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| # Filename: ciderD.py | |
| # | |
| # Description: Describes the class to compute the CIDEr-D (Consensus-Based Image Description Evaluation) Metric | |
| # by Vedantam, Zitnick, and Parikh (http://arxiv.org/abs/1411.5726) | |
| # | |
| # Creation Date: Sun Feb 8 14:16:54 2015 | |
| # | |
| # Authors: Ramakrishna Vedantam <vrama91@vt.edu> and Tsung-Yi Lin <tl483@cornell.edu> | |
| from __future__ import absolute_import | |
| from __future__ import division | |
| from __future__ import print_function | |
| from .ciderD_scorer import CiderScorer | |
| import pdb | |
| class CiderD: | |
| """ | |
| Main Class to compute the CIDEr metric | |
| """ | |
| def __init__(self, n=4, sigma=6.0, df="corpus"): | |
| # set cider to sum over 1 to 4-grams | |
| self._n = n | |
| # set the standard deviation parameter for gaussian penalty | |
| self._sigma = sigma | |
| # set which where to compute document frequencies from | |
| self._df = df | |
| self.cider_scorer = CiderScorer(n=self._n, df_mode=self._df) | |
| 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 | |
| def method(self): | |
| return "CIDEr-D" | |