Simplified evaluate get results function
Signed-off-by: Jim Martens <github@2martens.de>
This commit is contained in:
@ -393,25 +393,25 @@ def _ssd_evaluate_entropy_loop(use_entropy_threshold: bool, entropy_threshold_mi
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average_precisions = evaluate.get_mean_average_precisions(cum_precisions, cum_recalls, nr_classes)
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mean_average_precision = evaluate.get_mean_average_precision(average_precisions)
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results = _ssd_evaluate_get_results(true_positives,
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false_positives,
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cum_true_positives,
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cum_false_positives,
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cum_true_positives_overall,
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cum_false_positives_overall,
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cum_precisions,
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cum_recalls,
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cum_precisions_micro,
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cum_recalls_micro,
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cum_precisions_macro,
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cum_recalls_macro,
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f1_scores,
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f1_scores_micro,
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f1_scores_macro,
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average_precisions,
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mean_average_precision,
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open_set_error,
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cumulative_open_set_error)
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results = _ssd_evaluate_get_results(true_positives=true_positives,
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false_positives=false_positives,
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cum_true_positives=cum_true_positives,
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cum_false_positives=cum_false_positives,
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cum_true_positives_overall=cum_true_positives_overall,
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cum_false_positives_overall=cum_false_positives_overall,
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cum_precisions=cum_precisions,
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cum_recalls=cum_recalls,
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cum_precisions_micro=cum_precisions_micro,
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cum_recalls_micro=cum_recalls_micro,
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cum_precisions_macro=cum_precisions_macro,
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cum_recalls_macro=cum_recalls_macro,
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f1_scores=f1_scores,
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f1_scores_micro=f1_scores_micro,
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f1_scores_macro=f1_scores_macro,
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average_precisions=average_precisions,
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mean_average_precision=mean_average_precision,
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open_set_error=open_set_error,
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cumulative_open_set_error=cumulative_open_set_error)
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_pickle(f"{result_file}-{entropy_threshold}.bin"
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if use_entropy_threshold else f"{result_file}.bin", results)
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@ -1026,47 +1026,10 @@ def _ssd_save_history(summary_path: str, history: tf.keras.callbacks.History) ->
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pickle.dump(history.history, file)
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def _ssd_evaluate_get_results(true_positives: Sequence[np.ndarray],
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false_positives: Sequence[np.ndarray],
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cum_true_positives: Sequence[np.ndarray],
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cum_false_positives: Sequence[np.ndarray],
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cum_true_positives_micro: np.ndarray,
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cum_false_positives_micro: np.ndarray,
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cum_precisions: Sequence[np.ndarray],
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cum_recalls: Sequence[np.ndarray],
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cum_precision_micro: np.ndarray,
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cum_recall_micro: np.ndarray,
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cum_precision_macro: np.ndarray,
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cum_recall_macro: np.ndarray,
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f1_scores: Sequence[np.ndarray],
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f1_scores_micro: np.ndarray,
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f1_scores_macro: np.ndarray,
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average_precisions: Sequence[float],
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mean_average_precision: float,
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open_set_error: np.ndarray,
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cumulative_open_set_error: np.ndarray
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) -> Dict[str, Union[np.ndarray, float, int]]:
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results = {
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"true_positives": true_positives,
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"false_positives": false_positives,
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"cumulative_true_positives": cum_true_positives,
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"cumulative_false_positives": cum_false_positives,
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"cumulative_true_positives_micro": cum_true_positives_micro,
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"cumulative_false_positives_micro": cum_false_positives_micro,
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"cumulative_precisions": cum_precisions,
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"cumulative_recalls": cum_recalls,
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"cumulative_precision_micro": cum_precision_micro,
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"cumulative_recall_micro": cum_recall_micro,
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"cumulative_precision_macro": cum_precision_macro,
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"cumulative_recall_macro": cum_recall_macro,
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"f1_scores": f1_scores,
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"f1_scores_micro": f1_scores_micro,
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"f1_scores_macro": f1_scores_macro,
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"mean_average_precisions": average_precisions,
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"mean_average_precision": mean_average_precision,
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"open_set_error": open_set_error,
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"cumulative_open_set_error": cumulative_open_set_error
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}
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def _ssd_evaluate_get_results(**kwargs) -> Dict[str, Union[np.ndarray, float, int]]:
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results = {}
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for key in kwargs:
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results[key] = kwargs[key]
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return results
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