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Caffe实战系列：如何将CRFAsRNN移植到caffe-windows上去

（1）移植辅助的文件

将include/caffe/util/下的coords.hpp和modified_permutohedral.hpp复制到caffe-windows对应的目录
将src/caffe/util/modified_permutohedral.cpp复制到对应的目录中去

（2）移植Layer中的特性

在include/caffe/layer.hpp中添加
如下代码：
#include "caffe/util/coords.hpp"

和以下代码：
  virtual DiagonalAffineMap<Dtype> coord_map() {
      NOT_IMPLEMENTED;
      // suppress warnings
      return DiagonalAffineMap<Dtype>(vector<pair<Dtype, Dtype> >());
  }
修改后的文件如下：
#ifndef CAFFE_LAYER_H_
#define CAFFE_LAYER_H_

#include <algorithm>
#include <string>
#include <vector>

#include "caffe/blob.hpp"
#include "caffe/common.hpp"
#include "caffe/layer_factory.hpp"
#include "caffe/proto/caffe.pb.h"
#include "caffe/util/coords.hpp"
#include "caffe/util/math_functions.hpp"

/**
 Forward declare boost::thread instead of including boost/thread.hpp
 to avoid a boost/NVCC issues (#1009, #1010) on OSX.
 */
namespace boost { class mutex; }

namespace caffe {

/**
 * @brief An interface for the units of computation which can be composed into a
 *        Net.
 *
 * Layer%s must implement a Forward function, in which they take their input
 * (bottom) Blob%s (if any) and compute their output Blob%s (if any).
 * They may also implement a Backward function, in which they compute the error
 * gradients with respect to their input Blob%s, given the error gradients with
 * their output Blob%s.
 */
template <typename Dtype>
class Layer {
 public:
  /**
   * You should not implement your own constructor. Any set up code should go
   * to SetUp(), where the dimensions of the bottom blobs are provided to the
   * layer.
   */
  explicit Layer(const LayerParameter& param)
    : layer_param_(param), is_shared_(false) {
      // Set phase and copy blobs (if there are any).
      phase_ = param.phase();
      if (layer_param_.blobs_size() > 0) {
        blobs_.resize(layer_param_.blobs_size());
        for (int i = 0; i < layer_param_.blobs_size(); ++i) {
          blobs_[i].reset(new Blob<Dtype>());
          blobs_[i]->FromProto(layer_param_.blobs(i));
        }
      }
    }
  virtual ~Layer() {}

  /**
   * @brief Implements common layer setup functionality.
   *
   * @param bottom the preshaped input blobs
   * @param top
   *     the allocated but unshaped output blobs, to be shaped by Reshape
   *
   * Checks that the number of bottom and top blobs is correct.
   * Calls LayerSetUp to do special layer setup for individual layer types,
   * followed by Reshape to set up sizes of top blobs and internal buffers.
   * Sets up the loss weight multiplier blobs for any non-zero loss weights.
   * This method may not be overridden.
   */
  void SetUp(const vector<Blob<Dtype>*>& bottom,
      const vector<Blob<Dtype>*>& top) {
    InitMutex();
    CheckBlobCounts(bottom, top);
    LayerSetUp(bottom, top);
    Reshape(bottom, top);
    SetLossWeights(top);
  }

  /**
   * @brief Does layer-specific setup: your layer should implement this function
   *        as well as Reshape.
   *
   * @param bottom
   *     the preshaped input blobs, whose data fields store the input data for
   *     this layer
   * @param top
   *     the allocated but unshaped output blobs
   *
   * This method should do one-time layer specific setup. This includes reading
   * and processing relevent parameters from the <code>layer_param_</code>.
   * Setting up the shapes of top blobs and internal buffers should be done in
   * <code>Reshape</code>, which will be called before the forward pass to
   * adjust the top blob sizes.
   */
  virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
      const vector<Blob<Dtype>*>& top) {}

  /**
   * @brief Whether a layer should be shared by multiple nets during data
   *        parallelism. By default, all layers except for data layers should
   *        not be shared. data layers should be shared to ensure each worker
   *        solver access data sequentially during data parallelism.
   */
  virtual inline bool ShareInParallel() const { return false; }

