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import argparse |
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import torch |
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from mmengine import Config, DictAction |
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from mmengine.registry import init_default_scope |
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from mmdet3d.registry import MODELS |
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try: |
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from mmcv.cnn import get_model_complexity_info |
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except ImportError: |
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raise ImportError('Please upgrade mmcv to >0.6.2') |
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def parse_args(): |
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parser = argparse.ArgumentParser(description='Train a detector') |
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parser.add_argument('config', help='train config file path') |
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parser.add_argument( |
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'--shape', |
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type=int, |
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nargs='+', |
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default=[40000, 4], |
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help='input point cloud size') |
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parser.add_argument( |
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'--modality', |
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type=str, |
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default='point', |
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choices=['point', 'image', 'multi'], |
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help='input data modality') |
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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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args = parser.parse_args() |
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return args |
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def main(): |
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args = parse_args() |
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if args.modality == 'point': |
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assert len(args.shape) == 2, 'invalid input shape' |
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input_shape = tuple(args.shape) |
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elif args.modality == 'image': |
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if len(args.shape) == 1: |
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input_shape = (3, args.shape[0], args.shape[0]) |
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elif len(args.shape) == 2: |
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input_shape = (3, ) + tuple(args.shape) |
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else: |
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raise ValueError('invalid input shape') |
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elif args.modality == 'multi': |
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raise NotImplementedError( |
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'FLOPs counter is currently not supported for models with ' |
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'multi-modality input') |
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cfg = Config.fromfile(args.config) |
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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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init_default_scope(cfg.get('default_scope', 'mmdet3d')) |
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model = MODELS.build(cfg.model) |
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if torch.cuda.is_available(): |
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model.cuda() |
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model.eval() |
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flops, params = get_model_complexity_info(model, input_shape) |
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split_line = '=' * 30 |
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print(f'{split_line}\nInput shape: {input_shape}\n' |
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f'Flops: {flops}\nParams: {params}\n{split_line}') |
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print('!!!Please be cautious if you use the results in papers. ' |
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'You may need to check if all ops are supported and verify that the ' |
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'flops computation is correct.') |
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if __name__ == '__main__': |
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main() |
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