98 lines
2.9 KiB
Python
98 lines
2.9 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import argparse
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import numpy as np
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import torch
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from mmcv import Config, DictAction
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from mmdet.models import build_detector
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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=[1280, 800],
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help='input image size')
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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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parser.add_argument(
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'--size-divisor',
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type=int,
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default=32,
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help='Pad the input image, the minimum size that is divisible '
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'by size_divisor, -1 means do not pad the image.')
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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 len(args.shape) == 1:
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h = w = args.shape[0]
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elif len(args.shape) == 2:
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h, w = args.shape
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else:
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raise ValueError('invalid input shape')
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orig_shape = (3, h, w)
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divisor = args.size_divisor
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if divisor > 0:
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h = int(np.ceil(h / divisor)) * divisor
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w = int(np.ceil(w / divisor)) * divisor
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input_shape = (3, h, w)
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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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model = build_detector(
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cfg.model,
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train_cfg=cfg.get('train_cfg'),
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test_cfg=cfg.get('test_cfg'))
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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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if hasattr(model, 'forward_dummy'):
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model.forward = model.forward_dummy
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else:
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raise NotImplementedError(
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'FLOPs counter is currently not currently supported with {}'.
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format(model.__class__.__name__))
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flops, params = get_model_complexity_info(model, input_shape)
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split_line = '=' * 30
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if divisor > 0 and \
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input_shape != orig_shape:
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print(f'{split_line}\nUse size divisor set input shape '
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f'from {orig_shape} to {input_shape}\n')
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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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