124 lines
4.2 KiB
Python
124 lines
4.2 KiB
Python
"""Shared fixtures for the eval regression suite.
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Two session fixtures keep model loads off the per-test path:
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* ``gold_results`` — runs every reference-backed metric once via the
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top-level :func:`samples_from` + :meth:`Evaluator.evaluate` API and
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caches the per-sample dict for the asset.
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* ``invariant_results`` — runs every identity-invariant metric on
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N duplicate copies of the asset (paired-and-corpus shape).
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Both use ``skip_missing_deps=True`` so a slim test venv missing one
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metric's deps drops only that metric, never the whole run.
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"""
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from __future__ import annotations
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import json
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import shutil
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import tempfile
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from pathlib import Path
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import pytest
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from fastvideo.eval import EvalResults, create_evaluator, samples_from
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ASSET_DIR = Path(__file__).resolve().parent / "asset"
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ASSET_VIDEO = ASSET_DIR / "ltx2.mp4"
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ASSET_AUDIO = ASSET_DIR / "ltx2.wav"
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ASSET_META = ASSET_DIR / "ltx2.metadata.json"
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REF_DIR = Path(__file__).resolve().parent / "reference_scores"
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N_DUP = 4 # duplicates for set-metric invariants (fvd/fad need ≥2)
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def _all_reference_files() -> list[Path]:
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return sorted(REF_DIR.glob("*.json"))
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def _reference_metric_names() -> list[str]:
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return [json.loads(p.read_text())["metric"] for p in _all_reference_files()]
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# Identity invariants — both fastvideo and the math itself give the
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# perfect-match value on duplicate (gen, ref). No reference JSON needed.
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INVARIANT_SPEC = [
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# (metric, expected, abs_tolerance, location)
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("common.ssim", 1.0, 1e-3, "per_sample"),
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("common.psnr", 99.0, None, "per_sample"), # ≥ threshold (identical → ~inf, capped by floats)
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("common.lpips", 0.0, 1e-2, "per_sample"),
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("optical_flow.gt_optical_flow", 0.0, 1e-2, "per_sample"),
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("common.fvd", 0.0, 1.0, "corpus"),
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("audio.frechet_distance", 0.0, 1.0, "corpus"),
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("audio.kl_divergence", 0.0, 1e-3, "per_sample"),
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]
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def load_reference(metric: str) -> dict:
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return json.loads((REF_DIR / f"{metric}.json").read_text())
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@pytest.fixture(scope="session")
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def asset_meta() -> dict:
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return json.loads(ASSET_META.read_text())
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@pytest.fixture(scope="session")
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def gold_results(asset_meta) -> dict:
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"""Top-level API run on the single regression asset.
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Equivalent to four lines in user code::
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ev = create_evaluator(metrics=[...], skip_missing_deps=True)
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samples = samples_from(video=ASSET, audio=ASSET_WAV,
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text_prompt=..., fps=..., extras={...})
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return ev.evaluate(samples=samples)[0]
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"""
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metrics = _reference_metric_names()
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ev = create_evaluator(metrics=metrics, skip_missing_deps=True)
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samples = samples_from(
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video=ASSET_VIDEO,
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audio=ASSET_AUDIO,
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text_prompt=asset_meta["text_prompt"],
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fps=asset_meta["fps"],
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extras={
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"reference_text": asset_meta["reference_text"],
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"video_path": str(ASSET_VIDEO),
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"language": asset_meta["language"],
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},
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)
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results: EvalResults = ev.evaluate(samples=samples)
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yield results[0]
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ev.shutdown()
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@pytest.fixture(scope="session")
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def invariant_results(asset_meta) -> EvalResults:
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"""Run identity-invariant metrics on N duplicate copies of the asset.
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Same top-level shape — five lines. ``extract_audio`` pulls a ``.wav``
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next to each copy so audio set-metrics
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(``audio.frechet_distance``, ``audio.kl_divergence``) see paired
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audio without us pre-staging it.
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"""
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tmp = Path(tempfile.mkdtemp(prefix="eval_regression_inv_"))
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try:
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gen = tmp / "gen"; ref = tmp / "ref"
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gen.mkdir(); ref.mkdir()
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for i in range(N_DUP):
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shutil.copyfile(ASSET_VIDEO, gen / f"clip_{i:03d}.mp4")
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shutil.copyfile(ASSET_VIDEO, ref / f"clip_{i:03d}.mp4")
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ev = create_evaluator(
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metrics=[m for m, *_ in INVARIANT_SPEC],
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skip_missing_deps=True,
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)
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samples = samples_from(
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video=gen, reference=ref,
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text_prompt=asset_meta["text_prompt"],
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fps=asset_meta["fps"],
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extract_audio=tmp / "audio_cache",
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)
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yield ev.evaluate(samples=samples)
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ev.shutdown()
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finally:
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shutil.rmtree(tmp, ignore_errors=True)
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