Dynamic Model Pruning with Feedback

Paper Link : Dynamic Model Pruning with Feedback - ICLR2020

It's UNOFFICIAL code!

If you want to get information of hyperparameters, you should read appendix part of this paper

Abstract

(1) Allowing dynamic allocation of the sparsity pattern

(2) Incorporating feedback signal to reactivate prematurely pruned weights

Method

Run

python main.py cifar10 --datapath DATAPATH --a resnet layers 56 -C -g 0 save train.pth \
--epochs 300 --batch-size 128  --lr 0.2 --wd 1e-4 --nesterov --scheduler multistep --milestones 150 225 --gamma 0.1

Experiment

Experiment on ResNet56 for CIFAR10

DPF run :

python main.py cifar10 --datapath DATAPATH -a resnet --layers 56 -C -g 0 --save prune.pth \
-P --prune-type unstructured --prune-freq 16 --prune-rate 0.9 --prune-imp L2 \
--epochs 300 --batch-size 128  --lr 0.2 --wd 1e-4 --nesterov --scheduler multistep --milestones 150 225 --gamma 0.1