metrics.py 文件源码

def _precision_recall(n_gbboxes, n_detections, scores, tp, fp, scope=None):
    """Compute precision and recall from scores, true positives and false
    positives booleans arrays
    """
    # Sort by score.
    with tf.name_scope(scope, 'prec_rec', [n_gbboxes, scores, tp, fp]):
        # Sort detections by score.
        scores, idxes = tf.nn.top_k(scores, k=n_detections, sorted=True)
        tp = tf.gather(tp, idxes)
        fp = tf.gather(fp, idxes)
        # Computer recall and precision.
        dtype = tf.float64
        tp = tf.cumsum(tf.cast(tp, dtype), axis=0)
        fp = tf.cumsum(tf.cast(fp, dtype), axis=0)
        recall = _safe_div(tp, tf.cast(n_gbboxes, dtype), 'recall')
        precision = _safe_div(tp, tp + fp, 'precision')

        return tf.tuple([precision, recall])