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MiniCPM-o/eval_mm/vlmevalkit/vlmeval/dataset/image_vqa.py
Haoyu Li 59224808a1 Modify eval_mm for MiniCPM-V 2.6
2024-08-30 18:18:22 +00:00

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from functools import partial
from .image_base import ImageBaseDataset
from .utils import build_judge, DEBUG_MESSAGE
from ..smp import *
from ..utils import track_progress_rich
class ImageVQADataset(ImageBaseDataset):
TYPE = 'VQA'
DATASET_URL = {
'OCRVQA_TEST': 'https://opencompass.openxlab.space/utils/VLMEval/OCRVQA_TEST.tsv',
'OCRVQA_TESTCORE': 'https://opencompass.openxlab.space/utils/VLMEval/OCRVQA_TESTCORE.tsv',
'TextVQA_VAL': 'https://opencompass.openxlab.space/utils/VLMEval/TextVQA_VAL.tsv',
'DocVQA_VAL': 'https://opencompass.openxlab.space/utils/VLMEval/DocVQA_VAL.tsv',
'DocVQA_TEST': 'https://opencompass.openxlab.space/utils/VLMEval/DocVQA_TEST.tsv',
'InfoVQA_VAL': 'https://opencompass.openxlab.space/utils/VLMEval/InfoVQA_VAL.tsv',
'InfoVQA_TEST': 'https://opencompass.openxlab.space/utils/VLMEval/InfoVQA_TEST.tsv',
'ChartQA_TEST': 'https://opencompass.openxlab.space/utils/VLMEval/ChartQA_TEST.tsv',
}
DATASET_MD5 = {
'OCRVQA_TEST': 'ca46a6d74b403e9d6c0b670f6fc00db9',
'OCRVQA_TESTCORE': 'c5239fe77db8bdc1f2ad8e55e0d1fe97',
'TextVQA_VAL': 'b233b31f551bbf4056f2f955da3a92cd',
'DocVQA_VAL': 'd5ee77e1926ff10690d469c56b73eabf',
'DocVQA_TEST': '6a2f28cac26ef2d3447374e8c6f6c8e9',
'InfoVQA_VAL': '2342e9c225222f0ef4dec545ebb126fe',
'InfoVQA_TEST': 'df535bf51b88dc9718252c34131a6227',
'ChartQA_TEST': 'c902e0aa9be5582a7aad6dcf52734b42',
}
def build_prompt(self, line):
msgs = super().build_prompt(line)
assert msgs[-1]['type'] == 'text'
msgs[-1]['value'] += '\nAnswer the question using a single word or phrase.'
return msgs
# It returns a DataFrame
def evaluate(self, eval_file, **judge_kwargs):
from .utils.vqa_eval import hit_calculate, process_line
data = load(eval_file)
dataset = self.dataset_name
assert 'answer' in data and 'prediction' in data
data['prediction'] = [str(x) for x in data['prediction']]
data['answer'] = [str(x) for x in data['answer']]
lt = len(data)
pool = mp.Pool(16)
lines = [data.iloc[i] for i in range(lt)]
if listinstr(['TextVQA'], dataset):
res = pool.map(partial(process_line, method='vqa_score'), lines)
elif listinstr(['ChartQA'], dataset):
res = pool.map(partial(process_line, method='relaxed_accuracy'), lines)
elif listinstr(['OCRVQA'], dataset):
res = pool.map(partial(process_line, method='accuracy'), lines)
elif listinstr(['DocVQA', 'InfoVQA'], dataset):
res = pool.map(partial(process_line, method='anls'), lines)
else: # default using vqa_score to calculate score
res = pool.map(process_line, lines)
hit = hit_calculate(res, dataset)
ret = dict()
if 'split' in data:
splits = set(data['split'])
for sp in splits:
sub = [r for l, r in zip(lines, res) if l['split'] == sp]
# [np.mean(x['match']) >= full_score_weight for x in sub]
hit = hit_calculate(sub, dataset)
ret[sp] = np.mean(hit) * 100
sub = [r for l, r in zip(lines, res)]
hit = hit_calculate(sub, dataset)
ret['Overall'] = np.mean(hit) * 100
else:
ret['Overall'] = np.mean(hit) * 100
if 'category' in data:
cates = list(set(data['category']))
cates.sort()
for c in cates:
sub = [r for l, r in zip(lines, res) if l['category'] == c]
# [np.mean(x['match']) >= full_score_weight for x in sub]
hit = hit_calculate(sub, dataset)
ret[c] = np.mean(hit) * 100
