mirror of https://github.com/open-mmlab/mmyolo.git
124 lines
4.0 KiB
Python
124 lines
4.0 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import argparse
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import tempfile
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from pathlib import Path
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import torch
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from mmdet.registry import MODELS
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from mmengine.analysis import get_model_complexity_info
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from mmengine.config import Config, DictAction
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from mmengine.logging import MMLogger
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from mmengine.model import revert_sync_batchnorm
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from mmengine.registry import init_default_scope
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from mmyolo.utils import switch_to_deploy
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def parse_args():
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parser = argparse.ArgumentParser(description='Get a detector flops')
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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=[640, 640],
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help='input image size')
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parser.add_argument(
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'--show-arch',
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action='store_true',
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help='whether return the statistics in the form of network layers')
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parser.add_argument(
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'--not-show-table',
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action='store_true',
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help='whether return the statistics in the form of table'),
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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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return parser.parse_args()
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def inference(args, logger):
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config_name = Path(args.config)
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if not config_name.exists():
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logger.error(f'{config_name} not found.')
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cfg = Config.fromfile(args.config)
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cfg.work_dir = tempfile.TemporaryDirectory().name
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cfg.log_level = 'WARN'
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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', 'mmyolo'))
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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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# model
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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 = revert_sync_batchnorm(model)
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model.eval()
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switch_to_deploy(model)
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# input tensor
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# automatically generate a input tensor with the given input_shape.
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data_batch = {'inputs': [torch.rand(3, h, w)], 'batch_samples': [None]}
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data = model.data_preprocessor(data_batch)
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result = {'ori_shape': (h, w), 'pad_shape': data['inputs'].shape[-2:]}
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outputs = get_model_complexity_info(
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model,
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input_shape=None,
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inputs=data['inputs'], # the input tensor of the model
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show_table=not args.not_show_table, # show the complexity table
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show_arch=args.show_arch) # show the complexity arch
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result['flops'] = outputs['flops_str']
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result['params'] = outputs['params_str']
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result['out_table'] = outputs['out_table']
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result['out_arch'] = outputs['out_arch']
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return result
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def main():
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args = parse_args()
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logger = MMLogger.get_instance(name='MMLogger')
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result = inference(args, logger)
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split_line = '=' * 30
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ori_shape = result['ori_shape']
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pad_shape = result['pad_shape']
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flops = result['flops']
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params = result['params']
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print(result['out_table']) # print related information by table
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print(result['out_arch']) # print related information by network layers
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if pad_shape != ori_shape:
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print(f'{split_line}\nUse size divisor set input shape '
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f'from {ori_shape} to {pad_shape}')
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print(f'{split_line}\n'
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f'Input shape: {pad_shape}\nModel Flops: {flops}\n'
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f'Model Parameters: {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 '
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'that the flops computation is correct.')
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if __name__ == '__main__':
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main()
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