轉載自：http://blog.csdn.net/kkk584520/article/details/52721838
http://blog.csdn.net/kkk584520
博客內容基于新書《深度學習：21 天實戰 Caffe》，書中課后習題答案歡迎讀者留言討論。以下進入正文。
在使用 Caffe 過程中經常會有這樣的需求：已有 Layer 不符合我的應用場景；我需要這樣這樣的功能，原版代碼沒有實現；或者已經實現但效率太低，我有更好的實現。
方案一：簡單粗暴的解法——偷天換日
如果你對?ConvolutionLayer 的實現不滿意，那就直接改這兩個文件：$CAFFE_ROOT/include/caffe/layers/conv_layer.hpp 和 $CAFFE_ROOT/src/caffe/layers/conv_layer.cpp 或 conv_layer.cu ，將 im2col + gemm 替換為你自己的實現（比如基于 winograd 算法的實現）。
優點：快速迭代，不需要對 Caffe 框架有過多了解，糙快狠準。
缺點：代碼難維護，不能 merge 到 caffe master branch，容易給使用代碼的人帶來困惑（效果和 #define TRUE false 差不多）。
方案二：稍微溫柔的解法——千人千面
和方案一類似，只是通過預編譯宏來確定使用哪種實現。例如可以保留?ConvolutionLayer 默認實現，同時在代碼中增加如下段：
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#ifdef?SWITCH_MY_IMPLEMENTATION?? ?? #else?? ?? #endif??
這樣可以在需要使用該 Layer 的代碼中，增加宏定義：
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#define?SWITCH_MY_IMPLEMENTATION??
就可以使用你的實現。而未定義該宏的代碼，仍然使用原版實現。
優點：可以在新舊實現代碼之間靈活切換；
缺點：每次切換需要重新編譯；
方案三：優雅轉身——山路十八彎
同一個功能的 Layer 有不同實現，希望能靈活切換又不需要重新編譯代碼，該如何實現？
這時不得不使用 ProtoBuffer 工具了。
首先，要把你的實現，要像正常的 Layer 類一樣，分解為聲明部分和實現部分，分別放在 .hpp 與 .cpp、.cu 中。Layer 名稱要起一個能區別于原版實現的新名稱。.hpp 文件置于?$CAFFE_ROOT/include/caffe/layers/，而 .cpp 和 .cu 置于?$CAFFE_ROOT/src/caffe/layers/，這樣你在 $CAFFE_ROOT 下執行 make 編譯時，會自動將這些文件加入構建過程，省去了手動設置編譯選項的繁瑣流程。
其次，在?$CAFFE_ROOT/src/caffe/proto/caffe.proto 中，增加新 LayerParameter 選項，這樣你在編寫 train.prototxt 或者 test.prototxt 或者 deploy.prototxt 時就能把新 Layer 的描述寫進去，便于修改網絡結構和替換其他相同功能的 Layer 了。
最后也是最容易忽視的一點，在 Layer 工廠注冊新 Layer 加工函數，不然在你運行過程中可能會報如下錯誤：
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F1002?01:51:22.656038?1954701312?layer_factory.hpp:81]?Check?failed:?registry.count(type)?==?1?(0?vs.?1)?Unknown?layer?type:?AllPass?(known?types:?AbsVal,?Accuracy,?ArgMax,?BNLL,?BatchNorm,?BatchReindex,?Bias,?Concat,?ContrastiveLoss,?Convolution,?Crop,?Data,?Deconvolution,?Dropout,?DummyData,?ELU,?Eltwise,?Embed,?EuclideanLoss,?Exp,?Filter,?Flatten,?HDF5Data,?HDF5Output,?HingeLoss,?Im2col,?ImageData,?InfogainLoss,?InnerProduct,?Input,?LRN,?Log,?MVN,?MemoryData,?MultinomialLogisticLoss,?PReLU,?Pooling,?Power,?ReLU,?Reduction,?Reshape,?SPP,?Scale,?Sigmoid,?SigmoidCrossEntropyLoss,?Silence,?Slice,?Softmax,?SoftmaxWithLoss,?Split,?TanH,?Threshold,?Tile,?WindowData)?? ***?Check?failure?stack?trace:?***?? ????@????????0x10243154e??google::LogMessage::Fail()?? ????@????????0x102430c53??google::LogMessage::SendToLog()?? ????@????????0x1024311a9??google::LogMessage::Flush()?? ????@????????0x1024344d7??google::LogMessageFatal::~LogMessageFatal()?? ????@????????0x10243183b??google::LogMessageFatal::~LogMessageFatal()?? ????@????????0x102215356??caffe::LayerRegistry<>::CreateLayer()?? ????@????????0x102233ccf??caffe::Net<>::Init()?? ????@????????0x102235996??caffe::Net<>::Net()?? ????@????????0x102118d8b??time()?? ????@????????0x102119c9a??main?? ????@?????0x7fff851285ad??start?? ????@????????????????0x4??(unknown)?? Abort?trap:?6??
