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- # Copyright (c) OpenMMLab. All rights reserved.
- import argparse
-
- import numpy as np
- 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.')
- parser.add_argument(
- '--size-divisor',
- type=int,
- default=32,
- help='Pad the input image, the minimum size that is divisible '
- 'by size_divisor, -1 means do not pad the image.')
- args = parser.parse_args()
- return args
-
-
- def main():
-
- args = parse_args()
-
- if len(args.shape) == 1:
- h = w = args.shape[0]
- elif len(args.shape) == 2:
- h, w = args.shape
- else:
- raise ValueError('invalid input shape')
- orig_shape = (3, h, w)
- divisor = args.size_divisor
- if divisor > 0:
- h = int(np.ceil(h / divisor)) * divisor
- w = int(np.ceil(w / divisor)) * divisor
-
- input_shape = (3, h, w)
-
- 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
-
- if divisor > 0 and \
- input_shape != orig_shape:
- print(f'{split_line}\nUse size divisor set input shape '
- f'from {orig_shape} to {input_shape}\n')
- 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()
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