tools for per layer quantization
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.
To run the script, use the following command format:
python main.py --option <option_name>
The <option_name> 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
Contributions to this project are welcome! If you'd like to contribute to add new quantization methods, please follow the standard GitHub workflow:
- Fork the repository.
- Create a new branch for your feature (
git checkout -b feature/your-feature). - Commit your changes (
git commit -am 'Add some feature'). - Push to the branch (
git push origin feature/your-feature). - Create a new Pull Request.