下面給出一個實際案例，走一遍方案三的流程。
這里我們實現一個新 Layer，名稱為 AllPassLayer，顧名思義就是全通 Layer，“全通”借鑒于信號處理中的全通濾波器，將信號無失真地從輸入轉到輸出。
雖然這個 Layer 并沒有什么卵用，但是在這個基礎上增加你的處理是非常簡單的事情。另外也是出于實驗考慮，全通層的 Forward/Backward 函數非常簡單不需要讀者有任何高等數學和求導的背景知識。讀者使用該層時可以插入到任何已有網絡中，而不會影響訓練、預測的準確性。
首先看頭文件：
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#ifndef?CAFFE_ALL_PASS_LAYER_HPP_?? #define?CAFFE_ALL_PASS_LAYER_HPP_?? ?? #include?<vector>?? ?? #include?"caffe/blob.hpp"?? #include?"caffe/layer.hpp"?? #include?"caffe/proto/caffe.pb.h"?? ?? #include?"caffe/layers/neuron_layer.hpp"?? ?? namespace?caffe?{?? template?<typename?Dtype>?? class?AllPassLayer?:?public?NeuronLayer<Dtype>?{?? ?public:?? ??explicit?AllPassLayer(const?LayerParameter&?param)?? ??????:?NeuronLayer<Dtype>(param)?{}?? ?? ??virtual?inline?const?char*?type()?const?{?return?"AllPass";?}?? ?? ?protected:?? ?? ??virtual?void?Forward_cpu(const?vector<Blob<Dtype>*>&?bottom,?? ??????const?vector<Blob<Dtype>*>&?top);?? ??virtual?void?Forward_gpu(const?vector<Blob<Dtype>*>&?bottom,?? ??????const?vector<Blob<Dtype>*>&?top);?? ??virtual?void?Backward_cpu(const?vector<Blob<Dtype>*>&?top,?? ??????const?vector<bool>&?propagate_down,?const?vector<Blob<Dtype>*>&?bottom);?? ??virtual?void?Backward_gpu(const?vector<Blob<Dtype>*>&?top,?? ??????const?vector<bool>&?propagate_down,?const?vector<Blob<Dtype>*>&?bottom);?? };?? ?? }???? ?? #endif??//?CAFFE_ALL_PASS_LAYER_HPP_??
