def evaluate(
    generator,
    model,
    iou_threshold=0.5,
    score_threshold=0.05,
    max_detections=100,
    save_path=None
):
    """ Evaluate a given dataset using a given model.

    # Arguments
        generator       : The generator that represents the dataset to evaluate.
        model           : The model to evaluate.
        iou_threshold   : The threshold used to consider when a detection is positive or negative.
        score_threshold : The score confidence threshold to use for detections.
        max_detections  : The maximum number of detections to use per image.
        save_path       : The path to save images with visualized detections to.
    # Returns
        A dict mapping class names to mAP scores.
    """
    # gather all detections and annotations
    all_detections, all_inferences = _get_detections(generator, model, score_threshold=score_threshold, max_detections=max_detections, save_path=save_path)
    all_annotations    = _get_annotations(generator)
    average_precisions = {}

    # all_detections = pickle.load(open('all_detections.pkl', 'rb'))
    # all_annotations = pickle.load(open('all_annotations.pkl', 'rb'))
    # pickle.dump(all_detections, open('all_detections.pkl', 'wb'))
    # pickle.dump(all_annotations, open('all_annotations.pkl', 'wb'))

    # process detections and annotations
    for label in range(generator.num_classes()):
        if not generator.has_label(label):
            continue

        false_positives = np.zeros((0,))
        true_positives  = np.zeros((0,))
        scores          = np.zeros((0,))
        num_annotations = 0.0

        for i in range(generator.size()):
            detections           = all_detections[i][label]
            annotations          = all_annotations[i][label]
            num_annotations     += annotations.shape[0]
            detected_annotations = []

            for d in detections:
                scores = np.append(scores, d[4])

                if annotations.shape[0] == 0:
                    false_positives = np.append(false_positives, 1)
                    true_positives  = np.append(true_positives, 0)
                    continue

                overlaps            = compute_overlap(np.expand_dims(d, axis=0), annotations)
                assigned_annotation = np.argmax(overlaps, axis=1)
                max_overlap         = overlaps[0, assigned_annotation]

                if max_overlap >= iou_threshold and assigned_annotation not in detected_annotations:
                    false_positives = np.append(false_positives, 0)
                    true_positives  = np.append(true_positives, 1)
                    detected_annotations.append(assigned_annotation)
                else:
                    false_positives = np.append(false_positives, 1)
                    true_positives  = np.append(true_positives, 0)

        # no annotations -> AP for this class is 0 (is this correct?)
        if num_annotations == 0:
            average_precisions[label] = 0, 0
            continue

        # sort by score
        indices         = np.argsort(-scores)
        false_positives = false_positives[indices]
        true_positives  = true_positives[indices]

        # compute false positives and true positives
        false_positives = np.cumsum(false_positives)
        true_positives  = np.cumsum(true_positives)

        # compute recall and precision
        recall    = true_positives / num_annotations
        precision = true_positives / np.maximum(true_positives + false_positives, np.finfo(np.float64).eps)

        # compute average precision
        average_precision  = _compute_ap(recall, precision)
        average_precisions[label] = average_precision, num_annotations

    # inference time
    inference_time = np.sum(all_inferences) / generator.size()

    return average_precisions, inference_time
 Evaluate a given dataset using a given model.

    # Arguments
        generator       : The generator that represents the dataset to evaluate.
        model           : The model to evaluate.
        iou_threshold   : The threshold used to consider when a detection is positive or negative.
        score_threshold : The score confidence threshold to use for detections.
        max_detections  : The maximum number of detections to use per image.
        save_path       : The path to save images with visualized detections to.
    # Returns
        A dict mapping class names to mAP scores.
    
keras_retinanet/utils/eval.py