HongChuang/ultralytics-ascend
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Apply Ruff 0.9.0 (#18622)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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Glenn Jocher 2025-01-10 00:05:09 +01:00 • committed by GitHub
parent cc1e77138c
commit 3902e740cf
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22 changed files with 69 additions and 65 deletions
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12
ultralytics/engine/tuner.py
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@ -224,12 +224,12 @@ class Tuner:
# Save and print tune results
header = (
f'{self.prefix}{i + 1}/{iterations} iterations complete ✅ ({time.time() - t0:.2f}s)\n'
f'{self.prefix}Results saved to {colorstr("bold", self.tune_dir)}\n'
f'{self.prefix}Best fitness={fitness[best_idx]} observed at iteration {best_idx + 1}\n'
f'{self.prefix}Best fitness metrics are {best_metrics}\n'
f'{self.prefix}Best fitness model is {best_save_dir}\n'
f'{self.prefix}Best fitness hyperparameters are printed below.\n'
f"{self.prefix}{i + 1}/{iterations} iterations complete ✅ ({time.time() - t0:.2f}s)\n"
f"{self.prefix}Results saved to {colorstr('bold', self.tune_dir)}\n"
f"{self.prefix}Best fitness={fitness[best_idx]} observed at iteration {best_idx + 1}\n"
f"{self.prefix}Best fitness metrics are {best_metrics}\n"
f"{self.prefix}Best fitness model is {best_save_dir}\n"
f"{self.prefix}Best fitness hyperparameters are printed below.\n"
)
LOGGER.info("\n" + header)
data = {k: float(x[best_idx, i + 1]) for i, k in enumerate(self.space.keys())}