再看源文件：
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#include?<algorithm>?? #include?<vector>?? ?? #include?"caffe/layers/all_pass_layer.hpp"?? ?? #include?<iostream>?? using?namespace?std;?? #define?DEBUG_AP(str)?cout<<str<<endl?? namespace?caffe?{?? ?? template?<typename?Dtype>?? void?AllPassLayer<Dtype>::Forward_cpu(const?vector<Blob<Dtype>*>&?bottom,?? ????const?vector<Blob<Dtype>*>&?top)?{?? ??const?Dtype*?bottom_data?=?bottom[0]->cpu_data();?? ??Dtype*?top_data?=?top[0]->mutable_cpu_data();?? ??const?int?count?=?bottom[0]->count();?? ??for?(int?i?=?0;?i?<?count;?++i)?{?? ????top_data[i]?=?bottom_data[i];?? ??}?? ??DEBUG_AP("Here?is?All?Pass?Layer,?forwarding.");?? ??DEBUG_AP(this->layer_param_.all_pass_param().key());?? }?? ?? template?<typename?Dtype>?? void?AllPassLayer<Dtype>::Backward_cpu(const?vector<Blob<Dtype>*>&?top,?? ????const?vector<bool>&?propagate_down,?? ????const?vector<Blob<Dtype>*>&?bottom)?{?? ??if?(propagate_down[0])?{?? ????const?Dtype*?bottom_data?=?bottom[0]->cpu_data();?? ????const?Dtype*?top_diff?=?top[0]->cpu_diff();?? ????Dtype*?bottom_diff?=?bottom[0]->mutable_cpu_diff();?? ????const?int?count?=?bottom[0]->count();?? ????for?(int?i?=?0;?i?<?count;?++i)?{?? ??????bottom_diff[i]?=?top_diff[i];?? ????}?? ??}?? ??DEBUG_AP("Here?is?All?Pass?Layer,?backwarding.");?? ??DEBUG_AP(this->layer_param_.all_pass_param().key());?? }?? ?? ?? #ifdef?CPU_ONLY?? STUB_GPU(AllPassLayer);?? #endif?? ?? INSTANTIATE_CLASS(AllPassLayer);?? REGISTER_LAYER_CLASS(AllPass);?? }????
時間考慮，我沒有實現 GPU 模式的 forward、backward，故本文例程僅支持 CPU_ONLY 模式。
編輯 caffe.proto，找到 LayerParameter 描述，增加一項：
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message?LayerParameter?{?? ??optional?string?name?=?1;??? ??optional?string?type?=?2;??? ??repeated?string?bottom?=?3;??? ??repeated?string?top?=?4;??? ?? ???? ??optional?Phase?phase?=?10;?? ?? ???? ???? ???? ??repeated?float?loss_weight?=?5;?? ?? ???? ???? ??repeated?ParamSpec?param?=?6;?? ?? ???? ??repeated?BlobProto?blobs?=?7;?? ?? ???? ???? ??repeated?bool?propagate_down?=?11;?? ?? ???? ???? ???? ???? ???? ???? ??repeated?NetStateRule?include?=?8;?? ??repeated?NetStateRule?exclude?=?9;?? ?? ???? ??optional?TransformationParameter?transform_param?=?100;?? ?? ???? ??optional?LossParameter?loss_param?=?101;?? ?? ???? ???? ???? ???? ???? ???? ??optional?AccuracyParameter?accuracy_param?=?102;?? ??optional?ArgMaxParameter?argmax_param?=?103;?? ??optional?BatchNormParameter?batch_norm_param?=?139;?? ??optional?BiasParameter?bias_param?=?141;?? ??optional?ConcatParameter?concat_param?=?104;?? ??optional?ContrastiveLossParameter?contrastive_loss_param?=?105;?? ??optional?ConvolutionParameter?convolution_param?=?106;?? ??optional?CropParameter?crop_param?=?144;?? ??optional?DataParameter?data_param?=?107;?? ??optional?DropoutParameter?dropout_param?=?108;?? ??optional?DummyDataParameter?dummy_data_param?=?109;?? ??optional?EltwiseParameter?eltwise_param?=?110;?? ??optional?ELUParameter?elu_param?=?140;?? ??optional?EmbedParameter?embed_param?=?137;?? ??optional?ExpParameter?exp_param?=?111;?? ??optional?FlattenParameter?flatten_param?=?135;?? ??optional?HDF5DataParameter?hdf5_data_param?=?112;?? ??optional?HDF5OutputParameter?hdf5_output_param?=?113;?? ??optional?HingeLossParameter?hinge_loss_param?=?114;?? ??optional?ImageDataParameter?image_data_param?=?115;?? ??optional?InfogainLossParameter?infogain_loss_param?=?116;?? ??optional?InnerProductParameter?inner_product_param?=?117;?? ??optional?InputParameter?input_param?=?143;?? ??optional?LogParameter?log_param?=?134;?? ??optional?LRNParameter?lrn_param?=?118;?? ??optional?MemoryDataParameter?memory_data_param?=?119;?? ??optional?MVNParameter?mvn_param?=?120;?? ??optional?PoolingParameter?pooling_param?=?121;?? ??optional?PowerParameter?power_param?=?122;?? ??optional?PReLUParameter?prelu_param?=?131;?? ??optional?PythonParameter?python_param?=?130;?? ??optional?ReductionParameter?reduction_param?=?136;?? ??optional?ReLUParameter?relu_param?=?123;?? ??optional?ReshapeParameter?reshape_param?=?133;?? ??optional?ScaleParameter?scale_param?=?142;?? ??optional?SigmoidParameter?sigmoid_param?=?124;?? ??optional?SoftmaxParameter?softmax_param?=?125;?? ??optional?SPPParameter?spp_param?=?132;?? ??optional?SliceParameter?slice_param?=?126;?? ??optional?TanHParameter?tanh_param?=?127;?? ??optional?ThresholdParameter?threshold_param?=?128;?? ??optional?TileParameter?tile_param?=?138;?? ??optional?WindowDataParameter?window_data_param?=?129;?? ??optional?AllPassParameter?all_pass_param?=?155;?? }??
