Hi Guys,
first time poster here and kinda a noob in deployment of Models, so sorry if i forget to mention something.
My Projekt:
Currently i developed a ML model in python, which is working and takes as Input float numbers. Now i wanted to deploy it on an STM32H747I-DISCO Development-Board as training with the STM32Cube-AI component. The validation per "validation application" was fine, so i wanted to write an manuel code so i could do pre- and postprocess the data. Therefor i used a dummy dataset, where i know the results of the inferenz and wanted to do an inferenz with the following code. It gives me no error, but the results of the inferenz are wrong/ not defined. The result of the inferenz should be ~1, but it gaves me the following:
Code
First i call in my main function the MX_X_CUBE_AI_Init() and then MX_X_CUBE_AI_Process(). The Code, in which this are declared:
#ifdef __cplusplus
extern "C" {
#endif
/* Includes ------------------------------------------------------------------*/
#if defined ( __ICCARM__ )
#elif defined ( __CC_ARM ) || ( __GNUC__ )
#endif
/* System headers */
#include <stdint.h>
#include <stdlib.h>
#include <stdio.h>
#include <inttypes.h>
#include <string.h>
#include "app_x-cube-ai.h"
#include "main.h"
#include "ai_datatypes_defines.h"
#include "network.h"
#include "network_data.h"
#include "val_data.h"
/* USER CODE BEGIN includes */
/* USER CODE END includes */
/* IO buffers ----------------------------------------------------------------*/
AI_ALIGNED(4) ai_i8 data_in_1[AI_NETWORK_IN_1_SIZE_BYTES] = {0,0,0,2,0,0,0,2,0,0,0,2,0,0,0,2,0,0,0,2,0,0,0,2,0,0,0,2,0,0,0,2,0,0,0,2};
//ai_float data_in_1[9];
ai_i8* data_ins[AI_NETWORK_IN_NUM] = {
data_in_1
};
AI_ALIGNED(4) ai_i8 data_out_1[AI_NETWORK_OUT_1_SIZE_BYTES];
//ai_float data_out_1;
ai_i8* data_outs[AI_NETWORK_OUT_NUM] = {
data_out_1
};
/* Activations buffers -------------------------------------------------------*/
AI_ALIGNED(32)
static uint8_t pool0[AI_NETWORK_DATA_ACTIVATION_1_SIZE];
ai_handle data_activations0[] = {pool0};
/* AI objects ----------------------------------------------------------------*/
static ai_handle network = AI_HANDLE_NULL;
static ai_buffer* ai_input;
static ai_buffer* ai_output;
static void ai_log_err(const ai_error err, const char *fct)
{
/* USER CODE BEGIN log */
if (fct)
printf("TEMPLATE - Error (%s) - type=0x%02x code=0x%02x\r\n", fct,
err.type, err.code);
else
printf("TEMPLATE - Error - type=0x%02x code=0x%02x\r\n", err.type, err.code);
do {} while (1);
/* USER CODE END log */
}
static int ai_boostrap(ai_handle *act_addr)
{
ai_error err;
/* Create and initialize an instance of the model */
err = ai_network_create_and_init(&network, act_addr, NULL);
if (err.type != AI_ERROR_NONE) {
ai_log_err(err, "ai_network_create_and_init");
return -1;
}
ai_input = ai_network_inputs_get(network, NULL);
ai_output = ai_network_outputs_get(network, NULL);
for (int idx=0; idx < AI_NETWORK_IN_NUM; idx++) {
ai_input[idx].data = data_ins[idx];
}
for (int idx=0; idx < AI_NETWORK_OUT_NUM; idx++) {
ai_output[idx].data = data_outs[idx];
}
return 0;
}
static int ai_run(void)
{
ai_i32 batch;
batch = ai_network_run(network, &ai_input[0], &ai_output[0]);
if (batch != 1) {
ai_log_err(ai_network_get_error(network),
"ai_network_run");
return -1;
}
return 0;
}
/* USER CODE BEGIN 2 */
int acquire_and_process_data(ai_i8* data[])
{
for (int idx=0; idx < AI_NETWORK_IN_1_SIZE; idx++ )
{
// ((ai_float*)data_in_1)[idx] = (ai_float)1;
}
return 0;
}
int post_process(ai_i8* data[])
{
//result = *data;
return 0;
}
/* USER CODE END 2 */
/* Entry points --------------------------------------------------------------*/
void MX_X_CUBE_AI_Init(void)
{
/* USER CODE BEGIN 5 */
printf("\r\nTEMPLATE - initialization\r\n");
ai_boostrap(data_activations0);
/* USER CODE END 5 */
}
void MX_X_CUBE_AI_Process(void)
{
/* USER CODE BEGIN 6 */
int res = -1;
float y_val = 0;
printf("TEMPLATE - run - main loop\r\n");
if (network) {
/* 1 - acquire and pre-process input data */
res = acquire_and_process_data(data_ins);
HAL_Delay(100);
/* 2 - process the data - call inference engine */
res = ai_run();
HAL_Delay(100);
/* 3- post-process the predictions */
y_val = ((float *)data_out_1)[0];
res = post_process(data_outs);
HAL_Delay(100);
}
if (res) {
ai_error err = {AI_ERROR_INVALID_STATE, AI_ERROR_CODE_NETWORK};
ai_log_err(err, "Process has FAILED");
}
/* USER CODE END 6 */
}
#ifdef __cplusplus
}
#endif
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