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Create KL630 Multiple Models Example

1. Download Source Code

  1. Download the latest kneron_plus_vXXX.zip into Windows/Ubuntu from https://www.kneron.com/tw/support/developers/. It is located at Kneron PLUS section.

  2. Unzip kneron_plus_vXXX.zip

    Note: {PLUS_FOLDER_PATH} will be used below for representing the unzipped folder path of PLUS.

  3. Please contact Kneron to obtain the latest KL630_SDK_vXXX.zip.

    Note: KL630 SDK can only be developed on Ubuntu

  4. Unzip KL630_SDK_vXXX.zip

    Note: {KL630_SDK_TOP_FOLDER_PATH} will be used below for representing the unzipped folder path of KL630 Develop Package.

  5. Unzip {KL630_SDK_TOP_FOLDER_PATH}/03_SDK/02_Software_Tool_Kit/sdk_vX.X.X.tar.gz

    Note: {KL630_SDK_FOLDER_PATH} will be used below for representing the unzipped folder path of KL630 SDK.

2. PLUS (Software) Development

  1. Create my_kl630_mul_example folder

    $ cd {PLUS_FOLDER_PATH}/examples/
$ mkdir my_kl630_mul_example

  2. Add CMakelists.txt

    # build with current *.c/*.cpp plus common source files in parent folder
# executable name is current folder name.

get_filename_component(app_name ${CMAKE_CURRENT_SOURCE_DIR} NAME)
string(REPLACE " " "_" app_name ${app_name})

file(GLOB local_src
    "*.c"
    "*.cpp"
    )

set(common_src
    ../../ex_common/helper_functions.c
    )

add_executable(${app_name}
    ${local_src}
    ${common_src})

target_link_libraries(${app_name} ${KPLUS_LIB_NAME} ${USB_LIB} ${MATH_LIB} pthread)

  3. Add my_kl630_mul_example.h

    • Please define the customized header structure and customized result structure in this file.

    • Header (my_kl630_mul_example_header_t) is used for sending data to SCPU firmware. What kind of data should be contained can be customized based on the your requirement.

    • Result (my_kl630_mul_example_result_t) is used for receiving data from SCPU firmware. What kind of data should be contained can be customized based on the output of model inference.

    • kp_inference_header_stamp_t must be contained in both header and result structures.

    • The JOB_ID describes the unique id of the task you want to execute in firmware, and it must be unique and above 1000.

    • This file should be synchronized with the .h file in SCPU firmware.

    #pragma once

#define MY_KL630_MUL_EXAMPLE_JOB_ID 3003
#define PD_BOX_MAX                  80

/**
 * @brief describe a pedestrian detect classification result of one detected person
*/
typedef struct
{
    float pd_class_score;   /**< a pedestrian classification score */
    kp_bounding_box_t pd;   /**< a pedestrian box information */
} __attribute__((aligned(4))) my_KL630_one_pd_classification_result_t;

/**
* @brief describe a pedestrian detect classification output result
*/
typedef struct
{
    uint32_t box_count;                                     /**< boxes of all classes */
    my_KL630_one_pd_classification_result_t pds[PD_BOX_MAX];         /**< pedestrian detect information */
} __attribute__((aligned(4))) my_KL630_pd_classification_result_t;

typedef struct
{
    /* header stamp is necessary for data transfer between host and device */
    kp_inference_header_stamp_t header_stamp;
    uint32_t width;
    uint32_t height;
} __attribute__((aligned(4))) my_kl630_mul_example_header_t;

// result (header + data) for 'Customize Inference Multiple Models'
typedef struct
{
    /* header stamp is necessary for data transfer between host and device */
    kp_inference_header_stamp_t header_stamp;
    my_KL630_pd_classification_result_t pd_classification_result;
} __attribute__((aligned(4))) my_kl630_mul_example_result_t;

  4. Add my_kl630_mul_example.c

    • There are 5 steps for inferencing in Kneron AI device:

      1. Connect Kneron AI device.

      2. Upload the model to AI device.

      3. Prepare data for the header.

      4. Send the header and image buffer to SCPU firmware via kp_customized_inference_send().

      5. Receive the result from SCPU firmware via kp_customized_inference_receive().

    • In this example, the image is transcoded into RGB565, and the width and height of the image is carried by the header.

