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| import argparse | |
| import torch | |
| from mmcv import Config, DictAction | |
| from mmdet.models import build_detector | |
| try: | |
| from mmcv.cnn import get_model_complexity_info | |
| except ImportError: | |
| raise ImportError('Please upgrade mmcv to >0.6.2') | |
| def parse_args(): | |
| parser = argparse.ArgumentParser(description='Train a detector') | |
| parser.add_argument('config', help='train config file path') | |
| parser.add_argument( | |
| '--shape', | |
| type=int, | |
| nargs='+', | |
| default=[1280, 800], | |
| help='input image size') | |
| 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.') | |
| args = parser.parse_args() | |
| return args | |
| def main(): | |
| args = parse_args() | |
| if len(args.shape) == 1: | |
| input_shape = (3, args.shape[0], args.shape[0]) | |
| elif len(args.shape) == 2: | |
| input_shape = (3, ) + tuple(args.shape) | |
| else: | |
| raise ValueError('invalid input shape') | |
| cfg = Config.fromfile(args.config) | |
| if args.cfg_options is not None: | |
| cfg.merge_from_dict(args.cfg_options) | |
| # import modules from string list. | |
| if cfg.get('custom_imports', None): | |
| from mmcv.utils import import_modules_from_strings | |
| import_modules_from_strings(**cfg['custom_imports']) | |
| model = build_detector( | |
| cfg.model, | |
| train_cfg=cfg.get('train_cfg'), | |
| test_cfg=cfg.get('test_cfg')) | |
| if torch.cuda.is_available(): | |
| model.cuda() | |
| model.eval() | |
| if hasattr(model, 'forward_dummy'): | |
| model.forward = model.forward_dummy | |
| else: | |
| raise NotImplementedError( | |
| 'FLOPs counter is currently not currently supported with {}'. | |
| format(model.__class__.__name__)) | |
| flops, params = get_model_complexity_info(model, input_shape) | |
| split_line = '=' * 30 | |
| print(f'{split_line}\nInput shape: {input_shape}\n' | |
| f'Flops: {flops}\nParams: {params}\n{split_line}') | |
| print('!!!Please be cautious if you use the results in papers. ' | |
| 'You may need to check if all ops are supported and verify that the ' | |
| 'flops computation is correct.') | |
| if __name__ == '__main__': | |
| main() | |