haolibai / cross-distillation

Codes for paper "Few Shot Network Compression via Cross Distillation", AAAI 2020.

Cross distillation

This repository is for our paper Few Shot Network Compression via Cross Distillation, Haoli Bai, Jiaxiang Wu, Michael Lyu, Irwin King, AAAI 2020.

Dependencies:

• python: 3.6+
• torch: 1.1+
• torchvision 0.2.2+
• numpy 1.14+

Run

Step 1: Configuration

All the scripts to run the algorithm are in ./scripts. Please make necessary arg changes, e.g, data_path, save_path and load_path. Please prepare the datasets Cifar10 and ImageNet yourself.

Step 2: Pretrain

The alogrithm is based on a pre-trained model. For Cifar10 experiments, you can run sh scripts/start_vanilla.sh to train a new model from scratch. For ImageNet experiments, you can download the pretrained models from the official website.

Step 3: Run

• sh scripts/start_chnl.sh ${gpu_id} for structured pruning
• sh scripts/start_prune.sh ${gpu_id} for unstructured pruning The default parameters are already shown in the scripts. You can uncomment other configurations for different experiments.

Tips:

• The codes automatically generate ./few_shot_ind/, which stores the index of sampled data for few shot training in step 3.
• Please read the arg description in main.py to learn more about the meanings of hyper-parameters.

Citation

If you find the code helpful for your research, please kindly star this repo and cite our paper:

@inproceedings{bai2019few,
  title={Few Shot Network Compression via Cross Distillation},
  author={Bai, Haoli and Wu, Jiaxiang and King, Irwin and Lyu, Michael},
  booktitle={Proceedings of the 34-th AAAI conference on Artificial Intelligence, AAAI 2020},
  year={2020}
}

cross-distillation's Issues

about cross-distillation for quantization

Hi haoli,

I appreciate for your excellent works of cross-distillation, and I also wonder if there are codes for quantization equipped with cross-distillation as mentioned in your paper.

Thanks!