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EigenDamage: Structured Pruning in the Kronecker-Factored Eigenbasis

This repo contains the official implementations of EigenDamage: Structured Pruning in the Kronecker-Factored Eigenbasis. Details are listed below: <img align="right" width="380" height="280" src="imgs/figure1.png">

  1. The config file for the experiments are under the directory of configs/.
  2. The pruning algorithms are in pruner/. Please note that: <br> (1) fisher_diag_pruner.py implements C-OBD. <br> (2) kfac_eigen_pruner.py implements EigenDamage. <br> (3) kfac_full_pruner.py implements C-OBS. <br> (4) kfac_OBD_F2.py implements kron-OBD. <br> (5) kfac_OBS_F2.py implements kron-OBS. <br> (6) kfac_eigen_svd_pruner.py implements EigenDamage Depthwise Separable. <br>

Requirements

Python3.6, Pytorch 0.4.1

pip install https://download.pytorch.org/whl/cu90/torch-0.4.1-cp36-cp36m-linux_x86_64.whl
pip install torchvision
pip install tqdm
pip install tensorflow
pip install tensorboardX
pip install easydict
pip install scikit-tensor

Dataset

  1. Download tiny imagenet from "https://tiny-imagenet.herokuapp.com", and place it in ../data/tiny_imagenet. Please make sure there will be two folders, train and val, under the directory of ../data/tiny_imagenet. In either train or val, there will be 200 folders storing the images of each category.

  2. For cifar datasets, it will be automatically downloaded.

How to run?

1. Pretrain model

You can also download the pretrained model from https://drive.google.com/file/d/1hMxj6NUCE1RP9p_ZZpJPhryk2RPU4I-_/view?usp=sharing.

# for pretraining CIFAR10/CIFAR100
$ python main_pretrain.py --learning_rate 0.1 --weight_decay 0.0002 --dataset cifar10 --epoch 200

# for pretraining Tiny-ImageNet
$ python main_pretrain.py --learning_rate 0.1 --weight_decay 0.0002 --dataset tiny_imagenet --epoch 300

2. Pruning

# for pruning with EigenDamage, CIFAR10, VGG19 (one pass)
$ python main_prune.py --config ./configs/exp_for_cifar/cifar10/vgg19/one_pass/base/kfacf_eigen_base.json

# for pruning with EigenDamage, CIFAR100, VGG19
$ python main_prune.py --config ./configs/exp_for_cifar/cifar100/vgg19/one_pass/base/kfacf_eigen_base.json

# for pruning with EigenDamage, TinyImageNet, VGG19
$ python main_prune.py --config ./configs/exp_for_tiny_imagenet/tiny_imagenet/vgg19/one_pass/base/kfacf_eigen_base.json

# for pruning with EigenDamage + Depthwise separable, CIFAR100, VGG19
$ python main_prune_separable.py --config ./configs/exp_for_svd/cifar100/vgg19/one_pass/base/svd_eigendamage.json

Contact

If you have any questions or suggestions about the code or paper, please do not hesitate to contact with Chaoqi Wang(alecwangcq@gmail.com or cqwang@cs.toronto.edu) and Guodong Zhang(gdzhang.cs@gmail.com or gdzhang@cs.toronto.edu).

Citation

To cite this work, please use

@InProceedings{wang2019eigen,
  title = 	 {{E}igen{D}amage: Structured Pruning in the {K}ronecker-Factored Eigenbasis},
  author = 	 {Wang, Chaoqi and Grosse, Roger and Fidler, Sanja and Zhang, Guodong},
  booktitle = 	 {Proceedings of the 36th International Conference on Machine Learning},
  pages = 	 {6566--6575},
  year = 	 {2019},
  volume = 	 {97},
  publisher = {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v97/wang19g/wang19g.pdf},
  url = 	 {http://proceedings.mlr.press/v97/wang19g.html},
}