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# --------------------------------------------------------
# mcan-vqa (Deep Modular Co-Attention Networks)
# Licensed under The MIT License [see LICENSE for details]
# Written by Yuhao Cui https://github.com/cuiyuhao1996
# --------------------------------------------------------
import sys
sys.path.append('../')
from openvqa.utils.ans_punct import prep_ans
from openvqa.core.path_cfgs import PATH
import json, re
path = PATH()
ques_dict_preread = {
'train': json.load(open(path.RAW_PATH['gqa']['train'], 'r')),
'val': json.load(open(path.RAW_PATH['gqa']['val'], 'r')),
'testdev': json.load(open(path.RAW_PATH['gqa']['testdev'], 'r')),
'test': json.load(open(path.RAW_PATH['gqa']['test'], 'r')),
}
# Loading question word list
stat_ques_dict = {
**ques_dict_preread['train'],
**ques_dict_preread['val'],
**ques_dict_preread['testdev'],
**ques_dict_preread['test'],
}
stat_ans_dict = {
**ques_dict_preread['train'],
**ques_dict_preread['val'],
**ques_dict_preread['testdev'],
}
def tokenize(stat_ques_dict):
token_to_ix = {
'PAD': 0,
'UNK': 1,
'CLS': 2,
}
max_token = 0
for qid in stat_ques_dict:
ques = stat_ques_dict[qid]['question']
words = re.sub(
r"([.,'!?\"()*#:;])",
'',
ques.lower()
).replace('-', ' ').replace('/', ' ').split()
if len(words) > max_token:
max_token = len(words)
for word in words:
if word not in token_to_ix:
token_to_ix[word] = len(token_to_ix)
return token_to_ix, max_token
def ans_stat(stat_ans_dict):
ans_to_ix = {}
ix_to_ans = {}
for qid in stat_ans_dict:
ans = stat_ans_dict[qid]['answer']
ans = prep_ans(ans)
if ans not in ans_to_ix:
ix_to_ans[ans_to_ix.__len__()] = ans
ans_to_ix[ans] = ans_to_ix.__len__()
return ans_to_ix, ix_to_ans
token_to_ix, max_token = tokenize(stat_ques_dict)
ans_to_ix, ix_to_ans = ans_stat(stat_ans_dict)
# print(ans_to_ix)
# print(ix_to_ans)
# print(token_to_ix)
# print(token_to_ix.__len__())
# print(max_token)
json.dump([ans_to_ix, ix_to_ans, token_to_ix, max_token], open('../openvqa/datasets/gqa/dicts.json', 'w'))
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