thuyngch / gate-decorator-pruning

Code for the NuerIPS'19 paper "Gate Decorator: Global Filter Pruning Method for Accelerating Deep Convolutional Neural Networks"

Gate Decorator (NeurIPS 2019)

This repo contains required scripts to reproduce results from paper:

Gate Decorator: Global Filter Pruning Method for Accelerating Deep Convolutional Neural Networks

Requirements

python 3.6+ and PyTorch 1.0+

Installation

1. clone the code
2. pip install --upgrade git+https://github.com/youzhonghui/pytorch-OpCounter.git
3. pip install tqdm

How to use

In the run/resnet-56 folder, we provide an example which reduces the FLOPs of resnet-56 by 70%, but still maintains 93.15% accuracy on CIFAR-10:

1. The run/resnet-56/resnet56_prune.ipynb prunes the network with Tick-Tock framework.
2. The run/resnet-56/finetune.ipynb shows how to finetune the pruned network to get better results.

If you want to run the demo code, you may need to install jupyter notebook

Todo

• Basic running example.
• PyTorch 1.2 compatibility test.
• ResNet-50 pruned model.

Citation

If you use this code for your research, please cite our paper:

@inproceedings{zhonghui2019gate,
  title={Gate Decorator: Global Filter Pruning Method for Accelerating Deep Convolutional Neural Networks},
  author={Zhonghui You and
          Kun Yan and
          Jinmian Ye and
          Meng Ma and
          Ping Wang},
  booktitle={Advances in Neural Information Processing Systems (NeurIPS)},
  year={2019}
}