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https://github.com/open-mmlab/mmsegmentation.git
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ff95416c3b
## Motivation 1. It is used to save the segmentation predictions as files and upload these files to a test server ## Modification 1. Add output_file and format only in `IoUMetric` ## BC-breaking (Optional) No ## Use cases (Optional) If this PR introduces a new feature, it is better to list some use cases here, and update the documentation. ## Checklist 1. Pre-commit or other linting tools are used to fix the potential lint issues. 3. The modification is covered by complete unit tests. If not, please add more unit test to ensure the correctness. 4. If the modification has potential influence on downstream projects, this PR should be tested with downstream projects, like MMDet or MMDet3D. 5. The documentation has been modified accordingly, like docstring or example tutorials.
121 lines
4.0 KiB
Python
121 lines
4.0 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.runner import Runner
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# TODO: support fuse_conv_bn, visualization, and format_only
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def parse_args():
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parser = argparse.ArgumentParser(
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description='MMSeg test (and eval) a model')
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parser.add_argument('config', help='train 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=('if specified, the evaluation metric results will be dumped'
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'into the directory as json'))
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parser.add_argument(
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'--out',
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type=str,
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help='The directory to save output prediction 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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parser.add_argument('--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['draw'] = 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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visulizer = cfg.visualizer
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visulizer['save_dir'] = args.show_dir
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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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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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# add output_dir in metric
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if args.out is not None:
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cfg.test_evaluator['output_dir'] = args.out
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cfg.test_evaluator['keep_results'] = True
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# build the runner from config
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runner = Runner.from_cfg(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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