    • Sending header and receiving result can be executed sequentially or on two different threads.

    #include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <unistd.h>

#include "kp_core.h"
#include "kp_inference.h"
#include "helper_functions.h"

#include "my_kl630_mul_example.h"

static char _scpu_fw_path[128] = "../../res/firmware/KL630/kp_firmware.tar";
static char _model_file_path[128] = "../../res/models/KL630/YoloV5s480_256_3_pdc/models_630.nef";
static char _image_file_path[128] = "../../res/images/travel_walk_480x256.bmp";
static int _loop = 10;

int main(int argc, char *argv[])
{
    kp_device_group_t device;
    kp_model_nef_descriptor_t model_desc;

    // each device has a unique port ID, 0 for auto-search
    int port_id = (argc > 1) ? atoi(argv[1]) : 0;
    int ret;

    /******* connect the device *******/
    {
        // internal parameter to indicate the desired port id
        if (argc > 1) {
            port_id = atoi(argv[1]);
        }

        // connect device
        device = kp_connect_devices(1, &port_id, &error_code);
        if (!device) {
            printf("error ! connect device failed, port ID = '%d', error = '%d'\n", port_id, error_code);
            exit(0);
        }

        kp_set_timeout(device, 5000);
        printf("connect device ... OK\n");
    }

    /******* upload firmware to device *******/
    {
        ret = kp_load_firmware_from_file(device, _scpu_fw_path, NULL);
        if (ret != KP_SUCCESS)
        {
            printf("error ! upload firmware failed, error = %d (%s)\n", ret, kp_error_string(ret));
            exit(0);
        }

        printf("upload firmware ... OK\n");
    }

    /******* upload model to device *******/
    {
        ret = kp_load_model_from_file(device, _model_file_path, &model_desc);
        if (KP_SUCCESS != ret) {
            printf("error ! upload model failed, error = %d\n", ret);
            exit(0);
        }

        printf("upload model ... OK\n");
    }

    /******* prepare the image buffer read from file *******/
    // here convert a bmp file to RGB565 format buffer

    int img_width, img_height;
    char *img_buf = helper_bmp_file_to_raw_buffer(_image_file_path, &img_width, &img_height, KP_IMAGE_FORMAT_RGB565);

    if (!img_buf) {
        printf("error ! read image file failed\n");
        exit(0);
    }

    printf("read image ... OK\n");
    printf("\nstarting inference loop %d times:\n", _loop);

    /******* prepare input and output header/buffers *******/
    my_kl630_mul_example_header_t input_header;
    my_kl630_mul_example_result_t output_result;
    my_KL630_pd_classification_result_t* pd_classification_result = &output_result.pd_classification_result;

    input_header.header_stamp.job_id = MY_KL630_MUL_EXAMPLE_JOB_ID;
    input_header.header_stamp.total_image = 1;
    input_header.header_stamp.image_index = 0;
    input_header.img_width = img_width;
    input_header.img_height = img_height;

    int header_size = sizeof(my_kl630_mul_example_header_t);
    int image_size = img_width * img_height * 2; // RGB565
    int result_size = sizeof(my_kl630_mul_example_result_t);
    int recv_size = 0;

    /******* starting inference work *******/

    for (int i = 0; i < _loop; i++) {
        ret = kp_customized_inference_send(device, (void *)&input_header, header_size, (uint8_t *)img_buf, image_size);

        if (KP_SUCCESS != ret) {
            printf("\ninference failed, error = %d\n", ret);
            break;
        }

        ret = kp_customized_inference_receive(device, (void *)&output_result, result_size, &recv_size);

        if (KP_SUCCESS != ret) {
            printf("\ninference failed, error = %d\n", ret);
            break;
        }

        printf("\n[loop %d]\n", i + 1);

        for (int j = 0; j < pd_classification_result->box_count; j++) {
            printf("Box %d (x1, y1, x2, y2, class, score, pb score) = (%d, %d), (%d, %d), %d, %f, %f\n",
                j + 1,
                (int)pd_classification_result->pds[j].pd.x1,
                (int)pd_classification_result->pds[j].pd.y1,
                (int)pd_classification_result->pds[j].pd.x2,
                (int)pd_classification_result->pds[j].pd.y2,
                (int)pd_classification_result->pds[j].pd.class_num,
                pd_classification_result->pds[j].pd.score,
                pd_classification_result->pds[j].pd_class_score);
        }
    }

    printf("\n");

    free(img_buf);
    kp_release_model_nef_descriptor(&model_desc);
    kp_disconnect_devices(device);

    return 0;
}

3. Firmware Development for pedestrian detection + pedestrian classification

Note: For further information of KL630 VMF_NNM, please refer Vienna_NNM_Programming_Guide.pdf in {KL630_SDK_TOP_FOLDER_PATH}/03_SDK/01_Documents/