注意新增數字不要和以前的 Layer 數字重復。
仍然在 caffe.proto 中，增加 AllPassParameter 聲明，位置任意。我設定了一個參數，可以用于從 prototxt 中讀取預設值。
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message?AllPassParameter?{?? ??optional?float?key?=?1?[default?=?0];?? }??
在 cpp 代碼中，通過
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this->layer_param_.all_pass_param().key()??
這句來讀取 prototxt 預設值。
在 $CAFFE_ROOT 下執行 make clean，然后重新 make all。要想一次編譯成功，務必規范代碼，對常見錯誤保持敏銳的嗅覺并加以避免。
萬事具備，只欠 prototxt 了。
不難，我們寫個最簡單的 deploy.prototxt，不需要 data layer 和 softmax layer，just for fun。
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name:?"AllPassTest"?? layer?{?? ??name:?"data"?? ??type:?"Input"?? ??top:?"data"?? ??input_param?{?shape:?{?dim:?10?dim:?3?dim:?227?dim:?227?}?}?? }?? layer?{?? ??name:?"ap"?? ??type:?"AllPass"?? ??bottom:?"data"?? ??top:?"conv1"?? ??all_pass_param?{?? ????key:?12.88?? ??}?? }??
注意，這里的 type ：后面寫的內容，應該是你在 .hpp 中聲明的新類 class name 去掉 Layer 后的名稱。
上面設定了 key 這個參數的預設值為 12.88，嗯，你想到了劉翔對不對。
為了檢驗該 Layer 是否能正常創建和執行 ?forward, backward，我們運行 caffe time 命令并指定剛剛實現的 prototxt ：
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$?./build/tools/caffe.bin?time?-model?deploy.prototxt?? I1002?02:03:41.667682?1954701312?caffe.cpp:312]?Use?CPU.?? I1002?02:03:41.671360?1954701312?net.cpp:49]?Initializing?net?from?parameters:?? name:?"AllPassTest"?? state?{?? ??phase:?TRAIN?? }?? layer?{?? ??name:?"data"?? ??type:?"Input"?? ??top:?"data"?? ??input_param?{?? ????shape?{?? ??????dim:?10?? ??????dim:?3?? ??????dim:?227?? ??????dim:?227?? ????}?? ??}?? }?? layer?{?? ??name:?"ap"?? ??type:?"AllPass"?? ??bottom:?"data"?? ??top:?"conv1"?? ??all_pass_param?{?? ????key:?12.88?? ??}?? }?? I1002?02:03:41.671463?1954701312?layer_factory.hpp:77]?Creating?layer?data?? I1002?02:03:41.671484?1954701312?net.cpp:91]?Creating?Layer?data?? I1002?02:03:41.671499?1954701312?net.cpp:399]?data?->?data?? I1002?02:03:41.671555?1954701312?net.cpp:141]?Setting?up?data?? I1002?02:03:41.671566?1954701312?net.cpp:148]?Top?shape:?10?3?227?227?(1545870)?? I1002?02:03:41.671592?1954701312?net.cpp:156]?Memory?required?for?data:?6183480?? I1002?02:03:41.671605?1954701312?layer_factory.hpp:77]?Creating?layer?ap?? I1002?02:03:41.671620?1954701312?net.cpp:91]?Creating?Layer?ap?? I1002?02:03:41.671630?1954701312?net.cpp:425]?ap?<-?data?? I1002?02:03:41.671644?1954701312?net.cpp:399]?ap?->?conv1?? I1002?02:03:41.671663?1954701312?net.cpp:141]?Setting?up?ap?? I1002?02:03:41.671674?1954701312?net.cpp:148]?Top?shape:?10?3?227?227?