A python wrapper for the stm32ai
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 SLA0048 (see more below).
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:
pip3 install pystm32ai
From wheel:
Download wheel from Releases and install with pip:
pip3 install pystm32ai-*.whl
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 SLA0048 license, hence, by using this software you agree to the terms and condition of the SLA048 license. A copy of the license for X-CUBE-AI is provided below:
SLA0048 Rev4/March 2018
BY INSTALLING COPYING, DOWNLOADING, ACCESSING OR OTHERWISE USING THIS SOFTWARE PACKAGE OR ANY PART THEREOF (AND THE RELATED DOCUMENTATION) FROM STMICROELECTRONICS INTERNATIONAL N.V, SWISS BRANCH AND/OR ITS AFFILIATED COMPANIES (STMICROELECTRONICS), THE RECIPIENT, ON BEHALF OF HIMSELF OR HERSELF, OR ON BEHALF OF ANY ENTITY BY WHICH SUCH RECIPIENT IS EMPLOYED AND/OR ENGAGED AGREES TO BE BOUND BY THIS SOFTWARE PACKAGE LICENSE AGREEMENT.
Under STMicroelectronics’ intellectual property rights and subject to applicable licensing terms for any third-party software incorporated in this software package and applicable Open Source Terms (as defined here below), the redistribution, reproduction and use in source and binary forms of the software package or any part thereof, with or without modification, are permitted provided that the following conditions are met:
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The X-CUBE-AI is in turn using licensed components, for more info about these check out this pdf