  1. Go to App Flow Folder {KL630_SDK_FOLDER_PATH}/apps/vmf_nnm/solution/app_flow

  2. Add my_kl630_mul_example_inf.h into include folder

    • The content of this file should be synchronized with my_kl630_mul_example.h in PLUS.
    #include "kp_struct.h"
#include "model_res.h"

#define MY_KL630_MUL_EXAMPLE_JOB_ID 3004
#define PD_BOX_MAX                  80

/**
 * @brief describe a pedestrian detect classification result of one detected person
*/
typedef struct
{
    float pd_class_score;   /**< a pedestrian classification score */
    kp_bounding_box_t pd;   /**< a pedestrian box information */
} __attribute__((aligned(4))) my_KL630_one_pd_classification_result_t;

/**
* @brief describe a pedestrian detect classification output result
*/
typedef struct
{
    uint32_t box_count;                                     /**< boxes of all classes */
    my_KL630_one_pd_classification_result_t pds[DME_OBJECT_MAX];     /**< pedestrian detect information */
} __attribute__((aligned(4))) my_KL630_pd_classification_result_t;

typedef struct
{
    /* header stamp is necessary for data transfer between host and device */
    kp_inference_header_stamp_t header_stamp;
    uint32_t width;
    uint32_t height;
} __attribute__((aligned(4))) my_kl630_mul_example_header_t;

// result (header + data) for 'Customize Inference Multiple Models'
typedef struct
{
    /* header stamp is necessary for data transfer between host and device */
    kp_inference_header_stamp_t header_stamp;
    my_KL630_pd_classification_result_t pd_classification_result;
} __attribute__((aligned(4))) my_kl630_mul_example_result_t;

void my_kl630_mul_example_inf(int job_id, int num_input_buf, void **inf_input_buf_list);
void my_kl630_mul_example_inf_deinit();

  3. Add my_kl630_mul_example_inf.c

    • There are 8 steps for inferencing in pedestrian detect model and pedestrian classification model:

      1. Prepare the memory space for the result.

      2. Prepare header of output result.

      3. Prepare the temporary memory space for the result of middle model via calloc()

      4. Prepare VMF_NNM_INFERENCE_APP_CONFIG_T for pedestrian detect model, which is used for configure the inference in NCPU firmware.

      5. Activate NCPU firmware for pedestrian detect model via VMF_NNM_Inference_App_Execute().

      6. Prepare VMF_NNM_INFERENCE_APP_CONFIG_T for pedestrian classification model.

      7. Activate NCPU firmware for pedestrian classification model via VMF_NNM_Inference_App_Execute().

      8. Send the result to PLUS via VMF_NNM_Fifoq_Manager_Result_Enqueue().

    • For the customized model, model_id of VMF_NNM_INFERENCE_APP_CONFIG_T should be set to the id of the customized model.

    • The inference result of NCPU will be written to ncpu_result_buf of VMF_NNM_INFERENCE_APP_CONFIG_T. Therefore, you must provide a memory space for it (In this example, ncpu_result_buf is pointed to pd_result for pedestrian detect model, and imagenet_result for pedestrian classification model.)

