Quantization-Tools

tools for per layer quantization

Overview

This script is designed to handle command-line options for training and evaluating machine learning models. By specifying different options, users can train a new model, evaluate a model in FP32 precision, evaluate a model in FP16 precision or int8 Post Train quantization or int 8 Train Aware Quantization. The script leverages Python's argparse library to manage command-line arguments efficiently.

Usage

To run the script, use the following command format:

python main.py --option <option_name>

The should be one of the following:

train_model to train a resnet model with fp32.

evaluate_fp32 to evaluate a model in FP32 precision.

evaluate_fp16 to evaluate a model in FP16 precision.

int8_PTQ to evaluate a model in int8 using post train quantization.

int8_QAT to train and evaluate a model in int8 using train aware qauntization.

Use for more information: python main.py --help

Models information

How to contrinute

Contributions to this project are welcome! If you'd like to contribute to add new quantization methods, please follow the standard GitHub workflow:

1. Fork the repository.
2. Create a new branch for your feature (git checkout -b feature/your-feature).
3. Commit your changes (git commit -am 'Add some feature').
4. Push to the branch (git push origin feature/your-feature).
5. Create a new Pull Request.