def calc_score(self, candidate, refs):
        """
        Compute ROUGE-L score given one candidate and references for an image
        :param candidate: str : candidate sentence to be evaluated
        :param refs: list of str : COCO reference sentences for the particular image to be evaluated
        :returns score: int (ROUGE-L score for the candidate evaluated against references)
        """
        assert(len(candidate)==1)	
        assert(len(refs)>0)         
        prec = []
        rec = []

        # split into tokens
        token_c = candidate[0].split(" ")
    	
        for reference in refs:
            # split into tokens
            token_r = reference.split(" ")
            # compute the longest common subsequence
            lcs = my_lcs(token_r, token_c)
            prec.append(lcs/float(len(token_c)))
            rec.append(lcs/float(len(token_r)))

        prec_max = max(prec)
        rec_max = max(rec)

        if(prec_max!=0 and rec_max !=0):
            score = ((1 + self.beta**2)*prec_max*rec_max)/float(rec_max + self.beta**2*prec_max)
        else:
            score = 0.0
        return score

        Compute ROUGE-L score given one candidate and references for an image
        :param candidate: str : candidate sentence to be evaluated
        :param refs: list of str : COCO reference sentences for the particular image to be evaluated
        :returns score: int (ROUGE-L score for the candidate evaluated against references)
        
torchmm/metrics/caption/rouge/rouge.py