Skip to main content

A Model Compression Toolkit for neural networks

Project description

Model Compression Toolkit (MCT)

tests

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 networks on efficient hardware. Specifically, this project aims to apply quantization and pruning schemes to compress neural networks.

Currently, this project supports hardware-friendly post-training quantization (HPTQ) with Tensorflow 2 and Pytorch [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

This section provides a quick starting guide. We begin with installation 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 the 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).

Tensorboard Visualization (Experimental):

  • CS Analyzer: compare a model compressed with the original model to analyze large accuracy drops.
  • Activation statistics 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

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

mct-nightly-1.4.0.25052022.post442.tar.gz (211.2 kB view hashes)

Uploaded Source

Built Distribution

mct_nightly-1.4.0.25052022.post442-py3-none-any.whl (393.6 kB view hashes)

Uploaded Python 3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page