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https://github.com/open-mmlab/mmdeploy.git
synced 2025-01-14 08:09:43 +08:00
[Fix] fix device error in dump-info (#912)
* fix device error in dump-info * fix UT
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@ -42,20 +42,21 @@ def get_task(deploy_cfg: mmcv.Config) -> Dict:
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def get_model_name_customs(deploy_cfg: mmcv.Config, model_cfg: mmcv.Config,
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work_dir: str) -> Tuple:
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work_dir: str, device: str) -> Tuple:
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"""Get the model name and dump custom file.
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Args:
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deploy_cfg (mmcv.Config): Deploy config dict.
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model_cfg (mmcv.Config): The model config dict.
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work_dir (str): Work dir to save json files.
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device (str): The device passed in.
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Return:
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tuple(): Composed of the model name and the custom info.
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"""
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task = get_task_type(deploy_cfg)
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task_processor = build_task_processor(
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model_cfg=model_cfg, deploy_cfg=deploy_cfg, device='cpu')
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model_cfg=model_cfg, deploy_cfg=deploy_cfg, device=device)
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name = task_processor.get_model_name()
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customs = []
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if task == Task.TEXT_RECOGNITION:
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@ -75,19 +76,21 @@ def get_model_name_customs(deploy_cfg: mmcv.Config, model_cfg: mmcv.Config,
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def get_models(deploy_cfg: Union[str, mmcv.Config],
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model_cfg: Union[str, mmcv.Config], work_dir: str) -> List:
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model_cfg: Union[str, mmcv.Config], work_dir: str,
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device: str) -> List:
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"""Get the output model informantion for deploy.json.
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Args:
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deploy_cfg (mmcv.Config): Deploy config dict.
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model_cfg (mmcv.Config): The model config dict.
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work_dir (str): Work dir to save json files.
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device (str): The device passed in.
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Return:
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list[dict]: The list contains dicts composed of the model name, net,
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weghts, backend, precision batchsize and dynamic_shape.
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"""
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name, _ = get_model_name_customs(deploy_cfg, model_cfg, work_dir)
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name, _ = get_model_name_customs(deploy_cfg, model_cfg, work_dir, device)
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precision = 'FP32'
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ir_name = get_ir_config(deploy_cfg)['save_file']
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net = ir_name
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@ -149,19 +152,20 @@ def get_models(deploy_cfg: Union[str, mmcv.Config],
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def get_inference_info(deploy_cfg: mmcv.Config, model_cfg: mmcv.Config,
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work_dir: str) -> Dict:
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work_dir: str, device: str) -> Dict:
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"""Get the inference information for pipeline.json.
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Args:
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deploy_cfg (mmcv.Config): Deploy config dict.
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model_cfg (mmcv.Config): The model config dict.
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work_dir (str): Work dir to save json files.
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device (str): The device passed in.
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Return:
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dict: Composed of the model name, type, module, input, output and
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input_map.
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"""
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name, _ = get_model_name_customs(deploy_cfg, model_cfg, work_dir)
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name, _ = get_model_name_customs(deploy_cfg, model_cfg, work_dir, device)
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type = 'Task'
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module = 'Net'
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input = ['prep_output']
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@ -182,9 +186,17 @@ def get_inference_info(deploy_cfg: mmcv.Config, model_cfg: mmcv.Config,
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return return_dict
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def get_preprocess(deploy_cfg: mmcv.Config, model_cfg: mmcv.Config):
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def get_preprocess(deploy_cfg: mmcv.Config, model_cfg: mmcv.Config,
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device: str):
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"""Get the pre process information for pipeline.json.
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Args:
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deploy_cfg (mmcv.Config): Deploy config dict.
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model_cfg (mmcv.Config): The model config dict.
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device (str): The device passed in.
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"""
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task_processor = build_task_processor(
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model_cfg=model_cfg, deploy_cfg=deploy_cfg, device='cpu')
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model_cfg=model_cfg, deploy_cfg=deploy_cfg, device=device)
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pipeline = task_processor.get_preprocess()
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type = 'Task'
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module = 'Transform'
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@ -233,12 +245,14 @@ def get_preprocess(deploy_cfg: mmcv.Config, model_cfg: mmcv.Config):
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transforms=transforms)
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def get_postprocess(deploy_cfg: mmcv.Config, model_cfg: mmcv.Config) -> Dict:
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def get_postprocess(deploy_cfg: mmcv.Config, model_cfg: mmcv.Config,
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device: str, **kwargs) -> Dict:
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"""Get the post process information for pipeline.json.
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Args:
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deploy_cfg (mmcv.Config): Deploy config dict.
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model_cfg (mmcv.Config): The model config dict.
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device (str): The device passed in.
