为caffe添加最简单的全通层AllPassLayer

亦凉 2022-06-05 06:53 155阅读 0赞

官网操作步骤：https://github.com/BVLC/caffe/wiki/Development

1、"layers/"添加.hpp

2、"layers/"添加.cpp

3、（可选）"layers/"添加.cu

4、"proto/caffe.proto" 添加message LayerParameter

5、在.cpp中末尾写注册函数，如下所示：

`INSTANTIATE_CLASS(MyAwesomeLayer);``REGISTER_LAYER_CLASS(MyAwesome);`  
6、编写"test/test\_your\_layer.cpp"，用"test/test\_gradient\_check\_util.hpp"检查前向和反向传播  
`virtual void Backward_cpu(const vector<Blob<Dtype>*>& top, const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {`  
`NOT_IMPLEMENTED;`  
`}`

7、（可选）在"caffe/layer\_factory.cpp"注册Creator

8、不需要反向传播的时候，只需要改”include/caffe/your\_layer.hpp“（参考common.hpp, accuracy\_layer.hpp, threshold\_layer.hpp）

原文博客链接：http://www.cnblogs.com/zf-blog/p/6551278.html

参考赵永科的博客，这里我们实现一个新 Layer，名称为 AllPassLayer，顾名思义就是全通 Layer，“全通”借鉴于信号处理中的全通滤波器，将信号无失真地从输入转到输出。

虽然这个 Layer 并没有什么卵用，但是在这个基础上增加你的处理是非常简单的事情。另外也是出于实验考虑，全通层的 Forward/Backward 函数非常简单不需要读者有任何高等数学和求导的背景知识。读者使用该层时可以插入到任何已有网络中，而不会影响训练、预测的准确性。

首先，要把你的实现，要像正常的 Layer 类一样，分解为声明部分和实现部分，分别放在 .hpp 与 .cpp、.cu 中。Layer 名称要起一个能区别于原版实现的新名称。.hpp 文件置于 $CAFFE\_ROOT/include/caffe/layers/，而 .cpp 和 .cu 置于 $CAFFE\_ROOT/src/caffe/layers/，这样你在 $CAFFE\_ROOT 下执行 make 编译时，会自动将这些文件加入构建过程，省去了手动设置编译选项的繁琐流程。

其次，在 $CAFFE\_ROOT/src/caffe/proto/caffe.proto 中，增加新 LayerParameter 选项，这样你在编写 train.prototxt 或者 test.prototxt 或者 deploy.prototxt 时就能把新 Layer 的描述写进去，便于修改网络结构和替换其他相同功能的 Layer 了。

最后也是最容易忽视的一点，在 Layer 工厂注册新 Layer 加工函数，不然在你运行过程中可能会报如下错误：

![1062917-20170314224400010-591582360.jpg][]

首先看头文件（all\_pass\_layer.hpp）：

![复制代码][copycode.gif]

#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);  
    };  
      
    }  // namespace caffe

![复制代码][copycode.gif]

再看源文件(all\_pass\_layer.cpp和all\_pass\_layer.cu，这两个文件我暂时还不知道有什么区别，我是直接复制粘贴的，两个文件内容一样)：

![复制代码][copycode.gif]

#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(); //cpu_data()只读访问cpu data
      Dtype* top_data = top[0] -> mutable_cpu_data(); //mutable_cpu_data读写访问cpu data
      const int count = bottom[0] -> count(); //计算Blob中的元素总数
      for (int i = 0 ; i < count ; i ++)
      {
        top_data[i] = bottom_data[i];  //只是单纯的通过，全通
      }
      DEBUG_AP("Here is All Pass Layer , forwarding.");
      DEBUG_AP(this -> layer_param_.all_pass_param().key()); //读取prototxt预设值
    }
    
    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])  //propagate_down[0] = 1则指定计算权值的剃度 ， ...[1] = 1则指定计算偏置项的剃度，这里是计算权值的剃度
      {
        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());
    }
    
    #ifndef CPU_ONLY
    #define CPU_ONLY
    #endif INSTANTIATE_CLASS(AllPassLayer); REGISTER_LAYER_CLASS(AllPass); } //namespace caffe

![复制代码][copycode.gif]

时间考虑，我没有实现 GPU 模式的 forward、backward，故本文例程仅支持 CPU\_ONLY 模式。

编辑 caffe.proto，找到 LayerParameter 描述，增加一项：

![复制代码][copycode.gif]

message LayerParameter {  
      optional string name = 1; // the layer name  
      optional string type = 2; // the layer type  
      repeated string bottom = 3; // the name of each bottom blob  
      repeated string top = 4; // the name of each top blob  
      
