A python wrapper for the stedgeai command-line tool to analyse deep learning models for STM32 microcontrollers.

Disclaimer: This project is not related to STMicroelecronics, it is as a wrapper around a component of X-CUBE-AI property of STMicroelecronics and licensed under SLA (see more below).

Limitations: Currently this project works only on linux.

The API provide two functions : analyse and generate, the first one gives information about the size of a model and the second one can be used to generate C files to link with X-CUBE-AI runtime library (Not provided in this package).

The package doesn't include stm32ai executable so it will download and install it automatically on first call.

A command line utility is also provided (pystm32ai), however it currently doesn't match the full functionality provided by the original stm32ai executable.

With pip:

pip install pystm32ai

The goal of this tool is to use stm32ai in a deep learning training pipeline, where it can give information about the final model running on STM32

from pystm32ai import stm32ai

report = stm32ai.analyse('path/to/model.tflite')
print(report)

stm32ai.generate('path/to/model.tflite') # C files are generated in the current directory

The project can also be used as a command line tool:

pystm32ai analyse path/to/model.tflite
pystm32ai generate path/to/model.tflite

Here is the full usage :

usage: pystm32ai [-h] [--allocate-inputs] [--allocate-outputs] [--full-report] [--output_dir OUTPUT_DIR] [--name NAME] {analyse,generate} model_path

Python wrapper around stm32ai command line tool

positional arguments:
  {analyse,generate}    Action to run on stm32ai
  model_path            Path to model

optional arguments:
  -h, --help            show this help message and exit
  --allocate-inputs
  --allocate-outputs
  --full-report
  --output_dir OUTPUT_DIR
  --name NAME           Name of the model

def analyse(
    model_path, allocate_inputs=True, allocate_outputs=False, full_report=False
):
    """
    Analyse a model with CubeAI to get info about RAM, ROM and MACC
    Params:
        model_path: path to a model (ONNX, h5 or TFLITE)
        allocate_inputs: whether to allocate input tensor with activations
        allocate_outputs: whether to allocate output tensor with activations
        full_report: Get a full report with per-layers information
    Returns:
        A report as a dictionary
    """

def generate(
    model_path, allocate_inputs=True, allocate_outputs=False, name=None, output_dir="."
):
    """
    Generate a model C files to use in a STM32 application
    Params:
        model_path: path to a model (ONNX, h5 or TFLITE)
        allocate_inputs: whether to allocate input tensor with activations
        allocate_outputs: whether to allocate output tensor with activations
        output_dir: Path to output directory (default current working directory)
    """

The pystm32ai project is under the MIT License

It's using part of the X-CUBE-AI software which is under SLA0104 license, hence, by using this software you agree to the terms and condition of this license.

X-CUBE-AI in turn is using many licensed software, here's a summarized list of licenses used by X-CUBE-AI software components:

• SLA (STMicroelectronics License Agreement)
  • STM32Cube.AI tools and libraries
  • STM32N6xx_HAL_Driver
  • BSP components
• Apache-2.0
  • CMSIS (Arm)
  • TensorFlow related components
  • Google components (flatbuffers, auth, pasta)
  • Various ML tools (absl, gemmlowp, ruy)
• BSD-3-Clause
  • AI runner and examples
  • numpy
  • scipy
  • scikit-learn
  • h5py
  • protobuf
• MIT
  • Many Python utilities (attrs, charset_normalizer, urllib3, wrapt)
  • Various parsers and tools (jsonschema, mako, onnx)
  • UI/formatting tools (termcolor, terminaltables, tqdm)
• Python-2.0
  • Python core
  • astunparse
  • typing_extensions
• LGPL-2.1
  • astroid
  • chardet
• MPL-2.0 (certifi)
• GPLv2 (xdis)
• GPLv3 (uncompyle6)
• BSD-2-Clause (pycryptodome, pygments)
• The Unlicense (pyelftools)