Undefined reference from AI model generation with X-CUBE-AI

[dogg]

Hi,

I am following this tutorial to use my own image recognition model with the STM32H747I-DISCO board. I have already ran the demos and they seem to work fine.

I first configure the model in CUBE-MX successfully and then copy the required files to the demo project.

When I compile the code in the STM-IDE I get the following error a bunch of times:

undefined reference to `forward_conv2d_if32of32wf32'

I also get this: 

STM32H747I_DISCO_PersonDetect_Google_CM7.elf section `.bss' will not fit in region `DTCMRAM'
STM32H747I_DISCO_PersonDetect_Google_CM7.elf section `.axiram_section' will not fit in region `AXIRAM'
STM32H747I_DISCO_PersonDetect_Google_CM7.elf section `.sram_section' will not fit in region `SRAM123'
section .axiram_section VMA [24000000,241ad7ff] overlaps section .bss VMA [20017a40,2c6301a7]

even though CUBE-MX said that the used memory is within the flash and ram available (as shown in the image included).

The declaration is in layers_conv2d.h but I can't find the definition anywhere.

I have done the Updating to a newer version of X-CUBE-AI part in the tutorial successfully.

Any ideas?

thanks

[Julian E.]

Hello @dogg ,

We are working on this tutorial to replace it with model zoo.

I don't know if you are familiar with model zoo but to replicate the tutorial you are following, in your place I would:

• Follow the train readme to retrain a model with your data: stm32ai-modelzoo/object_detection/src/training/README.md at main · STMicroelectronics/stm32ai-modelzoo · GitHub
• Follow the deploy readme to deploy it on your board:stm32ai-modelzoo/object_detection/deployment at main · STMicroelectronics/stm32ai-modelzoo · GitHub

You can have a look on a similar thread where I describe how to replicate the object detection function pack:

Solved: stm32ai-modelzoo flash pre-trained model example. - STMicroelectronics Community

• Here I explain how to install "st edge ai" for local quantization
• Explain how to get a pretrained model from model zoo
• deploy it 

It is quite similar to your issue I believe.

Have a good day, 

Julian

[Nicolas_V]

Hello dogg,

The situation triggers me an idea.

Between the versions of X-CUBE-AI, the implementation of the AI kernels is continuously improving.

Your problem sounds like a misalignment between C, H, and lib files of X-CUBE-AI versions that you have inside your project. May be you have, during your exploration, ended to a situation where one file is from a different version.

I would erase all the X-CUBE-AI files from your project and redo the integration.

I hope it will help

With Kind Regards,

Nicolas_V

[dogg]

Hi, 

Thanks for the support, I have already deleted and recreated it a few times but will try a fresh try once more.

Are you saying that the same can be done with the model zoo? Transferring my own model to the development board?

I will investigate on that too and get back to you here.

thanks again

[dogg]

Hi again,

I have managed to make the deployment example work on my disco board and have also managed to train sd_ssd_mobilenet_v1 on the pascal dataset. The output is a .h5 file however and I haven't managed to get a .tflite version. Can that be done automatically? I am having trouble with a generic converter python script getting this error:

Exception encountered: Unrecognized keyword arguments passed to DepthwiseConv2D: {'groups': 1}

I also tried training that model on my own dataset with yolo annotations but I get this error:

FileNotFoundError: [Errno 2] Unable to synchronously open file (unable to open file: name = 'C:\Users\Haris\Desktop\stm32ai-modelzoo\object_detection\src\experiments_outputs\2024_11_06_16_14_16\saved_models\best_weights.h5', errno = 2, error message = 'No such file or directory', flags = 0, o_flags = 0)

this is my yaml file for training from scratch:

general:
  project_name: COCO_2017_person_Demo
  model_type: st_ssd_mobilenet_v1
#   model_path: C:/Users/Haris/Desktop/stm32ai-modelzoo/object_detection/pretrained_models/st_ssd_mobilenet_v1/ST_pretrainedmodel_public_dataset/coco_2017_person/st_ssd_mobilenet_v1_025_256/st_ssd_mobilenet_v1_025_256.h5
  logs_dir: logs
  saved_models_dir: saved_models
  gpu_memory_limit: 12
  global_seed: 127

operation_mode: training
#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:
#   validation_path:
#   test_path:
#   quantization_path:
#   quantization_split: 0.3

dataset:
  name: bugs                                    # Dataset name. Optional, defaults to "<unnamed>".
  class_names: [nc, mr, wf] #[ aeroplane,bicycle,bird,boat,bottle,bus,car,cat,chair,cow,diningtable,dog,horse,motorbike,person,pottedplant,sheep,sofa,train,tvmonitor ] # Names of the classes in the dataset.
  training_path: C:/Users/Haris/Desktop/stm32ai-modelzoo/object_detection/src/bugs/train
  validation_path: C:/Users/Haris/Desktop/stm32ai-modelzoo/object_detection/src/bugs/valid 
  validation_split: 0.2                                      # Training/validation sets split ratio.
  test_path:
  quantization_path:
  quantization_split:                                        # Quantization split ratio.
  seed: 123                                                  # Random generator seed used when splitting a dataset.

