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on
T4
Running
on
T4
| import argparse | |
| import os.path as osp | |
| from mmengine.config import Config, DictAction | |
| from mmengine.runner import Runner | |
| from mmengine.dataset import Compose | |
| from mmyolo.registry import RUNNERS | |
| from tools.demo import demo | |
| def parse_args(): | |
| parser = argparse.ArgumentParser( | |
| description='YOLO-World Demo') | |
| parser.add_argument('--config', default='configs/pretrain/yolo_world_l_t2i_bn_2e-4_100e_4x8gpus_obj365v1_goldg_train_lvis_minival.py') | |
| parser.add_argument('--checkpoint', default='model_zoo/yolow-v8_l_clipv2_frozen_t2iv2_bn_o365_goldg_pretrain.pth') | |
| parser.add_argument( | |
| '--work-dir', | |
| help='the directory to save the file containing evaluation metrics') | |
| parser.add_argument( | |
| '--cfg-options', | |
| nargs='+', | |
| action=DictAction, | |
| help='override some settings in the used config, the key-value pair ' | |
| 'in xxx=yyy format will be merged into config file. If the value to ' | |
| 'be overwritten is a list, it should be like key="[a,b]" or key=a,b ' | |
| 'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" ' | |
| 'Note that the quotation marks are necessary and that no white space ' | |
| 'is allowed.') | |
| args = parser.parse_args() | |
| return args | |
| if __name__ == '__main__': | |
| args = parse_args() | |
| # load config | |
| cfg = Config.fromfile(args.config) | |
| if args.cfg_options is not None: | |
| cfg.merge_from_dict(args.cfg_options) | |
| if args.work_dir is not None: | |
| cfg.work_dir = args.work_dir | |
| elif cfg.get('work_dir', None) is None: | |
| cfg.work_dir = osp.join('./work_dirs', | |
| osp.splitext(osp.basename(args.config))[0]) | |
| cfg.load_from = args.checkpoint | |
| if 'runner_type' not in cfg: | |
| runner = Runner.from_cfg(cfg) | |
| else: | |
| runner = RUNNERS.build(cfg) | |
| runner.call_hook('before_run') | |
| runner.load_or_resume() | |
| pipeline = cfg.test_dataloader.dataset.pipeline | |
| runner.pipeline = Compose(pipeline) | |
| runner.model.eval() | |
| demo(runner, args) | |