| import torch |
| import os |
| import json |
|
|
| import numpy as np |
| from tqdm import tqdm |
|
|
| from datasets import load_dataset, concatenate_datasets |
| 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 = {} |
| out_response = {} |
| 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): |
| 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": |
| pred_ans = parse_multi_choice_response(response,sample["all_choices"],sample["index2ans"]) |
| else: |
| pred_ans = parse_open_response(response) |
| out_samples[sample['id']] = pred_ans |
| out_response[sample['id']] = response |
| return out_samples,out_response |
|
|
|
|
| def eval_MMMU_test(model,dataset_path,output_path,subset): |
| sub_dataset_list = [] |
| for subject in tqdm(DOMAIN_CAT2SUB_CAT[subset]): |
| sub_dataset = load_dataset(dataset_path, subject, split="test") |
| sub_dataset_list.append(sub_dataset) |
| |
| with open("utils/MMMU/answer.json", "r") as f: |
| gold_answers = json.load(f) |
| |
|
|
| chunk_idx = int(os.environ.get("chunk_idx",0)) |
| num_chunks = int(os.environ.get("num_chunks",1)) |
| samples = [] |
| dataset = concatenate_datasets(sub_dataset_list) |
| for idx,sample in tqdm(enumerate(dataset)): |
| if idx % num_chunks == chunk_idx: |
| sample = process_single_sample(sample) |
| sample = construct_prompt(sample) |
| samples.append(sample) |
|
|
| if num_chunks == 1: |
| results_path = os.path.join(output_path,"results.json") |
| response_path = os.path.join(output_path,"response.json") |
| out_samples,out_response = run_model(samples,model) |
| save_json(results_path,out_samples) |
| save_json(response_path,out_response) |
| return evaluate(out_samples,gold_answers) |
|
|
| elif num_chunks > 1: |
| results_path = os.path.join(output_path,f"results_{chunk_idx}.json") |
| response_path = os.path.join(output_path,f"response_{chunk_idx}.json") |
| out_samples,out_response = run_model(samples,model) |
| save_json(results_path,out_samples) |
| save_json(response_path,out_response) |
|
|
|
|
| total_results_path = os.listdir(output_path) |
| total_results_path = [result for result in total_results_path if result.startswith("results_")] |
| if len(total_results_path) == num_chunks: |
| total_results = {} |
| for result in total_results_path: |
| results_path = os.path.join(output_path,result) |
| with open(results_path,"r") as f: |
| partial_results = json.load(f) |
| total_results.update(partial_results) |
| os.remove(results_path) |
| with open(os.path.join(output_path,"results.json"),"w") as f: |
| json.dump(total_results,f) |
| |
| return evaluate(total_results,gold_answers) |
| else: |
| return |
| else: |
| raise ValueError("num_chunks must be greater than 0") |
|
|
|
|