preprocessing:
  rescaling: { scale: 1/127.5, offset: -1 }
  resizing:
    aspect_ratio: fit
    interpolation: nearest
  color_mode: rgb

data_augmentation:
  rotation: 30
  shearing: 15
  translation: 0.1
  vertical_flip: 0.5
  horizontal_flip: 0.2
  gaussian_blur: 3.0
  linear_contrast: [ 0.75, 1.5 ]

training:
  model:
    type: st_ssd_mobilenet_v1
    alpha: 0.25
    input_shape: (256, 256, 3)
    weights: None
    # pretrained_weights: imagenet
  dropout:
  batch_size: 12
  epochs: 1
  optimizer:
    Adam:
      learning_rate: 0.001
  callbacks:
    ReduceLROnPlateau:
      monitor: val_loss
      patience: 20
    EarlyStopping:
      monitor: val_loss
      patience: 40

postprocessing:
  confidence_thresh: 0.6
  NMS_thresh: 0.5
  IoU_eval_thresh: 0.3
  plot_metrics: True   # Plot precision versus recall curves. Default is False.
  max_detection_boxes: 10

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: 9.1.0
    optimization: balanced
    on_cloud: False
    path_to_stedgeai: C:/Users/haris/STM32Cube/Repository/Packs/STMicroelectronics/X-CUBE-AI/9.1.0/Utilities/windows/stedgeai.exe
  path_to_cubeIDE: C:/ST/STM32CubeIDE_1.16.1/STM32CubeIDE/stm32cubeide.exe

deployment:
  c_project_path: ../../stm32ai_application_code/object_detection/
  IDE: GCC
  verbosity: 1
  hardware_setup:
    serie: STM32H7
    board: STM32H747I-DISCO

mlflow:
  uri: ./experiments_outputs/mlruns

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

thanks 

[Julian E.]

Hello @dogg ,

There are different operation mode, you used training that just retrain a model, here are all the operation modes:

https://github.com/STMicroelectronics/stm32ai-modelzoo/tree/main/image_classification 

What I generally do is chain_tbqeb  to do everything in one go: to train, benchmark the memory, inference time etc, quantize, evaluate the performance and do a new benchmark to see the difference between the .h5 and the .tfile.

You can use the quantize in your case.

For your first error, I am not sure what caused this, I never used the scripts other than the stm32ai_main.py

For the second error, it seems that a path is wrong and the file cannot be found.

The deployment operation mode generate a binary file flashed directly. If you want to generate a cubeIDE project or cubeMX project, you can take a look at the ST Edge AI Dev Cloud

https://wiki.st.com/stm32mcu/wiki/AI:Getting_started_with_STM32Cube.AI_Developer_Cloud 

Have a good day,

Julian

[dogg]

Hello,

I've managed to train the sd_ssd_mobilenet_v1 model on my own dataset but only after adding more data to my original dataset. Is there a lower limit to that? When I don't add more photos and annotations to my original dataset I get this:

FileNotFoundError: [Errno 2] Unable to synchronously open file (unable to open file: name = 'C:\Users\Haris\Desktop\stm32ai-modelzoo\object_detection\src\experiments_outputs\2024_11_06_16_14_16\saved_models\best_weights.h5', errno = 2, error message = 'No such file or directory', flags = 0, o_flags = 0)

Using chain_tqeb doesn't create a .tflite file which I believe is what is needed to download to the board, correct?

Don't we need to convert the .h5 file to .tflite?

I would then add my newly trained model to general.model_path in the .yaml file.

Please let me know. Really appreciate the much needed help so we can quickly go to developing a product with this. 

thanks

[Julian E. · Accepted solution]

Hello @dogg ,

I don't know about a lower limit concerning data, I will ask the dev team.

Concerning the quantization, if everything went correctly, you should have a .tflite model in /experiment_output/<date of experiment>/quantized_model.

You can try to use only the quantization operation mode: 

•  Add a quantization path in the dataset part of the yaml, it is advised to put the training data.
• You may need to use the model_path in the general part and comment everything from training/model:

general:
  project_name: COCO_2017_person_Demo
  model_type: st_ssd_mobilenet_v1
  model_path: <PATH TO YOUR TRAINED MODEL>
  logs_dir: logs
  saved_models_dir: saved_models
  gpu_memory_limit: 12
  global_seed: 127

operation_mode: quantization

training:
 # model:
    # type: st_ssd_mobilenet_v1
    # alpha: 0.25
    # input_shape: (256, 256, 3)
    # weights: None
    # pretrained_weights: imagenet
...

 Leave everything else the same.

If it doesn't work, you can also try to use the ST Edge AI Dev cloud to do the quantization instead of the local installation of ST Edge AI. Just change on_cloud to True. (you need a st accound and you will be ask to log after running the python script)

tools:
  stedgeai:
    version: 9.1.0
    optimization: balanced
    on_cloud: True
    path_to_stedgeai: C:/Users/haris/STM32Cube/Repository/Packs/STMicroelectronics/X-CUBE-AI/9.1.0/Utilities/windows/stedgeai.exe
  path_to_cubeIDE: C:/ST/STM32CubeIDE_1.16.1/STM32CubeIDE/stm32cubeide.exe

Finally, if it does not work, you can also quantize it manually on the ST Edge AI Dev Cloud

Documentation: https://wiki.st.com/stm32mcu/wiki/AI:Getting_started_with_STM32Cube.AI_Developer_Cloud 

Let me know if it helps. 

Julian

[Nicolas_V]

Hello dogg,

There are many aspects in your question.

There is a simple service to convert a h5 model to TFLite

operation_mode: quantization

You need to provide an h5 model, in the model_path.

A quantization dataset in quantization_path.

to be quick, the section

quantization:

  quantizer: TFlite_converter

  quantization_type: PTQ

  quantization_input_type: uint8

  quantization_output_type: float

  export_dir: quantized_models

With Kind Regards,

Nicolas_V

[dogg]

Hi,

Thanks for the info.

I did find the .tflite file inside quantized_models...

I have one more question for now and I will open a different thread if I stumble on something else in the process.

How can I use my nvidia gpu for the training with this script?

This is a bit confusing to me:

thanks