Depthwise Convolutional Layer

Introduction

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

• MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

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

• MobileNet-Caffe

GPUPerformance on example net

GPUPerformance	Origin1	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

2018/02/08 Updated

1. Add the funtion of depth_multiplier(e.g. Now you can set the input channel = 32, group = 32 and output channel = 64. It mean the depth_multiplier = 2)

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".

Footnotes

1. When turn on cudnn, the memory consuming of mobilenet would increase to unbelievable level. You may try. ↩