为caffe添加最简单的全通层AllPassLayer

2023-08-21 16:30:58

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：

为caffe添加最简单的全通层AllPassLayer

首先看头文件（all_pass_layer.hpp）：

#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

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)：

#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[] -> cpu_data(); //cpu_data()只读访问cpu data

  Dtype* top_data = top[] -> mutable_cpu_data(); //mutable_cpu_data读写访问cpu data

  const int count = bottom[] -> count(); //计算Blob中的元素总数

  for (int i =  ; 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[])  //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[] -> cpu_data();

    const Dtype* top_diff = top[] -> cpu_diff();

    Dtype* bottom_diff = bottom[] -> mutable_cpu_diff();

    const int count = bottom[] -> count();

    for ( int i =  ; 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

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：

message LayerParameter {

  optional string name = ; // the layer name

  optional string type = ; // the layer type

  repeated string bottom = ; // the name of each bottom blob

  repeated string top = ; // the name of each top blob  

  // The train / test phase for computation.

  optional Phase phase = ;  

  // 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 = ;  

  // Specifies training parameters (multipliers on global learning constants,

  // and the name and other settings used for weight sharing).

  repeated ParamSpec param = ;  

  // The blobs containing the numeric parameters of the layer.

  repeated BlobProto blobs = ;  

  // 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 = ;  

  // 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 = ;

  repeated NetStateRule exclude = ;  

  // Parameters for data pre-processing.

  optional TransformationParameter transform_param = ;  

  // Parameters shared by loss layers.

  optional LossParameter loss_param = ;  

  // 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 = ;

  optional ArgMaxParameter argmax_param = ;

  optional BatchNormParameter batch_norm_param = ;

  optional BiasParameter bias_param = ;

  optional ConcatParameter concat_param = ;

  optional ContrastiveLossParameter contrastive_loss_param = ;

  optional ConvolutionParameter convolution_param = ;

  optional CropParameter crop_param = ;

  optional DataParameter data_param = ;

  optional DropoutParameter dropout_param = ;

  optional DummyDataParameter dummy_data_param = ;

  optional EltwiseParameter eltwise_param = ;

  optional ELUParameter elu_param = ;

  optional EmbedParameter embed_param = ;

  optional ExpParameter exp_param = ;

  optional FlattenParameter flatten_param = ;

  optional HDF5DataParameter hdf5_data_param = ;

  optional HDF5OutputParameter hdf5_output_param = ;

  optional HingeLossParameter hinge_loss_param = ;

  optional ImageDataParameter image_data_param = ;

  optional InfogainLossParameter infogain_loss_param = ;

  optional InnerProductParameter inner_product_param = ;

  optional InputParameter input_param = ;

  optional LogParameter log_param = ;

  optional LRNParameter lrn_param = ;

  optional MemoryDataParameter memory_data_param = ;

  optional MVNParameter mvn_param = ;

  optional PoolingParameter pooling_param = ;

  optional PowerParameter power_param = ;

  optional PReLUParameter prelu_param = ;

  optional PythonParameter python_param = ;

  optional ReductionParameter reduction_param = ;

  optional ReLUParameter relu_param = ;

  optional ReshapeParameter reshape_param = ;

  optional ScaleParameter scale_param = ;

  optional SigmoidParameter sigmoid_param = ;

  optional SoftmaxParameter softmax_param = ;

  optional SPPParameter spp_param = ;

  optional SliceParameter slice_param = ;

  optional TanHParameter tanh_param = ;

  optional ThresholdParameter threshold_param = ;

  optional TileParameter tile_param = ;

  optional WindowDataParameter window_data_param = ;

  optional AllPassParameter all_pass_param = ;

}

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 =  [default = ];

}

这句来读取 prototxt 预设值。

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。

name: "AllPassTest"

layer {

  name: "data"

  type: "Input"

  top: "data"

  input_param { shape: { dim:  dim:  dim:  dim:  } }

}

layer {

  name: "ap"

  type: "AllPass"

  bottom: "data"

  top: "conv1"

  all_pass_param {

    key: 12.88

  }

}

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 ：

$ ./build/tools/caffe.bin time -model deploy.prototxt

I1002 ::41.667682  caffe.cpp:] Use CPU.

I1002 ::41.671360  net.cpp:] Initializing net from parameters:

name: "AllPassTest"

state {

  phase: TRAIN

}

layer {

  name: "data"

  type: "Input"

  top: "data"

  input_param {

    shape {

      dim:

      dim:

      dim:

      dim:

    }

  }

}

layer {

  name: "ap"

  type: "AllPass"

  bottom: "data"

  top: "conv1"

  all_pass_param {

    key: 12.88

  }

}

I1002 ::41.671463  layer_factory.hpp:] Creating layer data

I1002 ::41.671484  net.cpp:] Creating Layer data

I1002 ::41.671499  net.cpp:] data -> data

I1002 ::41.671555  net.cpp:] Setting up data

I1002 ::41.671566  net.cpp:] Top shape:     ()

I1002 ::41.671592  net.cpp:] Memory required for data:

I1002 ::41.671605  layer_factory.hpp:] Creating layer ap

I1002 ::41.671620  net.cpp:] Creating Layer ap

I1002 ::41.671630  net.cpp:] ap <- data

I1002 ::41.671644  net.cpp:] ap -> conv1

I1002 ::41.671663  net.cpp:] Setting up ap

I1002 ::41.671674  net.cpp:] Top shape:     ()

I1002 ::41.671685  net.cpp:] Memory required for data:

I1002 ::41.671695  net.cpp:] ap does not need backward computation.

