Quantize Model
===============

.. autofunction:: netspresso.np_qai.quantizer.NPQAIQuantizer.quantize_model



Example
-------

.. code-block:: python

    from netspresso import NPQAI
    from netspresso.np_qai import Device
    from netspresso.np_qai.options import QuantizePrecision

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

    INPUT_MODEL_PATH = "YOUR_INPUT_MODEL_PATH"
    OUTPUT_DIR = "YOUR_OUTPUT_DIR"
    JOB_NAME = "YOUR_JOB_NAME"
    DEVICE_NAME = "YOUR_DEVICE_NAME"

    CALIBRATION_DATA = {"images": inputs_array}

    quantized_result = quantizer.quantize_model(
        input_model_path=INPUT_MODEL_PATH,
        output_dir=OUTPUT_DIR,
        calibration_data=CALIBRATION_DATA,
        weights_dtype=QuantizePrecision.INT8,
        activations_dtype=QuantizePrecision.INT8,
        job_name=JOB_NAME,
    )
    print("Quantization task started")

    # Monitor task status
    while True:
        status = quantizer.get_quantize_task_status(quantized_result.quantize_info.quantize_task_uuid)
        if status.finished:
            quantized_result = quantizer.update_quantize_task(quantized_result)
            print("Quantization task completed")
            break
        else:
            print("Quantization task is still running")


Create Calibration Datasets
~~~~~~~~~~~~~~~~~~~~~~~~~~~

Using good, representative input samples for calibration helps improve performance on target hardware and retains model accuracy

.. code-block:: python

    import cv2
    from glob import glob

    import numpy as np
    from netspresso.inferencer.preprocessors.base import Preprocessor

    IMG_SIZE = 640
    preprocess_list = [
        {
            "name": "resize",
            "size": IMG_SIZE,
            "interpolation": "bilinear",
            "max_size": None,
            "resize_criteria": "long",
        },
        {
            "name": "pad",
            "size": IMG_SIZE,
            "fill": 114,
        }
    ]

    preprocessor = Preprocessor(preprocess_list)

    DATASET_PATH = "YOUR_DATASET_PATH"
    NUM_DATASET = 100
    image_paths = glob(f"{DATASET_PATH}/*.jpg")[:NUM_DATASET]

    inputs_array = []

    for image_path in image_paths:
        img = cv2.imread(image_path)
        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
        img = preprocessor(img)
        img = np.transpose(img, (0, 3, 1, 2))
        inputs_array.append(img)

    >> np.array(inputs_array).shape
    (100, 1, 3, 512, 512)