File size: 9,697 Bytes
765160f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 | """Validate the frozen cohort and prepare one model's matched local media."""
from __future__ import annotations
import hashlib
import shutil
import json
import subprocess
from collections import Counter
from concurrent.futures import ThreadPoolExecutor
from pathlib import Path
ROOT = Path(__file__).resolve().parent
COHORT = ROOT / "resources/cohort1015.jsonl"
COHORT_SHA256 = "1f27841f079259e7ebc5249bce114ec9542af36c7fc9a117f32eeac2f652dd62"
def sha256(path: Path) -> str:
digest = hashlib.sha256()
with path.open("rb") as stream:
for block in iter(lambda: stream.read(8 * 1024 * 1024), b""):
digest.update(block)
return digest.hexdigest()
def read_jsonl(path: Path) -> list[dict]:
return [json.loads(line) for line in path.read_text(encoding="utf-8").splitlines() if line.strip()]
def atomic_json(path: Path, data: object) -> None:
path.parent.mkdir(parents=True, exist_ok=True)
temporary = path.with_name(path.name + ".tmp")
temporary.write_text(json.dumps(data, ensure_ascii=False, indent=2, sort_keys=True) + "\n", encoding="utf-8")
temporary.replace(path)
def write_jsonl(path: Path, rows: list[dict]) -> None:
path.parent.mkdir(parents=True, exist_ok=True)
temporary = path.with_name(path.name + ".tmp")
temporary.write_text(
"".join(json.dumps(row, ensure_ascii=False, sort_keys=True) + "\n" for row in rows),
encoding="utf-8",
)
temporary.replace(path)
def frozen_rows() -> list[dict]:
if sha256(COHORT) != COHORT_SHA256:
raise ValueError("Frozen cohort SHA256 differs from the release")
rows = read_jsonl(COHORT)
ids = [row["videoId"] for row in rows]
counts = Counter(row["interaction"] for row in rows)
if len(rows) != 1015 or len(set(ids)) != 1015 or counts["event"] != 165 or counts["perspective"] != 150:
raise ValueError(f"Frozen membership is invalid: n={len(rows)}, scopes={counts}")
return rows
def _indexed_media(path: Path, expected_ids: set[str], label: str) -> dict[str, dict]:
rows = read_jsonl(path)
if len(rows) != len(expected_ids):
raise ValueError(f"{label}: expected {len(expected_ids)} rows, got {len(rows)}")
indexed = {}
for row in rows:
sample_id = row.get("sample_id", row.get("videoId"))
if not isinstance(sample_id, str) or sample_id in indexed:
raise ValueError(f"{label}: invalid or duplicate sample_id {sample_id!r}")
indexed[sample_id] = row
if set(indexed) != expected_ids:
raise ValueError(f"{label}: missing={len(expected_ids - set(indexed))}, extra={len(set(indexed) - expected_ids)}")
return indexed
def _check_media(row: dict, label: str) -> tuple[str, str]:
value = row.get("path", row.get("local_video_file"))
expected = row.get("sha256", row.get("media_sha256"))
if not isinstance(value, str) or not isinstance(expected, str) or len(expected) != 64:
raise ValueError(f"{label}: provide absolute path and sha256")
path = Path(value)
if not path.is_absolute() or not path.is_file() or path.suffix.lower() != ".mp4":
raise ValueError(f"{label}: MP4 path missing or not absolute: {path}")
actual = sha256(path)
if actual != expected.lower():
raise ValueError(f"{label}: SHA256 mismatch: {path}")
try:
probe = json.loads(subprocess.check_output([
"ffprobe", "-v", "error", "-show_streams", "-of", "json", str(path),
], stderr=subprocess.PIPE, timeout=60))
video = next(item for item in probe["streams"] if item["codec_type"] == "video")
audio = next(item for item in probe["streams"] if item["codec_type"] == "audio")
video_ok = (int(video["width"]), int(video["height"]), video["avg_frame_rate"],
int(video["nb_frames"])) == (768, 512, "24/1", 121)
audio_ok = (int(audio["sample_rate"]), int(audio["channels"])) == (48000, 2)
duration_ok = float(audio["duration"]) >= 5.0
if not (video_ok and audio_ok and duration_ok):
raise ValueError(f"{label}: expected 768x512, 121 frames at 24 fps, stereo 48 kHz and >=5 s audio")
except (KeyError, StopIteration, subprocess.CalledProcessError, subprocess.TimeoutExpired) as exc:
raise ValueError(f"{label}: ffprobe failed or missing required streams: {path}") from exc
return str(path.resolve()), actual
def prepare(generated: Path, gt: Path | None, run_dir: Path, model_id: str) -> dict:
if not model_id or any(character not in "abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789_-" for character in model_id):
raise ValueError("model_id may contain only letters, digits, hyphen and underscore")
cohort = frozen_rows()
