mct-quantizers 1.6.0
Infrastructure for support neural networks compression
Requires Python: >=3.6

# Model Compression Toolkit (MCT) Quantizers
        
        The MCT Quantizers library is an open-source library developed by researchers and engineers working at Sony Semiconductor Israel. 
        
        It provides tools for easily representing a quantized neural network in both Keras and PyTorch. The library offers researchers, developers, and engineers a set of useful quantizers, along with a simple interface for implementing new custom quantizers.
        
        ## High level description:
        
        The library's quantizers interface consists of two main components:
        
        1. `QuantizationWrapper`: This object takes a layer with weights and a set of weight quantizers to infer a quantized layer.
        2. `ActivationQuantizationHolder`: An object that holds an activation quantizer to be used during inference.
        
        Users can set the quantizers and all the quantization information for each layer by initializing the weights_quantizer and activation_quantizer API.
        
        Please note that the quantization wrapper and the quantizers are framework-specific.
        
        <img src="https://github.com/sony/mct_quantizers/raw/main/quantization_infra.png" width="700">
        
        ## Quantizers:
        
        The library provides the "Inferable Quantizer" interface for implementing new quantizers. 
        This interface is based on the [`BaseInferableQuantizer`](https://github.com/sony/mct_quantizers/blob/main/mct_quantizers/common/base_inferable_quantizer.py) class, which allows the definition of quantizers used for emulating inference-time quantization.
        
        On top of `BaseInferableQuantizer` the library defines a set of framework-specific quantizers for both weights and activations:
        1. [Keras Quantizers](https://github.com/sony/mct_quantizers/tree/main/mct_quantizers/keras/quantizers)
        2. [Pytorch Quantizers](https://github.com/sony/mct_quantizers/tree/main/mct_quantizers/pytorch/quantizers)
        
        ### The mark_quantizer Decorator
        
        The [`@mark_quantizer`](https://github.com/sony/mct_quantizers/blob/main/mct_quantizers/common/base_inferable_quantizer.py) decorator is used to assign each quantizer with static properties that define its task compatibility. Each quantizer class should be decorated with this decorator, which defines the following properties:
         - [`QuantizationTarget`](https://github.com/sony/mct_quantizers/blob/main/mct_quantizers/common/base_inferable_quantizer.py): An Enum that indicates whether the quantizer is intended for weights or activations quantization.
         - [`QuantizationMethod`](https://github.com/sony/mct_quantizers/blob/main/mct_quantizers/common/quant_info.py): A list of quantization methods (Uniform, Symmetric, etc.).
         - `identifier`: A unique identifier for the quantizer class. This is a helper property that allows the creation of advanced quantizers for specific tasks.
         
        ## Getting Started
        
        This section provides a quick guide to getting started. We begin with the installation process, either via source code or the pip server. Then, we provide a short example of usage.
        
        ### Installation
        
        #### From PyPi - mct-quantizers package
        To install the latest stable release of MCT Quantizer from PyPi, run the following command:
        ```
        pip install mct-quantizers
        ```
        
        If you prefer to use the nightly package (unstable version), you can install it with the following command:
        ```
        pip install mct-quantizers-nightly
        ```
        
        #### From Source
        To work with the MCT Quantizers source code, follow these steps:
        ```
        git clone https://github.com/sony/mct_quantizers.git
        cd mct_quantizers
        python setup.py install
        ```
        
        ### Requirements
        
        To use MCT Quantizers, you need to have one of the supported frameworks, Tensorflow or PyTorch, installed.
        
        For use with Tensorflow, please install the following package:
        [tensorflow](https://www.tensorflow.org/install),
        
        For use with PyTorch, please install the following package:
        [torch](https://pytorch.org/)
        
        You can also use the [requirements](https://github.com/sony/mct_quantizers/blob/main/requirements.txt) file to set up your environment.
        
        ## License
        [Apache License 2.0](https://github.com/sony/mct_quantizers/blob/main/LICENSE.md).