yonghenglh6 / depthwiseconvolution Goto Github PK

Dear Editor:
Which version of caffe have you used? How to add your code to caffe source?

你好，请问windows下面的caffe，是拷贝这个仓库caffe文件目录下面的程序到我电脑上面caffe相应的文件夹下面，然后重新编译一下就可以了吗？

Thanks for your job！
But I found that if (output channels / groups) != 1 and equals to other integer, the net couldn't work.
e.g. If input channels = 32, groups = 32 and output channels = 64, the loss of net will not decrease. In Imagenet1000 training, the loss is still at 6.9.
Do you know how to solve this problem?
Thx!

你好，请问在使用engine：CAFFE时，DepthwiseConvolution与conv的实现方式一模一样是吗，贡献是实现了group时可以使用cuda吗？还有在添加你的层入我的caffe时不需要添加进caffe.proto吗？

Dear Liu,
Thank you for your work.

I can not find changes in caffe.proto file. Can you provide this too?
there are compile issues when use the codes. multiple variables do not defined. I am not sure if missed some define in .h file...

And, I found comments in the codes, this is only for stride 1?

利用您的脚本我把vgg16转换成了mobilenet的形式，caffe也重新编译了。但是训练速度很慢，大概是4分钟20次。显卡是TITAN X (Pascal)。环境是，ubuntu14.04，cuda8.0,cudnn5.1
这是我的训练设置：
test_iter: 5000
test_interval: 5000
base_lr: 0.01
display: 20
max_iter: 300000
lr_policy: "poly"
power: 1
momentum: 0.9
weight_decay: 0.0005
snapshot: 5000
snapshot_prefix: "models/vgg16_"
random_seed: 0
net: "mobilenet.prototxt"
test_initialization: false
iter_size: 16
solver_mode: GPU

only for stride 1？

@yonghenglh6 Thanks for your good work! I trained caffe model by using your DepthwiseCovolution layer as following your suggestion, while it works when I train the model, I met some problems . When I deploy the model I trained , there was a error : Check failed registry count (type) == 1 (0 VS 1) Unknown layer type: DepthwiseConvolution.
So , did you meet this error? or Have you deployed some caffe model with DepthwiseConvolution?
Looking forward your attention. Thanks

When i try your DepthwiseConvolution layer, i got the error:Unknown layer type: DepthwiseConvolution, why? And should i change caffe.proto？

I found "group" parameter in your deploy.prototxt and should I use this during the training process or keep it ? How about the accuracy you got ? I trained model only to get 54% accuracy top 1.

Thanks for your job！
But I have some questions to ask you , I hope you can help me.

1. Merge the caffe folder in the repo with my own caffe, and replace the type of dw layer to DepthwiseConvolution in deploy.prototxt , test speed is faster. But when I train the caffemodel, i use convlution rather than DepthwiseConvolution, that means DepthwiseConvolution is the same as convlution? And I don't add DepthwiseConvolution layer to caffe.proto,why it can work?
   2.when I train the mobilenet ,I ues DepthwiseConvolution,but the speed of train is also slow ,because the caffe.proto doesn't have DepthwiseConvolution layer ?

When make,it turns out:
src/caffe/layers/depthwise_conv_layer.cpp:31:46: error: ‘class caffe::DepthwiseConvolutionLayer’ has no member named ‘bottom_dim_’
this->forward_cpu_gemm(bottom_data + n * this->bottom_dim_, weight,
^
src/caffe/layers/depthwise_conv_layer.cpp:32:24: error: ‘class caffe::DepthwiseConvolutionLayer’ has no member named ‘top_dim_’
top_data + n * this->top_dim_);
^
src/caffe/layers/depthwise_conv_layer.cpp:35:45: error: ‘class caffe::DepthwiseConvolutionLayer’ has no member named ‘top_dim_’
this->forward_cpu_bias(top_data + n * this->top_dim_, bias);
^
src/caffe/layers/depthwise_conv_layer.cpp: In instantiation of ‘void caffe::DepthwiseConvolutionLayer::Backward_cpu(const std::vector<caffe::Blob>&, const std::vector&, const std::vector<caffe::Blob>&) [with Dtype = float]’:

Hi:
Thanks for the effort in writing customized DepthwiseConvolution layers.
I have successfully merge them with my caffe.
Using my TitanX(Maxwell), I got 20ms for a 224*224 image inference with batch=1.
But, I noticed that you have achieved 8ms.
Would you please tell me what GPU you tested with?
Thank you very much

Alex

in https://github.com/shicai/MobileNet-Caffe/blob/master/mobilenet_deploy.prototxt#L50
batch_norm_param {
use_global_stats: true

why your deploy not have batch_norm_param { use_global_stats: true}???
@yonghenglh6 Thanks

this layer is more efficient than original caffe but with the same function？

您好，我大致看了一下代码，您实现的应该是：输入的feature map有多少channel， 卷积就有多少group，不知道我的理解正确吗？

Thanks for your code and sharing.
In the very beginning lines in .cu file there are some notes saying

/*
 * The depthwise layer for mobilenet.   only for stride 1
 */

However when I quickly look through the code, the case 'stride != 1' seems to be already considered and handled.
In case of some errors, I'm writing to ask whether I can use stride>1 in this layer now?
Thanks!

hi @yonghenglh6 ,
which gpu do you use for the benchmark time of depthwise conv?

how to change the two super-parameters ?and, why the train time of depth-wise convolution is longer than the same out number of convolution?