ids = {row["videoId"] for row in cohort}
generated_index = _indexed_media(generated, ids, "generated")
gt_index = _indexed_media(gt, ids, "gt") if gt else None
manifest_dir = run_dir / "manifests"
complete_path = run_dir / "INPUTS_COMPLETE.json"
if complete_path.exists():
raise FileExistsError(f"Run already prepared: {complete_path}; use another run_dir")
rows, gt_rows = [], []
for cohort_row in cohort:
sample_id = cohort_row["videoId"]
media, media_sha = _check_media(generated_index[sample_id], f"generated/{sample_id}")
gt_media, gt_sha = _check_media(gt_index[sample_id], f"gt/{sample_id}") if gt_index else ("", cohort_row["media_sha256"])
row = {
"sample_id": sample_id, "clip_id": f"{model_id}__{sample_id}",
"group": model_id, "checkpoint_label": model_id,
"local_video_file": media, "media_sha256": media_sha,
"source_video": media, "audio_uri": media,
"gt_file": gt_media, "gt_sha256": gt_sha,
"imagebind_id": f"{model_id}__{sample_id}", "imagebind_group": model_id,
"caption_av": cohort_row["caption_av"],
"interaction": cohort_row["interaction"], "scene": cohort_row["scene"],
"source_group": sample_id, "sample_partition": "frozen1015",
"dataset_family": "Helixbench", "first_frame_sha256": cohort_row["first_frame_sha256"],
}
rows.append(row)
gt_rows.append({"sample_id": sample_id, "media_sha256": gt_sha, **({"local_video_file": gt_media} if gt_index else {})})
for name, selected in (
("all1015", rows), ("text1015", rows), ("imagebind1015", rows),
("event165", [r for r in rows if r["interaction"] == "event"]),
("perspective150", [r for r in rows if r["interaction"] == "perspective"]),
("gt1015", gt_rows),
):
write_jsonl(manifest_dir / f"{name}.jsonl", selected)
if not gt_index:
_copy_reference_features(run_dir, ids)
payload = {
"model_id": model_id, "cohort_sha256": COHORT_SHA256,
"generated_input_sha256": sha256(generated), "gt_input_sha256": sha256(gt) if gt else "packaged_reference_features",
"counts": {"all": 1015, "event": 165, "perspective": 150},
"manifests": {name: sha256(manifest_dir / f"{name}.jsonl") for name in
("all1015", "text1015", "imagebind1015", "event165", "perspective150", "gt1015")},
}
atomic_json(complete_path, payload)
return payload
def validate_prepared(run_dir: Path) -> dict:
payload = json.loads((run_dir / "INPUTS_COMPLETE.json").read_text())
if payload["cohort_sha256"] != COHORT_SHA256:
raise ValueError("Prepared run uses a different frozen cohort")
for name, expected in payload["manifests"].items():
path = run_dir / "manifests" / f"{name}.jsonl"
if sha256(path) != expected:
raise ValueError(f"Manifest modified after preparation: {name}")
return payload
def verify_media_manifest(run_dir: Path, include_gt: bool = True) -> None:
"""Reject changed or missing MP4 files before using cached model features."""
names = ("all1015", "gt1015") if include_gt else ("all1015",)
rows = [row for name in names for row in read_jsonl(run_dir / "manifests" / f"{name}.jsonl") if "local_video_file" in row]
def check(row: dict) -> None:
path = Path(row["local_video_file"])
if not path.is_file() or sha256(path) != row["media_sha256"]:
raise ValueError(f"Media changed since prepare: {path}")
with ThreadPoolExecutor(max_workers=8) as pool:
list(pool.map(check, rows))
def _copy_reference_features(run_dir: Path, ids: set[str]) -> None:
"""Verify packaged GT features, then copy them into the standard run layout."""
source_root = ROOT.parents[2] / "reference"
index = json.loads((source_root / "SHA256SUMS.json").read_text())
expected = {f"{metric}/gt/{sample_id}.npz" for metric in ("passt", "vggish") for sample_id in ids}
if set(index) != expected:
raise ValueError("Packaged reference features must cover the frozen 1015 twice")
for relative, digest in index.items():
source = source_root / relative
if sha256(source) != digest:
raise ValueError(f"Reference feature changed: {relative}")
dest = run_dir / "results" / relative
dest.parent.mkdir(parents=True, exist_ok=True)
shutil.copy2(source, dest)
def verify_packaged_gt_features(run_dir: Path) -> None:
"""Ensure cached GT arrays still match the published reference package."""
source_root = ROOT.parents[2] / "reference"
index = json.loads((source_root / "SHA256SUMS.json").read_text())
for relative, digest in index.items():
path = run_dir / "results" / relative
if sha256(path) != digest:
raise ValueError(f"Packaged GT feature changed: {relative}")
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