I1002 ::41.671705  net.cpp:] data does not need backward computation.

I1002 ::41.671710  net.cpp:] This network produces output conv1

I1002 ::41.671720  net.cpp:] Network initialization done.

I1002 ::41.671746  caffe.cpp:] Performing Forward

Here is All Pass Layer, forwarding.

12.88

I1002 ::41.679689  caffe.cpp:] Initial loss:

I1002 ::41.679714  caffe.cpp:] Performing Backward

I1002 ::41.679738  caffe.cpp:] *** Benchmark begins ***

I1002 ::41.679746  caffe.cpp:] Testing for  iterations.

Here is All Pass Layer, forwarding.

12.88

Here is All Pass Layer, backwarding.

12.88

I1002 ::41.681139  caffe.cpp:] Iteration:  forward-backward time:  ms.

Here is All Pass Layer, forwarding.

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Here is All Pass Layer, backwarding.

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Here is All Pass Layer, forwarding.

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Here is All Pass Layer, backwarding.

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Here is All Pass Layer, forwarding.

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Here is All Pass Layer, forwarding.

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Here is All Pass Layer, forwarding.

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Here is All Pass Layer, forwarding.

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Here is All Pass Layer, forwarding.

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Here is All Pass Layer, forwarding.

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Here is All Pass Layer, forwarding.

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Here is All Pass Layer, forwarding.

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Here is All Pass Layer, forwarding.

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Here is All Pass Layer, forwarding.

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Here is All Pass Layer, forwarding.

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Here is All Pass Layer, forwarding.

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Here is All Pass Layer, forwarding.

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Here is All Pass Layer, forwarding.

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Here is All Pass Layer, forwarding.

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Here is All Pass Layer, backwarding.

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Here is All Pass Layer, forwarding.

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Here is All Pass Layer, backwarding.

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Here is All Pass Layer, forwarding.

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Here is All Pass Layer, backwarding.

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Here is All Pass Layer, forwarding.

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Here is All Pass Layer, backwarding.

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Here is All Pass Layer, forwarding.

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Here is All Pass Layer, backwarding.

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Here is All Pass Layer, forwarding.

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Here is All Pass Layer, backwarding.

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I1002 ::41.712528  caffe.cpp:] Iteration:  forward-backward time:  ms.

Here is All Pass Layer, forwarding.

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Here is All Pass Layer, backwarding.

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Here is All Pass Layer, forwarding.

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Here is All Pass Layer, backwarding.

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Here is All Pass Layer, forwarding.

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Here is All Pass Layer, backwarding.

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Here is All Pass Layer, forwarding.

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Here is All Pass Layer, backwarding.

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Here is All Pass Layer, forwarding.

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Here is All Pass Layer, forwarding.

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Here is All Pass Layer, forwarding.

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Here is All Pass Layer, forwarding.

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Here is All Pass Layer, forwarding.

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Here is All Pass Layer, forwarding.

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Here is All Pass Layer, forwarding.

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Here is All Pass Layer, forwarding.

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Here is All Pass Layer, forwarding.

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Here is All Pass Layer, forwarding.

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Here is All Pass Layer, forwarding.

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Here is All Pass Layer, forwarding.

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Here is All Pass Layer, forwarding.

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Here is All Pass Layer, forwarding.

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Here is All Pass Layer, forwarding.

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Here is All Pass Layer, forwarding.

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Here is All Pass Layer, forwarding.

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Here is All Pass Layer, forwarding.

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Here is All Pass Layer, forwarding.

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I1002 ::41.745970  caffe.cpp:] Iteration:  forward-backward time:  ms.

Here is All Pass Layer, forwarding.

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Here is All Pass Layer, backwarding.

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I1002 ::41.747185  caffe.cpp:] Iteration:  forward-backward time:  ms.

Here is All Pass Layer, forwarding.

12.88

Here is All Pass Layer, backwarding.

12.88

I1002 ::41.748430  caffe.cpp:] Iteration:  forward-backward time:  ms.

Here is All Pass Layer, forwarding.

12.88

Here is All Pass Layer, backwarding.

12.88

I1002 ::41.749826  caffe.cpp:] Iteration:  forward-backward time:  ms.

Here is All Pass Layer, forwarding.

12.88

Here is All Pass Layer, backwarding.

12.88

I1002 ::41.751124  caffe.cpp:] Iteration:  forward-backward time:  ms.

I1002 ::41.751147  caffe.cpp:] Average time per layer:

I1002 ::41.751157  caffe.cpp:]       data  forward: 0.00108 ms.

I1002 ::41.751183  caffe.cpp:]       data  backward: 0.001 ms.

I1002 ::41.751194  caffe.cpp:]         ap  forward: 1.37884 ms.

I1002 ::41.751205  caffe.cpp:]         ap  backward: 0.01156 ms.

I1002 ::41.751220  caffe.cpp:] Average Forward pass: 1.38646 ms.

I1002 ::41.751231  caffe.cpp:] Average Backward pass: 0.0144 ms.

I1002 ::41.751240  caffe.cpp:] Average Forward-Backward: 1.42 ms.

I1002 ::41.751250  caffe.cpp:] Total Time:  ms.

I1002 ::41.751260  caffe.cpp:] *** Benchmark ends ***

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

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