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Error with deploy a model on the stm32n6d570-dk

wyb217
Associate II

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

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