A personal depthwise convolution layer implementation on caffe by liuhao.(only GPU)
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README.md

Depthwise Convolutional Layer

Introduction

This is a personal caffe implementation of mobile convolution layer. For details, please read the original paper:

How to build

  1. Merge the caffe folder in the repo with your own caffe. $ cp -r $REPO/caffe/* $YOURCAFFE/
  2. Then make. $ cd $YOURCAFFE && make

Usage

Replacing the type of mobile convolution layer with "DepthwiseConvolution" is all. Please refer to the example/Withdw_MN_train_128_1_train.prototxt, which is altered from

GPUPerformance on example net

GPUPerformance Origin[^nocudnn] Mine
forward_batch1 41 ms 8 ms
backward_batch1 51 ms 11 ms
forward_batch16 532 ms 36 ms
backward_batch16 695 ms 96 ms

[^nocudnn]: When turn on cudnn, the memory consuming of mobilenet would increase to unbelievable level. You may try.

Transfer normal net to mobilenet

I write a script [transfer2Mobilenet.py] to convert normal net to mobilenet format. You may try too.
usage: python ./transfer2Mobilenet.py sourceprototxt targetprototxt [--midbn nobn --weight_filler msra --activation ReLU]    ["--origin_type" means the depthwise convolution layer's type will be "Convolution" instead of "DepthwiseConvolution"]

The "transferTypeToDepthwiseConvolution.py" will be used for changing the depthwise convolution layer's type from "Convolution" to "DepthwiseConvolution".