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* LR-BEHAV-RENDER-FIDELITY: split V3 Contrast and Blacks Port calibrated global Contrast and Blacks with shared CPU and GPU tables. Preserve V2 Whites, local adjustments, other source paths, and all V1 and V2 renders and persistent cache keys. Generate the constants from public Contrast and Blacks fits. Cover numeric references, legacy renders, keys that include the resolved process, windows and GPU parity. Document the measurements, limits and provisional number. Co-Authored-By: Codex <noreply@openai.com> * LR-BEHAV-RENDER-FIDELITY: reduce V3 preview cost and sanitize fits Compose calibrated CPU preview operators over linear input and skip unused mix endpoints. Keep high-bit-depth exports on the original operator path. Require three distinct photos per published error group and band, regenerate the fit hashes, and cover the sanitizer with synthetic tests. Document release measurements and identify the separate measurement tool proposed in #1181. Co-Authored-By: Codex <noreply@openai.com> * LR-BEHAV-RENDER-FIDELITY: preserve V3 luminance evaluation Retain the original operator evaluation for luminance mixes, including preceding Contrast, so preview interpolation cannot change the cutoff. Keep the fused per-channel path and exact deep exports. Add CPU and GPU regressions for both uniform cutoff fixtures. Document the measured CPU cost of positive Blacks and the retained fused path. Co-Authored-By: Codex <noreply@openai.com> * LR-BEHAV-RENDER-FIDELITY: finish split-module integration Import the persistent process-key helper directly in the session module. Keep the existing V3 operator selection and evaluation order in an inline helper so the combined finish-stage closure stays within the complexity limit. Refresh the V1/V2 golden hashes only after the exact fixture produces identical hashes on plain current main. Keep the cross-platform legacy samples unchanged. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> * LR-BEHAV-RENDER-FIDELITY: date the calibrated Light gap summary Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> --------- Co-authored-by: Codex <noreply@openai.com> Co-authored-by: Claude Opus 5.5 <noreply@anthropic.com> Co-authored-by: FredApps <153173854+FredApps@users.noreply.github.com>
70 lines
3.5 KiB
Python
70 lines
3.5 KiB
Python
"""Synthetic publication checks. Run with python3 -m unittest discover -s tools."""
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import contextlib
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import io
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import json
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import tempfile
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import unittest
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from pathlib import Path
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from types import SimpleNamespace
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import light_v3_constants as constants
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class SanitizeTests(unittest.TestCase):
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def test_counts_distinct_photos_for_each_group_and_band(self):
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fit = json.loads((constants.FITS / "contrast.json").read_text())
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# Three repeated pairs from one photo must not qualify as three photos.
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def row(photo, amount, bands):
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return dict(photo=photo, a_tgt=amount, a_ref=0, long_edge=800,
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encoded_error=0.25, cells={b: {"error": 0.5} for b in bands})
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fit["check"] = [row("synthetic-a", 20, constants.BANDS) for _ in range(3)]
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fit["check"] += [row(p, 40, constants.BANDS) for p in ("synthetic-a", "synthetic-b")]
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fit["check"] += [row(p, 60, constants.BANDS[:2]) for p in ("synthetic-a", "synthetic-b", "synthetic-c")]
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fit["check"] += [row("synthetic-a", 60, constants.BANDS) for _ in range(3)]
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fit["check"] += [row("synthetic-b", 60, constants.BANDS[:3])]
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fit["tune"] = fit["check"]
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with tempfile.TemporaryDirectory() as temp:
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source, dest = Path(temp) / "synthetic.json", Path(temp) / "public.json"
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source.write_text(json.dumps(fit))
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with contextlib.redirect_stdout(io.StringIO()):
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constants.sanitize(SimpleNamespace(fit=source, slider="contrast", o=dest))
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public = json.loads(dest.read_text())
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self.assertEqual(public["params"], fit["params"])
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self.assertEqual(public["check_photos"], 3)
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for split in ("tune", "check"):
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rows = public["median_errors"][split]
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self.assertEqual([r["amount"] for r in rows], [60])
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self.assertEqual(rows[0]["photos"], 3)
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self.assertEqual(set(rows[0]["band_error"]), set(constants.BANDS[:2]))
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self.assertEqual(rows[0]["band_photos"], {b: 3 for b in constants.BANDS[:2]})
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def test_single_photo_fit_has_no_error_aggregates(self):
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fit = json.loads((constants.FITS / "contrast.json").read_text())
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fit["tune"] = [dict(photo="synthetic-a", a_tgt=20, a_ref=0, long_edge=800,
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encoded_error=0.25, cells={})]
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fit["check"] = []
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with tempfile.TemporaryDirectory() as temp:
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source, dest = Path(temp) / "synthetic.json", Path(temp) / "public.json"
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source.write_text(json.dumps(fit))
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with contextlib.redirect_stdout(io.StringIO()):
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constants.sanitize(SimpleNamespace(fit=source, slider="contrast", o=dest))
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public = json.loads(dest.read_text())
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self.assertEqual(public["median_errors"], {"tune": [], "check": []})
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self.assertIsNone(public["cost"])
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def test_committed_aggregates_have_at_least_three_photos(self):
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for path in sorted(constants.FITS.glob("*.json")):
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fit = json.loads(path.read_text())
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for rows in fit["median_errors"].values():
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for row in rows:
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with self.subTest(fit=path.name, amount=row["amount"], edge=row["long_edge"]):
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self.assertGreaterEqual(row.get("photos", row["pairs"]), 3)
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self.assertEqual(set(row["band_error"]), set(row.get("band_photos", {})))
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self.assertTrue(all(n >= 3 for n in row.get("band_photos", {}).values()))
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if __name__ == "__main__":
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unittest.main()
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