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| # Copyright (c) OpenMMLab. All rights reserved. | |
| import argparse | |
| import os | |
| import os.path as osp | |
| from mmdet.engine.hooks.utils import trigger_visualization_hook | |
| from mmengine.config import Config, ConfigDict, DictAction | |
| from mmengine.evaluator import DumpResults | |
| from mmengine.runner import Runner | |
| from mmyolo.registry import RUNNERS | |
| from mmyolo.utils import is_metainfo_lower | |
| # TODO: support fuse_conv_bn | |
| def parse_args(): | |
| parser = argparse.ArgumentParser( | |
| description='MMYOLO 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( | |
| '--out', | |
| type=str, | |
| help='output result file (must be a .pkl file) in pickle format') | |
| parser.add_argument( | |
| '--json-prefix', | |
| type=str, | |
| help='the prefix of the output json file without perform evaluation, ' | |
| 'which is useful when you want to format the result to a specific ' | |
| 'format and submit it to the test server') | |
| parser.add_argument( | |
| '--tta', | |
| action='store_true', | |
| help='Whether to use test time augmentation') | |
| parser.add_argument( | |
| '--show', action='store_true', help='show prediction results') | |
| parser.add_argument( | |
| '--deploy', | |
| action='store_true', | |
| help='Switch model to deployment mode') | |
| parser.add_argument( | |
| '--show-dir', | |
| help='directory where painted images will be saved. ' | |
| 'If specified, it will be automatically saved ' | |
| 'to the work_dir/timestamp/show_dir') | |
| parser.add_argument( | |
| '--wait-time', type=float, default=2, help='the interval of show (s)') | |
| 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(): | |
| args = parse_args() | |
| # load config | |
| cfg = Config.fromfile(args.config) | |
| # replace the ${key} with the value of cfg.key | |
| # cfg = replace_cfg_vals(cfg) | |
| 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]) | |
| cfg.load_from = args.checkpoint | |
| if args.show or args.show_dir: | |
| cfg = trigger_visualization_hook(cfg, args) | |
| if args.deploy: | |
| cfg.custom_hooks.append(dict(type='SwitchToDeployHook')) | |
| # add `format_only` and `outfile_prefix` into cfg | |
| if args.json_prefix is not None: | |
| cfg_json = { | |
| 'test_evaluator.format_only': True, | |
| 'test_evaluator.outfile_prefix': args.json_prefix | |
| } | |
| cfg.merge_from_dict(cfg_json) | |
| # Determine whether the custom metainfo fields are all lowercase | |
| is_metainfo_lower(cfg) | |
| if args.tta: | |
| assert 'tta_model' in cfg, 'Cannot find ``tta_model`` in config.' \ | |
| " Can't use tta !" | |
| assert 'tta_pipeline' in cfg, 'Cannot find ``tta_pipeline`` ' \ | |
| "in config. Can't use tta !" | |
| cfg.model = ConfigDict(**cfg.tta_model, module=cfg.model) | |
| test_data_cfg = cfg.test_dataloader.dataset | |
| while 'dataset' in test_data_cfg: | |
| test_data_cfg = test_data_cfg['dataset'] | |
| # batch_shapes_cfg will force control the size of the output image, | |
| # it is not compatible with tta. | |
| if 'batch_shapes_cfg' in test_data_cfg: | |
| test_data_cfg.batch_shapes_cfg = None | |
| test_data_cfg.pipeline = cfg.tta_pipeline | |
| # build the runner from config | |
| if 'runner_type' not in cfg: | |
| # build the default runner | |
| runner = Runner.from_cfg(cfg) | |
| else: | |
| # build customized runner from the registry | |
| # if 'runner_type' is set in the cfg | |
| runner = RUNNERS.build(cfg) | |
| # add `DumpResults` dummy metric | |
| if args.out is not None: | |
| assert args.out.endswith(('.pkl', '.pickle')), \ | |
| 'The dump file must be a pkl file.' | |
| runner.test_evaluator.metrics.append( | |
| DumpResults(out_file_path=args.out)) | |
| # start testing | |
| runner.test() | |
| if __name__ == '__main__': | |
| main() | |