def compute_score(self, gts, res):
        """
        Main function to compute CIDEr score
        :param  hypo_for_image (dict) : dictionary with key <image> and value <tokenized hypothesis / candidate sentence>
                ref_for_image (dict)  : dictionary with key <image> and value <tokenized reference sentence>
        :return: cider (float) : computed CIDEr score for the corpus
        """

        # clear all the previous hypos and refs
        tmp_cider_scorer = self.cider_scorer.copy_empty()
        tmp_cider_scorer.clear()
        for res_id in res:

            hypo = res_id['caption']
            ref = gts[res_id['image_id']]

            # Sanity check.
            assert(type(hypo) is list)
            assert(len(hypo) == 1)
            assert(type(ref) is list)
            assert(len(ref) > 0)
            tmp_cider_scorer += (hypo[0], ref)

        (score, scores) = tmp_cider_scorer.compute_score()

        return score, scores

        Main function to compute CIDEr score
        :param  hypo_for_image (dict) : dictionary with key <image> and value <tokenized hypothesis / candidate sentence>
                ref_for_image (dict)  : dictionary with key <image> and value <tokenized reference sentence>
        :return: cider (float) : computed CIDEr score for the corpus
        
torchmm/metrics/caption/ciderD/ciderD.py