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# Filename: cider.py
#
# Description: Describes the class to compute the CIDEr (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 <[email protected]> and Tsung-Yi Lin <[email protected]>
from refTools.evaluation.cider.cider_scorer import CiderScorer
import pdb
class Cider:
"""
Main Class to compute the CIDEr metric
"""
def __init__(self, test=None, refs=None, n=4, sigma=6.0):
# set cider to sum over 1 to 4-grams
self._n = n
# set the standard deviation parameter for gaussian penalty
self._sigma = sigma
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
"""
assert(gts.keys() == res.keys())
imgIds = gts.keys()
cider_scorer = CiderScorer(n=self._n, sigma=self._sigma)
for id in imgIds:
hypo = res[id]
ref = gts[id]
# Sanity check.
assert(type(hypo) is list)
assert(len(hypo) == 1)
assert(type(ref) is list)
assert(len(ref) > 0)
cider_scorer += (hypo[0], ref)
(score, scores) = cider_scorer.compute_score()
return score, scores
def method(self):
return "CIDEr" |