Solved
Error with deploy a model on the stm32n6d570-dk
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 compilationI’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!