SSD: ReLU6

1、在srccaffeprotocaffe.proto中搜索message LayerParameter,在optional ReLUParameter relu_param = 123之后添加optional ReLU6Parameter relu6_param = 208; (最后的分号别忘了)

2、搜索message ReLUParameter,在这个ReLUParameter实现结构之后添加

// Message that stores parameters used by ReLU6Layer
message ReLU6Parameter {
  enum Engine {
    DEFAULT = 0;
    CAFFE = 1;
    CUDNN = 2;
  }
  optional Engine engine = 2 [default = DEFAULT];
}

支持proto头文件修改完毕，接下来添加所需的头文件和实现文件。

1.在blob/ssd/include/caffe/layers文件夹下新建relu6_layer.hpp,将

#ifndef CAFFE_RELU_LAYER_HPP_
#define CAFFE_RELU_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 {

/**
 * @brief Rectified Linear Unit non-linearity @f$ y = min(6, max(0, x)) @f$.
 *        The simple max is fast to compute, and the function does not saturate.
 */
template <typename Dtype>
class ReLU6Layer : public NeuronLayer<Dtype> {
 public:
  /**
   * @param param provides ReLUParameter relu_param,
   *     with ReLULayer options:
   *   - negative_slope ( optional, default 0).
   *     the value @f$ 
u @f$ by which negative values are multiplied.
   */
  explicit ReLU6Layer(const LayerParameter& param)
      : NeuronLayer<Dtype>(param) {}

  virtual inline const char* type() const { return "ReLU6"; }

 protected:
  /**
   * @param bottom input Blob vector (length 1)
   *   -# @f$ (N 	imes C 	imes H 	imes W) @f$
   *      the inputs @f$ x @f$
   * @param top output Blob vector (length 1)
   *   -# @f$ (N 	imes C 	imes H 	imes W) @f$
   *      the computed outputs @f$
   *        y = max(0, x)
   *      @f$ by default.  If a non-zero negative_slope @f$ 
u @f$ is provided,
   *      the computed outputs are @f$ y = max(0, x) + 
u min(0, x) @f$.
   */
  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);

  /**
   * @brief Computes the error gradient w.r.t. the ReLU inputs.
   *
   * @param top output Blob vector (length 1), providing the error gradient with
   *      respect to the outputs
   *   -# @f$ (N 	imes C 	imes H 	imes W) @f$
   *      containing error gradients @f$ frac{partial E}{partial y} @f$
   *      with respect to computed outputs @f$ y @f$
   * @param propagate_down see Layer::Backward.
   * @param bottom input Blob vector (length 1)
   *   -# @f$ (N 	imes C 	imes H 	imes W) @f$
   *      the inputs @f$ x @f$; Backward fills their diff with
   *      gradients @f$
   *        frac{partial E}{partial x} = left{
   *        egin{array}{lr}
   *            0 & mathrm{if} ; x le 0 \
   *            frac{partial E}{partial y} & mathrm{if} ; x > 0
   *        end{array} 
ight.
   *      @f$ if propagate_down[0], by default.
   *      If a non-zero negative_slope @f$ 
u @f$ is provided,
   *      the computed gradients are @f$
   *        frac{partial E}{partial x} = left{
   *        egin{array}{lr}
   *            
u frac{partial E}{partial y} & mathrm{if} ; x le 0 \
   *            frac{partial E}{partial y} & mathrm{if} ; x > 0
   *        end{array} 
ight.
   *      @f$.
   */
  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

#endif  // CAFFE_RELU_LAYER_HPP_

 

2.在blob/ssd/src/caffe/layers文件夹下新建relu6_layer.cpp，将

#include <algorithm>
#include <vector>

#include "caffe/layers/relu6_layer.hpp"

namespace caffe {

template <typename Dtype>
void ReLU6Layer<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] = std::min(std::max(bottom_data[i], Dtype(0)), Dtype(6));
  }
}

template <typename Dtype>
void ReLU6Layer<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] * 
          ((bottom_data[i] > 0 && bottom_data[i] < 6));
    }
  }
}


#ifdef CPU_ONLY
STUB_GPU(ReLU6Layer);
#endif

INSTANTIATE_CLASS(ReLU6Layer);
REGISTER_LAYER_CLASS(ReLU6);

}  // namespace caffe

3.在blob/ssd/src/caffe/layers文件夹下新建relu6_layer.cu，将

#include <algorithm>
#include <vector>

#include "caffe/layers/relu6_layer.hpp"

namespace caffe {

template <typename Dtype>
__global__ void ReLU6Forward(const int n, const Dtype* in, Dtype* out) {
  CUDA_KERNEL_LOOP(index, n) {
    out[index] = in[index] < 0 ? 0: (in[index] > 6 ? 6 : in[index]);
  }
}

template <typename Dtype>
void ReLU6Layer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,
    const vector<Blob<Dtype>*>& top) {
  const Dtype* bottom_data = bottom[0]->gpu_data();
  Dtype* top_data = top[0]->mutable_gpu_data();
  const int count = bottom[0]->count();
  // NOLINT_NEXT_LINE(whitespace/operators)
  ReLU6Forward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
      count, bottom_data, top_data);
  CUDA_POST_KERNEL_CHECK;
  // << " count: " << count << " bottom_data: "
  //     << (unsigned long)bottom_data
  //     << " top_data: " << (unsigned long)top_data
  //     << " blocks: " << CAFFE_GET_BLOCKS(count)
  //     << " threads: " << CAFFE_CUDA_NUM_THREADS;
}

template <typename Dtype>
__global__ void ReLU6Backward(const int n, const Dtype* in_diff,
    const Dtype* in_data, Dtype* out_diff) {
  CUDA_KERNEL_LOOP(index, n) {
    out_diff[index] = in_diff[index] * ((in_data[index] > 0)
        && (in_data[index] < 6));
  }
}

template <typename Dtype>
void ReLU6Layer<Dtype>::Backward_gpu(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]->gpu_data();
    const Dtype* top_diff = top[0]->gpu_diff();
    Dtype* bottom_diff = bottom[0]->mutable_gpu_diff();
    const int count = bottom[0]->count();
    // NOLINT_NEXT_LINE(whitespace/operators)
    ReLU6Backward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>(
        count, top_diff, bottom_data, bottom_diff);
    CUDA_POST_KERNEL_CHECK;
  }
}


INSTANTIATE_LAYER_GPU_FUNCS(ReLU6Layer);


}  // namespace caffe

重新编译ssd。