File size: 3,831 Bytes
63fba53 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 | 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: # open question
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
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