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NUQ: Finding Non-Uniform Quantization Schemesusing Multi-Task Gaussian Processes

Implementation of ECCV2020 "Finding Non-Uniform Quantization Using Multi-Task Gaussian Processes".

Requirements

  • Python >= 3.6
  • PyTorch >= 1.0
  • CUB == 1.8
  • GPyTorch
  • Pyro

Dependencies

This depends on the Quantization github repo, which implements cuda version of BFP and DSConv

Make sure to git submodule update --init --recursive and follow the installation steps in the Quantization repo:

  1. Download CUB and put it in /home/your_username/libs/ (or the file indicated at NUQ/BlackBox/Quantization/src/setup.py:22)
  2. cd /path/to/NUQ/BlackBox/Quantization/src/ then python build_ext --inplace.

Config

In the config.py file, you should insert the paths to the respective variables.

If you have the models trained for cifar10, and imagenet32, just insert their paths in the config.py file. If you don't, then you should insert the paths for the desirable loaction in the config.py file, and run the train_cifar.py or train_imagenet32.py scripts.

Citation

@misc{nascimento2020finding,
    title={Finding Non-Uniform Quantization Schemes using Multi-Task Gaussian Processes},
    author={Marcelo Gennari do Nascimento and Theo W. Costain and Victor Adrian Prisacariu},
    year={2020},
    eprint={2007.07743},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

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