mirror of
https://github.com/open-mmlab/mmdeploy.git
synced 2025-01-14 08:09:43 +08:00
27880afdcd
* better retinanet support * prepare split export tensorrt * optimizer cfg * free anchor when static shape * fix docstring
106 lines
3.4 KiB
Python
106 lines
3.4 KiB
Python
import argparse
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import logging
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import os.path as osp
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import mmcv
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import torch.multiprocessing as mp
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from torch.multiprocessing import Process, set_start_method
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from mmdeploy.apis import torch2onnx
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def parse_args():
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parser = argparse.ArgumentParser(description='Export model to backend.')
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parser.add_argument('deploy_cfg', help='deploy config path')
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parser.add_argument('model_cfg', help='model config path')
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parser.add_argument('checkpoint', help='model checkpoint path')
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parser.add_argument(
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'img', help='image used to convert model and test model')
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parser.add_argument('--work-dir', help='the dir to save logs and models')
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parser.add_argument(
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'--device', help='device used for conversion', default='cpu')
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parser.add_argument(
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'--log-level',
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help='set log level',
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default='INFO',
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choices=list(logging._nameToLevel.keys()))
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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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set_start_method('spawn')
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logger = logging.getLogger()
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logger.setLevel(args.log_level)
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deploy_cfg_path = args.deploy_cfg
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model_cfg_path = args.model_cfg
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checkpoint_path = args.checkpoint
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# load deploy_cfg
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deploy_cfg = mmcv.Config.fromfile(deploy_cfg_path)
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if not isinstance(deploy_cfg, mmcv.Config):
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raise TypeError('deploy_cfg must be a filename or Config object, '
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f'but got {type(deploy_cfg)}')
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# create work_dir if not
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mmcv.mkdir_or_exist(osp.abspath(args.work_dir))
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ret_value = mp.Value('d', 0, lock=False)
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# convert onnx
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logging.info('start torch2onnx conversion.')
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onnx_save_file = deploy_cfg['pytorch2onnx']['save_file']
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process = Process(
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target=torch2onnx,
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args=(args.img, args.work_dir, onnx_save_file, deploy_cfg_path,
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model_cfg_path, checkpoint_path),
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kwargs=dict(device=args.device, ret_value=ret_value))
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process.start()
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process.join()
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if ret_value.value != 0:
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logging.error('torch2onnx failed.')
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exit()
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else:
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logging.info('torch2onnx success.')
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# convert backend
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onnx_pathes = [osp.join(args.work_dir, onnx_save_file)]
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backend = deploy_cfg.get('backend', 'default')
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if backend == 'tensorrt':
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assert hasattr(deploy_cfg, 'tensorrt_param')
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tensorrt_param = deploy_cfg['tensorrt_param']
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model_params = tensorrt_param.get('model_params', [])
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assert len(model_params) == len(onnx_pathes)
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logging.info('start onnx2tensorrt conversion.')
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from mmdeploy.apis.tensorrt import onnx2tensorrt
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for model_id, model_param, onnx_path in zip(
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range(len(onnx_pathes)), model_params, onnx_pathes):
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onnx_name = osp.splitext(osp.split(onnx_path)[1])[0]
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save_file = model_param.get('save_file', onnx_name + '.engine')
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process = Process(
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target=onnx2tensorrt,
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args=(args.work_dir, save_file, model_id, deploy_cfg_path,
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onnx_path),
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kwargs=dict(device=args.device, ret_value=ret_value))
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process.start()
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process.join()
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if ret_value.value != 0:
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logging.error('onnx2tensorrt failed.')
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exit()
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else:
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logging.info('onnx2tensorrt success.')
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logging.info('All process success.')
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if __name__ == '__main__':
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main()
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