如何给20151230版caffe添加新的层 （一）

由于caffe在一步一步的更新，因此半年前的教程可能不适用于当前版的caffe

因此把这次尝试caffe修改过程记录下来，以饷后人。

首先下载caffe

https://github.com/yjxiong/caffe

这个版本的caffe是multi-gpu支持比较好的，用的是MPI技术，用的显存又少，而且在第一时间段支持了cudnn4，follow起来还是很爽的

关于第一阶段最naive的想法是添加类似conv层功能的Layer，主要参考了

http://blog.csdn.net/kuaitoukid/article/details/41865803

同样的，我们将新的layer明明为wtf layer

修改步骤

1.在 include/caffe/vision_layers.hpp 中添加 wtflayer的定义，由于此处不实现GPU函数，因此注释掉了gpu函数

template <typename Dtype>
class WtfLayer : public BaseConvolutionLayer<Dtype> {
 public:

  explicit WtfLayer(const LayerParameter& param)
      : BaseConvolutionLayer<Dtype>(param) {}

  virtual inline const char* type() const { return "Wtf"; }

 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);
  virtual inline bool reverse_dimensions() { return false; }
  virtual void compute_output_shape();
};

2.将Wtf_layer.cpp添加到src\caffe\layers文件夹中，代码内容复制convolutio_layer.cpp即可，注意在最后复制的时候添加 REGISTER_LAYER_CLASS(Wtf); 注册一下Wtf Layer

#include <vector>

#include "caffe/filler.hpp"
#include "caffe/layer.hpp"
#include "caffe/util/im2col.hpp"
#include "caffe/util/math_functions.hpp"
#include "caffe/vision_layers.hpp"

namespace caffe {

template <typename Dtype>
void WtfLayer<Dtype>::compute_output_shape() {
  this->height_out_ = (this->height_ + 2 * this->pad_h_ - this->kernel_h_)
      / this->stride_h_ + 1;
  this->width_out_ = (this->width_ + 2 * this->pad_w_ - this->kernel_w_)
      / this->stride_w_ + 1;
}

template <typename Dtype>
void WtfLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
      const vector<Blob<Dtype>*>& top) {
  const Dtype* weight = this->blobs_[0]->cpu_data();
  for (int i = 0; i < bottom.size(); ++i) {
    const Dtype* bottom_data = bottom[i]->cpu_data();
    Dtype* top_data = top[i]->mutable_cpu_data();
    for (int n = 0; n < this->num_; ++n) {
      this->forward_cpu_gemm(bottom_data + bottom[i]->offset(n), weight,
          top_data + top[i]->offset(n));
      if (this->bias_term_) {
        const Dtype* bias = this->blobs_[1]->cpu_data();
        this->forward_cpu_bias(top_data + top[i]->offset(n), bias);
      }
    }
  }
}

template <typename Dtype>
void WtfLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
  const Dtype* weight = this->blobs_[0]->cpu_data();
  Dtype* weight_diff = this->blobs_[0]->mutable_cpu_diff();
  for (int i = 0; i < top.size(); ++i) {
    const Dtype* top_diff = top[i]->cpu_diff();
    const Dtype* bottom_data = bottom[i]->cpu_data();
    Dtype* bottom_diff = bottom[i]->mutable_cpu_diff();
    // Bias gradient, if necessary.
    if (this->bias_term_ && this->param_propagate_down_[1]) {
      Dtype* bias_diff = this->blobs_[1]->mutable_cpu_diff();
      for (int n = 0; n < this->num_; ++n) {
        this->backward_cpu_bias(bias_diff, top_diff + top[i]->offset(n));
      }
    }
    if (this->param_propagate_down_[0] || propagate_down[i]) {
      for (int n = 0; n < this->num_; ++n) {
        // gradient w.r.t. weight. Note that we will accumulate diffs.
        if (this->param_propagate_down_[0]) {
          this->weight_cpu_gemm(bottom_data + bottom[i]->offset(n),
              top_diff + top[i]->offset(n), weight_diff);
        }
        // gradient w.r.t. bottom data, if necessary.
        if (propagate_down[i]) {
          this->backward_cpu_gemm(top_diff + top[i]->offset(n), weight,
              bottom_diff + bottom[i]->offset(n));
        }
      }
    }
  }
}

#ifdef CPU_ONLY
STUB_GPU(WtfLayer);
#endif

INSTANTIATE_CLASS(WtfLayer);
REGISTER_LAYER_CLASS(Wtf);

}  // namespace caffe

3.  修改proto/caffe.proto文件，找到LayerType，添加WTF，并更新ID。假如说Wtf_Layer有参数，比如Convolution肯定是有参数的，那么添加WtfParameter类
具体添加一下几行

optional WtfParameter wtf_param = 141;

以及

message WtfParameter {
  optional uint32 num_output = 1; // The number of outputs for the layer
  optional bool bias_term = 2 [default = true]; // whether to have bias terms
  // Pad, kernel size, and stride are all given as a single value for equal
  // dimensions in height and width or as Y, X pairs.
  optional uint32 pad = 3 [default = 0]; // The padding size (equal in Y, X)
  optional uint32 pad_h = 9 [default = 0]; // The padding height
  optional uint32 pad_w = 10 [default = 0]; // The padding width
  optional uint32 kernel_size = 4; // The kernel size (square)
  optional uint32 kernel_h = 11; // The kernel height
  optional uint32 kernel_w = 12; // The kernel width
  optional uint32 group = 5 [default = 1]; // The group size for group conv
  optional uint32 stride = 6 [default = 1]; // The stride (equal in Y, X)
  optional uint32 stride_h = 13; // The stride height
  optional uint32 stride_w = 14; // The stride width
  optional FillerParameter weight_filler = 7; // The filler for the weight
  optional FillerParameter bias_filler = 8; // The filler for the bias
  enum Engine {
    DEFAULT = 0;
    CAFFE = 1;
    CUDNN = 2;
  }
  optional Engine engine = 15 [default = DEFAULT];
}

以及

WTF = 52;

以及

optional WtfParameter wtf_param = 53;

接下来编译通过

如何测试我们写的layer的正确性呢？

1.将某些层的type: "Convolution"  转化为type: "Wtf"  测试Wtf的loss情况下降的和Convolution是否一样

2.有了训练好的model和prototxt，用matlab和python接口测试一下是否达到了卷积的效果。

暂时到这里~等待后来继续添加层