app.py

import os
os.system("pip uninstall mmcv-full")
os.system("pip install torch==2.0.1 torchvision==0.15.2 --index-url https://download.pytorch.org/whl/cu118")
os.system("mim install 'mmengine>=0.6.0'")
os.system("mim install 'mmcv>=2.0.0rc4,<2.1.0'")
os.system("mim install 'mmdet>=3.0.0,<4.0.0'")
os.system("mim install 'mmyolo'")
os.system("pip install -e .")

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)