o ;ήc @sRddlZddlZddlZejddddlmZmZddZedkr'edSdS) Nz%(levelname)s: %(message)s)format)"check_model_can_use_ort_mobile_pkgusability_checkercCstjdtjd}|jdddtjtd|jdgdd d d |jd d tjd|}t d}|j dkr>| t j n|j d krJ| t jn|j dkrV| t jn| t jtj|jd|d}t|j|j||d|r||ddS|ddS)NzAnalyze an ONNX model to determine how well it will work in mobile scenarios, and whether it is likely to be able to use the pre-built ONNX Runtime Mobile Android or iOS package.) descriptionformatter_classz --config_pathzbPath to required operators and types configuration used to build the pre-built ORT mobile package.F)helprequiredtypedefaultz --log_level)debuginfowarningerrorr z Logging level)choicesr r model_pathzPath to ONNX model to check)rr check_usabilityr r) skip_optimizeloggeraRun `python -m onnxruntime.tools.convert_onnx_models_to_ort ...` to convert the ONNX model to ORT format. By default, the conversion tool will create an ORT format model with saved optimizations which can potentially be applied at runtime (with a .with_runtime_opt.ort file extension) for use with NNAPI or CoreML, and a fully optimized ORT format model (with a .ort file extension) for use with the CPU EP.a As NNAPI or CoreML may provide benefits with this model it is recommended to compare the performance of the .with_runtime_opt.ort model using the NNAPI EP on Android, and the CoreML EP on iOS, against the performance of the .ort model using the CPU EP.zNFor optimal performance the .ort model should be used with the CPU EP. )argparseArgumentParserArgumentDefaultsHelpFormatter add_argumentpathlibPathrget_default_config_path parse_argslogging getLogger log_levelsetLevelDEBUGINFOWARNINGERRORr analyze_modelr run_check config_pathr )parserargsrtry_epsr+Z/tmp/pip-target-vg8gfxp4/lib/python/onnxruntime/tools/check_onnx_model_mobile_usability.pyrsD      r__main__) rrr basicConfigmobile_helpersrrr__name__r+r+r+r,s 4