isabella232 / model_optimization

Model Compression Toolkit (MCT) is an open source project for neural network model optimization under efficient, constrained hardware. This project provides researchers, developers, and engineers advanced quantization and compression tools for deploying state-of-the-art neural networks.

Model Compression Toolkit (MCT)

Model Compression Toolkit (MCT) is an open source project for neural network model optimization under efficient, constrained hardware.
This project provides researchers, developers and engineers tools for optimizing and deploying state-of-the-art neural network on efficient hardware.
Specifically, this project applies constrained quantization and pruning schemes on a neural network.

Currently, this project only supports hardware friendly post training quantization (HPTQ) with Tensorflow 2 [1].

The MCT project is developed by researchers and engineers working at Sony Semiconductors Israel.

For more information, please visit our project website.

Table of Contents

• Getting Started
• Supported features
• Results
• Contributions
• License

Getting Started

This section provides a quick starting guide. We begin with installtion via source code or pip server. Then, we provide a short usage example.

Installation

See the MCT install guide for the pip package, and build from source.

From Source

git clone https://github.com/sony/model_optimization.git
python setup.py install

From PyPi - latest stable release

pip install model-compression-toolkit

A nightly package is also available (unstable):

pip install mct-nightly

To run MCT, one of the supported frameworks, Tenosflow/Pytorch, needs to be installed.

For using with Tensorflow please install the packages: tensorflow, tensorflow-model-optimization

For using with Pytorch (experimental) please install the packages: torch

MCT is tested with:

• Tensorflow version 2.7
• Pytorch version 1.10.0

Usage Example

For an example of how to use the post training quantization, using Keras, please use this link.

For an example using Pytorch (experimental), please use this link.

For more examples please see the tutorials' directory.

Supported Features

Quantization:

* Post Training Quantization for Keras models.
* Post Training Quantization for Pytorch models (experimental).
* Gradient based post training (Experimental, Keras only).
* Mixed-precision post training quantization (Experimental, Keras only).

Tensorboard Visualization (Experimental):

* CS Analyizer: compare a model compressed with the orignal model to analyze large accuracy drops.
* Activation statisicis and errors

Results

Keras

As part of the MCT library, we have a set of example networks on image classification. These networks can be used as examples when using the package.

• Image Classification Example with MobileNet V1 on ImageNet dataset

Network Name	Float Accuracy	8Bit Accuracy	Comments
MobileNetV1 [2]	70.558	70.418

For more results please see [1]

Pytorch

We quantized classification networks from the torchvision library. In the following table we present the ImageNet validation results for these models:

Network Name	Float Accuracy	8Bit Accuracy
MobileNet V2 [3]	71.886	71.444
ResNet-18 [3]	69.86	69.63
SqueezeNet 1.1 [3]	58.128	57.678

Contributions

MCT aims at keeping a more up-to-date fork and welcomes contributions from anyone.

*You will find more information about contributions in the Contribution guide.

License

Apache License 2.0.

References

[1] Habi, H.V., Peretz, R., Cohen, E., Dikstein, L., Dror, O., Diamant, I., Jennings, R.H. and Netzer, A., 2021. HPTQ: Hardware-Friendly Post Training Quantization. arXiv preprint.

[2] MobilNet from Keras applications.

[3] TORCHVISION.MODELS