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from utils.common.data_record import read_json, write_json
from data.datasets.visual_question_answering.glossary import normalize_word
from collections import defaultdict, Counter
from tqdm import tqdm
ann_data = read_json('/data/zql/datasets/vqav2/Annotations/v2_mscoco_train2014_annotations.json')
question_data = read_json('/data/zql/datasets/vqav2/Questions/v2_OpenEnded_mscoco_train2014_questions.json')
question_to_id = {}
for q in tqdm(question_data['questions']):
question_to_id[q['question_id']] = q['question']
classes_set = []
for ann in ann_data['annotations']:
classes_set += [normalize_word(ann['multiple_choice_answer'])]
counter = {k: v for k, v in Counter(classes_set).items() if v >= 9}
ans2label = {k: i for i, k in enumerate(counter.keys())}
label2ans = list(counter.keys())
# print(list(ans2label.keys()))
# exit()
available_classes = list(ans2label.values())
classes_split_1 = available_classes[0: 100]
classes_split_2 = available_classes[100: ]
print(classes_split_1)
dataset_info_1 = [] # (image_file_path, question, labels, scores)
dataset_info_2 = [] # (image_file_path, question, labels, scores)
def get_score(occurences):
if occurences == 0:
return 0.0
elif occurences == 1:
return 0.3
elif occurences == 2:
return 0.6
elif occurences == 3:
return 0.9
else:
return 1.0
ii = 0
pbar = tqdm(ann_data['annotations'])
for q in pbar:
answers = q["answers"]
answer_count = {}
for answer in answers:
answer_ = answer["answer"]
answer_count[answer_] = answer_count.get(answer_, 0) + 1
labels = []
scores = []
for answer in answer_count:
if answer not in ans2label:
continue
labels.append(ans2label[answer])
score = get_score(answer_count[answer])
scores.append(score)
if len(labels) == 0:
continue
# annotations[q["image_id"]][q["question_id"]].append(
# {"labels": labels, "scores": scores,}
# )
# full_label = [0] * len(ans2label)
# for label_idx, score in zip(labels, scores):
# full_label[label_idx] = score
if all([label in classes_split_1 for label in labels]):
dataset_info_1 += [(q["image_id"], question_to_id[q["question_id"]], labels, scores)]
elif all([label in classes_split_2 for label in labels]):
dataset_info_2 += [(q["image_id"], question_to_id[q["question_id"]], [ii - 100 for ii in labels], scores)]
else:
# print('ignore')
pass
# dataset_info += [(q["image_id"], question_to_id[q["question_id"]], labels, scores)]
pbar.set_description(f'# samples: {len(dataset_info_1)}, {len(dataset_info_2)}')
# print(dataset_info[-1])
# break
# if ii < 10:
# print(dataset_info[-1])
# ii += 1
write_json('/data/zql/datasets/vqav2/label1.json', dataset_info_1)
write_json('/data/zql/datasets/vqav2/label2.json', dataset_info_2)
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