(1545870)?? I1002?02:03:41.671685?1954701312?net.cpp:156]?Memory?required?for?data:?12366960?? I1002?02:03:41.671695?1954701312?net.cpp:219]?ap?does?not?need?backward?computation.?? I1002?02:03:41.671705?1954701312?net.cpp:219]?data?does?not?need?backward?computation.?? I1002?02:03:41.671710?1954701312?net.cpp:261]?This?network?produces?output?conv1?? I1002?02:03:41.671720?1954701312?net.cpp:274]?Network?initialization?done.?? I1002?02:03:41.671746?1954701312?caffe.cpp:320]?Performing?Forward?? Here?is?All?Pass?Layer,?forwarding.?? 12.88?? I1002?02:03:41.679689?1954701312?caffe.cpp:325]?Initial?loss:?0?? I1002?02:03:41.679714?1954701312?caffe.cpp:326]?Performing?Backward?? I1002?02:03:41.679738?1954701312?caffe.cpp:334]?***?Benchmark?begins?***?? I1002?02:03:41.679746?1954701312?caffe.cpp:335]?Testing?for?50?iterations.?? Here?is?All?Pass?Layer,?forwarding.?? 12.88?? Here?is?All?Pass?Layer,?backwarding.?? 12.88?? I1002?02:03:41.681139?1954701312?caffe.cpp:363]?Iteration:?1?forward-backward?time:?1?ms.?? Here?is?All?Pass?Layer,?forwarding.?? 12.88?? Here?is?All?Pass?Layer,?backwarding.?? 12.88?? I1002?02:03:41.682394?1954701312?caffe.cpp:363]?Iteration:?2?forward-backward?time:?1?ms.?? Here?is?All?Pass?Layer,?forwarding.?? 12.88?? Here?is?All?Pass?Layer,?backwarding.?? 12.88?? I1002?02:03:41.683653?1954701312?caffe.cpp:363]?Iteration:?3?forward-backward?time:?1?ms.?? Here?is?All?Pass?Layer,?forwarding.?? 12.88?? Here?is?All?Pass?Layer,?backwarding.?? 12.88?? I1002?02:03:41.685096?1954701312?caffe.cpp:363]?Iteration:?4?forward-backward?time:?1?ms.?? Here?is?All?Pass?Layer,?forwarding.?? 12.88?? Here?is?All?Pass?Layer,?backwarding.?? 12.88?? I1002?02:03:41.686326?1954701312?caffe.cpp:363]?Iteration:?5?forward-backward?time:?1?ms.?? Here?is?All?Pass?Layer,?forwarding.?? 12.88?? Here?is?All?Pass?Layer,?backwarding.?? 12.88?? I1002?02:03:41.687713?1954701312?caffe.cpp:363]?Iteration:?6?forward-backward?time:?1?ms.?? Here?is?All?Pass?Layer,?forwarding.?? 12.88?? Here?is?All?Pass?Layer,?backwarding.?? 12.88?? I1002?02:03:41.689038?1954701312?caffe.cpp:363]?Iteration:?7?forward-backward?time:?1?ms.?? Here?is?All?Pass?Layer,?forwarding.?? 12.88?? Here?is?All?Pass?Layer,?backwarding.?? 12.88?? I1002?02:03:41.690251?1954701312?caffe.cpp:363]?Iteration:?8?forward-backward?time:?1?ms.?? Here?is?All?Pass?Layer,?forwarding.?? 12.88?? Here?is?All?Pass?Layer,?backwarding.?? 12.88?? I1002?02:03:41.691548?1954701312?caffe.cpp:363]?Iteration:?9?forward-backward?time:?1?ms.?? Here?is?All?Pass?Layer,?forwarding.?? 12.88?? Here?is?All?Pass?Layer,?backwarding.?? 12.88?? I1002?02:03:41.692805?1954701312?caffe.cpp:363]?Iteration:?10?forward-backward?time:?1?ms.?? Here?is?All?Pass?Layer,?forwarding.?? 12.88?? Here?is?All?Pass?Layer,?backwarding.?? 12.88?? I1002?02:03:41.694056?1954701312?caffe.cpp:363]?Iteration:?11?forward-backward?time:?1?ms.?? Here?is?All?Pass?Layer,?forwarding.?? 