Error with deploy a model on the stm32n6d570-dk

wyb217 · ‎2025-05-17

Hello,I met these logs when I try to deploy a model on stm32n6d570-dk:

1 physical GPUs, 1 logical GPUs
[INFO] : Setting upper memory limit to 4GBytes on gpu[0]
[INFO] : Running `deployment` operation mode
[INFO] : ClearML config check
[INFO] : The random seed for this simulation is 127
INFO: Created TensorFlow Lite XNNPACK delegate for CPU.
[INFO] : Generating C header file for Getting Started...
loading model.. model_path="yolov8n_256_quant_pc_uf_od_coco-person-st.tflite"
loading conf file.. "../../application_code/object_detection/STM32N6/stmaic_STM32N6570-DK.conf" config="None"
"n6 release" configuration is used
[INFO] : Selected board :  "STM32N6570-DK Getting Started Object Detection (STM32CubeIDE)" (stm32_cube_ide/n6 release/stm32n6)
[INFO] : Compiling the model and generating optimized C code + Lib/Inc files:  yolov8n_256_quant_pc_uf_od_coco-person-st.tflite
setting STM.AI tools.. root_dir="", req_version=""
 Cube AI Path: "E:\ST\STEdgeAI\2.0\Utilities\windows\stedgeai.exe".
[INFO] : Offline CubeAI used; Selected tools:  10.0.0 (x-cube-ai pack)
loading conf file.. "../../application_code/object_detection/STM32N6/stmaic_STM32N6570-DK.conf" config="None"
"n6 release" configuration is used
compiling... "yolov8n_256_quant_pc_uf_od_coco-person-st_tflite" session
 model_path  : ['yolov8n_256_quant_pc_uf_od_coco-person-st.tflite']
 tools       : 10.0.0 (x-cube-ai pack)
 target      : "STM32N6570-DK Getting Started Object Detection (STM32CubeIDE)" (stm32_cube_ide/n6 release/stm32n6)
 options     : --st-neural-art default@../../application_code/object_detection/STM32N6/Model/user_neuralart.json --input-data-type uint8 --inputs-ch-position chlast
D:\PyCharm 2024.3.5\plugins\python-ce\helpers\pycharm_display\datalore\display\supported_data_type.py:6: UserWarning: The NumPy module was reloaded (imported a second time). This can in some cases result in small but subtle issues and is discouraged.
  import numpy
ST Edge AI Core v2.0.0-20049

Error executing job with overrides: []
Traceback (most recent call last):
  File "D:\code\stm32ai-modelzoo-services\object_detection\src\stm32ai_main.py", line 242, in <module>
    main()
  File "D:\anaconda\envs\st_zoo\lib\site-packages\hydra\main.py", line 94, in decorated_main
    _run_hydra(
  File "D:\anaconda\envs\st_zoo\lib\site-packages\hydra\_internal\utils.py", line 394, in _run_hydra
    _run_app(
  File "D:\anaconda\envs\st_zoo\lib\site-packages\hydra\_internal\utils.py", line 457, in _run_app
    run_and_report(
  File "D:\anaconda\envs\st_zoo\lib\site-packages\hydra\_internal\utils.py", line 223, in run_and_report
    raise ex
  File "D:\anaconda\envs\st_zoo\lib\site-packages\hydra\_internal\utils.py", line 220, in run_and_report
    return func()
  File "D:\anaconda\envs\st_zoo\lib\site-packages\hydra\_internal\utils.py", line 458, in <lambda>
    lambda: hydra.run(
  File "D:\anaconda\envs\st_zoo\lib\site-packages\clearml\binding\hydra_bind.py", line 91, in _patched_hydra_run
    return PatchHydra._original_hydra_run(self, config_name, task_function, overrides, *args, **kwargs)
  File "D:\anaconda\envs\st_zoo\lib\site-packages\hydra\_internal\hydra.py", line 132, in run
    _ = ret.return_value
  File "D:\anaconda\envs\st_zoo\lib\site-packages\hydra\core\utils.py", line 260, in return_value
    raise self._return_value
  File "D:\anaconda\envs\st_zoo\lib\site-packages\hydra\core\utils.py", line 186, in run_job
    ret.return_value = task_function(task_cfg)
  File "D:\anaconda\envs\st_zoo\lib\site-packages\clearml\binding\hydra_bind.py", line 230, in _patched_task_function
    return task_function(a_config, *a_args, **a_kwargs)
  File "D:\code\stm32ai-modelzoo-services\object_detection\src\stm32ai_main.py", line 228, in main
    process_mode(cfg)
  File "D:\code\stm32ai-modelzoo-services\object_detection\src\stm32ai_main.py", line 102, in process_mode
    deploy(cfg)
  File "D:\code\stm32ai-modelzoo-services\object_detection\src\../deployment\deploy.py", line 111, in deploy
    stm32ai_deploy_stm32n6(target=board, stlink_serial_number=stlink_serial_number, stm32ai_version=stm32ai_version, c_project_path=c_project_path,
  File "D:\code\stm32ai-modelzoo-services\object_detection\src\../../common/deployment\common_deploy.py", line 469, in stm32ai_deploy_stm32n6
    stmaic_local_call(session)
  File "D:\code\stm32ai-modelzoo-services\object_detection\src\../../common/deployment\common_deploy.py", line 443, in stmaic_local_call
    stmaic.compile(session=session, options=opt, target=session._board_config)
  File "D:\code\stm32ai-modelzoo-services\object_detection\src\../../common\stm32ai_local\compile.py", line 208, in cmd_compile
    raise Exception('Error during compilation')
Exception: Error during compilation

I’m actually not sure exactly what’s going wrong—the build always fails with
Exception: Error during compilation.
I’ve pasted the full error output above.