    • For the detail of VMF_NNM_INFERENCE_APP_CONFIG_T, please refer to section NCPU Firmware Configuration

    #include <stdint.h>
#include <stdlib.h>
#include <string.h>
#include <stdio.h>

#include "model_type.h"
#include "vmf_nnm_inference_app.h"
#include "vmf_nnm_fifoq_manager.h"
#include "my_kl630_mul_example_inf.h"
#include "yolov5_post_process.h"
#include "classifier_post_process.h"

// for pedestrian detection result, should be in DDR
static struct yolo_result_s *_yolo_pd_result = NULL;
static struct classifier_result_s *_classifier_result = NULL;

/**
 * @brief describe class labels of pedestrian detection results.
 */
typedef enum {
    KP_APP_PD_CLASS_PERSON      = 0,
    KP_APP_PD_CLASS_BICYCLE     = 1,
    KP_APP_PD_CLASS_CAR         = 2,
    KP_APP_PD_CLASS_MOTORCYCLE  = 3,
    KP_APP_PD_CLASS_BUS         = 4,
    KP_APP_PD_CLASS_TRUCK       = 5,
    KP_APP_PD_CLASS_CAT         = 6,
    KP_APP_PD_CLASS_DOG         = 7
} kp_app_pd_class_t;

static yolo_post_proc_params_t post_proc_params_v5s = {
    .prob_thresh = 0.3,
    .nms_thresh = 0.65,
    .max_detection_per_class = 20,
    .anchor_row = 3,
    .anchor_col = 6,
    .stride_size = 3,
    .reserved_size = 0,
    .data = {
        // anchors[3][6]
        10, 13, 16, 30, 33, 23,
        30, 61, 62, 45, 59, 119,
        116, 90, 156, 198, 373, 326,
        // strides[3]
        8, 16, 32,
    },
};

// this app needs extra DDR buffers for ncpu result
static bool is_init = false;

static bool init_temp_buffer()
{
    // allocate DDR memory for ncpu/npu output restult
    _yolo_pd_result = (struct yolo_result_s *)calloc(1, sizeof(struct yolo_result_s));
    if (NULL == _yolo_pd_result)
        return false;

    _classifier_result = (struct classifier_result_s *)calloc(1, sizeof(struct classifier_result_s));
    if (NULL == _classifier_result)
        return false;

    return true;
}

static bool deinit_temp_buffer()
{
    if(is_init == true)
    {
        // free DDR memory for ncpu/npu output restult
        if(_yolo_pd_result)
            free(_yolo_pd_result);

        if(_classifier_result)
            free(_classifier_result);

        is_init = false;
    }

    return true;
}

static int inference_pedestrian_detection(my_kl630_mul_example_header_t *_input_header,
                                          struct yolo_result_s *_pd_result /* output */)
{
    // config image preprocessing and model settings
    VMF_NNM_INFERENCE_APP_CONFIG_T inf_config;
    memset(&inf_config, 0, sizeof(VMF_NNM_INFERENCE_APP_CONFIG_T)); // for safety let default 'bool' to 'false'

    // image buffer address should be just after the header
    inf_config.num_image = 1;
    inf_config.image_list[0].image_buf = (void *)((uint32_t)_input_header + sizeof(my_kl630_mul_example_header_t));
    inf_config.image_list[0].image_width = _input_header->width;
    inf_config.image_list[0].image_height = _input_header->height;
    inf_config.image_list[0].image_channel = 3;                                                 // assume RGB565
    inf_config.image_list[0].image_format = KP_IMAGE_FORMAT_RGB565;                             // default: RGB565
    inf_config.image_list[0].image_norm = KP_NORMALIZE_KNERON;                                  // this depends on model
    inf_config.image_list[0].image_resize = KP_RESIZE_ENABLE;                                   // default: enable resize
    inf_config.image_list[0].image_padding = KP_PADDING_CORNER;                                 // default: enable padding on corner
    inf_config.model_id = KNERON_YOLOV5S_PersonBicycleCarMotorcycleBusTruckCatDog8_256_480_3;   // this depends on model
    inf_config.user_define_data = (void *)&post_proc_params_v5s;                                // yolo post-process configurations for yolo v5 series
    inf_config.post_proc_func = yolov5_no_sigmoid_post_process;

    // set up fd result output buffer for ncpu/npu
    inf_config.ncpu_result_buf = (void *)_pd_result;

    return VMF_NNM_Inference_App_Execute(&inf_config);
}

static int inference_pedestrian_classification(my_kl630_mul_example_header_t *_input_header,
                                               struct bounding_box_s *_box,
                                               struct classifier_result_s * _imagenet_result/* output */)
{
    // config image preprocessing and model settings
    VMF_NNM_INFERENCE_APP_CONFIG_T inf_config;
    memset(&inf_config, 0, sizeof(VMF_NNM_INFERENCE_APP_CONFIG_T)); // for safety let default 'bool' to 'false'

    int32_t left = (int32_t)(_box->x1);
    int32_t top = (int32_t)(_box->y1);
    int32_t right = (int32_t)(_box->x2);
    int32_t bottom = (int32_t)(_box->y2);

