minigpt4/common/vqa_tools/aokvqa/evaluation/prepare_predictions.py

import argparse
import pathlib
import json

from load_aokvqa import load_aokvqa


if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--aokvqa-dir', type=pathlib.Path, required=True, dest='aokvqa_dir')
    parser.add_argument('--split', type=str, choices=['train', 'val', 'test'], required=True)
    parser.add_argument('--mc', type=argparse.FileType('r'), dest='mc_pred_file')
    parser.add_argument('--da', type=argparse.FileType('r'), dest='da_pred_file')
    parser.add_argument('--out', type=argparse.FileType('w'), dest='output_file')
    args = parser.parse_args()
    assert args.mc_pred_file or args.da_pred_file

    dataset = load_aokvqa(args.aokvqa_dir, args.split)
    mc_preds = json.load(args.mc_pred_file) if args.mc_pred_file else None
    da_preds = json.load(args.da_pred_file) if args.da_pred_file else None
    predictions = {}

    for d in dataset:
        q = d['question_id']
        predictions[q] = {}
        if mc_preds and q in mc_preds.keys():
            predictions[q]['multiple_choice'] = mc_preds[q]
        if da_preds and q in da_preds.keys():
            predictions[q]['direct_answer'] = da_preds[q]

    json.dump(predictions, args.output_file)