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
(1) Allowing dynamic allocation of the sparsity pattern
(2) Incorporating feedback signal to reactivate prematurely pruned weights
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
| Best Top-1 Acc(%) | Sparsity(%) | |
|---|---|---|
| Basline | 93.97 | 0 |
| DPF | 93.73 | 90.00 |
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