Awesome

Pruned models

Note:

• Speedup ratio is the theoretical value measured by FLOPs reduction in only conv layers.
• The baseline accuracies are obtained by evaluating the downloaded model without finetuning them on our produced ImageNet dataset.
• The provided pruned caffemodels are only zero-masked, without taking out the zero weight filters or columns. So they are literally of the same size as their baseline counterparts.

Environment

• Ubuntu 1404
• Caffe
• Python 2.7
• Use cuDNN

How to run the code

1. Download this repo and compile: make -j24, see Caffe's official guide. Make sure you get it through.
2. Here we show how to run the code, taking lenet5 as an example:
   • Preparation:
     • Data: Create your mnist training and testing lmdb (either you can download ours), put them in data/mnist/mnist_train_lmdb and data/mnist/mnist_test_lmdb.
     • Pretrained model: We provide a pretrained lenet5 model in compression_experiments/mnist/weights/baseline_lenet5.caffemodel (test accuracy = 0.991).
   • (We have set up an experiment folder in compression_experiments/lenet5, where there are three files: train.sh, solver.prototxt, train_val.prototxt. There are some path settings in them and pruning configs in solver.prototxt, where we have done that for you, but you are free to change them.)
   • In your caffe root path, run nohup sh compression_experiments/lenet5/train.sh <gpu_id> > /dev/null &, then you are all set! Check your log at compression_experiments/lenet5/weights.

For vgg16, resnet50, we also provided their experiment folders in compression_experiments, check them out and have a try!

Check the log

There are two logs generated during pruning: log_<TimeID>_acc.txt and log_<TimeID>_prune.txt. The former saves the logs printed by the original Caffe; the latter saves the logs printed by our added codes.

Go to the project folder, e.g., compression_experiments/lenet5 for lenet5, then run cat weights/*prune.txt | grep app you will see the pruning and retraining course.

License and Citation

Caffe is released under the BSD 2-Clause license. The BVLC reference models are released for unrestricted use.

Please cite these in your publications if this code helps your research:

@article{wang2019jstsp,
  Author = {Wang, Huan and Hu, Xinyi and Zhang, Qiming and Wang, Yuehai and Yu, Lu and Hu, Haoji},
  Title = {Structured Pruning for Efficient Convolutional Neural Networks via Incremental Regularization},
  Journal = {IEEE Journal of Selected Topics in Signal Processing},
  Year = {2019}
}
@proceedins{wang2019increg,
  Author = {Wang, Huan and Zhang, Qiming and Wang, Yuehai and Yu, Lu and Hu, Haoji},
  Title = {Structured Pruning for Efficient ConvNets via Incremental Regularization},
  Booktitle = {IJCNN},
  Year = {2019}
}
@proceedins{wang2018spp,
  Author = {Wang, Huan and Zhang, Qiming and Wang, Yuehai and Hu, Haoji},
  Title = {Structured probabilistic pruning for convolutional neural network acceleration},
  Booktitle = {BMVC},
  Year = {2018}
}
@article{jia2014caffe,
  Author = {Jia, Yangqing and Shelhamer, Evan and Donahue, Jeff and Karayev, Sergey and Long, Jonathan and Girshick, Ross and Guadarrama, Sergio and Darrell, Trevor},
  Journal = {arXiv preprint arXiv:1408.5093},
  Title = {Caffe: Convolutional Architecture for Fast Feature Embedding},
  Year = {2014}
}