def loss_function(y_true, y_pred): if isinstance(transform, str) and transform.lower() == 'disc': return losses.discriminative_instance_loss(y_true, y_pred) if focal: return losses.weighted_focal_loss( y_true, y_pred, gamma=gamma, n_classes=n_classes) return losses.weighted_categorical_crossentropy( y_true, y_pred, n_classes=n_classes)
def loss_function(y_true, y_pred): if focal: return losses.weighted_focal_loss(y_true, y_pred, gamma=gamma, n_classes=n_classes, from_logits=False) return losses.weighted_categorical_crossentropy(y_true, y_pred, n_classes=n_classes, from_logits=False)
def semantic_loss(y_pred, y_true): return panoptic_weight * losses.weighted_categorical_crossentropy( y_pred, y_true, n_classes=n_semantic_classes)
def _semantic_loss(y_pred, y_true): if n_classes > 1: return panoptic_weight * losses.weighted_categorical_crossentropy( y_true, y_pred, n_classes=n_classes) return panoptic_weight * MSE(y_true, y_pred)