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import torch
import torch.distributed as dist
from vlmeval.config import supported_VLM
from vlmeval.dataset import build_dataset
from vlmeval.inference import infer_data_job
from vlmeval.inference_video import infer_data_job_video
from vlmeval.inference_mt import infer_data_job_mt
from vlmeval.smp import *
from vlmeval.utils.result_transfer import MMMU_result_transfer, MMTBench_result_transfer
def parse_args():
parser = argparse.ArgumentParser()
# Essential Args
parser.add_argument('--data', type=str, nargs='+', required=True)
parser.add_argument('--model', type=str, nargs='+', required=True)
# Args that only apply to Video Dataset
parser.add_argument('--nframe', type=int, default=8)
parser.add_argument('--pack', action='store_true')
parser.add_argument('--use-subtitle', action='store_true')
# Work Dir
parser.add_argument('--work-dir', type=str, default='./outputs', help='select the output directory')
# Infer + Eval or Infer Only
parser.add_argument('--mode', type=str, default='all', choices=['all', 'infer'])
# API Kwargs, Apply to API VLMs and Judge API LLMs
parser.add_argument('--nproc', type=int, default=4, help='Parallel API calling')
parser.add_argument('--retry', type=int, default=None, help='retry numbers for API VLMs')
# Explicitly Set the Judge Model
parser.add_argument('--judge', type=str, default=None)
# Logging Utils
parser.add_argument('--verbose', action='store_true')
# Configuration for Resume
# Ignore: will not rerun failed VLM inference
parser.add_argument('--ignore', action='store_true', help='Ignore failed indices. ')
# Rerun: will remove all evaluation temp files
parser.add_argument('--rerun', action='store_true')
args = parser.parse_args()
return args
def main():
logger = get_logger('RUN')
args = parse_args()
assert len(args.data), '--data should be a list of data files'
if args.retry is not None:
for k, v in supported_VLM.items():
if hasattr(v, 'keywords') and 'retry' in v.keywords:
v.keywords['retry'] = args.retry
supported_VLM[k] = v
if hasattr(v, 'keywords') and 'verbose' in v.keywords:
v.keywords['verbose'] = args.verbose
supported_VLM[k] = v
rank, world_size = get_rank_and_world_size()
if world_size > 1:
local_rank = os.environ.get('LOCAL_RANK', 0)
torch.cuda.set_device(int(local_rank))
dist.init_process_group(backend='nccl', timeout=datetime.timedelta(seconds=10800))
for _, model_name in enumerate(args.model):
model = None
pred_root = osp.join(args.work_dir, model_name)
os.makedirs(pred_root, exist_ok=True)
for _, dataset_name in enumerate(args.data):
dataset_kwargs = {}
if dataset_name in ['MMLongBench_DOC', 'DUDE', 'DUDE_MINI', 'SLIDEVQA', 'SLIDEVQA_MINI']:
dataset_kwargs['model'] = model_name
if dataset_name == 'MMBench-Video':
dataset_kwargs['pack'] = args.pack
if dataset_name == 'Video-MME':
dataset_kwargs['use_subtitle'] = args.use_subtitle
# If distributed, first build the dataset on the main process for doing preparation works
if world_size > 1:
dataset = build_dataset(dataset_name, **dataset_kwargs) if rank == 0 else None
dist.barrier()
dataset_list = [dataset]
dist.broadcast_object_list(dataset_list, src=0)
dataset = dataset_list[0]
else:
dataset = build_dataset(dataset_name, **dataset_kwargs)
if dataset is None:
logger.error(f'Dataset {dataset_name} is not valid, will be skipped. ')
continue
result_file = f'{pred_root}/{model_name}_{dataset_name}.xlsx'
if dataset_name in ['MMBench-Video']:
packstr = 'pack' if args.pack else 'nopack'
result_file = f'{pred_root}/{model_name}_{dataset_name}_{args.nframe}frame_{packstr}.xlsx'
elif dataset.MODALITY == 'VIDEO':
if args.pack:
logger.info(f'{dataset_name} not support Pack Mode, directly change to unpack')
args.pack = False
packstr = 'pack' if args.pack else 'nopack'
result_file = f'{pred_root}/{model_name}_{dataset_name}_{args.nframe}frame_{packstr}.xlsx'
if dataset_name in ['Video-MME']:
subtitlestr = 'subs' if args.use_subtitle else 'nosubs'
