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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) |