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 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 compute_score(self, gts, res):
"""
Main function to compute CIDEr score
: param gts (dict) : {image:tokenized reference sentence}
: param res (dict) : {image:tokenized candidate sentence}
: return: cider (float) : computed CIDEr score for the corpus
"""
cider_scorer = CiderScorer(n=self._n)
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)
cider_scorer += (hypo[0], ref)
(score, scores) = cider_scorer.compute_score(self._df)
return score, scores
class Cider:
"""
Main Class to compute the CIDEr metric
"""
def __init__(self, n=4, df="corpus"):
"""
Initialize the CIDEr scoring function
: param n (int): n-gram size
: param df (string): specifies where to get the IDF values from
takes values 'corpus', 'coco-train'
: return: None
"""
# set cider to sum over 1 to 4-grams
self._n = n
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 gts (dict) : {image:tokenized reference sentence}
: param res (dict) : {image:tokenized candidate sentence}
: return: cider (float) : computed CIDEr score for the corpus
"""
# clear all the previous hypos and refs
self.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)
self.cider_scorer += (hypo[0], ref)
(score, scores) = self.cider_scorer.compute_score()
return score, scores
def method(self):
return "CIDEr"
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
"""
cider_scorer = CiderScorer(n=self._n, sigma=self._sigma)
for hypo,ref in zip(gts, res):
cider_scorer += (hypo, ref)
(score, scores) = cider_scorer.compute_score()
return score
