_paths(dataset_dir: Path, output_dir: Path) -> dict:
+    if not _inside(dataset_dir, EXPERIMENTAL_ROOT) or not _inside(output_dir, EXPERIMENTAL_ROOT):
+        raise ValueError(f"dataset and output must remain below {EXPERIMENTAL_ROOT}")
+    marker_path = dataset_dir / "EXPERIMENTAL_ONLY.json"
+    yaml_path = dataset_dir / "dataset.yaml"
+    if not marker_path.is_file() or not yaml_path.is_file():
+        raise ValueError("experimental marker or dataset.yaml is missing")
+    marker = json.loads(marker_path.read_text(encoding="utf-8"))
+    if marker.get("promotion_allowed") is not False or marker.get("release_claim_allowed") is not False:
+        raise ValueError("experimental marker does not prohibit promotion and release claims")
+    if marker.get("dataset_yaml_sha256") != sha256(yaml_path):
+        raise ValueError("experimental dataset.yaml checksum mismatch")
+    return marker
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--dataset-dir", type=Path, required=True)
+    parser.add_argument("--base-model", type=Path, required=True)
+    parser.add_argument("--output-dir", type=Path, required=True)
+    parser.add_argument("--run-name", default="v73-flanders-remediation")
+    parser.add_argument("--epochs", type=int, default=20)
+    parser.add_argument("--imgsz", type=int, default=640)
+    parser.add_argument("--batch", type=int, default=8)
+    parser.add_argument("--learning-rate", type=float, default=0.0001)
+    parser.add_argument("--freeze", type=int, default=10)
+    parser.add_argument("--workers", type=int, default=0)
+    return parser.parse_args()
+
+
+def main() -> int:
+    args = parse_args()
+    validate_paths(args.dataset_dir, args.output_dir)
+    if not args.base_model.is_file():
+        raise SystemExit(f"base model missing: {args.base_model}")
+    import torch
+    from ultralytics import YOLO
+
+    if not torch.cuda.is_available():
+        raise SystemExit("CUDA is required for GeoIntel experimental training")
+    model = YOLO(str(args.base_model))
+    result = model.train(
+        data=str(args.dataset_dir / "dataset.yaml"),
+        epochs=args.epochs,
+        imgsz=args.imgsz,
+        batch=args.batch,
+        workers=args.workers,
+        device=0,
+        project=str(args.output_dir),
+        name=args.run_name,
+        exist_ok=False,
+        pretrained=True,
+        deterministic=True,
+        seed=73,
+        amp=False,
+        val=False,
+        optimizer="AdamW",
+        lr0=args.learning_rate,
+        lrf=0.1,
+        freeze=args.freeze,
+        mosaic=0.0,
+        translate=0.05,
+        scale=0.1,
+        plots=False,
+        verbose=True,
+    )
+    run_dir = Path(result.save_dir)
+    best = run_dir / "weights" / "best.pt"
+    if not best.is_file():
+        best = run_dir / "weights" / "last.pt"
+    candidate = run_dir / "candidate.experimental.pt"
+    shutil.copy2(best, candidate)
+    summary = {
+        "schema_version": 1,
+        "status": "trained_experimental_only",
+        "promotion_allowed": False,
+        "release_claim_allowed": False,
+        "human_review_pending": True,
+        "dataset_yaml_sha256": sha256(args.dataset_dir / "dataset.yaml"),
+        "base_model": str(args.base_model),
+        "base_model_sha256": sha256(args.base_model),
+        "candidate_model": str(candidate),
+        "candidate_model_sha256": sha256(candidate),
+        "epochs": args.epochs,
+        "imgsz": args.imgsz,
+        "batch": args.batch,
+        "learning_rate": args.learning_rate,
+        "optimizer": "AdamW",
+        "freeze": args.freeze,
+        "workers": args.workers,
+        "device": torch.cuda.get_device_name(0),
+        "required_next_gate": "independent frozen V72 calibration evaluation",
+    }
+    (run_dir / "experimental_training_summary.json").write_text(
+        json.dumps(summary, indent=2, sort_keys=True) + "\n", encoding="utf-8"
+    )
+    print(json.dumps(summary, indent=2))
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())