| |
| 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') |
| ori_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) |
|
|
| 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 != ori_shape: |
| print(f'{split_line}\nUse size divisor set input shape ' |
| f'from {ori_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() |
|
|
