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MiniCPM-o/eval_mm/vlmevalkit/run.py
Poppy Xu d8f382e157 Modify eval_mm for MiniCPM-o 2.6
2025-01-21 15:34:54 +08:00

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import torch
import torch.distributed as dist
from vlmeval.config import supported_VLM
from vlmeval.dataset.video_dataset_config import supported_video_datasets
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 build_model_from_config(cfg, model_name):
import vlmeval.api
import vlmeval.vlm
config = cp.deepcopy(cfg[model_name])
if config == {}:
return supported_VLM[model_name]()
assert 'class' in config
cls_name = config.pop('class')
if hasattr(vlmeval.api, cls_name):
return getattr(vlmeval.api, cls_name)(**config)
elif hasattr(vlmeval.vlm, cls_name):
return getattr(vlmeval.vlm, cls_name)(**config)
else:
raise ValueError(f'Class {cls_name} is not supported in `vlmeval.api` or `vlmeval.vlm`')
def build_dataset_from_config(cfg, dataset_name):
import vlmeval.dataset
import inspect
config = cp.deepcopy(cfg[dataset_name])
if config == {}:
return supported_video_datasets[dataset_name]()
assert 'class' in config
cls_name = config.pop('class')
if hasattr(vlmeval.dataset, cls_name):
cls = getattr(vlmeval.dataset, cls_name)
sig = inspect.signature(cls.__init__)
valid_params = {k: v for k, v in config.items() if k in sig.parameters}
if valid_params.get('fps', 0) > 0 and valid_params.get('nframe', 0) > 0:
raise ValueError('fps and nframe should not be set at the same time')
if valid_params.get('fps', 0) <= 0 and valid_params.get('nframe', 0) <= 0:
raise ValueError('fps and nframe should be set at least one valid value')
return cls(**valid_params)
else:
raise ValueError(f'Class {cls_name} is not supported in `vlmeval.dataset`')
def parse_args():
help_msg = """\
You can launch the evaluation by setting either --data and --model or --config.
--data and --model:
Each Arg should be a list of strings, specifying the names of datasets and models.
To find all supported model names, please refer to the `vlmeval/config.py` of check the output of the command \
`vlmutil mlist all` in the terminal (you should first have vlmeval installed).
To find all supported dataset names, please refer to the `vlmeval/dataset/__init__.py` file. The python script \
to print all supported dataset names is as follows:
```python
from vlmeval.dataset import SUPPORTED_DATASETS
print(SUPPORTED_DATASETS)
```
or you can check the output of the command `vlmutil dlist all` in the terminal.
To find all supported video dataset default settings, please refer to the \
`vlmeval/dataset/video_dataset_config.py` file.
--config:
Launch the evaluation by specifying the path to the config json file. Sample Json Content:
```json
{
"model": {
"GPT4o_20240806_T00_HIGH": {
"class": "GPT4V",
"model": "gpt-4o-2024-08-06",
"temperature": 0,
"img_detail": "high"
},
"GPT4o_20240806_T10_Low": {
"class": "GPT4V",
"model": "gpt-4o-2024-08-06",
"temperature": 1.0,
"img_detail": "low"
},
"GPT4o_20241120": {}
},
"data": {
"MME-RealWorld-Lite": {
"class": "MMERealWorld",
"dataset": "MME-RealWorld-Lite"
},
"MMBench_DEV_EN_V11": {
"class": "ImageMCQDataset",
"dataset": "MMBench_DEV_EN_V11"
},
"MMBench_Video_8frame_nopack": {},
"Video-MME_16frame_subs": {
"class": "VideoMME",
"dataset": "Video-MME",
"nframe": 16,
"use_subtitle": true,
}
}
}
```
Currently, only `model` and `data` are supported fields. The content of each field is a dictionary.
For `model`, the key is the name of the model, and the value is a dictionary containing the following keys:
- `class`: The class name of the model, which should be a class in `vlmeval.vlm` or `vlmeval.api`.
- Other keys are specific to the model, please refer to the corresponding class.
- Tip: The defined model in the `supported_VLM` of `vlmeval/config.py` can be used as a shortcut.
For `data`, the key is the name of the dataset (should be the same as the `dataset` field in most cases, \
except for video datasets), and the value is a dictionary containing the following keys:
- `class`: The class name of the dataset, which should be a class in `vlmeval.dataset`.
