Create KL630 Single Model 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_sin_example folder

    $ cd {PLUS_FOLDER_PATH}/examples/
$ mkdir my_kl630_sin_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_sin_example.h

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

    • Header (my_kl630_sin_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_sin_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_SIN_EXAMPLE_JOB_ID     3002
#define YOLO_BOX_MAX                    100

typedef struct {
    uint32_t class_count;
    uint32_t box_count;
    kp_bounding_box_t boxes[YOLO_BOX_MAX];
} __attribute__((aligned(4))) my_kl630_sin_example_yolo_result_t;

typedef struct {
    /* header stamp is necessary */
    kp_inference_header_stamp_t header_stamp;

    uint32_t img_width;
    uint32_t img_height;
} __attribute__((aligned(4))) my_kl630_sin_example_header_t;

typedef struct {
    /* header stamp is necessary */
    kp_inference_header_stamp_t header_stamp;

    my_kl630_sin_example_yolo_result_t yolo_result;
} __attribute__((aligned(4))) my_kl630_sin_example_result_t;

  4. Add my_kl630_sin_example.c

    • There are 6 steps for inferencing in Kneron AI device:

      1. Connect Kneron AI device.

      2. Upload the firmware to AI device.

      3. Upload the model to AI device.

      4. Prepare data for the header.

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

      6. Receive the result from 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_sin_example.h"

static char _scpu_fw_path[128] = "../../res/firmware/KL630/kp_firmware.tar";
static char _model_file_path[128] = "../../res/models/KL630/YoloV5s_640_640_3/models_630.nef";
static char _image_file_path[128] = "../../res/images/one_bike_many_cars_608x608.bmp";
static int _loop = 10;

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

    /******* connect the device *******/
    {
        int port_id = 0; // 0 for one device auto-search
        int error_code;

        // 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("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("upload firmware failed, error = %d\n", 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 (ret != KP_SUCCESS) {
            printf("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("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_sin_example_header_t input_header;
    my_kl630_sin_example_result_t output_result;

    input_header.header_stamp.job_id = MY_KL630_SIN_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_sin_example_header_t);
    int image_size = img_width * img_height * 2; // RGB565
    int result_size = sizeof(my_kl630_sin_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 (ret != KP_SUCCESS) {
            break;
        }

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

        if (ret != KP_SUCCESS) {
            break;
        }

        printf("\n[loop %d]\n", i + 1);
        helper_print_yolo_box_on_bmp((kp_yolo_result_t *)&output_result.yolo_result, _image_file_path);
    }

    printf("\n");

    if (ret != KP_SUCCESS) {
        printf("\ninference failed, error = %d\n", ret);
        return -1;
    }

    free(img_buf);
    kp_disconnect_devices(device);

    return 0;
}

3. Firmware Development

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_sin_example_inf.h into include folder

    • The content of this file should be synchronized with my_kl630_sin_example.h in PLUS.
    #include "kp_struct.h"

#define MY_KL630_SIN_EXAMPLE_JOB_ID     3002
#define YOLO_BOX_MAX                    100

typedef struct {
    uint32_t class_count;
    uint32_t box_count;
    kp_bounding_box_t boxes[YOLO_BOX_MAX];
} __attribute__((aligned(4))) my_kl630_sin_example_yolo_result_t;

typedef struct {
    /* header stamp is necessary */
    kp_inference_header_stamp_t header_stamp;

    uint32_t img_width;
    uint32_t img_height;
} __attribute__((aligned(4))) my_kl630_sin_example_header_t;

typedef struct {
    /* header stamp is necessary */
    kp_inference_header_stamp_t header_stamp;

    my_kl630_sin_example_yolo_result_t yolo_result;
} __attribute__((aligned(4))) my_kl630_sin_example_result_t;

void my_kl630_sin_example_inf(uint32_t job_id, void *inf_input_buf);

  3. Add my_kl630_sin_example_inf.c

    • There are four steps for inferencing in one model:

      1. Prepare the memory space for the result.

      2. Prepare VMF_NNM_INFERENCE_APP_CONFIG_T, which is used for configure the inference process.

      3. Activate NCPU firmware via VMF_NNM_Inference_App_Execute().

      4. 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.

    • For the customized pre-process and post-process, please provide the function pointer to pre_proc_func and post_proc_func of VMF_NNM_INFERENCE_APP_CONFIG_T. Please refer Pre/Post Process for more information.

    • The inference result 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 yolo_result in output_result.)

    #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_sin_example_inf.h"
#include "yolov5_post_process.h"

static yolo_post_proc_params_t post_proc_params_v5s = {
    .prob_thresh = 0.15,
    .nms_thresh = 0.5,
    .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,
    },
};

void my_kl630_sin_example_inf(uint32_t 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 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_sin_example_header_t *input_header = (my_kl630_sin_example_header_t *)inf_input_buf_list[0];
    my_kl630_sin_example_result_t *output_result = (my_kl630_sin_example_result_t *)inf_result_buf;

    /******* Prepare the configuration *******/

    VMF_NNM_INFERENCE_APP_CONFIG_T inf_config;

    // Set the initial value of config to 0, false and NULL
    memset(&inf_config, 0, sizeof(VMF_NNM_INFERENCE_APP_CONFIG_T));

    // 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_sin_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;               // enable resize
    inf_config.image_list[0].image_padding = KP_PADDING_CORNER;             // enable padding on corner
    inf_config.model_id = KNERON_YOLOV5S_COCO80_640_640_3;                  // this depends on model
    inf_config.ncpu_result_buf = (void *)&(output_result->yolo_result);     // give result buffer for ncpu/npu, callback will carry it
    inf_config.user_define_data = (void *)&post_proc_params_v5s;            // yolo post-process configurations for yolo v3 series
    inf_config.post_proc_func = yolov5_no_sigmoid_post_process;             // yolo post-process function

    /******* Activate inferencing in NCPU *******/

    int inf_status = VMF_NNM_Inference_App_Execute(&inf_config);

    /******* Send the result to PLUS *******/

    // header_stamp is a must to correctly transfer result data back to host SW
    output_result->header_stamp.magic_type = KDP2_MAGIC_TYPE_INFERENCE;
    output_result->header_stamp.total_size = sizeof(my_kl630_sin_example_result_t);
    output_result->header_stamp.job_id = job_id;
    output_result->header_stamp.status_code = inf_status;

    // 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);
}

  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_SIN_EXAMPLE_JOB_ID and corespond the switch case to my_kl630_sin_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_sin_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_SIN_EXAMPLE_JOB_ID:
        my_kl630_sin_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;
    }
}

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)
{
    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.