ret = d2df(ret)
ret.round(2)
suffix = eval_file.split('.')[-1]
result_file = eval_file.replace(f'.{suffix}', '_acc.csv')
dump(ret, result_file)
return ret
class OCRBench(ImageBaseDataset):
TYPE = 'VQA'
DATASET_URL = {
'OCRBench': 'https://opencompass.openxlab.space/utils/VLMEval/OCRBench.tsv'
}
DATASET_MD5 = {'OCRBench': 'e953d98a987cc6e26ef717b61260b778'}
# It returns a dictionary
@classmethod
def evaluate(self, eval_file, **judge_kwargs):
OCRBench_score = {
'Regular Text Recognition': 0,
'Irregular Text Recognition': 0,
'Artistic Text Recognition': 0,
'Handwriting Recognition': 0,
'Digit String Recognition': 0,
'Non-Semantic Text Recognition': 0,
'Scene Text-centric VQA': 0,
'Doc-oriented VQA': 0,
'Key Information Extraction': 0,
'Handwritten Mathematical Expression Recognition': 0,
}
data = load(eval_file)
lt = len(data)
lines = [data.iloc[i] for i in range(lt)]
for i in tqdm(range(len(lines))):
line = lines[i]
predict = str(line['prediction'])
answers = eval(line['answer'])
category = line['category']
if category == 'Handwritten Mathematical Expression Recognition':
for j in range(len(answers)):
answer = answers[j].strip().replace('\n', ' ').replace(' ', '')
predict = predict.strip().replace('\n', ' ').replace(' ', '')
if answer in predict:
OCRBench_score[category] += 1
break
else:
for j in range(len(answers)):
answer = answers[j].lower().strip().replace('\n', ' ')
predict = predict.lower().strip().replace('\n', ' ')
if answer in predict:
OCRBench_score[category] += 1
break
final_score_dict = {}
final_score_dict['Text Recognition'] = \
(OCRBench_score['Regular Text Recognition'] + OCRBench_score['Irregular Text Recognition']
+ OCRBench_score['Artistic Text Recognition'] + OCRBench_score['Handwriting Recognition']
+ OCRBench_score['Digit String Recognition'] + OCRBench_score['Non-Semantic Text Recognition'])
final_score_dict['Scene Text-centric VQA'] = OCRBench_score['Scene Text-centric VQA']
final_score_dict['Doc-oriented VQA'] = OCRBench_score['Doc-oriented VQA']
final_score_dict['Key Information Extraction'] = OCRBench_score['Key Information Extraction']
final_score_dict['Handwritten Mathematical Expression Recognition'] = \
(OCRBench_score['Handwritten Mathematical Expression Recognition'])
final_score_dict['Final Score'] = \
(final_score_dict['Text Recognition'] + final_score_dict['Scene Text-centric VQA']
+ final_score_dict['Doc-oriented VQA'] + final_score_dict['Key Information Extraction']
+ final_score_dict['Handwritten Mathematical Expression Recognition'])
final_score_dict['Final Score Norm'] = (float(final_score_dict['Final Score']) / 10)
score_pth = eval_file.replace('.xlsx', '_score.json')
dump(final_score_dict, score_pth)
return final_score_dict
class MathVista(ImageBaseDataset):
TYPE = 'VQA'
DATASET_URL = {
'MathVista_MINI': 'https://opencompass.openxlab.space/utils/VLMEval/MathVista_MINI.tsv'
}
DATASET_MD5 = {'MathVista_MINI': 'f199b98e178e5a2a20e7048f5dcb0464'}
# It returns a DataFrame
@classmethod
def evaluate(self, eval_file, **judge_kwargs):
from .utils.mathvista import MathVista_auxeval, MathVista_acc
model = judge_kwargs['model']
suffix = eval_file.split('.')[-1]
storage = eval_file.replace(f'.{suffix}', f'_{model}.xlsx')
tmp_file = eval_file.replace(f'.{suffix}', f'_{model}.pkl')
nproc = judge_kwargs.pop('nproc', 4)
if not osp.exists(storage):
data = load(eval_file)
model = build_judge(max_tokens=128, **judge_kwargs)
