losses.py 文件源码

def smooth_l1_loss(bbox_prediction, bbox_target, sigma=1.0):
    """
    Return Smooth L1 Loss for bounding box prediction.

    Args:
        bbox_prediction: shape (1, H, W, num_anchors * 4)
        bbox_target:     shape (1, H, W, num_anchors * 4)


    Smooth L1 loss is defined as:

    0.5 * x^2                  if |x| < d
    abs(x) - 0.5               if |x| >= d

    Where d = 1 and x = prediction - target

    """
    sigma2 = sigma ** 2
    diff = bbox_prediction - bbox_target
    abs_diff = tf.abs(diff)
    abs_diff_lt_sigma2 = tf.less(abs_diff, 1.0 / sigma2)
    bbox_loss = tf.reduce_sum(
        tf.where(
            abs_diff_lt_sigma2, 0.5 * tf.square(abs_diff),
            abs_diff - 0.5
        ), [1]
    )
    return bbox_loss