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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 Optimal Thres 0.922/0.485/0.915 313.5->127.5 2.24->0.98
MobileV2 AutoSlim <200 flops 0.922/0.795/0.919 313.5->137.5 2.24->1.037
VGG Slimming Optimal Thres 0.926/0.183/0.920 399.3->147.8 20.03->1.49
Resnet50 Slimming Optimal Thres 0.926/0.665/0.921 3448->975 23.52->6.00
ShuffleNetV2 Slimming Optimal Thres 0.897/0.894/0.895 348.6->188.5 2.22->1.24

NOTE:

  1. args for VGG: --arch VGG --s 0.001 --sr --lr 0.02 --epochs 100
  2. args for resnet: --arch resnet50 --s 0.001 --sr --lr 0.02 --epochs 100
  3. args for shufflenet: --arch ShuffleNetV2 --s 0.007 --sr --lr 0.001 --epochs 100
    Try yourself with different arguments!

TODO

Pruning Methodsd

Backbones

  • MobileV2
  • ShuffleNet
  • VGG
  • ResNet
    ....

Reference

rethinking-network-pruning

Pruned-MobileNet_v2

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Try out different pruning-approaches on lightweight Backbones.

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