Can you share your solver.prototxt ?
I only get 43% val acc when the epoch is 200000.
Mine is
net: "train_val.prototxt"
test_initialization: false
test_iter: 100
test_interval: 5000
display: 100
average_loss: 40
base_lr: 0.01
lr_policy: "poly"
power: 1.0
max_iter: 1000000
momentum: 0.9
weight_decay: 0.0001
snapshot: 5000
snapshot_prefix: "models/mobilenet"

NCSDK v2.05
u16@u16-System-Product-Name:~/work/realtime-object-detection$ mvNCCompile models/MobileNetSSD_deploy.prototxt -w models/MobileNetSSD_deploy.caffemodel -s 12 -is 300 300 -o graphs/mobilenetgraph
/usr/local/bin/ncsdk/Controllers/Parsers/TensorFlowParser/Convolution.py:45: SyntaxWarning: assertion is always true, perhaps remove parentheses?
assert(False, "Layer type not supported by Convolution: " + obj.type)
mvNCCompile v02.00, Copyright @ Intel Corporation 2017

[Error 4] Toolkit Error: Stage Type Not Supported: DepthwiseConvolution

It's just a copy of the caffe convolution layer with name changed to"depthwise"... Amusing it got so many stars~

I know it can run faster than original conv, but I find all the params are available in original caffe convolution, so if I just replace DepthwiseConvolution with Convolution, do they have same performance?
Or if depthwiseConvolution does something else?
Thanks very much

I use the latest version of caffe
Just copy three files and make?
Do I need to modify caffe.proto and layer_factory.cpp?

In Mobilenet-tensorflow (https://github.com/Zehaos/MobileNet), The speed in GTX-1080 is only 3ms, but caffe takes 8ms.

What makes the difference between caffe and tensorflow?

thanks

when I make cope the depthConv_layer to the caffe root, when I make caffe, always the crt1.o happen, the key error is display as below:
/usr/lib/gcc/x86_64-linux-gnu/5/../../../x86_64-linux-gnu/crt1.o: In function _start': (.text+0x20): undefined reference to main'
collect2: error: ld returned 1 exit status

any suggetion how to solve it?

Hi,I campare your depthwise_conv_layer.cpp with original caffe's conv_layer.cpp:except the layer name,all other is the same.Is that true?

Please forgive me for being impolite.

You maybe only have implemented the layer for CUDA, but the implementation of CPU is still only the original "Caffe's conv+group"?

The same issue is here: https://github.com/Zehaos/MobileNet/issues/22

I wonder if you will implement the DepthwiseConvolutionLayer for CPU? Any contribution will be grateful!

Best.

In your code comment ,it says only for stride 1. But in other issue you said it is for all stride. I checked the code , it is only for dalition1 ,not stride 1, right?

Did someone meet the problem ,called error == cudaSuccess (77 vs. 0 ) , when I changed the original convolution to the format of the depthwise convolution,and run it , I got the math_functions.cuL79 ] check failed: error == cudaSuccess (77 vs. 0 ) an illegal memory access was encountered. However, when I used the CAFFE TIME to test the same prototxt(the depthwise convolution prototxt), it worked and cumulated the network's time. @ @yonghenglh6 Thanks

Hi, I've placed those threes files under the corresponding folders. Then I go to caffe root dir to execute make clean && make -j4 all. But when I ran my network, it says:
I1114 07:16:55.252534 10776 layer_factory.hpp:77] Creating layer conv2_1/dw F1114 07:16:55.252562 10776 layer_factory.hpp:81] Check failed: registry.count(type) == 1 (0 vs. 1) Unknown layer type: DeptwiseConvolution (known types: AbsVal, Accuracy, ArgMax, BNLL, BatchNorm, BatchReindex, Bias, Concat, ContrastiveLoss, Convoltion, Crop, Data, Deconvolution, Dropout, DummyData, ELU, Eltwise, Embed, EuclideanLoss, Exp, Filter, Flatten, HDF5Data, HDFOutput, HingeLoss, Im2col, ImageData, InfogainLoss, InnerProduct, Input, LRN, LSTM, LSTMUnit, Log, MVN, MemoryData, MultinomalLogisticLoss, PReLU, Parameter, Pooling, Power, Python, RNN, ReLU, Reduction, Reshape, SPP, Scale, Sigmoid, SigmoidCrossEnropyLoss, Silence, Slice, Softmax, SoftmaxWithLoss, Split, TanH, Threshold, Tile, WindowData) *** Check failure stack trace: *** Aborted
Is there any possible reasons? Or are there extra files to modify? thanks a lot

i find the parameter(group) in DepthwiseConvolution layer of depthwised_mobilenet_deploy.prototxt
Is it still useful?

I use your code to train my net, and I found the dw layer's parameter is same with normal convolution. do you change the blob of weight size when create DepthwiseConvolution layer?

Hello, I added your .hpp/.cpp/.cu files to my caffe folders accordingly and compiled successfully(I didn't modify any other files). But when I try to train my mobilenet model, it always prompted " Unknown layer type: DepthwiseConvolution". Could you please tell me where the problem is? By the way, my caffe runs on windows 7.