    // image buffer address should be just after the header
    inf_config.num_image = 1;
    inf_config.image_list[0].image_buf = (void *)((uint32_t)_input_header + sizeof(my_kl630_mul_example_header_t));
    inf_config.image_list[0].image_width = _input_header->width;
    inf_config.image_list[0].image_height = _input_header->height;
    inf_config.image_list[0].image_channel = 3;                         // assume RGB565
    inf_config.image_list[0].image_format = KP_IMAGE_FORMAT_RGB565;     // assume RGB565
    inf_config.image_list[0].image_norm = KP_NORMALIZE_KNERON;          // this depends on model
    inf_config.image_list[0].image_resize = KP_RESIZE_ENABLE;           // default: enable resize
    inf_config.image_list[0].image_padding = KP_PADDING_DISABLE;        // default: disable padding
    inf_config.image_list[0].enable_crop = true;                        // enable crop image in ncpu/npu

    // set crop box
    inf_config.image_list[0].crop_area.crop_number = 0;
    inf_config.image_list[0].crop_area.x1 = left;
    inf_config.image_list[0].crop_area.y1 = top;
    inf_config.image_list[0].crop_area.width = right - left;
    inf_config.image_list[0].crop_area.height = bottom - top;

    inf_config.model_id = KNERON_PERSONCLASSIFIER_MB_56_48_3;           // this depends on model
    inf_config.post_proc_func = classifier_post_process;

    // set up fd result output buffer for ncpu/npu
    inf_config.ncpu_result_buf = (void *)_classifier_result;

    return VMF_NNM_Inference_App_Execute(&inf_config);
}

void my_kl630_mul_example_inf(int job_id, int num_input_buf, void **inf_input_buf_list)
{
    if (1 != num_input_buf) {
        VMF_NNM_Fifoq_Manager_Status_Code_Enqueue(job_id, KP_FW_WRONG_INPUT_BUFFER_COUNT_110);
        return;
    }

    int inf_status;
    int result_buf_size;
    uint32_t inf_result_buf;
    uint32_t inf_result_phy_addr;

    /******* Prepare the memory space of result *******/

    if (0 != VMF_NNM_Fifoq_Manager_Result_Get_Free_Buffer(&inf_result_buf, &inf_result_phy_addr, &result_buf_size, -1)) {
        printf("[%s] get result free buffer failed\n", __FUNCTION__);
        return;
    }

    my_kl630_mul_example_header_t *input_header = (my_kl630_mul_example_header_t *)inf_input_buf_list[0];
    my_kl630_mul_example_result_t *output_result = (my_kl630_mul_example_result_t *)inf_result_buf;


    /******* Prepare header of output result *******/

    output_result->header_stamp.magic_type = KDP2_MAGIC_TYPE_INFERENCE;
    output_result->header_stamp.total_size = sizeof(my_kl630_mul_example_result_t);
    output_result->header_stamp.job_id = job_id;

    /******* Prepare the temporary memory space for the result of middle model *******/

    if (!is_init) {
        int status = init_temp_buffer();
        if (!status) {
            // notify host error !
            output_result->header_stamp.status_code = KP_FW_DDR_MALLOC_FAILED_102;
            VMF_NNM_Fifoq_Manager_Result_Enqueue(inf_result_buf, inf_result_phy_addr, result_buf_size, -1, false);
            return;
        }

        is_init = true;
    }

    /******* Run face detect model *******/
    inf_status = inference_pedestrian_detection(input_header, _yolo_pd_result);
    if (inf_status != KP_SUCCESS) {
        // notify host error !
        output_result->header_stamp.status_code = inf_status;
        VMF_NNM_Fifoq_Manager_Result_Enqueue(inf_result_buf, inf_result_phy_addr, result_buf_size, -1, false);
        return;
    }

