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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"
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