edge-mdt-nightly 1.1.0
Edge AI Model Development Toolkit
License: Apache-2.0
Requires Python: >=3.9

## 
<div align="center">
<figure>
<img src="https://raw.githubusercontent.com/SonySemiconductorSolutions/aitrios-edge-mdt/main/docs/images/EdgeMDT-header-image-small.png" width="1000"
alt="Edge-MDT header" />
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# Edge AI Model Development Toolkit User Manual

## Introduction

The Edge-MDT (Model Development Toolkit) package installs all of the
packages that are necessary to quantise, compress, and convert a model
so that it can run on your IMX500 device.

<figure id="model-dev-flow">
<img src="https://raw.githubusercontent.com/SonySemiconductorSolutions/aitrios-edge-mdt/main/docs/images/EdgeMDT-blocks.png"
alt="Flowchart showing the model development, conversion, and deployment flow from the framework environment to the AI Camera" />
<figcaption>Figure 1: Model development flow</figcaption>
</figure>
<br><br>

Edge-MDT contains the following packages:

- [MCT](https://github.com/sony/model_optimization) (Model Compression
  Toolkit) – An open-source python package for quantizing and
  compressing a neural network model, so that the model can be converted
  to run efficiently on the hardware device, while maintaining the
  accuracy of the model as closely as possible to the original
  floating-point model.

- [IMX500
  Converter](https://developer.aitrios.sony-semicon.com/en/raspberrypi-ai-camera/documentation/imx500-converter)
  – A CLI application that converts (compiles) the neural network model
  that is the output of MCT (.onnx or .keras formats) into a binary file
  that can be loaded onto the IMX500 device, and executed in real time.

- [TPC](https://github.com/SonySemiconductorSolutions/aitrios-edge-mdt-tpc)
  (Target Platform Capabilities) – An open-source package that contains
  descriptions for the various attributes (capabilities) of the target
  device’s hardware and software. MCT uses the relevant device
  description during the optimization process, so that the output model
  will be the best fit for the specific target device.

Edge-MDT offers a streamlined installation process through its single
installer package. With just one command, you’ll get all necessary
components installed, and Edge-MDT automatically manages version
compatibility between packages. While the packages work together
seamlessly, you still operate each component independently.

We strongly recommend using Edge-MDT for standard installations. The
alternative approach of installing components separately puts the burden
of version management on you and often requires more complex manual
setup. This individual installation method can lead to compatibility
issues and doesn’t guarantee a smooth working environment.

## How to use Edge-MDT

Typical use of Edge-MDT:

- Take an off-the-shelf or custom, pre-trained **floating point** model,
  in the **framework environment** (TensorFlow or PyTorch)

- Use **MCT** to quantize and compress the floating point model, and
  export it

- Use **Converter** to convert the output of MCT to a binary image that
  can be packaged and loaded onto the device

The figure [Model development flow](#model-dev-flow) shows the entire
development and deployment flow, and where MCT and the Converter fit in
as part of the entire flow. After using Converter, you should use the
output from Converter as input to the
[Packager](https://developer.aitrios.sony-semicon.com/en/raspberrypi-ai-camera/documentation/imx500-packager).
The Packager packs the converted model with additional information for
deployment on the target device.

For further information about MCT and Converter, please refer to the
relevant user manuals.

> [!NOTE]
> The Packager component is not a part of Edge-MDT, and is out of scope
> of this manual.

### Install with parameters

The Edge-MDT package takes a parameter to select between installing the
PyTorch or TensorFlow version of it.

If you are using PyTorch:

    $ pip install edge-mdt[pt]

if you are using TensorFlow:

    $ pip install edge-mdt[tf]

### Advanced installation cases

There may be a few scenarios in which you might want to perform a
special installation.

For example, if you need to install both PyTorch and TensorFlow versions
on the same machine, or if you need to install a specific combination of
Edge-MDT packages that is different from the combination included in the
Edge-MDT package.

In these cases, we strongly recommend doing special installations in
separate Python virtual environments (for example, using
`python -m venv <virtual-environment-name>`). This avoids problems with
conflicts between packages or versions.

## Operating environment

### System requirements

The system running the neural network converter should at least meet the
requirements: <br>
Linux PC (recommended)
- RAM: 4 GB

- Python: 3.11

- JVM 17

- OS: Tested and verified with Ubuntu 20.04 and 22.04.

<!-- -->
Raspberry Pi
- Raspberry Pi 4+

- RAM: 4 GB

- Python: 3.11

- JVM 17

### Supported frameworks

| **Framework** | **Tested FW versions** | **Tested Python versions** | **Serialization** | **Opset** |
|----|----|----|----|----|
| PyTorch | 2.2-2.5 | 3.9-3.11 | .onnx | 15-20 |
| TensorFlow | 2.12-2.15 | 3.9-3.11 | .keras |  |

## Framework extensions

Edge-MDT makes use of several framework extensions. The extensions are
installed as dependent libraries for the MCT and Converter packages:

- [MCTQ](https://github.com/sony/mct_quantizers) – An open-source python
  library of quantization layers and classes that is used by MCT to add
  quantization to a network. You do not need to directly make use of
  this library.

- [Custom layers](https://github.com/sony/custom_layers) – An
  open-source python library containing several post processing layers.
  If some of the layers in an existing model cannot be converted, you
  can replace these specific layers in your model with one of the
  available custom layers from this library. This can allow the model to
  be converted for the hardware device.