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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) | |