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from typing import List
import datasets
import evaluate
import os
import tempfile
import subprocess
from pycocoevalcap.cider.cider import CiderScorer, Cider
_DESCRIPTION = """
The CIDEr (Consensus-based Image Description Evaluation) metric is used to evaluate the quality of image captions generated by models in image captioning tasks.
It measures how well the generated caption matches human-written reference captions by considering both the frequency and the relevance of words or phrases.
Here is the formula for the CIDEr metric in LaTeX code:
$
\\text{CIDEr}(c_i, C) = \\frac{1}{N} \\sum_{n=1}^{N} w_n \\cdot \\frac{\\sum_{j=1}^{m} \\text{IDF}(g_j) \\cdot \\text{TF}(g_j, c_i)}{\\sum_{j=1}^{m} \\text{IDF}(g_j) \\cdot \\text{TF}(g_j, C)}
$
where:
- $ c_i $ is the candidate caption,
- $ C $ is the set of reference captions,
- $ N $ is the number of n-grams (typically 1 to 4),
- $ w_n $ is the weight for the n-gram,
- $ g_j $ represents the j-th n-gram,
- $ \\text{TF}(g_j, c_i) $ is the term frequency of the n-gram $ g_j $ in the candidate caption $ c_i $,
- $ \\text{TF}(g_j, C) $ is the term frequency of the n-gram $ g_j $ in the reference captions $ C $,
- $ \\text{IDF}(g_j) $ is the inverse document frequency of the n-gram $ g_j $.
"""
_KWARGS_DESCRIPTION = """
Args:
predictions (`list` of `str`): Predicted captions.
references (`list` of `str` lists): Ground truth captions.
n (int, defaults to 4): Number of ngrams for which (ngram) representation is calculated.
sigma (float, defaults to 6.0): The standard deviation parameter for gaussian penalty.
Returns:
CIDEr (`float`): CIDEr value. Minimum possible value is 0. Maximum possible value is 100.
"""
_CITATION = """
@inproceedings{vedantam2015cider,
title={Cider: Consensus-based image description evaluation},
author={Vedantam, Ramakrishna and Lawrence Zitnick, C and Parikh, Devi},
booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
pages={4566--4575},
year={2015}
}
"""
_URLS = {
"stanford-corenlp": "https://repo1.maven.org/maven2/edu/stanford/nlp/stanford-corenlp/3.4.1/stanford-corenlp-3.4.1.jar"
}
def tokenize(tokenizer_path: str, predictions: List[str], references: List[List[str]]):
PUNCTUATIONS = [
"''",
"'",
"``",
"`",
"-LRB-",
"-RRB-",
"-LCB-",
"-RCB-",
".",
"?",
"!",
",",
":",
"-",
"--",
"...",
";",
]
cmd = [
"java",
"-cp",
tokenizer_path,
"edu.stanford.nlp.process.PTBTokenizer",
"-preserveLines",
"-lowerCase",
]
sentences = "\n".join(
[
s.replace("\n", " ")
for s in predictions + [ref for refs in references for ref in refs]
]
)
with tempfile.NamedTemporaryFile(delete=False) as f:
f.write(sentences.encode())
cmd.append(f.name)
p_tokenizer = subprocess.Popen(cmd, stdout=subprocess.PIPE)
token_lines = p_tokenizer.communicate(input=sentences.rstrip())[0]
token_lines = token_lines.decode()
lines = [
" ".join([w for w in line.rstrip().split(" ") if w not in PUNCTUATIONS])
for line in token_lines.split("\n")
]
os.remove(f.name)
pred_size = len(predictions)
ref_sizes = [len(ref) for ref in references]
predictions = lines[:pred_size]
start = pred_size
references = []
for size in ref_sizes:
references.append(lines[start : start + size])
start += size
return predictions, references
@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
class CIDEr(evaluate.Metric):
def _info(self):
return evaluate.MetricInfo(
description=_DESCRIPTION,
citation=_CITATION,
inputs_description=_KWARGS_DESCRIPTION,
features=datasets.Features(
{
"predictions": datasets.Value("string", id="sequence"),
"references": datasets.Sequence(
datasets.Value("string", id="sequence"), id="references"
),
}
),
reference_urls=[
"https://github.com/salaniz/pycocoevalcap",
"https://github.com/tylin/coco-caption",
],
)
def _download_and_prepare(self, dl_manager):
self.tokenizer_path = dl_manager.download(_URLS["stanford-corenlp"])
def _compute(self, predictions, references, n=4, sigma=6.0):
predications, references = tokenize(
self.tokenizer_path, predictions, references
)
scorer = CiderScorer(n, sigma)
for pred, refs in zip(predications, references):
scorer += (pred, refs)
score, scores = scorer.compute_score()
return {"CIDEr": score}
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