def configure_evaluator() -> Evaluator:
        """Default evaluator.

        Override in subclass for model-specific evaluator behaviour.
        """
        # val metrics (needed for early stopping)
        image_auroc = AUROC(fields=["pred_score", "gt_label"], prefix="image_")
        pixel_auroc = AUROC(fields=["anomaly_map", "gt_mask"], prefix="pixel_")
        val_metrics = [image_auroc, pixel_auroc]

        # test_metrics
        image_auroc = AUROC(fields=["pred_score", "gt_label"], prefix="image_")
        image_f1score = F1Score(fields=["pred_label", "gt_label"], prefix="image_")
        pixel_auroc = AUROC(fields=["anomaly_map", "gt_mask"], prefix="pixel_")
        pixel_f1score = F1Score(fields=["pred_mask", "gt_mask"], prefix="pixel_")
        test_metrics = [image_auroc, image_f1score, pixel_auroc, pixel_f1score]
        return Evaluator(val_metrics=val_metrics, test_metrics=test_metrics)
Default evaluator.

        Override in subclass for model-specific evaluator behaviour.
        
src/anomalib/models/image/fastflow/lightning_model.py