237014845 / MobileNet-v2-pruning

Try out different pruning-approaches on lightweight Backbones.

MobileV2 Pruning

This repository aim to try out different pruning-approaches on lightweight Backbones.

Usage

1. Training

   python main.py --arch MobileNetV2 (for l1norm pruner )
   python main.py --sr --arch MobileNetV2 (for slimming pruner) 
   python main.py --arch USMobileNetV2 (for Autoslim pruner )
   

2. Pruning (prune+finetune)

   python prune.py --arch MobileNetV2 --pruner l1normpruner --pruneratio 0.6
   python prune.py --arch MobileNetV2 --pruner SlimmingPruner --sr --pruneratio 0.6
   python prune.py --arch USMobileNetV2 --pruner AutoSlimPruner
   

Results on Cifar10

BackBone	Pruner	Prune Ratio	Original/Pruned/Finetuned Accuracy	FLOPs(M)	Params(M)
MobileV2	L1-Norm	0.6	0.937/0.100/0.844	313.5->225.5	2.24->1.15
MobileV2	Slimming	0.6	0.922/0.485/0.915	313.5->214.5	2.24->0.98
MobileV2	AutoSlim	<200 flops	0.920/0.561/0.916	313.5->199.67	2.24->0.81

TODO

Pruning Methodsd

• l1-norm pruner
• Slimming pruner
• AutoSlim
• ThiNet
• Soft filter pruning
  ....

Backbones

• MobileV2
• ShuffleNet
  ....

Reference

rethinking-network-pruning

Pruned-MobileNet_v2