assert model.working(), ('MathVista evaluation requires a working OPENAI API\n' + DEBUG_MESSAGE)
lt = len(data)
lines = [data.iloc[i] for i in range(lt)]
tups = [(model, line) for line in lines]
indices = [line['index'] for line in lines]
ans = {}
if osp.exists(tmp_file):
ans = load(tmp_file)
tups = [x for x, i in zip(tups, indices) if i not in ans]
indices = [i for i in indices if i not in ans]
if len(indices):
new_results = track_progress_rich(
MathVista_auxeval,
tups,
nproc=nproc,
chunksize=nproc,
keys=indices,
save=tmp_file,
)
ans = load(tmp_file)
for k, v in zip(indices, new_results):
assert k in ans
assert ans[k]['log'] == v['log'] and ans[k]['res'] == v['res']
data['res'] = [ans[idx]['res'] for idx in data['index']]
data['log'] = [ans[idx]['log'] for idx in data['index']]
dump(data, storage)
score = MathVista_acc(storage)
score_pth = storage.replace('.xlsx', '_score.csv')
dump(score, score_pth)
return score
class MathVision(ImageBaseDataset):
TYPE = 'VQA'
DATASET_URL = {
'MathVision': 'https://opencompass.openxlab.space/utils/VLMEval/MathVision.tsv',
'MathVision_MINI': 'https://opencompass.openxlab.space/utils/VLMEval/MathVision_MINI.tsv'
}
DATASET_MD5 = {
'MathVision': '93f6de14f7916e598aa1b7165589831e',
'MathVision_MINI': '060fe4fa5d868987ce179307bd5f8a33'
}
# It returns a DataFrame
@classmethod
def evaluate(self, eval_file, **judge_kwargs):
from .utils.mathv import MATH_V_auxeval, MATH_V_acc
if 'model' in judge_kwargs:
model = judge_kwargs['model']
else:
model = os.path.basename(os.environ.get('LOCAL_LLM'))
suffix = eval_file.split('.')[-1]
storage = eval_file.replace(f'.{suffix}', f'_{model}.xlsx')
tmp_file = eval_file.replace(f'.{suffix}', f'_{model}.pkl')
nproc = judge_kwargs.pop('nproc', 4)
if not osp.exists(storage):
data = load(eval_file)
model = build_judge(max_tokens=128, **judge_kwargs)
assert model.working(), ('MATH-Vision evaluation requires a working OPENAI API\n' + DEBUG_MESSAGE)
lt = len(data)
lines = [data.iloc[i] for i in range(lt)]
tups = [(model, line) for line in lines]
indices = [line['index'] for line in lines]
ans = {}
if osp.exists(tmp_file):
ans = load(tmp_file)
tups = [x for x, i in zip(tups, indices) if i not in ans]
indices = [i for i in indices if i not in ans]
if len(indices):
new_results = track_progress_rich(
MATH_V_auxeval,
tups,
nproc=nproc,
chunksize=nproc,
keys=indices,
save=tmp_file,
)
ans = load(tmp_file)
for k, v in zip(indices, new_results):
assert k in ans
assert ans[k]['log'] == v['log'] and ans[k]['res'] == v['res']
data['res'] = [ans[idx]['res'] for idx in data['index']]
data['log'] = [ans[idx]['log'] for idx in data['index']]
dump(data, storage)
score = MATH_V_acc(storage)
score_pth = storage.replace('.xlsx', '_score.csv')
dump(score, score_pth)
return score
class LLaVABench(ImageBaseDataset):
TYPE = 'VQA'
DATASET_URL = {'LLaVABench': 'https://opencompass.openxlab.space/utils/VLMEval/LLaVABench.tsv'}
DATASET_MD5 = {'LLaVABench': 'd382a093f749a697820d3dadd61c8428'}
# It returns a DataFrame
@classmethod
def evaluate(self, eval_file, **judge_kwargs):
from .utils.llavabench import (
build_prompt,
LLaVABench_atomeval,
LLaVABench_score,
)
suffix = '.' + eval_file.split('.')[-1]
record_file = eval_file.replace(suffix, '_openai_result' + suffix)
score_file = eval_file.replace(suffix, '_score.csv')
nproc = judge_kwargs.pop('nproc', 4)
system_prompt = 'You are a helpful and precise assistant for checking the quality of the answer.'
