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Migrate from Kneron PLUS 1.x & Kneron PLUS 2.x to Kneron PLUS 3

This chapter shows how to migrate your code from Kneron PLUS V1.x and Kneron PLUS V2.x to Kneron PLUS V3. Before getting started, please reference the APIs V1, V2 and V3 Mapping Table for mapping V1.x and V2.x to V3 API. The major migration process is:

  1. Rewrite model information access usage
  2. Replace inference usage
  3. (Option) Replace built-in YOLO post process function

1. Rewrite model information access usage

In Kneron PLUS 1.x, the kp.ModelNefDescriptor provides basic and essential information about loaded models, such as model ID, input size, and raw output size. It is straightforward to use.

However, some API usage, such as data inference (without hardware image pre-processing), requires more model information (e.g., model input NPU data layout, model input quantization parameters, etc.) to handle software pre-processing on the host PC.

In the Kneron PLUS 3 platform, the classes kp.TensorShapeInfo and kp.QuantizationParameters play a crucial role in ensuring compatibility with all model information.

Note: For new information added in kp.ModelNefDescriptor, please reference kp.ModelNefDescriptor definition.

2. Replace inference usage

2.1. Change Generic Raw Inference to Generic Image Inference

  1. Replace inference header

    • V1.x

      ...

"""
prepare the image
"""
import cv2
img = cv2.imread(filename=IMAGE_FILE_PATH)
img_bgr565 = cv2.cvtColor(src=img, code=cv2.COLOR_BGR2BGR565)

"""
prepare generic raw inference image descriptor
"""
generic_raw_image_header = kp.GenericRawImageHeader(
    model_id=model_nef_descriptor.models[0].id,
    resize_mode=kp.ResizeMode.KP_RESIZE_ENABLE,
    padding_mode=kp.PaddingMode.KP_PADDING_CORNER,
    normalize_mode=kp.NormalizeMode.KP_NORMALIZE_KNERON,
    inference_number=0
)

...

    • V2/V3

      ...

"""
prepare the image
"""
import cv2
img = cv2.imread(filename=IMAGE_FILE_PATH)
img_bgr565 = cv2.cvtColor(src=img, code=cv2.COLOR_BGR2BGR565)

"""
prepare generic image inference input descriptor
"""
generic_inference_input_descriptor = kp.GenericImageInferenceDescriptor(
    model_id=model_nef_descriptor.models[0].id,
    inference_number=0,
    input_node_image_list=[
        kp.GenericInputNodeImage(
            image=img_bgr565,
            image_format=kp.ImageFormat.KP_IMAGE_FORMAT_RGB565
        )
    ]
)

...

  2. Replace inference send and receive function

    • V1.x

      ...

kp.inference.generic_raw_inference_send(device_group=device_group,
                                        generic_raw_image_header=generic_raw_image_header,
                                        image=img_bgr565,
                                        image_format=kp.ImageFormat.KP_IMAGE_FORMAT_RGB565)

generic_raw_result = kp.inference.generic_raw_inference_receive(device_group=device_group,
                                                                generic_raw_image_header=generic_raw_image_header,
                                                                model_nef_descriptor=model_nef_descriptor)

...

    • V2/V3

      ...

kp.inference.generic_image_inference_send(device_group=device_group,
                                          generic_inference_input_descriptor=generic_inference_input_descriptor)

generic_raw_result = kp.inference.generic_image_inference_receive(device_group=device_group)

...

2.2. Change Generic Raw Inference Bypass Pre-Processing to Generic Data Inference

  1. Replace inference header

    • V1.x

      ...

"""
prepare the normalized RGBA8888 binary data
"""
with open(IMAGE_FILE_PATH, 'rb') as file:
    img_buffer = file.read()


"""
prepare generic raw inference bypass pre-processing image descriptor
"""
generic_raw_image_header = kp.v1.GenericRawBypassPreProcImageHeader(
    model_id=model_nef_descriptor.models[0].id,
    image_buffer_size=len(img_buffer),
    inference_number=0
)

...

    • V2/V3

      ...

"""
prepare the normalized RGBA8888 binary data
"""
with open(IMAGE_FILE_PATH, 'rb') as file:
    img_buffer = file.read()

"""
prepare generic data inference input descriptor
"""
generic_inference_input_descriptor = kp.GenericDataInferenceDescriptor(
    model_id=model_nef_descriptor.models[0].id,
    inference_number=0,
    input_node_data_list=[kp.GenericInputNodeData(buffer=img_buffer)]
)

...

  2. Replace inference send and receive function

    • V1.x

      ...

kp.inference.generic_raw_inference_bypass_pre_proc_send(
    device_group=device_group,
    generic_raw_image_header=generic_raw_image_header,
    image_buffer=img_buffer)

generic_raw_bypass_pre_proc_result = kp.inference.generic_raw_inference_bypass_pre_proc_receive(
    device_group=device_group,
    generic_raw_image_header=generic_raw_image_header,
    model_nef_descriptor=model_nef_descriptor)


...

    • V2/V3

      ...

kp.inference.generic_data_inference_send(device_group=device_group,
                                         generic_inference_input_descriptor=generic_inference_input_descriptor)

generic_raw_result = kp.inference.generic_data_inference_receive(device_group=device_group)

...

3. (Option) Replace built-in YOLO post process function

Note: For more information of YOLO post processing parameters update, please reference python/example/utils/ExamplePostProcess.py