mirror of https://github.com/open-mmlab/mmocr.git
142 lines
5.1 KiB
Python
142 lines
5.1 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import argparse
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import os
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import os.path as osp
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from mmengine.config import Config, DictAction
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from mmengine.registry import RUNNERS
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from mmengine.runner import Runner
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def parse_args():
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parser = argparse.ArgumentParser(description='Test (and eval) a model')
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parser.add_argument('config', help='Test config file path')
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parser.add_argument('checkpoint', help='Checkpoint file')
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parser.add_argument(
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'--work-dir',
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help='The directory to save the file containing evaluation metrics')
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parser.add_argument(
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'--save-preds',
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action='store_true',
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help='Dump predictions to a pickle file for offline evaluation')
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parser.add_argument(
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'--show', action='store_true', help='Show prediction results')
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parser.add_argument(
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'--show-dir',
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help='Directory where painted images will be saved. '
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'If specified, it will be automatically saved '
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'to the work_dir/timestamp/show_dir')
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parser.add_argument(
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'--wait-time', type=float, default=2, help='The interval of show (s)')
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parser.add_argument(
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'--cfg-options',
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nargs='+',
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action=DictAction,
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help='Override some settings in the used config, the key-value pair '
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'in xxx=yyy format will be merged into config file. If the value to '
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'be overwritten is a list, it should be like key="[a,b]" or key=a,b '
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'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '
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'Note that the quotation marks are necessary and that no white space '
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'is allowed.')
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parser.add_argument(
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'--launcher',
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choices=['none', 'pytorch', 'slurm', 'mpi'],
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default='none',
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help='Job launcher')
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parser.add_argument(
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'--tta', action='store_true', help='Test time augmentation')
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# When using PyTorch version >= 2.0.0, the `torch.distributed.launch`
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# will pass the `--local-rank` parameter to `tools/test.py` instead
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# of `--local_rank`.
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parser.add_argument('--local_rank', '--local-rank', type=int, default=0)
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args = parser.parse_args()
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if 'LOCAL_RANK' not in os.environ:
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os.environ['LOCAL_RANK'] = str(args.local_rank)
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return args
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def trigger_visualization_hook(cfg, args):
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default_hooks = cfg.default_hooks
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if 'visualization' in default_hooks:
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visualization_hook = default_hooks['visualization']
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# Turn on visualization
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visualization_hook['enable'] = True
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visualization_hook['draw_gt'] = True
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visualization_hook['draw_pred'] = True
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if args.show:
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visualization_hook['show'] = True
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visualization_hook['wait_time'] = args.wait_time
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if args.show_dir:
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cfg.visualizer['save_dir'] = args.show_dir
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cfg.visualizer['vis_backends'] = [dict(type='LocalVisBackend')]
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else:
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raise RuntimeError(
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'VisualizationHook must be included in default_hooks.'
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'refer to usage '
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'"visualization=dict(type=\'VisualizationHook\')"')
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return cfg
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def main():
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args = parse_args()
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# load config
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cfg = Config.fromfile(args.config)
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cfg.launcher = args.launcher
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if args.cfg_options is not None:
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cfg.merge_from_dict(args.cfg_options)
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# work_dir is determined in this priority: CLI > segment in file > filename
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if args.work_dir is not None:
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# update configs according to CLI args if args.work_dir is not None
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cfg.work_dir = args.work_dir
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elif cfg.get('work_dir', None) is None:
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# use config filename as default work_dir if cfg.work_dir is None
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cfg.work_dir = osp.join('./work_dirs',
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osp.splitext(osp.basename(args.config))[0])
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cfg.load_from = args.checkpoint
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# TODO: It will be supported after refactoring the visualizer
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if args.show and args.show_dir:
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raise NotImplementedError('--show and --show-dir cannot be set '
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'at the same time')
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if args.show or args.show_dir:
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cfg = trigger_visualization_hook(cfg, args)
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if args.tta:
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cfg.test_dataloader.dataset.pipeline = cfg.tta_pipeline
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cfg.tta_model.module = cfg.model
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cfg.model = cfg.tta_model
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# save predictions
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if args.save_preds:
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dump_metric = dict(
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type='DumpResults',
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out_file_path=osp.join(
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cfg.work_dir,
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f'{osp.basename(args.checkpoint)}_predictions.pkl'))
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if isinstance(cfg.test_evaluator, (list, tuple)):
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cfg.test_evaluator = list(cfg.test_evaluator)
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cfg.test_evaluator.append(dump_metric)
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else:
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cfg.test_evaluator = [cfg.test_evaluator, dump_metric]
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# build the runner from config
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if 'runner_type' not in cfg:
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# build the default runner
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runner = Runner.from_cfg(cfg)
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else:
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# build customized runner from the registry
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# if 'runner_type' is set in the cfg
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runner = RUNNERS.build(cfg)
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# start testing
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runner.test()
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if __name__ == '__main__':
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main()
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