| | from ..smp import * |
| | from ..dataset.utils.judge_util import build_judge |
| | from ..dataset.utils.multiple_choice import extract_answer_from_item |
| | from .matching_util import can_infer |
| | from .mp_util import track_progress_rich |
| |
|
| |
|
| | def MMMU_result_transfer(result_path): |
| | res = {} |
| | result_data = load(result_path) |
| | mcq = result_data['A'].notna() |
| | lt = len(result_data) |
| | for i in range(lt): |
| | line = result_data.iloc[i] |
| | if mcq[i]: |
| | options = { |
| | cand: line[cand] |
| | for cand in string.ascii_uppercase |
| | if cand in line and not pd.isna(line[cand]) |
| | } |
| | prediction = line['prediction'] |
| | infer_prediction = can_infer(prediction, options) |
| | res[line['id']] = infer_prediction |
| | else: |
| | res[line['id']] = line['prediction'] |
| | result_json = result_path.replace('.xlsx', '.json') |
| | dump(res, result_json) |
| | return result_json |
| |
|
| |
|
| | def MMTBench_result_transfer(eval_file, dataset='default', **judge_kwargs): |
| | logger = get_logger('Evaluation') |
| | nproc = judge_kwargs.pop('nproc', 4) |
| |
|
| | rd.seed(2680) |
| | suffix = eval_file.split('.')[-1] |
| | model = judge_kwargs['model'] |
| | assert model in ['chatgpt-0125', 'exact_matching', 'gpt-4-0125'] |
| | name_str_map = { |
| | 'chatgpt-0125': 'openai', |
| | 'gpt-4-0125': 'gpt4' |
| | } |
| | name_str = name_str_map[model] if model in name_str_map else model |
| |
|
| | if model == 'exact_matching': |
| | model = None |
| | elif gpt_key_set(): |
| | model = build_judge(**judge_kwargs) |
| | if not model.working(): |
| | logger.error('The OPENAI API is not working properly, will use exact matching for evaluation') |
| | model = None |
| | else: |
| | logger.error('OPENAI_API_KEY is not set properly, will use exact matching for evaluation') |
| | model = None |
| |
|
| | logger.info(f'Evaluating {eval_file}') |
| | result_file = eval_file.replace(f'.{suffix}', f'_{name_str}_option.pkl') |
| | result = {} |
| | if osp.exists(result_file): |
| | result = load(result_file) |
| |
|
| | data = load(eval_file) |
| | assert 'index' in data, 'Essentail columns missing in the eval_file.' |
| |
|
| | data = data.sort_values(by='index') |
| | data['prediction'] = [str(x) for x in data['prediction']] |
| | for k in data.keys(): |
| | data[k.lower() if k not in list(string.ascii_uppercase) else k] = data.pop(k) |
| |
|
| | idx2lines = {data.iloc[i]['index']: data.iloc[i] for i in range(len(data))} |
| | idx2lines = {k: v for k, v in idx2lines.items() if k not in result} |
| |
|
| | indices = list(idx2lines.keys()) |
| | lines = [idx2lines[i] for i in indices] |
| | tups = [(model, line) for line in lines] |
| | res = track_progress_rich( |
| | extract_answer_from_item, |
| | tups, |
| | nproc=nproc, |
| | chunksize=nproc, |
| | save=result_file, |
| | keys=indices) |
| |
|
| | for i, r in zip(indices, res): |
| | if i in result: |
| | assert result[i]['opt'] == r['opt'] and result[i]['log'] == r['log'] |
| | else: |
| | result[i] = r |
| |
|
| | indices = list(data['index']) |
| | data['opt'] = [result[i]['opt'] for i in data['index']] |
| | data['log'] = [result[i]['log'] for i in data['index']] |
| |
|
| | |
| | output_path = eval_file.replace(f'.{suffix}', f'_{name_str}_submission.tsv') |
| | dump(data, eval_file.replace(f'.{suffix}', f'_{name_str}_submission.tsv')) |
| | return output_path |
| |
|
