Awesome

Lookahead Pruning (LAP)

Official PyTorch implementation of "Lookahead: A Far-sighted Alternative of Magnitude-based Pruning" (ICLR 2020) by Sejun Park*, Jaeho Lee*, Sangwoo Mo, and Jinwoo Shin.

Installation

Download BackPack package to run OBD experiments. If you only want MP/LAP experiments, simply comment the OBD parts.

Run experiments

Run MNIST experiments (MLP)

python main.py --dataset mnist --network mlp --method mp
python main.py --dataset mnist --network mlp --method lap

Run CIFAR-10 experiments (VGG19)

python main.py --dataset cifar10 --network vgg19 --method mp
python main.py --dataset cifar10 --network vgg19 --method lap_bn

Run Tiny-ImageNet experiments (ResNet50)

python main.py --dataset tiny-imagenet --network resnet50_64 --method mp
python main.py --dataset tiny-imagenet --network resnet50_64 --method lap_bn

Run data-dependent pruning experiments

python main.py --dataset mnist --network mlp --method obd
python main.py --dataset mnist --network mlp --method lap_act

Run global pruning experiments

python main.py --dataset mnist --network mlp --pruning_type global --method mp_global_normalize
python main.py --dataset mnist --network mlp --pruning_type global --method lap_global_normalize

Results are saved in

./checkpoint/{dataset}_{network}_{pruning_type}_{seed}/{method}/logs.txt

Citation

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

@inproceedings{
  park2020lookahead,
  title={Lookahead: A Far-sighted Alternative of Magnitude-based Pruning},
  author={Sejun Park and Jaeho Lee and Sangwoo Mo and Jinwoo Shin},
  booktitle={International Conference on Learning Representations},
  year={2020},
  url={https://openreview.net/forum?id=ryl3ygHYDB}
}