      // The train / test phase for computation.  
      optional Phase phase = 10;  
      
      // The amount of weight to assign each top blob in the objective.  
      // Each layer assigns a default value, usually of either 0 or 1,  
      // to each top blob.  
      repeated float loss_weight = 5;  
      
      // Specifies training parameters (multipliers on global learning constants,  
      // and the name and other settings used for weight sharing).  
      repeated ParamSpec param = 6;  
      
      // The blobs containing the numeric parameters of the layer.  
      repeated BlobProto blobs = 7;  
      
      // Specifies on which bottoms the backpropagation should be skipped.  
      // The size must be either 0 or equal to the number of bottoms.  
      repeated bool propagate_down = 11;  
      
      // Rules controlling whether and when a layer is included in the network,  
      // based on the current NetState.  You may specify a non-zero number of rules  
      // to include OR exclude, but not both.  If no include or exclude rules are  
      // specified, the layer is always included.  If the current NetState meets  
      // ANY (i.e., one or more) of the specified rules, the layer is  
      // included/excluded.  
      repeated NetStateRule include = 8;  
      repeated NetStateRule exclude = 9;  
      
      // Parameters for data pre-processing.  
      optional TransformationParameter transform_param = 100;  
      
      // Parameters shared by loss layers.  
      optional LossParameter loss_param = 101;  
      
      // Layer type-specific parameters.  
      //  
      // Note: certain layers may have more than one computational engine  
      // for their implementation. These layers include an Engine type and  
      // engine parameter for selecting the implementation.  
      // The default for the engine is set by the ENGINE switch at compile-time.  
      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; }

![复制代码][copycode.gif]

注意新增数字不要和以前的 Layer 数字重复。

仍然在 caffe.proto 中，增加 AllPassParameter 声明，位置任意。我设定了一个参数，可以用于从 prototxt 中读取预设值。

message AllPassParameter {  
      optional float key = 1 [default = 0];  
    }

这句来读取 prototxt 预设值。

个人补充1：原作漏掉了在message V1LayerParameter下添加optional AllPassParameter all\_pass\_param = 888;和enum LayerType里添加AllPass =  666;（新ID不冲突即可）（该操作冗余，最新的版本里添加LayerParameter即可）

个人补充2：http://blog.csdn.net/huima2017/article/details/65628957。这篇博客在\{caffe\_dir\}/src/caffe/util下的upgrade\_proto.cpp的

const char\* UpgradeV1LayerType(const V1LayerParameter\_LayerType type)函数中添加如下代码，方便已经训练好的模型进行转换。

case V1LayerParameter\_ALLPASS :    
   return "AllPass";

个人补充3：在\{caffe\_dir\}/src/caffe/test/中增加layer的测试代码，对所写的layer前传和反传进行测试，测试还包括速度。（待补充。。。）

在 $CAFFE\_ROOT 下执行 make clean，然后重新 make all。要想一次编译成功，务必规范代码，对常见错误保持敏锐的嗅觉并加以避免。

万事具备，只欠 prototxt 了。

不难，我们写个最简单的 deploy.prototxt，不需要 data layer 和 softmax layer，just for fun。

![复制代码][copycode.gif]

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  
      }  
    }

![复制代码][copycode.gif]

注意，这里的 type ：后面写的内容，应该是你在 .hpp 中声明的新类 class name 去掉 Layer 后的名称。

上面设定了 key 这个参数的预设值为 12.88，嗯，你想到了刘翔对不对。

为了检验该 Layer 是否能正常创建和执行  forward, backward，我们运行 caffe time 命令并指定刚刚实现的 prototxt ：

![复制代码][copycode.gif]

$ ./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 ***

![复制代码][copycode.gif]

可见该 Layer 可以正常创建、加载预设参数、执行 forward、backward 函数。

实际上对于算法 Layer，还要写 Test Case 保证功能正确。由于我们选择了极为简单的全通 Layer，故这一步可以省去。我这里偷点懒，您省点阅读时间。

补充：make all后还要重新配置python接口，make pycaffe ,然后再按之前的操作就行了。。。

参考链接：http://blog.csdn.net/kkk584520/article/details/52721838

[1062917-20170314224400010-591582360.jpg]: /images/20220605/5c718b720d024194999b0025653485cb.png
[copycode.gif]: /images/20220605/f2ce9a7055d84fa59f3f7c0c283d33b6.png