Initial public release

scripts/build_regional_yolo_dataset.py

@@ -0,0 +1,233 @@
+#!/usr/bin/env python3
+"""Build a leak-free regional YOLO view over an immutable tiled corpus."""
+
+from __future__ import annotations
+
+import argparse
+import hashlib
+import json
+import sys
+from collections import Counter
+from pathlib import Path
+from typing import Any
+
+SCRIPT_DIR = Path(__file__).resolve().parent
+if str(SCRIPT_DIR) not in sys.path:
+    sys.path.insert(0, str(SCRIPT_DIR))
+
+from training_dataset_eligibility import (  # noqa: E402
+    TrainingEligibilityError,
+    assert_frozen_manifest_training_eligible,
+)
+from training_release_manifest import (  # noqa: E402
+    TrainingReleaseError,
+    assert_yolo_summary_bound_to_embedded_training_release,
+    create_training_release_manifest,
+)
+
+
+PROTECTED_SPLITS = {"calibration", "test", "background-test", "challenge"}
+
+
+def sha256(path: Path) -> str:
+    digest = hashlib.sha256()
+    with path.open("rb") as stream:
+        for chunk in iter(lambda: stream.read(1024 * 1024), b""):
+            digest.update(chunk)
+    return digest.hexdigest()
+
+
+def write_lines(path: Path, values: list[str]) -> None:
+    path.write_text("".join(f"{value}\n" for value in values), encoding="utf-8")
+
+
+def build(
+    *,
+    summary: dict[str, Any],
+    manifest: dict[str, Any],
+    region: str,
+    priority_contexts: set[str],
+    priority_repeat: int,
+    negative_repeat: int,
+) -> tuple[list[str], list[str], dict[str, Any]]:
+    samples = {item["sample_slug"]: item for item in manifest["samples"]}
+    selected: list[tuple[dict[str, Any], dict[str, Any]]] = []
+    protected: list[str] = []
+    unknown: list[str] = []
+    for tile in summary["tiles"]:
+        if not tile.get("kept", True):
+            continue
+        sample = samples.get(tile["sample_slug"])
+        if sample is None:
+            unknown.append(tile["sample_slug"])
+            continue
+        tile_split = str(tile.get("split") or "")
+        manifest_split = str(sample.get("split") or "")
+        if tile_split in PROTECTED_SPLITS or manifest_split in PROTECTED_SPLITS:
+            protected.append(tile["sample_slug"])
+            continue
+        if tile_split != manifest_split:
+            raise ValueError(
+                f"tile/manifest split mismatch for {tile['sample_slug']}: "
+                f"{tile_split!r} != {manifest_split!r}"
+            )
+        if sample.get("region") == region and tile_split in {"train", "val"}:
+            selected.append((tile, sample))
+    if unknown:
+        raise ValueError(f"summary references unknown samples: {sorted(set(unknown))}")
+    if protected:
+        raise ValueError(f"summary contains protected tiles: {sorted(set(protected))}")
+
+    train: list[str] = []
+    val: list[str] = []
+    sample_counts: Counter[str] = Counter()
+    context_counts: Counter[str] = Counter()
+    negative_count = 0
+    for tile, sample in selected:
+        image_path = str(tile["image_path"])
+        split = str(tile.get("split") or sample.get("split"))
+        if split == "val":
+            val.append(image_path)
+            continue
+        context = str(sample.get("context") or "unknown")
+        is_negative = bool(tile.get("is_negative"))
+        repeat = negative_repeat if is_negative else priority_repeat if context in priority_contexts else 1
+        train.extend([image_path] * repeat)
+        sample_counts[sample["sample_slug"]] += repeat
+        context_counts[context] += repeat
+        negative_count += repeat if is_negative else 0
+    if not train or not val:
+        raise ValueError(f"regional dataset requires non-empty train and val lists: {region}")
+    if set(train) & set(val):
+        raise ValueError("regional train/validation image leakage")
+    evidence = {
+        "schema_version": 1,
+        "status": "ok",
+        "region": region,
+        "priority_contexts": sorted(priority_contexts),
+        "priority_repeat": priority_repeat,
+        "negative_repeat": negative_repeat,
+        "train_entry_count": len(train),
+        "train_unique_image_count": len(set(train)),
+        "validation_image_count": len(val),
+        "negative_train_entry_count": negative_count,
+        "sample_entry_counts": dict(sorted(sample_counts.items())),
+        "context_entry_counts": dict(sorted(context_counts.items())),
+        "protected_samples_in_training": [],
+        "train_validation_overlap": [],
+    }
+    return train, val, evidence
+
+
+def select_paths(
+    summary: dict[str, Any], manifest: dict[str, Any], region: str
+) -> tuple[list[str], list[str]]:
+    """Backward-compatible unweighted regional selection API."""