result_file = result_file.replace('.xlsx', f'_{subtitlestr}.xlsx')
if dataset.TYPE == 'MT':
result_file = result_file.replace('.xlsx', '.tsv')
if osp.exists(result_file) and args.rerun:
for keyword in ['openai', 'gpt', 'auxmatch']:
os.system(f'rm {pred_root}/{model_name}_{dataset_name}_{keyword}*')
if model is None:
model = model_name # which is only a name
# Perform the Inference
if dataset.MODALITY == 'VIDEO':
model = infer_data_job_video(
model,
work_dir=pred_root,
model_name=model_name,
dataset=dataset,
nframe=args.nframe,
pack=args.pack,
verbose=args.verbose,
subtitle=args.use_subtitle,
api_nproc=args.nproc)
elif dataset.TYPE == 'MT':
model = infer_data_job_mt(
model,
work_dir=pred_root,
model_name=model_name,
dataset=dataset,
verbose=args.verbose,
api_nproc=args.nproc,
ignore_failed=args.ignore)
else:
model = infer_data_job(
model,
work_dir=pred_root,
model_name=model_name,
dataset=dataset,
verbose=args.verbose,
api_nproc=args.nproc,
ignore_failed=args.ignore)
# Set the judge kwargs first before evaluation or dumping
judge_kwargs = {
'nproc': args.nproc,
'verbose': args.verbose,
}
if args.retry is not None:
judge_kwargs['retry'] = args.retry
if args.judge is not None:
judge_kwargs['model'] = args.judge
else:
if dataset.TYPE in ['MCQ', 'Y/N']:
judge_kwargs['model'] = 'chatgpt-0125'
elif listinstr(['MMVet', 'MathVista', 'LLaVABench', 'MMBench-Video', 'MathVision'], dataset_name):
judge_kwargs['model'] = 'gpt-4-turbo'
elif listinstr(['MMLongBench', 'MMDU', 'DUDE', 'DUDE_MINI', 'SLIDEVQA', 'SLIDEVQA_MINI'], dataset_name):
judge_kwargs['model'] = 'gpt-4o'
if 'OPENAI_API_KEY_JUDGE' in os.environ and len(os.environ['OPENAI_API_KEY_JUDGE']):
judge_kwargs['key'] = os.environ['OPENAI_API_KEY_JUDGE']
if 'OPENAI_API_BASE_JUDGE' in os.environ and len(os.environ['OPENAI_API_BASE_JUDGE']):
judge_kwargs['api_base'] = os.environ['OPENAI_API_BASE_JUDGE']
if rank == 0:
if dataset_name in ['MMMU_TEST']:
result_json = MMMU_result_transfer(result_file)
logger.info(f'Transfer MMMU_TEST result to json for official evaluation, '
f'json file saved in {result_json}') # noqa: E501
continue
elif 'MMT-Bench_ALL' in dataset_name:
submission_file = MMTBench_result_transfer(result_file, **judge_kwargs)
logger.info(f'Extract options from prediction of MMT-Bench FULL split for official evaluation '
f'(https://eval.ai/web/challenges/challenge-page/2328/overview), '
f'submission file saved in {submission_file}') # noqa: E501
continue
elif 'MLLMGuard_DS' in dataset_name:
logger.info('The evaluation of MLLMGuard_DS is not supported yet. ') # noqa: E501
continue
elif 'AesBench_TEST' == dataset_name:
logger.info(f'The results are saved in {result_file}. '
f'Please send it to the AesBench Team via [email protected].') # noqa: E501
continue
if dataset_name in [
'MMBench_TEST_CN', 'MMBench_TEST_EN', 'MMBench', 'MMBench_CN',
'MMBench_TEST_CN_V11', 'MMBench_TEST_EN_V11', 'MMBench_V11', 'MMBench_CN_V11'
]:
if not MMBenchOfficialServer(dataset_name):
logger.error(
f'Can not evaluate {dataset_name} on non-official servers, '
'will skip the evaluation. '
)
continue
eval_proxy = os.environ.get('EVAL_PROXY', None)
old_proxy = os.environ.get('HTTP_PROXY', '')
if rank == 0 and args.mode == 'all':
if eval_proxy is not None:
proxy_set(eval_proxy)
eval_results = dataset.evaluate(result_file, **judge_kwargs)
if eval_results is not None:
assert isinstance(eval_results, dict) or isinstance(eval_results, pd.DataFrame)
logger.info(f'The evaluation of model {model_name} x dataset {dataset_name} has finished! ')
logger.info('Evaluation Results:')
if isinstance(eval_results, dict):
logger.info('\n' + json.dumps(eval_results, indent=4))
elif isinstance(eval_results, pd.DataFrame):
if len(eval_results) < len(eval_results.columns):
eval_results = eval_results.T
logger.info('\n' + tabulate(eval_results))
if eval_proxy is not None:
proxy_set(old_proxy)
if __name__ == '__main__':
load_env()
main()
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