Questions on VWW Inference Tutorial in STM Cube IDE Project

[gunjupark]

Hi, I have a few questions about performing inference on mcu in real-world.
(Tinyengine's inference tutorial using STM CUBE IDE)

First, I found input data processing code in STM CUBE project's main.cpp.
From what I understand, the code directly passes a signed char 'input' buffer to the model.

I have a question regarding the models provided by the model_zoo.
It seems that Model Zoo's Torch models and TFLite models are trained with Torch's toTensor() and normalize() preprocessing.
However, I couldn't find such preprocessing steps in the tutorial code(STM Cube project codes), so I'm curious if the models used in the tutorial (codegen) were trained using a different approach.

Secondly, I fount the torch2tflite converting method in this repository's issues (#6).
So, I attempted to convert the torch model to a tflite model using Alibaba's tinyNN Tool.

However, I noticed some differences in tflite operators between the tflite model I converted and the one in your model_zoo. Do you use any other approaches for converting Torch2TFLite?

Thank you.

[gunjupark]

My first question has been resolved.
I checked that normalization has been fused in quantization step.
https://github.com/alibaba/TinyNeuralNetwork/blob/main/docs/FAQ.md

but I couldn't know how to cast uint8 to int8 in torch's data loader transform step.
How could I train with signed RGB value with torch's normalization?
Thank you