Importance Estimation for Neural Network Pruning

This repo contains required scripts to reproduce results from paper:

Importance Estimation for Neural Network Pruning
Pavlo Molchanov, Arun Mallya, Stephen Tyree, Iuri Frosio, Jan Kautz .
In CVPR 2019.

License

Copyright (C) 2019 NVIDIA Corporation.

All rights reserved. Licensed under the CC BY-NC-SA 4.0 (Attribution-NonCommercial-ShareAlike 4.0 International)

The code is released for academic research use only. For commercial use, please contact researchinquiries@nvidia.com.

Installation

Clone this repo.

git clone https://github.com/NVlabs/Taylor_pruning.git
cd Taylor_pruning/

This code requires PyTorch 1.0 and python 3+. Please install dependencies by

pip install -r requirements.txt

For the best reproducibility of results you will need NVIDIA DGX1 server with 8 V100. During pruning and finetuning we at most use 4 GPUs.

The code was tested with python3.6 the following software versions:

Software	version
cuDNN	v7500
Pytorch	1.0.1.post2
CUDA	v10.0

Preparation

Dataset preparation

Pruning examples use ImageNet 1k dataset which needs to be downloaded beforehand. Use standard instructions to setup ImageNet 1k for Pytorch, e.g. from here.

Models preparation

We use pretrained models provided with Pytorch, they need to be downloaded:

wget https://download.pytorch.org/models/resnet50-19c8e357.pth 
wget https://download.pytorch.org/models/resnet101-5d3b4d8f.pth
mkdir ./models/pretrained/
mv {resnet50-19c8e357.pth,resnet101-5d3b4d8f.pth} ./models/pretrained/

Pruning examples

Results in Table 3 can be reproduced the following script running on 4 V100 GPUs:

python main.py --name=runs/resnet50/resnet50_prune72 --dataset=Imagenet \
--lr=0.001 --lr-decay-every=10 --momentum=0.9 --epochs=25 --batch-size=256 \ 
--pruning=True --seed=0 --model=resnet50 --load_model=./models/pretrained/resnet50-19c8e357.pth \
--mgpu=True --group_wd_coeff=1e-8 --wd=0.0 --tensorboard=True --pruning-method=22 \
--data=/imagenet/ --no_grad_clip=True --pruning_config=./configs/imagenet_resnet50_prune72.json

Note: we run finetuning for 25 epochs and do not use weight decay. Better model can be obtained by training longer, increasing learning and using weight decay.

Example of config file ./configs/imagenet_resnet50_prune72.json:

{
  "method": 22,
  "frequency" : 30,
  "prune_per_iteration" : 100,
  "maximum_pruning_iterations" : 32,
  "starting_neuron" : 0,
  "fixed_layer" : -1,
  "l2_normalization_per_layer": false,
  "rank_neurons_equally": false,
  "prune_neurons_max": 3200,
  "use_momentum": true,
  "pruning_silent": false,

  "pruning_threshold" : 100.0,
  "start_pruning_after_n_iterations" : 0,
  "push_group_down" : false,
  "do_iterative_pruning" : true,
  "fixed_criteria" : false,
  "seed" : 0,
  "pruning_momentum" : 0.9
}

We provide config files for pruning to 56%, 72%, 81%, 91% of original ResNet-50 and to 40%, 50%, 55%, 75% of ResNet-101 models. Percentage means the ratio of gates to be active after pruning.

Parameter description

Pruning methods (different criteria) are encoded with integer as:

method id	name	description	comment
22	Taylor_gate	Gate after BN in Table 2, Taylor FO in Table 1, Taylor-FO-BN in Table 3	Best method
0	Taylor_weight	Conv weight/conv/linear weight with Taylor FO In Table 2 and Table 1
1	Random	Random
2	Weight norm	Weight magnitude/ weight
3	Weight_abs	Not used
6	Taylor_output	Taylor-output as in [27]
10	OBD	Optimal Brain Damage
11	Taylor_gate_SO	Taylor SO
23	Taylor_gate_FG	uses gradient per example to compute Taylor FO, Taylor FO- FG in Table 1, Gate after BN - FG in Table 2
30	BN_weight	BN scale in Table 2
31	BN_Taylor	BN scale Taylor FO in Table 2

Citation

If you use this code for your research, please cite our papers.

@inproceedings{molchanov2019taylor,
  title={Importance Estimation for Neural Network Pruning},
  author={Molchanov, Pavlo and Mallya, Arun and Tyree, Stephen and Frosio, Iuri and Kautz, Jan},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  year={2019}
}

To come

• Config files for DenseNet and VGG16 pruning on ImageNet
• Examples of CIFAR experiments
• Getting shrinked model after pruning (for ResNet-101 only)
• Oracle estimates of true importance, code to compute correlation with Oracle

Acknowledgments

This code heavily reuses Pytorch example for ImageNet training provided here.