metrics.py 文件源码

def average_precision_voc12(precision, recall, name=None):
    """Compute (interpolated) average precision from precision and recall Tensors.

    The implementation follows Pascal 2012 and ILSVRC guidelines.
    See also: https://sanchom.wordpress.com/tag/average-precision/
    """
    with tf.name_scope(name, 'average_precision_voc12', [precision, recall]):
        # Convert to float64 to decrease error on Riemann sums.
        precision = tf.cast(precision, dtype=tf.float64)
        recall = tf.cast(recall, dtype=tf.float64)

        # Add bounds values to precision and recall.
        precision = tf.concat([[0.], precision, [0.]], axis=0)
        recall = tf.concat([[0.], recall, [1.]], axis=0)
        # Ensures precision is increasing in reverse order.
        precision = tfe_math.cummax(precision, reverse=True)

        # Riemann sums for estimating the integral.
        # mean_pre = (precision[1:] + precision[:-1]) / 2.
        mean_pre = precision[1:]
        diff_rec = recall[1:] - recall[:-1]
        ap = tf.reduce_sum(mean_pre * diff_rec)
        return ap