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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 .")
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='Zero shot Detection 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, cfg)
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