This project aimed to cover the experiments of our paper in ICMLA2023. If your like this work, please cite our paper:
@article{zhao2023pslr,
title={PSLR: Unstructured Pruning with Layer-wise Sparsity Ratio},
author={Zhao, Haocheng and Yu, Limin and Guan, Runwei and Jia, Liye and Zhang, Junqing and Yue, Yutao},
journal={22nd IEEE Internaltional Conference on Machine Learning and Application},
year={2023}
}
# pytorch
pip install torch==1.11.0 torchvision==0.12.0
# others
pip install hydra-core==1.2 tqdm tensorboardX
sh exp/bl.sh
# pre-trained models
sh exp/exp0_bl.sh
# figure
sh exp/exp0_trained.sh
# Table II
sh exp/exp1.sh
# Table III
sh exp/exp2.sh
# Table IV
sh exp/exp3_erk.sh
sh exp/exp3_featio.sh
# Table V
sh exp/exp_ablation.sh
sh exp/exp_ablation2.sh