    int box_count = 0;
    int max_box_count = (pd_result->box_count > PD_BOX_MAX) ? PD_BOX_MAX : _yolo_pd_result->box_count;
    my_KL630_pd_classification_result_t *pd_result = &output_result->pd_classification_result;

    for (int i = 0; i < max_box_count; i++) {
        struct bounding_box_s *box = &_yolo_pd_result->boxes[i];

        if (KP_APP_PD_CLASS_PERSON == box->class_num) {
            // do face landmark for each faces
            inf_status = inference_pedestrian_classification(input_header, box, _classifier_result);

            if (KP_SUCCESS != inf_status) {
                // notify host error !
                output_result->header_stamp.status_code = inf_status;
                VMF_NNM_Fifoq_Manager_Result_Enqueue(inf_result_buf, inf_result_phy_addr, result_buf_size, -1, false);
                return;
            }

            // pedestrian_imagenet_classification result (class 0 : background, class 1: person)
            pd_result->pds[box_count].pd_class_score = _classifier_result->score[1];
        }
        else{
            pd_result->pds[box_count].pd_class_score = 0;
        }

        memcpy(&pd_result->pds[box_count].pd, box, sizeof(kp_bounding_box_t));
        box_count++;
    }

    pd_result->box_count = box_count;

    output_result->header_stamp.status_code = KP_SUCCESS;
    output_result->header_stamp.total_size = sizeof(my_kl630_mul_example_result_t) - sizeof(my_KL630_pd_classification_result_t) +
                                             sizeof(pd_result->box_count) + (box_count * sizeof(my_KL630_one_pd_classification_result_t));
    // send output result buffer back to host SW
    VMF_NNM_Fifoq_Manager_Result_Enqueue(inf_result_buf, inf_result_phy_addr, result_buf_size, -1, false);
}

void my_kl630_mul_example_inf_deinit()
{
    deinit_temp_buffer();
}

  4. Go to Companion Solution Folder {KL630_SDK_FOLDER_PATH}/apps/vmf_nnm/solution/solution_companion_user_ex

  5. Edit application_init.c

    • _app_func is the entry interface for all inference request.

    • Inference jobs will be dispatched to the coresponding function based on the job_id in kp_inference_header_stamp_t in the header.

    • You need to establish a switch case for MY_KL630_MUL_EXAMPLE_JOB_ID and corespond the switch case to my_kl630_mul_example_inf().

    #include <stdio.h>
#include <stdlib.h>
#include <signal.h>
#include <string.h>
#include <sys/stat.h>
#include <sys/time.h>
#include <getopt.h>
#include <unistd.h>

#include <vmf_nnm_inference_app.h>
#include <vmf_nnm_fifoq_manager.h>

// inference app
#include "kdp2_inf_app_yolo.h"
#include "demo_customize_inf_single_model.h"
#include "demo_customize_inf_multiple_models.h"
#include "application_init.h"
/* ======================================== */
/*              Add Line Begin              */
/* ======================================== */
#include "my_kl630_mul_example_inf.h"
/* ======================================== */
/*               Add Line End               */
/* ======================================== */

/**
* @brief To register AI applications
* @param[in] num_input_buf number of data inputs in list
* @param[in] inf_input_buf_list list of data input for inference task
* @return N/A
* @note Add a switch case item for a new inf_app application
*/
static void _app_func(int num_input_buf, void** inf_input_buf_list);

static void _app_func(int num_input_buf, void** inf_input_buf_list)
{
    // check header stamp
    if (0 >= num_input_buf) {
        kmdw_printf("No input buffer for app function\n");
        return;
    }

    kp_inference_header_stamp_t *header_stamp = (kp_inference_header_stamp_t *)inf_input_buf_list[0];
    uint32_t job_id = header_stamp->job_id;

