Buckets:
| import argparse | |
| from vlmeval.smp import * | |
| from vlmeval.config import supported_VLM | |
| def is_api(x): | |
| return getattr(supported_VLM[x].func, 'is_api', False) | |
| models = list(supported_VLM) | |
| models = [x for x in models if 'fs' not in x] | |
| models = [x for x in models if not is_api(x)] | |
| exclude_list = ['cogvlm-grounding-generalist', 'emu2'] | |
| models = [x for x in models if x not in exclude_list] | |
| def is_large(x): | |
| return '80b' in x or 'emu2' in x or '34B' in x | |
| small_models = [x for x in models if not is_large(x)] | |
| large_models = [x for x in models if is_large(x)] | |
| models = small_models + large_models | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument('--data', type=str, nargs='+', required=True) | |
| args = parser.parse_args() | |
| # Skip some models | |
| models = [x for x in models if not listinstr(['MiniGPT', 'grounding-generalist'], x)] | |
| for m in models: | |
| from vlmeval.smp import get_pred_file_format | |
| pred_format = get_pred_file_format() | |
| unknown_datasets = [x for x in args.data if not osp.exists(f'{m}/{m}_{x}.{pred_format}')] | |
| if len(unknown_datasets) == 0: | |
| continue | |
| dataset_str = ' '.join(unknown_datasets) | |
| if '80b' in m: | |
| cmd = f'python run.py --data {dataset_str} --model {m}' | |
| else: | |
| cmd = f'bash run.sh --data {dataset_str} --model {m}' | |
| print(cmd) | |
| os.system(cmd) |
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