class_docstr: Bleu Evaluator. Now, we support metrics `'bleu'`
      
class_docstr_tok: ["Bleu","Evaluator",".","Now",",","we","support","metrics","`","'bleu","'","`"]
docstr: get metrics result
  
  Args:
      generate_corpus: the generated corpus
      reference_corpus: the referenced corpus
  
  Returns:
      dict: a dict of metrics <metric> which record the results according to self.ngrams
docstr_tok: ["get","metrics","result","Args",":","generate_corpus",":","the","generated","corpus","reference_corpus",":","the","referenced","corpus","Returns",":","dict",":","a","dict","of","metrics","<","metric",">","which","record","the","results","according","to","self.ngrams"]
code: def _calc_metrics_info(self, generate_corpus, reference_corpus):
      r"""get metrics result
  
      Args:
          generate_corpus: the generated corpus
          reference_corpus: the referenced corpus
  
      Returns:
          dict: a dict of metrics <metric> which record the results according to self.ngrams
      """
      results = {}
      refs = {
          idx: r
          for idx, r in enumerate(reference_corpus.tokenized_text)
      }
      gen = {
          idx: [g]
          for idx, g in enumerate(generate_corpus.tokenized_text)
      }
      score = Spice().compute_score(refs, gen)[0]
      results["spice"] = score * 100
      return results
  
imports: from .abstract_evaluator import AbstractEvaluator
  from pycocoevalcap.spice.spice import Spice
label_desc: Use image node_id 2 for calling the SpiceEvaluator obj's underlying member method code with example usage: obj._calc_metrics_info(generate_corpus, reference_corpus) and returns: results