| import argparse |
| import json |
| import os |
|
|
| argparse = argparse.ArgumentParser() |
| argparse.add_argument('--image_result_file', type=str, default='') |
| argparse.add_argument('--anno_path', type=str, default='data/SEED/SEED-Bench.json') |
|
|
| args = argparse.parse_args() |
| image_result_file = args.image_result_file |
| anno_path = args.anno_path |
|
|
| assert image_result_file.endswith('.jsonl') |
|
|
|
|
| def is_integer_string(s): |
| try: |
| int(s) |
| return True |
| except ValueError: |
| return False |
|
|
|
|
| def filter_questions(data, task='all'): |
| if task == 'image': |
| return [q for q in data if 1 <= q['question_type_id'] <= 9] |
| elif task == 'video': |
| return [q for q in data if 10 <= q['question_type_id'] <= 12] |
| elif task == 'all': |
| return data |
| elif is_integer_string(task): |
| return [q for q in data if q['question_type_id'] == int(task)] |
| else: |
| raise ValueError(f'Invalid task: {task}') |
|
|
|
|
| if __name__ == '__main__': |
|
|
| qa_anno = json.load(open(anno_path, 'rb')) |
| if 'questions' in qa_anno.keys(): |
| question_type = qa_anno['question_type'] |
| question_id_type = {v: k for k, v in question_type.items()} |
| qa_anno = qa_anno['questions'] |
|
|
| qa_anno = filter_questions(qa_anno, 'all') |
| print(f'length: {len(qa_anno)}') |
|
|
| with open(image_result_file, 'r') as f: |
|
|
| image_result = [json.loads(line) for line in f.readlines()] |
|
|
| results = [] |
|
|
| results.extend(image_result) |
|
|
| qa_id_anno = {} |
| for item in qa_anno: |
| question_id = str(item['question_id']) |
| qa_id_anno[question_id] = item |
|
|
| type_counts = {k: [] for k, v in question_id_type.items()} |
|
|
| for item in results: |
| pred, gt, question_id = item['prediction'], item['answer'], item['question_id'] |
| question_id = str(question_id) |
| question_type = qa_id_anno[question_id]['question_type_id'] |
| data_type = qa_id_anno[question_id]['data_type'] |
| gt = qa_id_anno[question_id]['answer'] |
| if len(pred) != 1: |
| pred = pred[0] |
| if pred == gt: |
| type_counts[question_type].append(1) |
| else: |
| type_counts[question_type].append(0) |
|
|
| print('Accuracy for each data type:') |
| total_count, image_count, video_count = 0, 0, 0 |
| total_correct, image_correct, video_correct = 0, 0, 0 |
| for data_type_id, result in type_counts.items(): |
| accuracy = sum(result) / len(result) * 100 |
| data_type = question_id_type[data_type_id] |
| print(f'Data type {data_type}: {accuracy:.2f}%') |
|
|
| total_count += len(result) |
| total_correct += sum(result) |
| if data_type_id >= 1 and data_type_id <= 9: |
| image_count += len(result) |
| image_correct += sum(result) |
| else: |
| video_count += len(result) |
| video_correct += sum(result) |
|
|
| total_accuracy = total_correct / total_count * 100 |
| image_accuracy = image_correct / image_count * 100 |
| video_accuracy = video_correct / video_count * 100 |
|
|
| print(f'Total accuracy: {total_accuracy:.2f}%') |
| print(f'Image accuracy: {image_accuracy:.2f}%') |
| print(f'Video accuracy: {video_accuracy:.2f}%') |
|
|
