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| """Load the Phase 3 evaluation slice from a committed run. | |
| Phase 3 scores every model on the exact images and references of an existing | |
| run instead of re-sampling COCO (``docs/EVAL_METHODOLOGY.md`` § 8.2). A run's | |
| ``predictions.jsonl`` already records that slice: one row per image, in | |
| evaluation order, with the references its metrics were computed against. | |
| The stored image paths belong to the machine that produced the run (Kaggle for | |
| the committed runs), so each one is remapped by file name onto a local images | |
| directory. Nothing else is transformed: row order, reference order and | |
| reference text are kept exactly. Image files don't need to exist here; the | |
| runner checks them before any model loads. | |
| """ | |
| from __future__ import annotations | |
| import hashlib | |
| import json | |
| from dataclasses import dataclass, field | |
| from pathlib import Path | |
| class EvalSlice: | |
| """The images and stored references of one committed evaluation run. | |
| Two slices compare equal when their image paths and references match, | |
| whichever ``predictions.jsonl`` they were read from. | |
| """ | |
| source: Path = field(compare=False) | |
| image_paths: tuple[Path, ...] | |
| references: tuple[tuple[str, ...], ...] | |
| def __len__(self) -> int: | |
| return len(self.image_paths) | |
| def load_eval_slice(predictions_path: str | Path, images_dir: str | Path) -> EvalSlice: | |
| """Read the slice recorded in a run's ``predictions.jsonl``. | |
| Args: | |
| predictions_path: A committed ``results/<run_id>/predictions.jsonl``. | |
| images_dir: Local directory holding the slice images. Each stored image | |
| path is replaced by ``images_dir / <file name>``. | |
| Returns: | |
| The slice, in file order, with references exactly as stored. | |
| Raises: | |
| ValueError: If a row lacks a non-empty ``image`` string or a non-empty | |
| list of reference strings. The message names the file and line. | |
| """ | |
| source = Path(predictions_path) | |
| root = Path(images_dir) | |
| image_paths: list[Path] = [] | |
| references: list[tuple[str, ...]] = [] | |
| with source.open(encoding="utf-8") as f: | |
| for lineno, line in enumerate(f, start=1): | |
| if not line.strip(): | |
| continue | |
| row = json.loads(line) | |
| image = row.get("image") if isinstance(row, dict) else None | |
| refs = row.get("references") if isinstance(row, dict) else None | |
| name = _file_name(image) if isinstance(image, str) else "" | |
| if not name: | |
| raise ValueError(f"{source}:{lineno}: row has no image file name") | |
| if not (isinstance(refs, list) and refs and all(isinstance(r, str) for r in refs)): | |
| raise ValueError(f"{source}:{lineno}: row needs a non-empty list of references") | |
| image_paths.append(root / name) | |
| references.append(tuple(refs)) | |
| return EvalSlice(source=source, image_paths=tuple(image_paths), references=tuple(references)) | |
| def slice_fingerprint(eval_slice: EvalSlice) -> str: | |
| """SHA-256 of the slice's image file names and references, in order. | |
| Hashes the parsed content rather than the file bytes, so line endings and | |
| the machine-specific image directory don't change the identity. | |
| """ | |
| payload = [ | |
| [path.name, list(refs)] | |
| for path, refs in zip(eval_slice.image_paths, eval_slice.references, strict=True) | |
| ] | |
| encoded = json.dumps(payload, ensure_ascii=False, separators=(",", ":")).encode("utf-8") | |
| return hashlib.sha256(encoded).hexdigest() | |
| def _file_name(stored_path: str) -> str: | |
| """Return the file name of a stored path written with either separator.""" | |
| return stored_path.replace("\\", "/").rsplit("/", 1)[-1] | |