1271e900113a2730b8fec9db1e750b26db4b6af9,tensorflow_ranking/python/model.py,_GroupwiseRankingModel,_compute_logits_impl,#_GroupwiseRankingModel#Any#Any#Any#Any#Any#Any#,330

Before Change

        // Reset invalid scores to 0 based on mask.
        scores_mask = tf.gather(
            tf.expand_dims(self._indices_mask, 2),
            tf.zeros([tf.shape(input=scores)[2]], tf.int32),
            axis=2)
        scores = tf.compat.v1.where(scores_mask, scores, tf.zeros_like(scores))
        // Scatter scores from [batch_size, num_groups, logits_size] to
        // [batch_size, list_size].
        logits = tf.scatter_nd(self._score_scatter_indices, scores,
                               [batch_size, list_size])
        counts = tf.scatter_nd(self._score_scatter_indices,
                               tf.cast(scores_mask, tf.float32),
                               [batch_size, list_size])
        // Use average.
        logits = tf.compat.v1.div_no_nan(logits, counts)
    return logits


def _make_model_fn(ranking_model, ranking_head):

After Change

          task_logits = tf.compat.v1.div_no_nan(task_logits, counts)
          return task_logits

        if isinstance(scores, dict):
          logits = {}
          for name, task_scores in six.iteritems(scores):
            logits[name] = _accumulate_scores(task_scores)
        else:
          logits = _accumulate_scores(scores)

    return logits


def _make_model_fn(ranking_model, ranking_head):

Instances