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import pandas as pd
import numpy as np
import random
def parse_multi_choice_response(response, all_choices, index2ans):
"""
Parse the prediction from the generated response.
Return the predicted index e.g., A, B, C, D.
"""
response = str(response)
for char in [',', '.', '!', '?', ';', ':', "'"]:
response = response.strip(char)
response = " " + response + " " # add space to avoid partial match
index_ans = True
ans_with_brack = False
candidates = []
for choice in all_choices: # e.g., (A) (B) (C) (D)
if f'({choice})' in response or f'{choice}. ' in response:
candidates.append(choice)
ans_with_brack = True
if len(candidates) == 0:
for choice in all_choices: # e.g., A B C D
if f' {choice} ' in response:
candidates.append(choice)
# if all above doesn't get candidates, check if the content is larger than 5 tokens and try to parse the example
if len(candidates) == 0 and len(response.split()) > 5:
for index, ans in index2ans.items():
if ans.lower() in response.lower():
candidates.append(index)
index_ans = False # it's content ans.
if len(candidates) == 0: # still not get answer, randomly choose one.
pred_index = random.choice(all_choices)
elif len(candidates) > 1:
start_indexes = []
if index_ans:
if ans_with_brack:
for can in candidates:
index = response.rfind(f'({can})')
start_indexes.append(index) # -1 will be ignored anyway
# start_indexes = [generated_response.index(f'({can})') for can in candidates]
else:
for can in candidates:
index = response.rfind(f" {can} ")
start_indexes.append(index)
else:
for can in candidates:
index = response.lower().rfind(index2ans[can].lower())
start_indexes.append(index)
# get the last one
pred_index = candidates[np.argmax(start_indexes)]
else: # if only one candidate, use it.
pred_index = candidates[0]
return pred_index
def get_mc_score(row, use_parse = True):
if use_parse:
if pd.isna(row["A"]):
return False
response = row["prediction"]
all_choices = []
for i in range(9):
if chr(65+i) in row and pd.isna(row[chr(65+i)])== False:
all_choices.append(chr(65+i))
index2ans = {index: row[index] for index in all_choices}
pred_index = parse_multi_choice_response(response, all_choices, index2ans)
else:
pred_index = row["output"]
return int(pred_index == row["answer"])
def report_vmc_acc(data):
general_datasets = ["SEEDBench", "MMStar", "A-OKVQA", "VizWiz", "MMVet",
"VQAv2", "OKVQA"]
reason_datasets = ["MMMU", "MathVista", "ScienceQA", "RealWorldQA", "GQA", "MathVision"]
ocr_datasets = ["TextVQA", "OCRVQA"]
doc_datasets = ["AI2D", "ChartQA","DocVQA", "InfoVQA", "TableVQABench"]
results = {}
for category in data['category'].unique():
results[category] = data[data['category'] == category]['hit'].mean()
results = pd.DataFrame(results, index=[0])
results["Overall"] = data['hit'].mean()
results['General'] = results[general_datasets].mean(axis=1)
results['Reasoning'] = results[reason_datasets].mean(axis=1)
results['OCR'] = results[ocr_datasets].mean(axis=1)
results['Doc & Chart'] = results[doc_datasets].mean(axis=1)
for key in results:
results[key] = round(results[key]*100, 2)
results = results[['Overall', 'General', 'Reasoning', 'OCR', 'Doc & Chart']
+ general_datasets + reason_datasets + ocr_datasets + doc_datasets]
return results
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