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}
}