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@@ -59,3 +59,4 @@ docs/_build/ | |
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# PyBuilder | ||
target/ | ||
*.dat |
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scipy | ||
tqdm | ||
h5py | ||
nltk |
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#!/usr/bin/env python | ||
# -*- coding: utf-8 -*- | ||
# File: visualqa.py | ||
# Author: Yuxin Wu <ppwwyyxxc@gmail.com> | ||
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from ..base import DataFlow | ||
from six.moves import zip, map | ||
from collections import Counter | ||
import json | ||
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__all__ = ['VisualQA'] | ||
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# TODO shuffle | ||
class VisualQA(DataFlow): | ||
""" | ||
Visual QA dataset. See http://visualqa.org/ | ||
Simply read q/a json file and produce q/a pairs in their original format. | ||
""" | ||
def __init__(self, question_file, annotation_file): | ||
qobj = json.load(open(question_file)) | ||
self.task_type = qobj['task_type'] | ||
self.questions = qobj['questions'] | ||
self._size = len(self.questions) | ||
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aobj = json.load(open(annotation_file)) | ||
self.anno = aobj['annotations'] | ||
assert len(self.anno) == len(self.questions), \ | ||
"{}!={}".format(len(self.anno), len(self.questions)) | ||
self._clean() | ||
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def _clean(self): | ||
for a in self.anno: | ||
for aa in a['answers']: | ||
del aa['answer_id'] | ||
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def size(self): | ||
return self._size | ||
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def get_data(self): | ||
for q, a in zip(self.questions, self.anno): | ||
assert q['question_id'] == a['question_id'] | ||
yield [q, a] | ||
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def get_common_answer(self, n): | ||
""" Get the n most common answers (could be phrases) """ | ||
cnt = Counter() | ||
for anno in self.anno: | ||
cnt[anno['multiple_choice_answer']] += 1 | ||
return [k[0] for k in cnt.most_common(n)] | ||
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def get_common_question_words(self, n): | ||
""" | ||
Get the n most common words in questions | ||
""" | ||
from nltk.tokenize import word_tokenize # will need to download 'punckt' | ||
cnt = Counter() | ||
for q in self.questions: | ||
cnt.update(word_tokenize(q['question'].lower())) | ||
del cnt['?'] # probably don't need this | ||
ret = cnt.most_common(n) | ||
return [k[0] for k in ret] | ||
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if __name__ == '__main__': | ||
vqa = VisualQA('/home/wyx/data/VQA/MultipleChoice_mscoco_train2014_questions.json', | ||
'/home/wyx/data/VQA/mscoco_train2014_annotations.json') | ||
for k in vqa.get_data(): | ||
#print json.dumps(k) | ||
break | ||
vqa.get_common_question_words(100) | ||
#from IPython import embed; embed() |