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from vlmeval.smp import *
from vlmeval.dataset import SUPPORTED_DATASETS
def get_score(model, dataset):
file_name = f'{model}/{model}_{dataset}'
if listinstr([
'CCBench', 'MMBench', 'SEEDBench_IMG', 'MMMU', 'ScienceQA',
'AI2D_TEST', 'MMStar', 'RealWorldQA', 'BLINK', 'VisOnlyQA-VLMEvalKit'
], dataset):
file_name += '_acc.csv'
elif listinstr(['MME', 'Hallusion', 'LLaVABench'], dataset):
file_name += '_score.csv'
elif listinstr(['MMVet', 'MathVista'], dataset):
file_name += '_gpt-4-turbo_score.csv'
elif listinstr(['COCO', 'OCRBench'], dataset):
file_name += '_score.json'
else:
raise NotImplementedError
if not osp.exists(file_name):
return {}
data = load(file_name)
ret = {}
if dataset == 'CCBench':
ret[dataset] = data['Overall'][0] * 100
elif dataset == 'MMBench':
for n, a in zip(data['split'], data['Overall']):
if n == 'dev':
ret['MMBench_DEV_EN'] = a * 100
elif n == 'test':
ret['MMBench_TEST_EN'] = a * 100
elif dataset == 'MMBench_CN':
for n, a in zip(data['split'], data['Overall']):
if n == 'dev':
ret['MMBench_DEV_CN'] = a * 100
elif n == 'test':
ret['MMBench_TEST_CN'] = a * 100
elif listinstr(['SEEDBench', 'ScienceQA', 'MMBench', 'AI2D_TEST', 'MMStar', 'RealWorldQA', 'BLINK'], dataset):
ret[dataset] = data['Overall'][0] * 100
elif 'MME' == dataset:
ret[dataset] = data['perception'][0] + data['reasoning'][0]
elif 'MMVet' == dataset:
data = data[data['Category'] == 'Overall']
ret[dataset] = float(data.iloc[0]['acc'])
elif 'HallusionBench' == dataset:
data = data[data['split'] == 'Overall']
for met in ['aAcc', 'qAcc', 'fAcc']:
ret[dataset + f' ({met})'] = float(data.iloc[0][met])
elif 'MMMU' in dataset:
data = data[data['split'] == 'validation']
ret['MMMU (val)'] = float(data.iloc[0]['Overall']) * 100
elif 'MathVista' in dataset:
data = data[data['Task&Skill'] == 'Overall']
ret[dataset] = float(data.iloc[0]['acc'])
elif 'LLaVABench' in dataset:
data = data[data['split'] == 'overall'].iloc[0]
ret[dataset] = float(data['Relative Score (main)'])
elif 'OCRBench' in dataset:
ret[dataset] = data['Final Score']
elif dataset == 'VisOnlyQA-VLMEvalKit':
for n, a in zip(data['split'], data['Overall']):
ret[f'VisOnlyQA-VLMEvalKit_{n}'] = a * 100
return ret
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('--data', type=str, nargs='+', default=[])
parser.add_argument("--model", type=str, nargs='+', required=True)
args = parser.parse_args()
return args
def gen_table(models, datasets):
res = defaultdict(dict)
for m in models:
for d in datasets:
try:
res[m].update(get_score(m, d))
except Exception as e:
logging.warning(f'{type(e)}: {e}')
logging.warning(f'Missing Results for Model {m} x Dataset {d}')
keys = []
for m in models:
for d in res[m]:
keys.append(d)
keys = list(set(keys))
keys.sort()
final = defaultdict(list)
for m in models:
final['Model'].append(m)
for k in keys:
if k in res[m]:
final[k].append(res[m][k])
else:
final[k].append(None)
final = pd.DataFrame(final)
dump(final, 'summ.csv')
if len(final) >= len(final.iloc[0].keys()):
print(tabulate(final))
else:
print(tabulate(final.T))
if __name__ == '__main__':
args = parse_args()
if args.data == []:
args.data = list(SUPPORTED_DATASETS)
gen_table(args.model, args.data) |