+    samples = {item["sample_slug"]: item for item in manifest["samples"]}
+    compatible_summary = {
+        **summary,
+        "tiles": [
+            tile
+            for tile in summary["tiles"]
+            if tile["sample_slug"] in samples
+            and samples[tile["sample_slug"]].get("split") in {"train", "val"}
+        ],
+    }
+    train, val, _evidence = build(
+        summary=compatible_summary,
+        manifest=manifest,
+        region=region,
+        priority_contexts=set(),
+        priority_repeat=1,
+        negative_repeat=1,
+    )
+    return sorted(train), sorted(val)
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--summary", type=Path, required=True)
+    parser.add_argument("--corpus-manifest", type=Path, required=True)
+    parser.add_argument("--region", required=True)
+    parser.add_argument("--output-dir", type=Path, required=True)
+    parser.add_argument("--priority-context", action="append", default=[])
+    parser.add_argument("--priority-repeat", type=int, default=2)
+    parser.add_argument("--negative-repeat", type=int, default=2)
+    parser.add_argument(
+        "--review-audit",
+        type=Path,
+        help="Accepted corpus-audit evidence; mandatory for an operational training release.",
+    )
+    parser.add_argument(
+        "--fixture-mode",
+        action="store_true",
+        help="Accept only an explicitly fixture-only corpus manifest; never use for operational training data.",
+    )
+    args = parser.parse_args()
+    if args.priority_repeat < 1 or args.negative_repeat < 1:
+        parser.error("repeat counts must be positive")
+    summary = json.loads(args.summary.read_text(encoding="utf-8"))
+    manifest = json.loads(args.corpus_manifest.read_text(encoding="utf-8"))
+    try:
+        assert_frozen_manifest_training_eligible(
+            args.corpus_manifest,
+            fixture_mode=args.fixture_mode,
+            verify_live=True,
+        )
+        assert_yolo_summary_bound_to_embedded_training_release(
+            summary_path=args.summary,
+            corpus_manifest=args.corpus_manifest,
+            fixture_mode=args.fixture_mode,
+        )
+    except (TrainingEligibilityError, TrainingReleaseError) as exc:
+        raise SystemExit(str(exc)) from exc
+    train, val, evidence = build(
+        summary=summary,
+        manifest=manifest,
+        region=args.region,
+        priority_contexts=set(args.priority_context),
+        priority_repeat=args.priority_repeat,
+        negative_repeat=args.negative_repeat,
+    )
+    args.output_dir.mkdir(parents=True, exist_ok=False)
+    train_path = args.output_dir / "train.txt"
+    val_path = args.output_dir / "val.txt"
+    write_lines(train_path, train)
+    write_lines(val_path, val)
+    yaml_path = args.output_dir / "dataset.yaml"
+    yaml_path.write_text(
+        f"path: /\ntrain: {train_path}\nval: {val_path}\nnames:\n  0: building\n",
+        encoding="utf-8",
+    )
+    try:
+        release_paths = create_training_release_manifest(
+            train_yaml=yaml_path,
+            corpus_manifest=args.corpus_manifest,
+            review_audit_path=args.review_audit,
+            fixture_mode=args.fixture_mode,
+        )
+    except TrainingReleaseError as exc:
+        raise SystemExit(str(exc)) from exc
+    evidence.update({
+        "source_summary": str(args.summary),
+        "source_summary_sha256": sha256(args.summary),
+        "corpus_manifest": str(args.corpus_manifest),
+        "corpus_manifest_sha256": sha256(args.corpus_manifest),
+        "train_sha256": sha256(train_path),
+        "validation_sha256": sha256(val_path),
+        "dataset_yaml": str(yaml_path),
+        "training_release_manifest": str(release_paths["release_manifest"]),
+        "training_release_manifest_sha256": sha256(release_paths["release_manifest"]),
+        "training_release_freeze": str(release_paths["release_freeze"]),
+        "training_asset_manifest": str(release_paths["asset_manifest"]),
+        "fixture_mode": bool(args.fixture_mode),
+    })
+    encoded_evidence = json.dumps(evidence, indent=2)
+    (args.output_dir / "regional-dataset-evidence.json").write_text(encoded_evidence, encoding="utf-8")
+    (args.output_dir / "regional-dataset.json").write_text(encoded_evidence, encoding="utf-8")
+    print(json.dumps(evidence, indent=2))
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())