214 lines
7.0 KiB
Python
214 lines
7.0 KiB
Python
import argparse
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import logging
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import os.path as osp
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import subprocess
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from functools import partial
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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 (assert_cfg_valid, extract_model, inference_model,
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torch2onnx)
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from mmdeploy.apis.utils import get_split_cfg
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def parse_args():
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parser = argparse.ArgumentParser(description='Export model to backends.')
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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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parser.add_argument(
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'--show', action='store_true', help='Show detection outputs')
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args = parser.parse_args()
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return args
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def target_wrapper(target, log_level, *args, **kwargs):
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logger = logging.getLogger()
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logger.level
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logger.setLevel(log_level)
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return target(*args, **kwargs)
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def create_process(name, target, args, kwargs, ret_value=None):
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logging.info(f'{name} start.')
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log_level = logging.getLogger().level
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wrap_func = partial(target_wrapper, target, log_level)
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process = Process(target=wrap_func, args=args, kwargs=kwargs)
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process.start()
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process.join()
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if ret_value is not None:
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if ret_value.value != 0:
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logging.error(f'{name} failed.')
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exit()
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else:
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logging.info(f'{name} success.')
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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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assert_cfg_valid(deploy_cfg, model_cfg_path)
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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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onnx_save_file = deploy_cfg['pytorch2onnx']['save_file']
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create_process(
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'torch2onnx',
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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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ret_value=ret_value)
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# convert backend
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onnx_files = [osp.join(args.work_dir, onnx_save_file)]
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# split model
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apply_marks = deploy_cfg.get('apply_marks', False)
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if apply_marks:
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assert hasattr(deploy_cfg, 'split_params')
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split_params = deploy_cfg['split_params']
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if 'split_cfg' in split_params:
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split_cfgs = split_params.get('split_cfg', None)
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else:
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assert 'split_type' in split_params
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split_cfgs = get_split_cfg(deploy_cfg['codebase'],
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split_params['split_type'])
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origin_onnx_file = onnx_files[0]
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onnx_files = []
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for split_cfg in split_cfgs:
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save_file = split_cfg['save_file']
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save_path = osp.join(args.work_dir, save_file)
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start = split_cfg['start']
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end = split_cfg['end']
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dynamic_axes = split_cfg.get('dynamic_axes', None)
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create_process(
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f'split model {save_file} with start: {start}, end: {end}',
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extract_model,
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args=(origin_onnx_file, start, end),
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kwargs=dict(
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dynamic_axes=dynamic_axes,
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save_file=save_path,
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ret_value=ret_value),
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ret_value=ret_value)
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onnx_files.append(save_path)
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backend_files = onnx_files
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# convert backend
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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_params')
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tensorrt_params = deploy_cfg['tensorrt_params']
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model_params = tensorrt_params.get('model_params', [])
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assert len(model_params) == len(onnx_files)
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from mmdeploy.apis.tensorrt import onnx2tensorrt
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from mmdeploy.apis.tensorrt import is_available as trt_is_available
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assert trt_is_available(
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), 'TensorRT is not available,' \
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+ ' please install TensorRT and build TensorRT custom ops first.'
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backend_files = []
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for model_id, model_param, onnx_path in zip(
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range(len(onnx_files)), model_params, onnx_files):
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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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create_process(
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f'onnx2tensorrt of {onnx_path}',
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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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ret_value=ret_value)
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backend_files.append(osp.join(args.work_dir, save_file))
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elif backend == 'ncnn':
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from mmdeploy.apis.ncnn import get_onnx2ncnn_path
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from mmdeploy.apis.ncnn import is_available as is_available_ncnn
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if not is_available_ncnn():
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logging.error('ncnn support is not available.')
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exit(-1)
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onnx2ncnn_path = get_onnx2ncnn_path()
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backend_files = []
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for onnx_path in onnx_files:
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onnx_name = osp.splitext(osp.split(onnx_path)[1])[0]
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save_param = onnx_name + '.param'
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save_bin = onnx_name + '.bin'
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save_param = osp.join(args.work_dir, save_param)
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save_bin = osp.join(args.work_dir, save_bin)
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subprocess.call([onnx2ncnn_path, onnx_path, save_param, save_bin])
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backend_files += [save_param, save_bin]
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# check model outputs by visualization
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# visualize model of the backend
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create_process(
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f'visualize {backend} model',
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target=inference_model,
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args=(model_cfg_path, deploy_cfg_path, backend_files, args.img),
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kwargs=dict(
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device=args.device,
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output_file=f'output_{backend}.jpg',
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show_result=args.show,
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ret_value=ret_value),
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ret_value=ret_value)
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# visualize pytorch model
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create_process(
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'visualize pytorch model',
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target=inference_model,
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args=(model_cfg_path, deploy_cfg_path, [checkpoint_path], args.img),
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kwargs=dict(
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device=args.device,
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backend='pytorch',
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output_file='output_pytorch.jpg',
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show_result=args.show,
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ret_value=ret_value),
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ret_value=ret_value)
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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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