optuna tune
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@ -26,7 +26,7 @@ class ContiniousSavingCallback:
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f'best_{self.study.best_trial.number}_' \
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f'score_{self.study.best_value}.pkl'
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def __init__(self, root:Union[str, Path], study: optuna.Study):
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def __init__(self, root: Union[str, Path], study: optuna.Study):
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self._study = study
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self.root = Path(root)
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pass
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@ -49,7 +49,7 @@ class ContiniousSavingCallback:
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temp_study_file.unlink(missing_ok=True)
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def __call__(self, study: optuna.study.Study, trial: optuna.trial.FrozenTrial) -> None:
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self._write_to_disk(study, self.tmp_study_path(trial.number))
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self._write_to_disk(study, self.tmp_study_path)
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def __enter__(self):
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return self
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@ -70,10 +70,10 @@ def optimize(trial: optuna.Trial):
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lr_scheduler_parameter = None
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optuna_suggestions = dict(
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model_name='VisualTransformer',
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data_name='CCSLibrosaDatamodule',
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model_name='CNNBaseline',
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data_name='PrimatesLibrosaDatamodule',
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batch_size=trial.suggest_int('batch_size', 5, 50, step=5),
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max_epochs=200,
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max_epochs=400,
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target_mel_length_in_seconds=trial.suggest_float('target_mel_length_in_seconds', 0.2, 1.5, step=0.1),
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random_apply_chance=trial.suggest_float('random_apply_chance', 0.1, 0.5, step=0.1),
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loudness_ratio=trial.suggest_float('loudness_ratio', 0.0, 0.5, step=0.1),
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@ -99,8 +99,8 @@ def optimize(trial: optuna.Trial):
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transformer_dict = dict(
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mlp_dim=2 ** trial.suggest_int('mlp_dim', 1, 5, step=1),
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head_dim=2 ** trial.suggest_int('head_dim', 1, 5, step=1),
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patch_size=trial.suggest_int('patch_size', 6, 12, step=3),
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attn_depth=trial.suggest_int('attn_depth', 2, 14, step=4),
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patch_size=trial.suggest_int('patch_size', 6, 20, step=3),
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attn_depth=trial.suggest_int('attn_depth', 2, 20, step=4),
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heads=trial.suggest_int('heads', 2, 16, step=2),
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embedding_size=trial.suggest_int('embedding_size', 12, 64, step=12)
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)
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