My goal is simply to deploy a pretrained model onto the STM32 N6570-DK board using the automated .yaml–based workflow.
I use this model:yolov8n_256_quant_pc_uf_od_coco-person-st.tflite and the content of my .yaml file as follow:

general:
 project_name: COCO_2017_person_Demo
 model_type: yolo_v8
#choices=[st_ssd_mobilenet_v1, ssd_mobilenet_v2_fpnlite, tiny_yolo_v2, st_yolo_lc_v1, 
#         st_yolo_x, yolo_v8, yolo_v5u]
 model_path: yolov8n_256_quant_pc_uf_od_coco-person-st.tflite
 logs_dir: logs
 saved_models_dir: saved_models
 gpu_memory_limit: 6
 num_threads_tflite: 4
 global_seed: 127

operation_mode: deployment
#choices=['training' , 'evaluation', 'deployment', 'quantization', 'benchmarking',
#        'chain_tqeb','chain_tqe','chain_eqe','chain_qb','chain_eqeb','chain_qd ']

dataset:
 name: COCO_2017_person
 class_names: [ person ]
 training_path: /dataset/coco_person_2017_tfs/train
 validation_path:
 validation_split: 0.1
 test_path: /dataset/coco_person_2017_tfs/val
 quantization_path: /dataset/coco_person_2017_tfs/val
 quantization_split: 0.01

preprocessing:
 rescaling: { scale: 1/127.5, offset: -1 }
 resizing:
   aspect_ratio: fit
   interpolation: nearest
 color_mode: rgb
                      
data_augmentation:
 ########## For tiny_yolo_v2 and st_yolo_lc_v1 only ###########
 random_periodic_resizing:
   period: 10
   image_sizes: [(192, 192), (224, 224), (256, 256), (288, 288), (320, 320), (352, 352),
                 (384, 384), (416, 416), (448, 448), (480, 480), (512, 512),
                 (544, 544), (576, 576), (608, 608)]
 random_flip:
   mode: horizontal
 random_crop:
   crop_center_x: (0.25, 0.75)
   crop_center_y: (0.25, 0.75)
   crop_width: (0.5, 0.9)
   crop_height: (0.5, 0.9)
   change_rate: 0.9
 random_contrast:
   factor: 0.4
 random_brightness:
   factor: 0.3 

training:
 model:
   # alpha: 0.35
   input_shape: (192, 192, 3)
   # pretrained_weights: imagenet
 dropout:
 batch_size: 64
 epochs: 4
 optimizer:
   Adam:
     learning_rate: 0.005
 callbacks:
   ReduceLROnPlateau:
     monitor: val_map
     patience: 10
     factor: 0.25
   ModelCheckpoint:
     monitor: val_map
   EarlyStopping:
     monitor: val_map
     patience: 20

postprocessing:
 confidence_thresh: 0.1
 NMS_thresh: 0.5
 IoU_eval_thresh: 0.4
 plot_metrics: False   # Plot precision versus recall curves. Default is False.
 max_detection_boxes: 100

quantization:
 quantizer: TFlite_converter
 quantization_type: PTQ
 quantization_input_type: uint8
 quantization_output_type: float
 granularity: per_channel   #per_tensor
 optimize: False   #can be True if per_tensor
 export_dir: quantized_models

benchmarking:
 board: STM32H747I-DISCO

tools:
 stedgeai:
   version: 10.0.0
   optimization: balanced
   on_cloud: False
   path_to_stedgeai: E:/ST/STEdgeAI/2.0/Utilities/windows/stedgeai.exe
 path_to_cubeIDE: E:/ST/STM32CubeIDE_1.17.0/STM32CubeIDE/stm32cubeide.exe

deployment:
 c_project_path: D:\code\stm32ai-modelzoo-services\en.n6-ai-getstarted-v1.0.0\application_code\object_detection\STM32N6
 IDE: GCC
 verbosity: 1
 hardware_setup:
   serie: STM32N6
   board: STM32N6570-DK

mlflow:
 uri: ./experiments_outputs/mlruns

hydra:
 run:
   dir: ./experiments_outputs/${now:%Y_%m_%d_%H_%M_%S}

Do you have some ideas about my trouble?Thanks very much!

wyb217 · ‎2025-05-19

It has been solved. Solution is here :https://community.st.com/t5/edge-ai/deploying-the-image-classification-model-on-the-stm32n6570-dk/m-p/792217

View solution in original post

wyb217 · ‎2025-05-19

It has been solved. Solution is here :https://community.st.com/t5/edge-ai/deploying-the-image-classification-model-on-the-stm32n6570-dk/m-p/792217