113 lines
4.3 KiB
Python
113 lines
4.3 KiB
Python
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"""Dataset-specific metadata adapters.
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Adapters do not normalize signals. They only convert source layouts into
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`SourceRecord`; the shared writer owns every media transformation.
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"""
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import ast
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import csv
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import json
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from pathlib import Path
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from typing import Iterable
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from preprocess.asd.schema import SAMPLE_RATE, SourceRecord
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def _labels(text: str) -> list[int]:
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values = ast.literal_eval(text)
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if not isinstance(values, (list, tuple)):
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raise ValueError("labels must be a list")
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result = [int(value) for value in values]
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if any(value not in (0, 1) for value in result):
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raise ValueError("labels must contain only 0 and 1")
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return result
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def _existing(path: Path, sample_id: str) -> Path:
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if not path.exists():
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raise FileNotFoundError(f"{sample_id}: missing {path}")
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return path
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def clip_directory_records(
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loader_path: Path,
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visual_root: Path,
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audio_root: Path,
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split: str,
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dataset: str = "ava",
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skip_missing: bool = False,
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) -> Iterable[SourceRecord]:
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"""Read the AVA-style loader TSV and nested JPEG/WAV layout.
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AVA and WASD ship the identical layout - `clips_videos/<split>/<clip>/
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<entity>/<timestamp>.jpg` beside `clips_audios/<split>/<clip>/<entity>.wav`
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- and the identical five-column loader TSV, so one reader serves both.
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"""
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face_by_id = {
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path.name: path for path in (visual_root / split).glob("*/*") if path.is_dir()
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}
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audio_by_id = {path.stem: path for path in (audio_root / split).glob("*/*.wav")}
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with loader_path.open(newline="") as handle:
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for row_number, row in enumerate(csv.reader(handle, delimiter="\t"), start=1):
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if len(row) < 4:
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raise ValueError(
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f"{loader_path}:{row_number}: expected at least 4 columns"
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)
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sample_id, _, fps, label_text = row[:4]
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face_path = face_by_id.get(sample_id)
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audio_path = audio_by_id.get(sample_id)
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if face_path is None or audio_path is None:
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if skip_missing:
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continue
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raise FileNotFoundError(
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f"{sample_id}: missing face directory or clip-local WAV"
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)
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yield SourceRecord(
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dataset=dataset,
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sample_id=sample_id,
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visual_path=face_path,
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audio_path=audio_path,
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source_fps=float(fps),
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labels=_labels(label_text),
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visual_kind="jpeg_directory",
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)
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def manifest_records(
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manifest_path: Path,
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dataset: str,
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root: Path | None = None,
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default_npy_audio_sample_rate: int = SAMPLE_RATE,
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) -> Iterable[SourceRecord]:
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"""Read the canonical TSV adapter used for AVCC and future datasets.
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Required header fields: sample_id, visual_path, audio_path, source_fps,
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labels. Optional: start_sec, visual_kind, audio_sample_rate.
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"""
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root = root or manifest_path.parent
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with manifest_path.open(newline="") as handle:
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reader = csv.DictReader(handle, delimiter="\t")
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required = {"sample_id", "visual_path", "audio_path", "source_fps", "labels"}
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missing = required.difference(reader.fieldnames or [])
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if missing:
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raise ValueError(f"{manifest_path}: missing columns {sorted(missing)}")
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for row_number, row in enumerate(reader, start=2):
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try:
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yield SourceRecord(
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dataset=dataset,
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sample_id=row["sample_id"],
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visual_path=_existing(root / row["visual_path"], row["sample_id"]),
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audio_path=_existing(root / row["audio_path"], row["sample_id"]),
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source_fps=float(row["source_fps"]),
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labels=json.loads(row["labels"]),
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start_sec=float(row.get("start_sec") or 0.0),
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npy_audio_sample_rate=int(
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row.get("audio_sample_rate") or default_npy_audio_sample_rate
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),
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visual_kind=row.get("visual_kind") or "npy",
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)
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except (TypeError, ValueError, json.JSONDecodeError) as error:
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raise ValueError(
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f"{manifest_path}:{row_number}: invalid row"
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) from error
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