Convert Model
=============

.. autofunction:: netspresso.np_qai.converter.NPQAIConverter.convert_model


Example
-------

.. code-block:: python

    from netspresso import NPQAI
    from netspresso.np_qai import Device
    from netspresso.np_qai.options import CompileOptions, Runtime, ComputeUnit, QuantizeFullType

    QAI_HUB_API_TOKEN = "YOUR_QAI_HUB_API_TOKEN"
    np_qai = NPQAI(api_token=QAI_HUB_API_TOKEN)
    
    converter = np_qai.converter()

    convert_options = CompileOptions(
        target_runtime=Runtime.TFLITE,
        compute_unit=[ComputeUnit.NPU],
        quantize_full_type=QuantizeFullType.INT8,
        quantize_io=True,
        quantize_io_type=QuantizeFullType.INT8,
    )

    IMG_SIZE = 640
    INPUT_MODEL_PATH = "YOUR_INPUT_MODEL_PATH"
    OUTPUT_DIR = "YOUR_OUTPUT_DIR"
    JOB_NAME = "YOUR_JOB_NAME"
    DEVICE_NAME = "QCS6490 (Proxy)"
    converted_result = converter.convert_model(
        input_model_path=INPUT_MODEL_PATH,
        output_dir=OUTPUT_DIR,
        target_device_name=Device(DEVICE_NAME),
        options=convert_options,
        input_shapes=dict(image=(1, 3, IMG_SIZE, IMG_SIZE)),
        job_name=JOB_NAME,
    )

    print("Conversion task started")

    # Monitor task status
    while True:
        status = converter.get_convert_task_status(converted_result.convert_task_info.convert_task_uuid)
        if status.finished:
            converted_result = converter.update_convert_task(converted_result)
            print("Conversion task completed")
            break
        else:
            print("Conversion task is still running")