| import torch |
| import os |
| import random |
|
|
| import numpy as np |
| from tqdm import tqdm |
|
|
| from datasets import load_dataset, concatenate_datasets |
|
|
| from argparse import ArgumentParser |
| from mathruler.grader import extract_boxed_content |
| from .data_utils import load_yaml, construct_prompt, save_json, process_single_sample, CAT_SHORT2LONG,DOMAIN_CAT2SUB_CAT |
| from .eval_utils import evaluate,parse_multi_choice_response, parse_open_response |
| from ..utils import extract |
|
|
| def run_model(samples, model,): |
| out_samples = [] |
| with torch.no_grad(): |
| messages_list = [] |
| current_messages = [] |
| current_samples = [] |
| for sample in tqdm(samples): |
| messages = {"prompt":sample["final_input_prompt"],"image":sample["image"]} |
| current_messages.append(messages) |
| current_samples.append(sample) |
| if len(current_messages) >= 2000: |
| messages_list.append([current_messages,current_samples]) |
| current_messages = [] |
| current_samples = [] |
| if current_messages: |
| messages_list.append([current_messages,current_samples]) |
|
|
| for current_messages,current_samples in tqdm(messages_list): |
| outputs = model.generate_outputs(current_messages) |
| for sample,response in zip(current_samples,outputs): |
| out_sample = { |
| "id":sample['id'], |
| "question_type":sample['question_type'], |
| "answer":sample["answer"] |
| } |
| if "<answer>" in response: |
| response = extract(response,"answer") |
| if extract_boxed_content(response) != "None": |
| response = extract_boxed_content(response) |
|
|
| if sample['question_type'] == 'multiple-choice': |
| out_sample["all_choices"] = sample["all_choices"] |
| out_sample["index2ans"] = sample["index2ans"] |
| out_sample["response"] = response |
| out_sample["parsed_pred"] = parse_multi_choice_response(response, sample['all_choices'], sample['index2ans']) |
| else: |
| out_sample["response"] = response |
| out_sample["parsed_pred"] = response |
| out_samples.append(out_sample) |
| return out_samples |
|
|
|
|
| def eval_MMMU_val(model,dataset_path,output_path,subset): |
| total_results = {"total":{"total":0,"right":0}} |
| for subject in tqdm(DOMAIN_CAT2SUB_CAT[subset]): |
| sub_samples = [] |
| sub_dataset = load_dataset(dataset_path, subject, split="validation") |
|
|
| eval_sub_path = os.path.join(output_path,subject) |
| if not os.path.exists(eval_sub_path): |
| os.mkdir(eval_sub_path) |
| for sample in sub_dataset: |
| sample = process_single_sample(sample) |
| sample = construct_prompt(sample) |
| sub_samples.append(sample) |
|
|
| eval_samples = run_model(sub_samples, model) |
| save_json(os.path.join(eval_sub_path,"output_sample.json"), eval_samples) |
| judge_dict, metric_dict = evaluate(eval_samples) |
| metric_dict.update({"num_example": len(eval_samples)}) |
| for eval_sample in eval_samples: |
| eval_sample.update({"judge": judge_dict[eval_sample['id']]}) |
|
|
| save_json(os.path.join(eval_sub_path, 'parsed_output.json'), eval_samples) |
| save_json(os.path.join(eval_sub_path, 'result.json'), metric_dict) |
| total_results[subject] = metric_dict |
| total_results["total"]["total"] += metric_dict["total"] |
| total_results["total"]["right"] += metric_dict["right"] |
| total_results["total"]["acc"] = total_results["total"]["right"] / total_results["total"]["total"] |
| save_json(os.path.join(output_path, 'result.json'), total_results) |
| return total_results |
|
|
