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import argparse
import itertools
import json
import os
import re
from functools import partial
from pathlib import Path
import numpy as np
import torch
from qwen_vl_utils import process_vision_info
from torchvision.ops.boxes import box_area
from tqdm import tqdm
from transformers import AutoProcessor, Qwen2VLForConditionalGeneration
import cv2
import pandas as pd
import random
# from utils import seed_everything, get_model_id, update_res
import datetime
from num2words import num2words
import ast
import warnings
import csv
CAPABILITY_NAME = 'category'
def update_data_format(df_data):
df_data = df_data.rename(columns={'capability': CAPABILITY_NAME})
if 'dataset_name' in df_data.columns:
df_data['sub_task'] = df_data['dataset_name']
df_data.drop(columns=['dataset_name'], inplace=True)
df_data.loc[df_data['sub_task'] == 'OCR-VQA', 'sub_task'] = 'BookOCR'
df_data.loc[df_data[CAPABILITY_NAME] != 'Visual Reasoning', CAPABILITY_NAME] = 'Visual Perception'
df_data['reasoning_type'] = df_data['reasoning_type'].fillna('')
df_data.loc[df_data['reasoning_type'] == 'algebraic', 'reasoning_type'] = 'arithmetic'
df_data.loc[df_data['task'] == 'Text Localization Bbox2Text', 'sub_task'] = 'Bbox2Text'
df_data.loc[df_data['task'] == 'Text Localization Text2Bbox', 'sub_task'] = 'Text2Bbox'
df_data.loc[df_data['task'] == 'Text Localization Bbox2Text', 'task'] = 'Text Localization'
df_data.loc[df_data['task'] == 'Text Localization Text2Bbox', 'task'] = 'Text Localization'
df_perception = df_data[df_data[CAPABILITY_NAME] == 'Visual Perception']
df_reasoning = df_data[df_data[CAPABILITY_NAME] == 'Visual Reasoning'].copy()
df_reasoning['task'] = df_reasoning['reasoning_type'].str.title()
df_reasoning['task'] = df_reasoning['task'].apply(
lambda x: x + ' Reasoning' if x in ['Arithmetic', 'Logical', 'Spatial'] else x
)
df_reasoning = df_reasoning.drop(columns=['reasoning_type']) # mute SettingWithCopyWarning warnings
return pd.concat([df_perception, df_reasoning], ignore_index=True)
return df_data
answer_pattern = re.compile(r'<answer>(.*?)</answer>')
markdown_json_pattern = re.compile(r'```json(.*?)```', re.DOTALL)
def process_answer(answer):
answer = answer.split('### Final Answer ###')[-1].strip() if '### Final Answer ###' in answer else answer
answer = answer.split('Answer:')[-1].strip() if 'Answer:' in answer else answer
matches = re.findall(answer_pattern, answer)
answer = matches[-1] if matches else answer
matches = re.findall(markdown_json_pattern, answer)
answer = matches[-1] if matches else answer
try:
answer_json = json.loads(answer)
answer = answer_json["answer"]
except:
pass
return str(answer)
class NoMatchedEquationError(Exception):
pass
def calculate(expression):
try:
exp = expression.split('=')[0].strip()
node = ast.parse(exp, mode='eval')
result = eval(compile(node, '<string>', 'eval'))
return result
except:
return None
def extract_and_judge(expression, target_result):
expression = expression.replace(" ", "").replace("$", "")
result = calculate(expression)
if result is None:
raise NoMatchedEquationError(f"none error")
else:
return target_result.strip() in str(result).strip()
def check_relation(gt, ans, strict=False):
# # cot: 200 direct: 25
if strict:
delta_len = 25
else:
delta_len = 200
if abs(len(str(ans)) - len(str(gt))) > 200:
return False
gt = str(gt).strip()
ans = str(ans).strip()
if gt.lower() == ans.lower():
return True
## rule 1: if string has million, then parse number
# if "million" in ans or "increase" in ans or "decrease" in ans:
# a = re.findall(r'\d+', gt)
# for i in a:
# if str(i) in ans:
# return True
## rule 2: string exact match
try:
aa = int(gt.replace(",", "").replace("$", "").replace("*", "").replace("**", "").replace("@", "").replace("-","").replace(" ", "").replace("%", ""))
# print(aa)
words_list = ans.split(" ")
for i in words_list:
if int(aa) == int(i.replace(",", "").replace("$", "").replace("*", "").replace("**", "").replace("@", "").replace("-", "").replace(" ", "").replace("%", "").replace("million","").replace("increase","").replace("decrease","")):
if len(words_list)<5:
return True
except:
pass
try:
aa = float(gt.replace(",", "").replace("$", "").replace("*", "").replace("**", "").replace("@", "").replace("-","").replace(" ", "").replace("%", ""))
# print(aa)
words_list = ans.split(" ")
for i in words_list:
if round(aa,4) ==round(float(i.replace(",", "").replace("$", "").replace("*", "").replace("**", "").replace("@", "").replace("-", "").replace(" ", "").replace("%", "").replace("million","").replace("increase","").replace("decrease","")),4):
if len(words_list) < 3:
return True
except:
pass
## rule 3: if string can be converted to number, then use number to test, avoid '1' and '12'
try:
a_f = float(ans.replace(",", "").replace("$", ""))
g_f = float(gt.replace(",", "").replace("$", ""))
if a_f == g_f:
return True
else:
return False
except:
pass
## rule 4: if answer contains equation e.g. 141-111=30
try:
aa = extract_and_judge(ans, gt)
return aa
except:
pass
## rule 5: string contains thousandths like 1,000 or percentage like 10%
try:
num_gt = float(gt.lstrip('$').replace(',', '').rstrip('%')) / 100 if "%" in gt else float(
gt.lstrip('$').replace(',', ''))
num_ans = float(ans.lstrip('$').replace(',', '').rstrip('%')) / 100 if "%" in ans else float(
ans.lstrip('$').replace(',', ''))
if num_gt == num_ans:
return True
else:
return False
except ValueError:
pass
## rule 6: number to word e.g. eleven=>11
try:
num_gt = float(gt.lstrip('$').replace(',', '').rstrip('%')) / 100 if "%" in gt else float(
gt.lstrip('$').replace(',', ''))
if num2words(int(num_gt)).lower() == ans.lower():
return True
else:
return False
except ValueError:
pass
## rule 7: string convert
aa = gt.lower() in ans.lower()
if aa:
try:
a = int(ans.lower().replace(gt.lower(), "").replace(" ", ""))
if a:
return False
else:
return True
except:
return True
def eval_mmdocbench(ans, gt_obj, strict=False):
# 1.process ans
ans = process_answer(ans)
# 2.process ground truth
# if isinstance(gt_obj[0], str):
# gt_obj = json.loads(gt_obj[0])
# for gt_obj_ in gt_obj:
# gt_ls = []
# if isinstance(gt_obj_, list):
# for sub_gt_obj_ in gt_obj_:
# gt_ls.append(str(sub_gt_obj_['answer']))
# else:
# gt_ls.append(str(gt_obj_['answer']))
gt_ls = []
gt_obj_lst = json.loads(gt_obj)
if type(gt_obj_lst[0]) == list:
gt_obj_lst = gt_obj_lst[0]
for item in gt_obj_lst: # gt_obj_lst is list
gt_ls.append(item["answer"])
# 3.calculate em
cnt = 0
em = 0
for gt in gt_ls:
if gt != "":
cnt += 1
flag = check_relation(gt, ans, strict)
if flag:
em += 1
em /= cnt
return em |