12.88?? Here?is?All?Pass?Layer,?backwarding.?? 12.88?? I1002?02:03:41.695264?1954701312?caffe.cpp:363]?Iteration:?12?forward-backward?time:?1?ms.?? Here?is?All?Pass?Layer,?forwarding.?? 12.88?? Here?is?All?Pass?Layer,?backwarding.?? 12.88?? I1002?02:03:41.696761?1954701312?caffe.cpp:363]?Iteration:?13?forward-backward?time:?1?ms.?? Here?is?All?Pass?Layer,?forwarding.?? 12.88?? Here?is?All?Pass?Layer,?backwarding.?? 12.88?? I1002?02:03:41.698225?1954701312?caffe.cpp:363]?Iteration:?14?forward-backward?time:?1?ms.?? Here?is?All?Pass?Layer,?forwarding.?? 12.88?? Here?is?All?Pass?Layer,?backwarding.?? 12.88?? I1002?02:03:41.699653?1954701312?caffe.cpp:363]?Iteration:?15?forward-backward?time:?1?ms.?? Here?is?All?Pass?Layer,?forwarding.?? 12.88?? Here?is?All?Pass?Layer,?backwarding.?? 12.88?? I1002?02:03:41.700945?1954701312?caffe.cpp:363]?Iteration:?16?forward-backward?time:?1?ms.?? Here?is?All?Pass?Layer,?forwarding.?? 12.88?? Here?is?All?Pass?Layer,?backwarding.?? 12.88?? I1002?02:03:41.702761?1954701312?caffe.cpp:363]?Iteration:?17?forward-backward?time:?1?ms.?? Here?is?All?Pass?Layer,?forwarding.?? 12.88?? Here?is?All?Pass?Layer,?backwarding.?? 12.88?? I1002?02:03:41.704056?1954701312?caffe.cpp:363]?Iteration:?18?forward-backward?time:?1?ms.?? Here?is?All?Pass?Layer,?forwarding.?? 12.88?? Here?is?All?Pass?Layer,?backwarding.?? 12.88?? I1002?02:03:41.706471?1954701312?caffe.cpp:363]?Iteration:?19?forward-backward?time:?2?ms.?? Here?is?All?Pass?Layer,?forwarding.?? 12.88?? Here?is?All?Pass?Layer,?backwarding.?? 12.88?? I1002?02:03:41.708784?1954701312?caffe.cpp:363]?Iteration:?20?forward-backward?time:?2?ms.?? Here?is?All?Pass?Layer,?forwarding.?? 12.88?? Here?is?All?Pass?Layer,?backwarding.?? 12.88?? I1002?02:03:41.710043?1954701312?caffe.cpp:363]?Iteration:?21?forward-backward?time:?1?ms.?? Here?is?All?Pass?Layer,?forwarding.?? 12.88?? Here?is?All?Pass?Layer,?backwarding.?? 12.88?? I1002?02:03:41.711272?1954701312?caffe.cpp:363]?Iteration:?22?forward-backward?time:?1?ms.?? Here?is?All?Pass?Layer,?forwarding.?? 12.88?? Here?is?All?Pass?Layer,?backwarding.?? 12.88?? I1002?02:03:41.712528?1954701312?caffe.cpp:363]?Iteration:?23?forward-backward?time:?1?ms.?? Here?is?All?Pass?Layer,?forwarding.?? 12.88?? Here?is?All?Pass?Layer,?backwarding.?? 12.88?? I1002?02:03:41.713964?1954701312?caffe.cpp:363]?Iteration:?24?forward-backward?time:?1?ms.?? Here?is?All?Pass?Layer,?forwarding.?? 12.88?? Here?is?All?Pass?Layer,?backwarding.?? 12.88?? I1002?02:03:41.715248?1954701312?caffe.cpp:363]?Iteration:?25?forward-backward?time:?1?ms.?? Here?is?All?Pass?Layer,?forwarding.?? 12.88?? Here?is?All?Pass?Layer,?backwarding.?? 