Dynamic-Network-Surgery-Caffe-Reimplementation

Caffe reimplementation of dynamic network surgery(GPU-only/cuDNN unsupported yet).
Official repo at here (https://github.com/yiwenguo/Dynamic-Network-Surgery).

Main Differences:

• We didn't prune the bias term.
• We make the selection of hyper-parameters more clear and intuitive.
• We re-adjust the organization of the codes.
  You may monitor the change of weights sparsity of convolution layers and fully-connected layers during training.
• We re-write the original convolution layer and inner-product layer instead of creating new classes.
  It will be easier to reuse the existing .prototxt without modifying the layer types.

How to use ?

The sames as the original Caffe framework.

$ make all -j8 # USE_NCCL=1 make all -j8 for multi-GPU support
$ ./build/tools/caffe train --weights /ModelPath/Ur.caffemodel --solver /SolverPath/solver.prototxt -gpu 0
$ ./build/tools/caffe test --weights /ModelPath/Ur.caffemodel --model /StructPath/train_val.prototxt -gpu 0 -iterations 100
# Please notice: 
# CPU Version is not supported yet, but you may find it quite easy to rewrite conv_layer.cpp and innerproduct_layer.cpp from .cu files.

You may load pre-trained caffemodel into this framework to fine-tune (highly recommended) or re-train from the begining (remember to set the threshold in train_val.prototxt, which will be mentioned below).

Usage Example :

Pre-trained Caffemodel:
AlexNet with BN (https://github.com/HolmesShuan/AlexNet-BN-Caffemodel-on-ImageNet)
Sparse (50%) convolution layers should outperform the full-precison baseline.

Pruned Layer

layer {
  name: "conv1"
  type: "Convolution"
  bottom: "data"
  top: "conv1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  # weight_mask param
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 96
    kernel_size: 11
    stride: 4
    pad: 2
    threshold: 0.6 ## based on the 68-95-99.7 rule [defalut 0.6]
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
    # weight_mask_filler { ## omissible 
    #   type: "constant"
    #   value: 1 ## This term has been reset to 1 in caffe.proto
    # }
  }
}

Dense Layer

layer {
  name: "fc6"
  type: "InnerProduct"
  bottom: "pool5"
  top: "fc6"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  inner_product_param {
    num_output: 4096
    sparsity_term: false ## Default is true
    weight_filler {
      type: "gaussian"
      std: 0.005
    }
    bias_filler {
      type: "constant"
      value: 0.1
    }
  }
}

solver.prototxt

net: "models/bvlc_alexnet/train_val.prototxt"
base_lr: 0.001
lr_policy: "multistep"
gamma: 0.1
stepvalue: 84000
display: 20
max_iter: 162000
momentum: 0.9
weight_decay: 0.00005
snapshot: 6000
snapshot_prefix: "models/bvlc_alexnet/alexnet-BN"
solver_mode: GPU
surgery_iter_gamma: 0.0001 ## [default 1e-4] Probability(do surgery) = (1+gamma*iter)^-power 
surgery_iter_power: 1 ## [default 1] 

Tips

1. The selection of threshold is pretty tricky. It may differ a lot between different layers.
2. If you encounter the vanishing gradient problem, then adjust gamma and power in solver.prototxt.
   If multiple attempts failed, then you may reduce the threshold based on the 68-95-99.7 rule.

Citation

Basic idea comes from:

@inproceedings{guo2016dynamic,    
  title = {Dynamic Network Surgery for Efficient DNNs},
  author = {Guo, Yiwen and Yao, Anbang and Chen, Yurong},
  booktitle = {Advances in neural information processing systems (NIPS)},
  year = {2016}
} 

And base on Caffe framework:

@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}
}