# Copyright (c) OpenMMLab. All rights reserved. import argparse import os import os.path as osp from copy import deepcopy import mmengine from mmengine.config import Config, ConfigDict, DictAction from mmengine.hooks import Hook from mmengine.runner import Runner from mmcls.utils import register_all_modules def parse_args(): parser = argparse.ArgumentParser( description='MMCLS 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', help='the file to save metric results.') parser.add_argument( '--dump', type=str, help='dump predictions to a pickle file for offline evaluation') 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( '--show-dir', help='directory where the visualization images will be saved.') parser.add_argument( '--show', action='store_true', help='whether to display the prediction results in a window.') parser.add_argument( '--interval', type=int, default=1, help='visualize per interval samples.') parser.add_argument( '--wait-time', type=float, default=2, help='display time of every window. (second)') parser.add_argument( '--no-pin-memory', action='store_true', help='whether to disable the pin_memory option in dataloaders.') parser.add_argument( '--tta', action='store_true', help='Whether to enable the Test-Time-Aug (TTA). If the config file ' 'has `tta_pipeline` and `tta_model` fields, use them to determine the ' 'TTA transforms and how to merge the TTA results. Otherwise, use flip ' 'TTA by averaging classification score.') 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 merge_args(cfg, args): """Merge CLI arguments to config.""" cfg.launcher = args.launcher # 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 # -------------------- visualization -------------------- if args.show or (args.show_dir is not None): assert 'visualization' in cfg.default_hooks, \ 'VisualizationHook is not set in the `default_hooks` field of ' \ 'config. Please set `visualization=dict(type="VisualizationHook")`' cfg.default_hooks.visualization.enable = True cfg.default_hooks.visualization.show = args.show cfg.default_hooks.visualization.wait_time = args.wait_time cfg.default_hooks.visualization.out_dir = args.show_dir cfg.default_hooks.visualization.interval = args.interval # -------------------- Dump predictions -------------------- if args.dump is not None: assert args.dump.endswith(('.pkl', '.pickle')), \ 'The dump file must be a pkl file.' dump_metric = dict(type='DumpResults', out_file_path=args.dump) if isinstance(cfg.test_evaluator, (list, tuple)): cfg.test_evaluator = list(cfg.test_evaluator).append(dump_metric) else: cfg.test_evaluator = [cfg.test_evaluator, dump_metric] # -------------------- TTA related args -------------------- if args.tta: if 'tta_model' not in cfg: cfg.tta_model = dict(type='mmcls.AverageClsScoreTTA') if 'tta_pipeline' not in cfg: test_pipeline = cfg.test_dataloader.dataset.pipeline cfg.tta_pipeline = deepcopy(test_pipeline) flip_tta = dict( type='TestTimeAug', transforms=[ [ dict(type='RandomFlip', prob=1.), dict(type='RandomFlip', prob=0.) ], [test_pipeline[-1]], ]) cfg.tta_pipeline[-1] = flip_tta cfg.model = ConfigDict(**cfg.tta_model, module=cfg.model) cfg.test_dataloader.dataset.pipeline = cfg.tta_pipeline # ----------------- Default dataloader args ----------------- default_dataloader_cfg = ConfigDict( pin_memory=True, collate_fn=dict(type='default_collate'), ) def set_default_dataloader_cfg(cfg, field): if cfg.get(field, None) is None: return dataloader_cfg = deepcopy(default_dataloader_cfg) dataloader_cfg.update(cfg[field]) cfg[field] = dataloader_cfg if args.no_pin_memory: cfg[field]['pin_memory'] = False set_default_dataloader_cfg(cfg, 'test_dataloader') if args.cfg_options is not None: cfg.merge_from_dict(args.cfg_options) return cfg def main(): args = parse_args() # register all modules in mmcls into the registries # do not init the default scope here because it will be init in the runner register_all_modules(init_default_scope=False) # load config cfg = Config.fromfile(args.config) cfg = merge_args(cfg, args) # build the runner from config runner = Runner.from_cfg(cfg) if args.out: class SaveMetricHook(Hook): def after_test_epoch(self, _, metrics=None): if metrics is not None: mmengine.dump(metrics, args.out) runner.register_hook(SaveMetricHook(), 'LOWEST') # start testing runner.test() if __name__ == '__main__': main()