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| # This file is originated from the official MMMU codebase: | |
| # https://github.com/MMMU-Benchmark/MMMU | |
| """Parse and Evalate""" | |
| import os | |
| import json | |
| import pdb | |
| from argparse import ArgumentParser | |
| from llava.eval.mmmu_utils.data_utils import save_json, CAT_SHORT2LONG, DOMAIN_CAT2SUB_CAT | |
| from llava.eval.mmmu_utils.eval_utils import evaluate, parse_multi_choice_response, parse_open_response, calculate_ins_level_acc | |
| if __name__ == '__main__': | |
| parser = ArgumentParser() | |
| parser.add_argument('--output_path', type=str, default="./example_outputs/qwen_vl/total_val_output.json", help="The path to model output file.") | |
| parser.add_argument('--answer_path', type=str, default="./answer_dict_val.json", help="Answer file path.") | |
| args = parser.parse_args() | |
| output_dict = json.load(open(args.output_path)) | |
| answer_dict = json.load(open(args.answer_path)) | |
| # group by category | |
| output_dict_w_cat = {} | |
| for data_id, parsed_pred in output_dict.items(): | |
| category = "_".join(data_id.split("_")[1:-1]) | |
| if category not in output_dict_w_cat: | |
| output_dict_w_cat.update({category: {}}) | |
| output_dict_w_cat[category].update({data_id: parsed_pred}) | |
| # group by category | |
| answer_dict_w_cat = {} | |
| for data_id, parsed_pred in answer_dict.items(): | |
| category = "_".join(data_id.split("_")[1:-1]) | |
| if category not in answer_dict_w_cat: | |
| answer_dict_w_cat.update({category: {}}) | |
| answer_dict_w_cat[category].update({data_id: parsed_pred}) | |
| evaluation_result = {} | |
| for category in CAT_SHORT2LONG.values(): | |
| print("Evaluating: {}".format(category)) | |
| # get cat_outputs and cat_answers | |
| try: | |
| cat_outputs = output_dict_w_cat[category] | |
| cat_answers = answer_dict_w_cat[category] | |
| except KeyError: | |
| print("Skipping {} for not found".format(category)) | |
| continue | |
| exampels_to_eval = [] | |
| for data_id, parsed_pred in cat_outputs.items(): | |
| question_type = cat_answers[data_id]['question_type'] | |
| if question_type != 'multiple-choice': | |
| parsed_pred = parse_open_response(parsed_pred) # mainly for type consistency (make it number, etc.) | |
| else: | |
| parsed_pred = parsed_pred | |
| exampels_to_eval.append({ | |
| "id": data_id, | |
| "question_type": question_type, | |
| "answer": cat_answers[data_id]['ground_truth'], | |
| "parsed_pred": parsed_pred | |
| }) | |
| judge_dict, metric_dict = evaluate(exampels_to_eval) | |
| metric_dict.update({"num_example": len(exampels_to_eval)}) | |
| evaluation_result[category] = metric_dict | |
| printable_results = {} | |
| # pdb.set_trace() | |
| # add domain Subject | |
| for domain, in_domain_cats in DOMAIN_CAT2SUB_CAT.items(): | |
| in_domain_cat_results = {} | |
| for cat_name in in_domain_cats: # use the order in DOMAIN_CAT2SUB_CAT | |
| if cat_name in evaluation_result.keys(): | |
| in_domain_cat_results[cat_name] = evaluation_result[cat_name] | |
| else: | |
| pass | |
| in_domain_ins_acc = calculate_ins_level_acc(in_domain_cat_results) | |
| in_domain_data_num = sum([cat_results['num_example'] for cat_results in in_domain_cat_results.values()]) | |
| printable_results['Overall-' + domain] = {"num": int(in_domain_data_num), | |
| "acc": round(in_domain_ins_acc, 3) | |
| } | |
| # add sub category | |
| for cat_name, cat_results in in_domain_cat_results.items(): | |
| printable_results[cat_name] = {"num": int(cat_results['num_example']), | |
| "acc": round(cat_results['acc'], 3) | |
| } | |
| # table.append(["-----------------------------", "-----", "----"]) | |
| all_ins_acc = calculate_ins_level_acc(evaluation_result) | |
| printable_results['Overall'] = {"num": sum([cat_results['num_example'] for cat_results in evaluation_result.values()]), | |
| "acc": round(all_ins_acc, 3) | |
| } | |
| print(printable_results) |