12.88?? I1002?02:03:41.716487?1954701312?caffe.cpp:363]?Iteration:?26?forward-backward?time:?1?ms.?? Here?is?All?Pass?Layer,?forwarding.?? 12.88?? Here?is?All?Pass?Layer,?backwarding.?? 12.88?? I1002?02:03:41.717725?1954701312?caffe.cpp:363]?Iteration:?27?forward-backward?time:?1?ms.?? Here?is?All?Pass?Layer,?forwarding.?? 12.88?? Here?is?All?Pass?Layer,?backwarding.?? 12.88?? I1002?02:03:41.718962?1954701312?caffe.cpp:363]?Iteration:?28?forward-backward?time:?1?ms.?? Here?is?All?Pass?Layer,?forwarding.?? 12.88?? Here?is?All?Pass?Layer,?backwarding.?? 12.88?? I1002?02:03:41.720289?1954701312?caffe.cpp:363]?Iteration:?29?forward-backward?time:?1?ms.?? Here?is?All?Pass?Layer,?forwarding.?? 12.88?? Here?is?All?Pass?Layer,?backwarding.?? 12.88?? I1002?02:03:41.721837?1954701312?caffe.cpp:363]?Iteration:?30?forward-backward?time:?1?ms.?? Here?is?All?Pass?Layer,?forwarding.?? 12.88?? Here?is?All?Pass?Layer,?backwarding.?? 12.88?? I1002?02:03:41.723042?1954701312?caffe.cpp:363]?Iteration:?31?forward-backward?time:?1?ms.?? Here?is?All?Pass?Layer,?forwarding.?? 12.88?? Here?is?All?Pass?Layer,?backwarding.?? 12.88?? I1002?02:03:41.724261?1954701312?caffe.cpp:363]?Iteration:?32?forward-backward?time:?1?ms.?? Here?is?All?Pass?Layer,?forwarding.?? 12.88?? Here?is?All?Pass?Layer,?backwarding.?? 12.88?? I1002?02:03:41.725587?1954701312?caffe.cpp:363]?Iteration:?33?forward-backward?time:?1?ms.?? Here?is?All?Pass?Layer,?forwarding.?? 12.88?? Here?is?All?Pass?Layer,?backwarding.?? 12.88?? I1002?02:03:41.726771?1954701312?caffe.cpp:363]?Iteration:?34?forward-backward?time:?1?ms.?? Here?is?All?Pass?Layer,?forwarding.?? 12.88?? Here?is?All?Pass?Layer,?backwarding.?? 12.88?? I1002?02:03:41.728013?1954701312?caffe.cpp:363]?Iteration:?35?forward-backward?time:?1?ms.?? Here?is?All?Pass?Layer,?forwarding.?? 12.88?? Here?is?All?Pass?Layer,?backwarding.?? 12.88?? I1002?02:03:41.729249?1954701312?caffe.cpp:363]?Iteration:?36?forward-backward?time:?1?ms.?? Here?is?All?Pass?Layer,?forwarding.?? 12.88?? Here?is?All?Pass?Layer,?backwarding.?? 12.88?? I1002?02:03:41.730716?1954701312?caffe.cpp:363]?Iteration:?37?forward-backward?time:?1?ms.?? Here?is?All?Pass?Layer,?forwarding.?? 12.88?? Here?is?All?Pass?Layer,?backwarding.?? 12.88?? I1002?02:03:41.732275?1954701312?caffe.cpp:363]?Iteration:?38?forward-backward?time:?1?ms.?? Here?is?All?Pass?Layer,?forwarding.?? 12.88?? Here?is?All?Pass?Layer,?backwarding.?? 12.88?? I1002?02:03:41.733809?1954701312?caffe.cpp:363]?Iteration:?39?forward-backward?time:?1?ms.?? Here?is?All?Pass?Layer,?forwarding.?? 12.88?? Here?is?All?Pass?Layer,?backwarding.?? 12.88?? I1002?02:03:41.735049?1954701312?caffe.cpp:363]?Iteration:?40?forward-backward?time:?1?ms.?? Here?is?All?Pass?Layer,?forwarding.?? 12.88?? Here?is?All?Pass?Layer,?backwarding.?? 