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Return:
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dict: Composed of the model name, type, module, input, params and
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@ -249,7 +263,7 @@ def get_postprocess(deploy_cfg: mmcv.Config, model_cfg: mmcv.Config) -> Dict:
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name = 'postprocess'
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task = get_task_type(deploy_cfg)
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task_processor = build_task_processor(
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model_cfg=model_cfg, deploy_cfg=deploy_cfg, device='cpu')
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model_cfg=model_cfg, deploy_cfg=deploy_cfg, device=device)
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params = task_processor.get_postprocess()
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# TODO remove after adding instance segmentation to task processor
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@ -270,14 +284,15 @@ def get_postprocess(deploy_cfg: mmcv.Config, model_cfg: mmcv.Config) -> Dict:
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output=output)
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def get_deploy(deploy_cfg: mmcv.Config, model_cfg: mmcv.Config,
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work_dir: str) -> Dict:
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def get_deploy(deploy_cfg: mmcv.Config, model_cfg: mmcv.Config, work_dir: str,
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device: str) -> Dict:
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"""Get the inference information for pipeline.json.
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Args:
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deploy_cfg (mmcv.Config): Deploy config dict.
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model_cfg (mmcv.Config): The model config dict.
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work_dir (str): Work dir to save json files.
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device (str): The device passed in.
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Return:
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dict: Composed of version, task, models and customs.
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@ -286,27 +301,28 @@ def get_deploy(deploy_cfg: mmcv.Config, model_cfg: mmcv.Config,
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task = get_task_type(deploy_cfg)
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cls_name = task_map[task]['cls_name']
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_, customs = get_model_name_customs(
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deploy_cfg, model_cfg, work_dir=work_dir)
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deploy_cfg, model_cfg, work_dir=work_dir, device=device)
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version = get_mmdpeloy_version()
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models = get_models(deploy_cfg, model_cfg, work_dir=work_dir)
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models = get_models(deploy_cfg, model_cfg, work_dir, device)
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return dict(version=version, task=cls_name, models=models, customs=customs)
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def get_pipeline(deploy_cfg: mmcv.Config, model_cfg: mmcv.Config,
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work_dir: str) -> Dict:
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work_dir: str, device: str) -> Dict:
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"""Get the inference information for pipeline.json.
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Args:
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deploy_cfg (mmcv.Config): Deploy config dict.
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model_cfg (mmcv.Config): The model config dict.
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work_dir (str): Work dir to save json files.
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device (str): The device passed in.
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Return:
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dict: Composed of input node name, output node name and the tasks.
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"""
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preprocess = get_preprocess(deploy_cfg, model_cfg)
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infer_info = get_inference_info(deploy_cfg, model_cfg, work_dir=work_dir)
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postprocess = get_postprocess(deploy_cfg, model_cfg)
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preprocess = get_preprocess(deploy_cfg, model_cfg, device)
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infer_info = get_inference_info(deploy_cfg, model_cfg, work_dir, device)
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postprocess = get_postprocess(deploy_cfg, model_cfg, device)
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task = get_task_type(deploy_cfg)
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input_names = preprocess['input']
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output_names = postprocess['output']
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@ -353,7 +369,8 @@ def get_detail(deploy_cfg: mmcv.Config, model_cfg: mmcv.Config,
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def export2SDK(deploy_cfg: Union[str, mmcv.Config],
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model_cfg: Union[str, mmcv.Config], work_dir: str, pth: str):
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model_cfg: Union[str, mmcv.Config], work_dir: str, pth: str,
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device: str, **kwargs):
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"""Export information to SDK. This function dump `deploy.json`,
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`pipeline.json` and `detail.json` to work dir.
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@ -364,8 +381,8 @@ def export2SDK(deploy_cfg: Union[str, mmcv.Config],
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pth (str): The path of the model checkpoint weights.
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"""
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deploy_cfg, model_cfg = load_config(deploy_cfg, model_cfg)
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deploy_info = get_deploy(deploy_cfg, model_cfg, work_dir=work_dir)
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pipeline_info = get_pipeline(deploy_cfg, model_cfg, work_dir=work_dir)
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deploy_info = get_deploy(deploy_cfg, model_cfg, work_dir, device)
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pipeline_info = get_pipeline(deploy_cfg, model_cfg, work_dir, device)
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detail_info = get_detail(deploy_cfg, model_cfg, pth=pth)
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mmcv.dump(
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deploy_info,
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@ -417,7 +417,7 @@ def test_AdvancedEnum():
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not importlib.util.find_spec('mmedit'), reason='requires mmedit')
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def test_export_info():
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with tempfile.TemporaryDirectory() as dir:
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export2SDK(correct_deploy_cfg, correct_model_cfg, dir, '')
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export2SDK(correct_deploy_cfg, correct_model_cfg, dir, '', 'cpu')
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deploy_json = os.path.join(dir, 'deploy.json')
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pipeline_json = os.path.join(dir, 'pipeline.json')
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detail_json = os.path.join(dir, 'detail.json')
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@ -122,7 +122,12 @@ def main():
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mmcv.mkdir_or_exist(osp.abspath(args.work_dir))
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if args.dump_info:
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export2SDK(deploy_cfg, model_cfg, args.work_dir, pth=checkpoint_path)
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export2SDK(
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deploy_cfg,
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model_cfg,
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args.work_dir,
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pth=checkpoint_path,
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device=args.device)
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ret_value = mp.Value('d', 0, lock=False)
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