add ConvolutionDepthwise layer

[sp2823]

https://arxiv.org/pdf/1704.04861v1.pdf
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

convolution depthwise layer
faster and less memory than the "convolution layer with group" (with CuDNN and without CuDNN)

[fengziyong]

caffe_set just for cpu_data @sp2823

[sp2823]

We only need to set mutable_cpu_data or mutable_gpu_data once.
There is a similar implementation of batch_sum_multiplier_ in BatchNormLayer.
If it is necessary, we should use caffe_set in Forward_cpu and caffe_gpu_set in Forward_gpu.

[fengziyong]

I mean caffe_set is just for pointer of cpu_data, and set data to pointer of gpu_data would crash.

[zj19921221]

请问，两个问题请教下：
1、您这个实现和caffe中用group实现有什么优点吗？
2、模糊理解了group不能够并行的，并不是很理解为什么加了for循环就不能并行？

[NHZlX]

cpu下有待于优化

[zjchuyp]

@sp2823
Is it faster by using forloop than using gemm in CPU mode?

[mathmanu]

Great to see this work - I hope it gets merged soon. The correct name for this should be "DepthwiseSeparable". Just "Depthwise" gives almost the opposite meaning.

[sp2823]

I didn't optimize the CPU mode because the Convolution layer with group is slow in GPU mode. You can use this code for training and use Convolution layer for prediction.

[youngwanLEE]

Could you share your .prototxt which show how to set parameters? or test_examples?

[mathmanu]

I have attached the files required to train the popular mobilenet model:

imagenet_mobilenet1.0_2017-07-04_10-44-00.zip

I added the following code in layer_factory.cpp, GetConvolutionLayer() so that this layer will be called whever its appropriate to use:
if(conv_param.num_output() == conv_param.group()) {
return shared_ptr< Layer< Dtype > >(new ConvolutionDepthwiseLayer(param));
}

There is a speedup when using the proposed ConvolutionDepthwise layer is used instead of Convolution layer. But it is not as much as I expected.

If fact if I just comment the group parameter in all convolution layers in both train.prototxt and test.prototxt, so that the 3x3 convolution becomes are traditional 3x3 convolution instead of DepthWise seperable, it becomes slightly faster! This was not what I was expecting.

Is there something that I am missing? Please try the files that I shared.

[sp2823]

You only need to edit the .prototxt file like this.
type: "Convolution"
type: "ConvolutionDepthwise"

[ryusaeba]

@sp2823 How do I merge your implementation into my CAFFE? Just download the hpp/cpp is OK? Thanks :)

[sp2823]

download the .hpp/.cpp/.cu file and compile

[leolee96]

Hi @sp2823 ,
I am new to caffe. I got an error saying like "class caffe::ConvolutionParameter’ has no member named ‘kernel_size_size’" in conv_dw_layer.cpp when I'm trying to compile.
Any idea of this error?

[SophieZhou]

Hi, @sp2823
I have trained mobilenet using your code for 20 epoches, and the top1 about 52%, and about 76% for top5. Do you have any experiments results?
By the way, the code is very well. The speed for training is much faster than group convolution way. And I hope the results are well too.

[zj19921221]

@SophieZhou 你好，请问下在cpu下训练速度有变很快吗？性能达到什么程度。

[birdwcp]

up

[birdwcp]

you did not implement CuDNNConvolutionDepthWiseLayer. Isn't it necessary?