12.88?? I1002?02:03:41.737144?1954701312?caffe.cpp:363]?Iteration:?41?forward-backward?time:?2?ms.?? Here?is?All?Pass?Layer,?forwarding.?? 12.88?? Here?is?All?Pass?Layer,?backwarding.?? 12.88?? I1002?02:03:41.739090?1954701312?caffe.cpp:363]?Iteration:?42?forward-backward?time:?1?ms.?? Here?is?All?Pass?Layer,?forwarding.?? 12.88?? Here?is?All?Pass?Layer,?backwarding.?? 12.88?? I1002?02:03:41.741575?1954701312?caffe.cpp:363]?Iteration:?43?forward-backward?time:?2?ms.?? Here?is?All?Pass?Layer,?forwarding.?? 12.88?? Here?is?All?Pass?Layer,?backwarding.?? 12.88?? I1002?02:03:41.743450?1954701312?caffe.cpp:363]?Iteration:?44?forward-backward?time:?1?ms.?? Here?is?All?Pass?Layer,?forwarding.?? 12.88?? Here?is?All?Pass?Layer,?backwarding.?? 12.88?? I1002?02:03:41.744732?1954701312?caffe.cpp:363]?Iteration:?45?forward-backward?time:?1?ms.?? Here?is?All?Pass?Layer,?forwarding.?? 12.88?? Here?is?All?Pass?Layer,?backwarding.?? 12.88?? I1002?02:03:41.745970?1954701312?caffe.cpp:363]?Iteration:?46?forward-backward?time:?1?ms.?? Here?is?All?Pass?Layer,?forwarding.?? 12.88?? Here?is?All?Pass?Layer,?backwarding.?? 12.88?? I1002?02:03:41.747185?1954701312?caffe.cpp:363]?Iteration:?47?forward-backward?time:?1?ms.?? Here?is?All?Pass?Layer,?forwarding.?? 12.88?? Here?is?All?Pass?Layer,?backwarding.?? 12.88?? I1002?02:03:41.748430?1954701312?caffe.cpp:363]?Iteration:?48?forward-backward?time:?1?ms.?? Here?is?All?Pass?Layer,?forwarding.?? 12.88?? Here?is?All?Pass?Layer,?backwarding.?? 12.88?? I1002?02:03:41.749826?1954701312?caffe.cpp:363]?Iteration:?49?forward-backward?time:?1?ms.?? Here?is?All?Pass?Layer,?forwarding.?? 12.88?? Here?is?All?Pass?Layer,?backwarding.?? 12.88?? I1002?02:03:41.751124?1954701312?caffe.cpp:363]?Iteration:?50?forward-backward?time:?1?ms.?? I1002?02:03:41.751147?1954701312?caffe.cpp:366]?Average?time?per?layer:?? I1002?02:03:41.751157?1954701312?caffe.cpp:369]???????data??forward:?0.00108?ms.?? I1002?02:03:41.751183?1954701312?caffe.cpp:372]???????data??backward:?0.001?ms.?? I1002?02:03:41.751194?1954701312?caffe.cpp:369]?????????ap??forward:?1.37884?ms.?? I1002?02:03:41.751205?1954701312?caffe.cpp:372]?????????ap??backward:?0.01156?ms.?? I1002?02:03:41.751220?1954701312?caffe.cpp:377]?Average?Forward?pass:?1.38646?ms.?? I1002?02:03:41.751231?1954701312?caffe.cpp:379]?Average?Backward?pass:?0.0144?ms.?? I1002?02:03:41.751240?1954701312?caffe.cpp:381]?Average?Forward-Backward:?1.42?ms.?? I1002?02:03:41.751250?1954701312?caffe.cpp:383]?Total?Time:?71?ms.?? I1002?02:03:41.751260?1954701312?caffe.cpp:384]?***?Benchmark?ends?***??
可見該 Layer 可以正常創建、加載預設參數、執行 forward、backward 函數。
總結
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