metrics.py 文件源码

def precision_recall(num_gbboxes, num_detections, tp, fp, scores,
                     dtype=tf.float64, scope=None):
    """Compute precision and recall from scores, true positives and false
    positives booleans arrays
    """
    # Input dictionaries: dict outputs as streaming metrics.
    if isinstance(scores, dict):
        d_precision = {}
        d_recall = {}
        for c in num_gbboxes.keys():
            scope = 'precision_recall_%s' % c
            p, r = precision_recall(num_gbboxes[c], num_detections[c],
                                    tp[c], fp[c], scores[c],
                                    dtype, scope)
            d_precision[c] = p
            d_recall[c] = r
        return d_precision, d_recall

    # Sort by score.
    with tf.name_scope(scope, 'precision_recall',
                       [num_gbboxes, num_detections, tp, fp, scores]):
        # Sort detections by score.
        scores, idxes = tf.nn.top_k(scores, k=num_detections, sorted=True)
        tp = tf.gather(tp, idxes)
        fp = tf.gather(fp, idxes)
        # Computer recall and precision.
        tp = tf.cumsum(tf.cast(tp, dtype), axis=0)
        fp = tf.cumsum(tf.cast(fp, dtype), axis=0)
        recall = _safe_div(tp, tf.cast(num_gbboxes, dtype), 'recall')
        precision = _safe_div(tp, tp + fp, 'precision')
        return tf.tuple([precision, recall])