# Copyright (c) OpenMMLab. All rights reserved. import argparse import os import os.path as osp from mmengine.config import Config, DictAction from mmengine.runner import Runner from mmrazor.utils import register_all_modules # TODO: support fuse_conv_bn, visualization, and format_only def parse_args(): parser = argparse.ArgumentParser( description='MMRazor test (and eval) a model') parser.add_argument('config', help='test config file path') parser.add_argument('checkpoint', help='checkpoint file') 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.') parser.add_argument( '--launcher', choices=['none', 'pytorch', 'slurm', 'mpi'], default='none', help='job launcher') parser.add_argument('--local_rank', type=int, default=0) args = parser.parse_args() if 'LOCAL_RANK' not in os.environ: os.environ['LOCAL_RANK'] = str(args.local_rank) return args def main(): register_all_modules(False) args = parse_args() # load config cfg = Config.fromfile(args.config) cfg.launcher = args.launcher if args.cfg_options is not None: cfg.merge_from_dict(args.cfg_options) # work_dir is determined in this priority: CLI > segment in file > filename if args.work_dir is not None: # update configs according to CLI args if args.work_dir is not None cfg.work_dir = args.work_dir elif cfg.get('work_dir', None) is None: # use config filename as default work_dir if cfg.work_dir is None cfg.work_dir = osp.join('./work_dirs', osp.splitext(osp.basename(args.config))[0]) if args.checkpoint == 'none': # NOTE: In this case, `args.checkpoint` isn't specified. If you haven't # specified a checkpoint in the `init_cfg` of the model yet, it may # cause the invalid results. cfg.load_from = None else: cfg.load_from = args.checkpoint if 'type' in cfg.test_cfg and cfg.test_cfg.type.endswith('PTQLoop'): cfg.test_cfg.only_val = True # build the runner from config runner = Runner.from_cfg(cfg) # start testing runner.test() if __name__ == '__main__': main()