Add causal person fusion and MuVAP stage-3 baselines

third_party/MuVAP/preprocess/asd/adapters.py

@@ -0,0 +1,112 @@
+"""Dataset-specific metadata adapters.
+
+Adapters do not normalize signals. They only convert source layouts into
+`SourceRecord`; the shared writer owns every media transformation.
+"""
+
+import ast
+import csv
+import json
+from pathlib import Path
+from typing import Iterable
+
+from preprocess.asd.schema import SAMPLE_RATE, SourceRecord
+
+
+def _labels(text: str) -> list[int]:
+    values = ast.literal_eval(text)
+    if not isinstance(values, (list, tuple)):
+        raise ValueError("labels must be a list")
+    result = [int(value) for value in values]
+    if any(value not in (0, 1) for value in result):
+        raise ValueError("labels must contain only 0 and 1")
+    return result
+
+
+def _existing(path: Path, sample_id: str) -> Path:
+    if not path.exists():
+        raise FileNotFoundError(f"{sample_id}: missing {path}")
+    return path
+
+
+def clip_directory_records(
+    loader_path: Path,
+    visual_root: Path,
+    audio_root: Path,
+    split: str,
+    dataset: str = "ava",
+    skip_missing: bool = False,
+) -> Iterable[SourceRecord]:
+    """Read the AVA-style loader TSV and nested JPEG/WAV layout.
+
+    AVA and WASD ship the identical layout - `clips_videos/<split>/<clip>/
+    <entity>/<timestamp>.jpg` beside `clips_audios/<split>/<clip>/<entity>.wav`
+    - and the identical five-column loader TSV, so one reader serves both.
+    """
+    face_by_id = {
+        path.name: path for path in (visual_root / split).glob("*/*") if path.is_dir()
+    }
+    audio_by_id = {path.stem: path for path in (audio_root / split).glob("*/*.wav")}
+    with loader_path.open(newline="") as handle:
+        for row_number, row in enumerate(csv.reader(handle, delimiter="\t"), start=1):
+            if len(row) < 4:
+                raise ValueError(
+                    f"{loader_path}:{row_number}: expected at least 4 columns"
+                )
+            sample_id, _, fps, label_text = row[:4]
+            face_path = face_by_id.get(sample_id)
+            audio_path = audio_by_id.get(sample_id)
+            if face_path is None or audio_path is None:
+                if skip_missing:
+                    continue
+                raise FileNotFoundError(
+                    f"{sample_id}: missing face directory or clip-local WAV"
+                )
+            yield SourceRecord(
+                dataset=dataset,
+                sample_id=sample_id,
+                visual_path=face_path,
+                audio_path=audio_path,
+                source_fps=float(fps),
+                labels=_labels(label_text),
+                visual_kind="jpeg_directory",
+            )
+
+
+def manifest_records(
+    manifest_path: Path,
+    dataset: str,
+    root: Path | None = None,
+    default_npy_audio_sample_rate: int = SAMPLE_RATE,
+) -> Iterable[SourceRecord]:
+    """Read the canonical TSV adapter used for AVCC and future datasets.
+
+    Required header fields: sample_id, visual_path, audio_path, source_fps,
+    labels. Optional: start_sec, visual_kind, audio_sample_rate.
+    """
+    root = root or manifest_path.parent
+    with manifest_path.open(newline="") as handle:
+        reader = csv.DictReader(handle, delimiter="\t")
+        required = {"sample_id", "visual_path", "audio_path", "source_fps", "labels"}
+        missing = required.difference(reader.fieldnames or [])
+        if missing:
+            raise ValueError(f"{manifest_path}: missing columns {sorted(missing)}")
+        for row_number, row in enumerate(reader, start=2):
+            try:
+                yield SourceRecord(
+                    dataset=dataset,
+                    sample_id=row["sample_id"],
+                    visual_path=_existing(root / row["visual_path"], row["sample_id"]),
+                    audio_path=_existing(root / row["audio_path"], row["sample_id"]),
+                    source_fps=float(row["source_fps"]),
+                    labels=json.loads(row["labels"]),
+                    start_sec=float(row.get("start_sec") or 0.0),
+                    npy_audio_sample_rate=int(
+                        row.get("audio_sample_rate") or default_npy_audio_sample_rate
+                    ),
+                    visual_kind=row.get("visual_kind") or "npy",
+                )
+            except (TypeError, ValueError, json.JSONDecodeError) as error:
+                raise ValueError(
+                    f"{manifest_path}:{row_number}: invalid row"
+                ) from error