def normalized_rmse(self, pred, gt_truth):
l, r = utils.norm_idx(self.num_patches)
assert (l is not None and r is not None)
norm = tf.sqrt(
tf.reduce_sum(((gt_truth[:, l, :] - gt_truth[:, r, :])**2), 1))
return tf.reduce_sum(
tf.sqrt(tf.reduce_sum(tf.square(pred - gt_truth), 2)),
1) / (norm * self.num_patches)
def _batch_normalized_error(pred, gt_truth, num_patches=73):
l, r = utils.norm_idx(num_patches)
assert (l is not None and r is not None)
norm = tf.sqrt(tf.reduce_sum((tf.square(gt_truth[:, l, :] - gt_truth[:, r, :])), 1, keepdims=True))
return tf.sqrt(tf.reduce_sum(tf.square(pred - gt_truth), 2)) / norm
