import os os.system("pip uninstall -y mmcv-full") os.system("mim install 'mmengine>=0.6.0'") # os.system("pip install mmcv==2.0.1 -f https://download.openmmlab.com/mmcv/dist/cu118/torch2.0/index.html") os.system("mim install 'mmcv-lite==2.0.1'") os.system("mim install 'mmdet>=3.0.0,<4.0.0'") os.system("mim install 'mmyolo'") os.system("pip install -e .") os.system("python tools/demo.py configs/pretrain/yolo_world_l_t2i_bn_2e-4_100e_4x8gpus_obj365v1_goldg_train_lvis_minival.py yolow-v8_l_clipv2_frozen_t2iv2_bn_o365_goldg_pretrain.pth") 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='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)