- `dataset`: The name of the dataset, which should be a string that is accepted by the `dataset` argument of the \
corresponding class.
- Other keys are specific to the dataset, please refer to the corresponding class.
- Tip: The defined dataset in the `supported_video_datasets` of `vlmeval/dataset/video_dataset_config.py` \
can be used as a shortcut.
The keys in the `model` and `data` fields will be used for naming the prediction files and evaluation results.
When launching with `--config`, args for API VLMs, such as `--retry`, `--verbose`, will be ignored.
"""
parser = argparse.ArgumentParser(description=help_msg, formatter_class=argparse.RawTextHelpFormatter)
# Essential Args, Setting the Names of Datasets and Models
parser.add_argument('--data', type=str, nargs='+', help='Names of Datasets')
parser.add_argument('--model', type=str, nargs='+', help='Names of Models')
parser.add_argument('--config', type=str, help='Path to the Config Json File')
# 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('--api_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. ')
# Reuse: will reuse the existing prediction files
parser.add_argument('--reuse', action='store_true')
args = parser.parse_args()
return args
def main():
logger = get_logger('RUN')
rank, world_size = get_rank_and_world_size()
args = parse_args()
use_config, cfg = False, None
if args.config is not None:
assert args.data is None and args.model is None, '--data and --model should not be set when using --config'
use_config, cfg = True, load(args.config)
args.model = list(cfg['model'].keys())
args.data = list(cfg['data'].keys())
else:
assert len(args.data), '--data should be a list of data files'
if rank == 0:
if not args.reuse:
logger.warning('--reuse is not set, will not reuse previous (before one day) temporary files')
else:
logger.warning('--reuse is set, will reuse the latest prediction & temporary pickle files')
if 'MMEVAL_ROOT' in os.environ:
args.work_dir = os.environ['MMEVAL_ROOT']
if not use_config:
for k, v in supported_VLM.items():
if hasattr(v, 'keywords') and 'retry' in v.keywords and args.retry is not None:
v.keywords['retry'] = args.retry
supported_VLM[k] = v
if hasattr(v, 'keywords') and 'verbose' in v.keywords and args.verbose is not None:
v.keywords['verbose'] = args.verbose
supported_VLM[k] = v
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=int(os.environ.get('DIST_TIMEOUT', 3600)))
)
for _, model_name in enumerate(args.model):
model = None
date, commit_id = timestr('day'), githash(digits=8)
eval_id = f"T{date}_G{commit_id}"
pred_root = osp.join(args.work_dir, model_name, eval_id)
pred_root_meta = osp.join(args.work_dir, model_name)
os.makedirs(pred_root_meta, exist_ok=True)
prev_pred_roots = ls(osp.join(args.work_dir, model_name), mode='dir')
if len(prev_pred_roots) and args.reuse:
prev_pred_roots.sort()
if not osp.exists(pred_root):
os.makedirs(pred_root, exist_ok=True)
if use_config:
model = build_model_from_config(cfg['model'], model_name)
for _, dataset_name in enumerate(args.data):
try:
result_file_base = f'{model_name}_{dataset_name}.xlsx'
if use_config:
if world_size > 1:
if rank == 0:
dataset = build_dataset_from_config(cfg['data'], dataset_name)
dist.barrier()
dataset = build_dataset_from_config(cfg['data'], dataset_name)
if dataset is None:
logger.error(f'Dataset {dataset_name} is not valid, will be skipped. ')
continue
else:
dataset_kwargs = {}
if dataset_name in ['MMLongBench_DOC', 'DUDE', 'DUDE_MINI', 'SLIDEVQA', 'SLIDEVQA_MINI']:
dataset_kwargs['model'] = model_name
# If distributed, first build the dataset on the main process for doing preparation works
if world_size > 1:
if rank == 0:
dataset = build_dataset(dataset_name, **dataset_kwargs)
dist.barrier()
dataset = build_dataset(dataset_name, **dataset_kwargs)
if dataset is None:
logger.error(f'Dataset {dataset_name} is not valid, will be skipped. ')
continue
# Handling Multi-Turn Dataset
if dataset.TYPE == 'MT':
result_file_base = result_file_base.replace('.xlsx', '.tsv')
result_file = osp.join(pred_root, result_file_base)
# Reuse the previous prediction file if exists
if rank == 0 and len(prev_pred_roots):
prev_result_file = None
prev_pkl_file_list = []
for root in prev_pred_roots[::-1]:
if osp.exists(osp.join(root, result_file_base)):
prev_result_file = osp.join(root, result_file_base)
break
elif commit_id in root and len(ls(root)) and root != pred_root:
temp_files = ls(root, match=[dataset_name, '.pkl'])
if len(temp_files):
prev_pkl_file_list.extend(temp_files)
break
if not args.reuse:
prev_result_file = None
prev_pkl_file_list = []
if prev_result_file is not None:
logger.warning(
f'--reuse is set, will reuse the prediction file {prev_result_file}.')
