vlmeval/dataset/utils/mathv.py

from ...smp import *
from ...utils import can_infer
import timeout_decorator
try:
    from latex2sympy2 import latex2sympy
except Exception as e:
    logging.critical(f'{type(e)}: {e}')
    logging.critical('Please install latex2sympy2 by running "pip install latex2sympy2"')


FAIL_MSG = 'Failed to obtain answer via API.'


@timeout_decorator.timeout(30, use_signals=False)
def is_equal(asw: str, gt_asw: str) -> bool:
    if not isinstance(asw, str) != str or not isinstance(gt_asw, str):
        print('Warning: input is not string')
        print(asw, gt_asw)
    asw = str(asw).lower().strip()
    gt_asw = str(gt_asw).lower().strip()
    if gt_asw == asw:
        return True
    try:
        a = eval(gt_asw)
        b = eval(asw)
        if abs(a - b) < 1e-6:
            return True
    except:
        pass
    try:
        a = latex2sympy(gt_asw)
        b = latex2sympy(asw)
        if abs(eval(str(a)) - eval(str(b))) < 1e-6:
            return True
        if abs(a - b) < 1e-6:
            return True
    except:
        pass
    return False


def get_gpt4_ICE():
    example_1 = """
Hint: Please answer the question and provide the final answer at the end.\n
Question: Which number is missing?\n
Model response: The number missing in the sequence is 14.\n
Extracted answer: 14
"""

    example_2 = """
Hint: Please answer the question and provide the final answer at the end.\n
Question: What is the fraction of females facing the camera?\n
Model response: The fraction of females facing the camera is 0.6,
which means that six out of ten females in the group are facing the camera.\n
Extracted answer: 0.6
"""

    example_3 = """
Hint: Please answer the question and provide the final answer at the end.\n
Question: How much money does Luca need to buy a sour apple candy and a butter-scotch candy? (Unit: $)\n
Model response: Luca needs $1.45 to buy a sour apple candy and a butterscotch candy.\n
Extracted answer: 1.45
"""

    example_4 = """
Hint: Please answer the question and provide the final answer at the end.\n
Question: Between which two years does the line graph saw its maximum peak?\n
Model response: The line graph saw its maximum peak between 2007 and 2008.\n
Extracted answer: [2007, 2008]
"""

    example_5 = """
Hint: Please answer the question and provide the correct option letter, e.g., A, B, C, D, at the end.\n
Question: What fraction of the shape is blue?\n
Choices: (A) 3/11 (B) 8/11 (C) 6/11 (D) 3/5\n
Model response: The correct answer is (B) 8/11.\n
Extracted answer: B
"""

    return [example_1, example_2, example_3, example_4, example_5]


def build_mathv_gpt4_prompt(line):
    task_description = """
Please read the following example.
Then extract the answer from the model response and type it at the end of the prompt.\n
"""
    question = line['question']
    prediction = str(line['prediction'])
    prompt = task_description
    examples = get_gpt4_ICE()
    for example in examples:
        prompt += example + '\n'
    prompt += question + '\n'
    prompt += 'Model respone: ' + prediction
    prompt += 'Extracted answer:'
    return prompt


def list_to_dict(lst):
    return {chr(65 + i): val for i, val in enumerate(lst)}


def post_check(line, prefetch=False):
    res = None
    ans = line['answer']
    response = line['prediction'] if prefetch else line['res']
    try:
        if len(eval(line['choices'])) > 0:
            ans = line['answer']
            choices = list_to_dict(eval(line['choices']))
            res = can_infer(response, choices)
            if prefetch:
                return res
        else:
            res = str(response)
            ans = str(ans)
    except ValueError:
        pass

    try:
        if is_equal(res, ans):
            return res if prefetch else True
        else:
            return False
    except Exception as err:
        logging.warning(f'{type(err)}: {err}')
        return False


def MATH_V_auxeval(model, line):
    prompt = build_mathv_gpt4_prompt(line)
    log = ''
    retry = 5
    if post_check(line, prefetch=True):
        res = post_check(line, prefetch=True)
        return dict(log='Prefetch succeed', res=res)
    for i in range(retry):
        prediction = line['prediction']
        res = model.generate(prompt, temperature=i * 0.5)

        if FAIL_MSG in res:
            log += f'Try {i}: output is {prediction}, failed to parse.\n'
        else:
            log += 'Succeed'
            return dict(log=log, res=res)
    log += 'All 5 retries failed.\n'
    return dict(log=log, res='')


def MATH_V_acc(result_file):
    data = load(result_file)
    tot = defaultdict(lambda: 0)
    fetch = defaultdict(lambda: 0)
    hit = defaultdict(lambda: 0)
    lt = len(data)
    from tqdm import tqdm
    for i in tqdm(range(lt)):
        item = data.iloc[i]
        cate = item['category']
        tot['Overall'] += 1
        tot[cate] += 1
        if item['log'] == 'Prefetch succeed':
            fetch['Overall'] += 1
            fetch[cate] += 1
        if post_check(item, prefetch=False):
            hit['Overall'] += 1
            hit[cate] += 1

    res = defaultdict(list)
    for k in tot.keys():
        res['Subject'].append(k)
        res['tot'].append(tot[k])
        res['prefetch'].append(fetch[k])
        res['hit'].append(hit[k])
        res['prefetch_rate'].append(fetch[k] / tot[k] * 100)
        res['acc'].append(hit[k] / tot[k] * 100)
    res = pd.DataFrame(res).sort_values('Subject', ignore_index=True)
    return res