if not osp.exists(record_file):
data = load(eval_file)
lines = [data.iloc[i] for i in range(len(data))]
model = build_judge(temperature=0.2, system_prompt=system_prompt, **judge_kwargs)
assert model.working(), ('LLaVABench evaluation requires a working OPENAI API\n' + DEBUG_MESSAGE)
prompts = [build_prompt(line) for line in lines]
tups = [(model, prompt) for prompt in prompts]
scores = track_progress_rich(LLaVABench_atomeval, tups, nproc=nproc, chunksize=nproc)
data['gpt4_score'] = [x[0] for x in scores]
data['score'] = [x[1] for x in scores]
dump(data, record_file)
data = load(record_file)
ret = LLaVABench_score(data).round(1)
dump(ret, score_file)
return ret
class MMVet(ImageBaseDataset):
TYPE = 'VQA'
DATASET_URL = {
'MMVet': 'https://opencompass.openxlab.space/utils/VLMEval/MMVet.tsv'
}
DATASET_MD5 = {'MMVet': '748aa6d4aa9d4de798306a63718455e3'}
# It returns a DataFrame
@classmethod
def evaluate(self, eval_file, **judge_kwargs):
from .utils.mmvet import MMVet_auxeval, MMVet_acc
suffix = eval_file.split('.')[-1]
model = judge_kwargs['model']
storage = eval_file.replace(f'.{suffix}', f'_{model}.xlsx')
tmp_file = eval_file.replace(f'.{suffix}', f'_{model}.pkl')
nproc = judge_kwargs.pop('nproc', 4)
if not osp.exists(storage):
data = load(eval_file)
model = build_judge(max_tokens=3, **judge_kwargs)
assert model.working(), ('MMVet evaluation requires a working OPENAI API\n' + DEBUG_MESSAGE)
lt = len(data)
lines = [data.iloc[i] for i in range(lt)]
tups = [(model, line) for line in lines]
indices = [line['index'] for line in lines]
ans = load(tmp_file) if osp.exists(tmp_file) else {}
tups = [x for x, i in zip(tups, indices) if i not in ans]
indices = [i for i in indices if i not in ans]
if len(indices):
new_results = track_progress_rich(
MMVet_auxeval,
tups,
nproc=nproc,
chunksize=nproc,
keys=indices,
save=tmp_file,
)
ans = load(tmp_file)
for k, v in zip(indices, new_results):
assert k in ans
assert ans[k]['log'] == v['log'] and ans[k]['score'] == v['score']
data['score'] = [ans[idx]['score'] for idx in data['index']]
data['log'] = [ans[idx]['log'] for idx in data['index']]
dump(data, storage)
score, score_fine = MMVet_acc(storage)
score_pth = storage.replace('.xlsx', '_score.csv')
score_fine_pth = storage.replace('.xlsx', '_score_fine.csv')
dump(score, score_pth)
dump(score_fine, score_fine_pth)
return score
class MTVQADataset(ImageBaseDataset):
TYPE = 'VQA'
DATASET_URL = {'MTVQA_TEST': 'https://opencompass.openxlab.space/utils/VLMEval/MTVQA_TEST.tsv'}
DATASET_MD5 = {'MTVQA_TEST': 'd87c17dbab934b7cd89c0a3c1c5657f4'}
@classmethod
def evaluate(self, eval_file, **judge_kwargs):
data = load(eval_file)
assert 'answer' in data and 'prediction' in data and 'category' in data
data['prediction'] = [str(x) for x in data['prediction']]
data['answer'] = [str(x) for x in data['answer']]
if 'split' in data:
assert np.all([x.lower() == 'test' for x in data['split']]), 'We only support MTVQA_TEST for now. '
lt = len(data)
category_scores = defaultdict(list)
for i in range(lt):
line = data.iloc[i]
ans = line['answer'].strip().lower().replace('.', '')
pred = line['prediction'].strip().lower().replace('.', '')
cate = line['category']
score = 1.0 if ans in pred else 0.0
category_scores[cate].append(score)
category_scores['Average'].append(score)
# Calculate the average score for each category, the score is normalized to [0, 100]
category_averages = {category: np.mean(scores) * 100 for category, scores in category_scores.items()}
suffix = eval_file.split('.')[-1]
result_file = eval_file.replace(f'.{suffix}', '_acc.json')
dump(category_averages, result_file)
return category_averages
# MT-VQA adopts a custom prompt
def build_prompt(self, line):
msgs = super().build_prompt(line)
assert sum([x['type'] == 'text' for x in msgs]) == 1
for item in msgs:
if item['type'] == 'text':
item['value'] += '\nAnswer the question using a word or phrase in the language of the question.'
return msgs
class CustomVQADataset(ImageBaseDataset):
TYPE = 'VQA'
def load_data(self, dataset):
data_path = osp.join(LMUDataRoot(), f'{dataset}.tsv')
if file_size(data_path, 'GB') > 1:
local_path = data_path.replace('.tsv', '_local.tsv')
if not osp.exists(local_path) or os.environ.get('FORCE_LOCAL', None):
from ..tools import LOCALIZE
LOCALIZE(data_path, local_path)
data_path = local_path
return load(data_path)
def evaluate(self, eval_file, **judge_kwargs):
raise NotImplementedError
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