test.py 4.47 KB
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# 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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from mmdet3d.utils import register_all_modules, replace_ceph_backend
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# TODO: support fuse_conv_bn and format_only
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def parse_args():
    parser = argparse.ArgumentParser(
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        description='MMDet3D test (and eval) a model')
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    parser.add_argument('config', help='test config file path')
    parser.add_argument('checkpoint', help='checkpoint file')
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    parser.add_argument(
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        '--work-dir',
        help='the directory to save the file containing evaluation metrics')
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    parser.add_argument(
        '--ceph', action='store_true', help='Use ceph as data storage backend')
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    parser.add_argument(
        '--show', action='store_true', help='show prediction results')
    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')
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    parser.add_argument(
        '--task',
        type=str,
        choices=[
            'mono_det', 'multi-view_det', 'lidar_det', 'lidar_seg',
            'multi-modality_det'
        ],
        help='Determine the visualization method depending on the task.')
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    parser.add_argument(
        '--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',
        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.')
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    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


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def trigger_visualization_hook(cfg, args):
    default_hooks = cfg.default_hooks
    if 'visualization' in default_hooks:
        visualization_hook = default_hooks['visualization']
        # Turn on visualization
        visualization_hook['draw'] = True
        if args.show:
            visualization_hook['show'] = True
            visualization_hook['wait_time'] = args.wait_time
        if args.show_dir:
            visualization_hook['test_out_dir'] = args.show_dir
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        visualization_hook['vis_task'] = args.task
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    else:
        raise RuntimeError(
            'VisualizationHook must be included in default_hooks.'
            'refer to usage '
            '"visualization=dict(type=\'VisualizationHook\')"')

    return cfg


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def main():
    args = parse_args()

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    # register all modules in mmdet3d 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)
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    # load config
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    cfg = Config.fromfile(args.config)
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    # TODO: We will unify the ceph support approach with other OpenMMLab repos
    if args.ceph:
        cfg = replace_ceph_backend(cfg)
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    cfg.launcher = args.launcher
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    if args.cfg_options is not None:
        cfg.merge_from_dict(args.cfg_options)
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    # 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])
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    cfg.load_from = args.checkpoint
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    if args.show or args.show_dir:
        cfg = trigger_visualization_hook(cfg, args)

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    # build the runner from config
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    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)
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    # start testing
    runner.test()
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if __name__ == '__main__':
    main()