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Rethinking the Smaller-Norm-Less-Informative Assumption in Channel Pruning of Convolution Layers

Tensorflow implementation from original author here.

A PyTorch implementation of this paper.

To do list:

  • Extend to MobileNet and VGG
  • Fix MAC op calculation for strided convolution
  • Include training scheme from paper

Usage

I haven't included any code for transfer learning/ using pretrained models, so everything here must be done from scratch. You will have to rewrite your models to use my extended version of batch normalization, so any occurences of nn.BatchNorm2d should be replaced with bn.BatchNorm2dEx. I have included a few examples in the models folder. Note that in the forward pass you need to provide the weight from the last convolution to the batchnorm (e.g. out = self.bn1(self.conv1(x), self.conv1.weight).

I will add command line support for hyperparameters soon, but for now they will have to be altered in the main script itself. Currently the default is set to train ResNet-18; this can easily be swapped out for another model.

python main.py

Results on CIFAR-10

Model Size MAC ops Inf. time Accuracy
ResNet-18
ResNet-18-Compressed
VGG-16
VGG-16-Compressed
MobileNet
MobileNet-Compressed

Citing

Now accepted to ICLR 2018, will update bibtex soon:

@article{ye2018rethinking,
  title={Rethinking the Smaller-Norm-Less-Informative Assumption in Channel Pruning of Convolution Layers},
  author={Ye, Jianbo and Lu, Xin and Lin, Zhe and Wang, James Z},
  journal={arXiv preprint arXiv:1802.00124},
  year={2018}
}

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Rethinking the Smaller-Norm-Less-Informative Assumption in Channel Pruning of Convolution Layers https://arxiv.org/abs/1802.00124

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