TensorFlow implementation of paper:

Trained Ternary Quantization, by Zhu et al.

This implementation is based on tensorpack. Thanks to this framework which made this implementation extremely easy.

• Python 2 or 3
• TensorFlow >= 0.8
• Python bindings for OpenCV
• other requirements:

pip install --user -r requirements.txt
pip install --user -r opt-requirements.txt (some optional dependencies, you can install later if needed)

• Use tcmalloc whenever possible
• Enable import tensorpack:

export PYTHONPATH=$PYTHONPATH:`readlink -f path/to/tensorpack`

• To train ResNet on CIFAR10 with fixed threshold:

cd examples/Ternary-Net/
python ./tw-cifar10-resnet.py --gpu 0,1 [--load MODEL_PATH] [--t threshold] [--n NSIZE]

Note: We used 2 GPUs for training and pretrained model can be obtained using /examples/ResNet/

• To train ResNet on CIFAR10 with fixed sparsity:

cd examples/Ternary-Net/
python ./p-cifar10-resnet.py --gpu 0,1 [--load MODEL_PATH] [—p sparsity] [--n NSIZE]

• To train AlexNet on ImageNet with fiexed threshold:

cd examples/Ternary-Net/
python ./tw-imagenet-alexnet.py --gpu 0,1,2,3 --data IMAGENET_PATH [--t threshold]

Note: We used 4 GPUs for training

Some training logs can be found here.

Please use github issues for any issues related to the code. Send email to the authors for general questions related to the paper.

If you use our code or models in your research, please cite:

@article{zhu2016trained,
  title={Trained Ternary Quantization},
  author={Zhu, Chenzhuo and Han, Song and Mao, Huizi and Dally, William J},
  journal={arXiv preprint arXiv:1612.01064},
  year={2016}
}