if prev_result_file != result_file:
shutil.copy(prev_result_file, result_file)
elif len(prev_pkl_file_list):
for fname in prev_pkl_file_list:
target_path = osp.join(pred_root, osp.basename(fname))
if not osp.exists(target_path):
shutil.copy(fname, target_path)
logger.info(f'--reuse is set, will reuse the prediction pickle file {fname}.')
else:
logger.warning(f'File already exists: {target_path}')
if world_size > 1:
dist.barrier()
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,
result_file_name=result_file_base,
verbose=args.verbose,
api_nproc=args.api_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.api_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.api_nproc,
ignore_failed=args.ignore)
# Set the judge kwargs first before evaluation or dumping
judge_kwargs = {
'nproc': args.api_nproc,
'verbose': args.verbose,
'retry': args.retry if args.retry is not None else 3
}
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', 'LLaVABench', 'MMBench-Video'], dataset_name):
judge_kwargs['model'] = 'gpt-4-turbo'
elif listinstr(['MathVista', 'MathVerse', 'MathVision', 'DynaMath', 'VL-RewardBench', 'WeMath', 'LogicVista'], dataset_name): # noqa: E501
judge_kwargs['model'] = 'gpt-4o-mini'
elif listinstr(['MMLongBench', 'MMDU', 'DUDE', 'SLIDEVQA', 'MIA-Bench', 'WildVision'], dataset_name): # noqa: E501
judge_kwargs['model'] = 'gpt-4o'
if rank == 0:
logger.info(judge_kwargs)
if world_size > 1:
dist.barrier()
# Only Rank 0 handles the evaluation part
if rank == 0:
# Prepare Submission Files for MMMU_TEST AND MMT-Bench_ALL
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}')
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}')
continue
# Skip the evaluation part if only infer
if args.mode == 'infer':
continue
# Skip the evaluation part if the dataset evaluation is not supported or annotations are missing
if 'MLLMGuard_DS' in dataset_name:
logger.info('The evaluation of MLLMGuard_DS is not supported yet. ')
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 huangyipo@hotmail.com.')
continue
elif dataset_name in ['DocVQA_TEST', 'InfoVQA_TEST', 'Q-Bench1_TEST', 'A-Bench_TEST']:
logger.info(f'{dataset_name} is a test split without ground-truth. '
'Thus only the inference part is supported for those datasets. ')
continue
elif 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'
] and not MMBenchOfficialServer(dataset_name):
logger.error(
f'Can not evaluate {dataset_name} on non-official servers, will skip the evaluation.')
continue
# Setup the proxy for the evaluation
eval_proxy = os.environ.get('EVAL_PROXY', None)
old_proxy = os.environ.get('HTTP_PROXY', '')
if eval_proxy is not None:
proxy_set(eval_proxy)
# Perform the Evaluation
eval_results = dataset.evaluate(result_file, **judge_kwargs)
# Display Evaluation Results in Terminal
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))
# Restore the proxy
if eval_proxy is not None:
proxy_set(old_proxy)
# Create the symbolic links for the prediction files
files = os.listdir(pred_root)
files = [x for x in files if (f'{model_name}_{dataset_name}' in x or "status.json" in x)]
for f in files:
cwd = os.getcwd()
file_addr = osp.join(cwd, pred_root, f)
link_addr = osp.join(cwd, pred_root_meta, f)
if osp.exists(link_addr) or osp.islink(link_addr):
os.remove(link_addr)
os.symlink(file_addr, link_addr)
except Exception as e:
logger.exception(f'Model {model_name} x Dataset {dataset_name} combination failed: {e}, '
'skipping this combination.')
continue
if world_size > 1:
dist.barrier()
if world_size > 1:
dist.destroy_process_group()
if __name__ == '__main__':
load_env()
main()
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