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"""Utilities to convert the raw metadata dumps into viewer-friendly JSONL files.""" |
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from __future__ import annotations |
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import json |
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from dataclasses import asdict, dataclass |
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from pathlib import Path |
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from typing import Dict, Iterator, MutableMapping |
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ROOT = Path(__file__).resolve().parents[1] |
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RAW_DIR = ROOT / "raw" |
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OUTPUT_DIR = ROOT / "data" |
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HF_DATASET_ID = "ishwarbb23/cuebench" |
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HF_IMAGE_PREFIX = f"hf://datasets/{HF_DATASET_ID}/" |
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CONFIG_SOURCES: Dict[str, Path] = { |
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"clue": RAW_DIR / "clue_metadata.jsonl", |
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"mep": RAW_DIR / "mep_metadata.jsonl", |
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} |
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@dataclass |
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class BuildStats: |
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"""Simple container for summary numbers we surface in README/stats.json.""" |
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num_examples: int |
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num_bytes: int |
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source_path: str |
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output_path: str |
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def as_dict(self) -> Dict[str, object]: |
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return asdict(self) |
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def _normalize_record(record: MutableMapping[str, object]) -> MutableMapping[str, object]: |
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"""Add the columns expected by the README and dataset viewer.""" |
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image_id = record.get("aligned_id") or record.get("image_id") |
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if image_id is None: |
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seq = record.get("seq_name", "seq") |
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frame = record.get("frame_count", 0) |
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image_id = f"{seq}.{int(frame):05d}" |
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record["image_id"] = image_id |
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observed = record.get("observed_classes") or record.get("detected_classes") or [] |
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record["observed_classes"] = observed |
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record.setdefault("detected_classes", observed) |
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record["target_classes"] = record.get("target_classes", []) |
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image_path = record.get("image_path") |
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record["image_path"] = image_path |
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if image_path: |
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normalized_path = str(Path(image_path)) |
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record["image"] = f"{HF_IMAGE_PREFIX}{normalized_path}" |
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else: |
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record["image"] = None |
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return record |
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def _iter_records(path: Path) -> Iterator[MutableMapping[str, object]]: |
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with path.open("r", encoding="utf-8") as src: |
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for line in src: |
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if not line.strip(): |
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continue |
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yield json.loads(line) |
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def build_split(config_name: str, source_path: Path, output_path: Path) -> BuildStats: |
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output_path.parent.mkdir(parents=True, exist_ok=True) |
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count = 0 |
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with output_path.open("w", encoding="utf-8") as dst: |
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for record in _iter_records(source_path): |
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normalized = _normalize_record(record) |
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dst.write(json.dumps(normalized, ensure_ascii=False) + "\n") |
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count += 1 |
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num_bytes = output_path.stat().st_size |
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return BuildStats( |
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num_examples=count, |
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num_bytes=num_bytes, |
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source_path=str(source_path.relative_to(ROOT)), |
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output_path=str(output_path.relative_to(ROOT)), |
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) |
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def main() -> None: |
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stats: Dict[str, Dict[str, object]] = {} |
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for config_name, source in CONFIG_SOURCES.items(): |
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if not source.exists(): |
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raise FileNotFoundError(f"Missing source file for {config_name}: {source}") |
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output_path = OUTPUT_DIR / config_name / "train.jsonl" |
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summary = build_split(config_name, source, output_path) |
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stats[config_name] = summary.as_dict() |
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print( |
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f"[{config_name}] wrote {summary.num_examples} examples -> {summary.output_path} " |
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f"({summary.num_bytes} bytes)." |
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) |
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stats_path = OUTPUT_DIR / "stats.json" |
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with stats_path.open("w", encoding="utf-8") as handle: |
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json.dump(stats, handle, indent=2) |
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handle.write("\n") |
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print(f"Wrote summary stats to {stats_path.relative_to(ROOT)}") |
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if __name__ == "__main__": |
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main() |
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