    switch (job_id)
    {
    case KDP2_INF_ID_APP_YOLO:
        kdp2_app_yolo_inference(job_id, num_input_buf, inf_input_buf_list);
        break;
    case KDP2_JOB_ID_APP_YOLO_CONFIG_POST_PROC:
        kdp2_app_yolo_config_post_process_parameters(job_id, num_input_buf, inf_input_buf_list);
        break;
    case DEMO_KL630_CUSTOMIZE_INF_SINGLE_MODEL_JOB_ID: // a demo code implementation in SCPU for user-defined/customized infernece from one model
        demo_customize_inf_single_model(job_id, num_input_buf, inf_input_buf_list);
        break;
    case DEMO_KL630_CUSTOMIZE_INF_MULTIPLE_MODEL_JOB_ID: // a demo code implementation in SCPU for user-defined/customized infernece from two models
        demo_customize_inf_multiple_models(job_id, num_input_buf, inf_input_buf_list);
        break;
    /* ======================================== */
    /*              Add Line Begin              */
    /* ======================================== */
    case MY_KL630_MUL_EXAMPLE_JOB_ID:
        my_kl630_mul_example_inf(job_id, num_input_buf, inf_input_buf_list);
        break;
    /* ======================================== */
    /*               Add Line End               */
    /* ======================================== */
    default:
        VMF_NNM_Fifoq_Manager_Status_Code_Enqueue(job_id, KP_FW_ERROR_UNKNOWN_APP);
        printf("unsupported job_id %d \n",job_id);
        break;
    }
}

static void _app_func_deinit(unsigned int job_id);

void _app_func_deinit(unsigned int job_id)
{
    switch (job_id)
    {
    case KDP2_INF_ID_APP_YOLO:
        kdp2_app_yolo_inference_deinit();
        break;
    case DEMO_KL630_CUSTOMIZE_INF_SINGLE_MODEL_JOB_ID:
        demo_customize_inf_single_model_deinit();
        break;
    case DEMO_KL630_CUSTOMIZE_INF_MULTIPLE_MODEL_JOB_ID:
        demo_customize_inf_multiple_model_deinit();
        break;
    /* ======================================== */
    /*              Add Line Begin              */
    /* ======================================== */
    case MY_KL630_MUL_EXAMPLE_JOB_ID:
        my_kl630_mul_example_inf_deinit();
        break;
    /* ======================================== */
    /*               Add Line End               */
    /* ======================================== */
    default:
        printf("%s, unsupported job_id %d \n", __func__, job_id);
        break;
    }
}

void app_initialize(void)
{
    printf(">> Start running KL630 KDP2 companion mode ...\n");

    /* initialize inference app */
    /* register APP functions */
    /* specify depth of inference queues */
    VMF_NNM_Inference_App_Init(_app_func);
    VMF_NNM_Fifoq_Manager_Init();

    return;
}

void app_destroy(void)
{
    _app_func_deinit(KDP2_INF_ID_APP_YOLO);
    _app_func_deinit(DEMO_KL630_CUSTOMIZE_INF_SINGLE_MODEL_JOB_ID);
    _app_func_deinit(DEMO_KL630_CUSTOMIZE_INF_MULTIPLE_MODEL_JOB_ID);
    /* ======================================== */
    /*              Add Line Begin              */
    /* ======================================== */
    _app_func_deinit(MY_KL630_MUL_EXAMPLE_JOB_ID);
    /* ======================================== */
    /*               Add Line End               */
    /* ======================================== */

    VMF_NNM_Inference_App_Destroy();
    VMF_NNM_Fifoq_Manager_Destroy();
}

4. Pre-process and Post-process Development

If the customized models need a customized pre-process or post-process on Kneron AI device, you can add the pre-process and post-process in the following files.

  1. Go to NCPU Project Main Folder {KL630_SDK_FOLDER_PATH}/apps/vmf_nnm/solution/app_flow/pre_post_proc

  2. Add your customized pre-process/post-process header file into include folder.

  3. Add your customized post-process/post-process implementation c file into current folder.

  4. Register the pre/post process into pre_proc_func and post_proc_func of VMF_NNM_INFERENCE_APP_CONFIG_T during firmware development.

    • If pre_proc_func of VMF_NNM_INFERENCE_APP_CONFIG_T is not set, hardware auto pre-process will be adapted during inference flow.

    • If post_proc_func of VMF_NNM_INFERENCE_APP_CONFIG_T is not set, inference raw output data will be put into result buffer without post-process.

Note: During developing the post-processing, you must be aware of what pre-process has done, including image resize, image padding, and image cropping.

Note: In post-processing, the memory layout of data in raw_cnn_res_t for KL520, KL630 and KL720 are different. Please reference Kneron NPU Raw Output Channel Order.