  /** @brief Return whether this layer is actually shared by other nets.
   *         If ShareInParallel() is true and using more than one GPU and the
   *         net has TRAIN phase, then this function is expected return true.
   */
  inline bool IsShared() const { return is_shared_; }

  /** @brief Set whether this layer is actually shared by other nets
   *         If ShareInParallel() is true and using more than one GPU and the
   *         net has TRAIN phase, then is_shared should be set true.
   */
  inline void SetShared(bool is_shared) {
    CHECK(ShareInParallel() || !is_shared)
        << type() << "Layer does not support sharing.";
    is_shared_ = is_shared;
  }

  /**
   * @brief Adjust the shapes of top blobs and internal buffers to accommodate
   *        the shapes of the bottom blobs.
   *
   * @param bottom the input blobs, with the requested input shapes
   * @param top the top blobs, which should be reshaped as needed
   *
   * This method should reshape top blobs as needed according to the shapes
   * of the bottom (input) blobs, as well as reshaping any internal buffers
   * and making any other necessary adjustments so that the layer can
   * accommodate the bottom blobs.
   */
  virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
      const vector<Blob<Dtype>*>& top) = 0;

  /**
   * @brief Given the bottom blobs, compute the top blobs and the loss.
   *
   * @param bottom
   *     the input blobs, whose data fields store the input data for this layer
   * @param top
   *     the preshaped output blobs, whose data fields will store this layers'
   *     outputs
   * \return The total loss from the layer.
   *
   * The Forward wrapper calls the relevant device wrapper function
   * (Forward_cpu or Forward_gpu) to compute the top blob values given the
   * bottom blobs.  If the layer has any non-zero loss_weights, the wrapper
   * then computes and returns the loss.
   *
   * Your layer should implement Forward_cpu and (optionally) Forward_gpu.
   */
  inline Dtype Forward(const vector<Blob<Dtype>*>& bottom,
      const vector<Blob<Dtype>*>& top);

  /**
   * @brief Given the top blob error gradients, compute the bottom blob error
   *        gradients.
   *
   * @param top
   *     the output blobs, whose diff fields store the gradient of the error
   *     with respect to themselves
   * @param propagate_down
   *     a vector with equal length to bottom, with each index indicating
   *     whether to propagate the error gradients down to the bottom blob at
   *     the corresponding index
   * @param bottom
   *     the input blobs, whose diff fields will store the gradient of the error
   *     with respect to themselves after Backward is run
   *
   * The Backward wrapper calls the relevant device wrapper function
   * (Backward_cpu or Backward_gpu) to compute the bottom blob diffs given the
   * top blob diffs.
   *
   * Your layer should implement Backward_cpu and (optionally) Backward_gpu.
   */
  inline void Backward(const vector<Blob<Dtype>*>& top,
      const vector<bool>& propagate_down,
      const vector<Blob<Dtype>*>& bottom);

  /**
   * @brief Returns the vector of learnable parameter blobs.
   */
  vector<shared_ptr<Blob<Dtype> > >& blobs() {
    return blobs_;
  }

  /**
   * @brief Returns the layer parameter.
   */
  const LayerParameter& layer_param() const { return layer_param_; }

  /**
   * @brief Writes the layer parameter to a protocol buffer
   */
  virtual void ToProto(LayerParameter* param, bool write_diff = false);

  /**
   * @brief Returns the scalar loss associated with a top blob at a given index.
   */
  inline Dtype loss(const int top_index) const {
    return (loss_.size() > top_index) ? loss_[top_index] : Dtype(0);
  }

  /**
   * @brief Sets the loss associated with a top blob at a given index.
   */
  inline void set_loss(const int top_index, const Dtype value) {
    if (loss_.size() <= top_index) {
      loss_.resize(top_index + 1, Dtype(0));
    }
    loss_[top_index] = value;
  }

  /**
   * @brief Returns the layer type.
   */
  virtual inline const char* type() const { return ""; }

  /**
   * @brief Returns the exact number of bottom blobs required by the layer,
   *        or -1 if no exact number is required.
   *
   * This method should be overridden to return a non-negative value if your
   * layer expects some exact number of bottom blobs.
   */
  virtual inline int ExactNumBottomBlobs() const { return -1; }
  /**
   * @brief Returns the minimum number of bottom blobs required by the layer,
   *        or -1 if no minimum number is required.
   *
   * This method should be overridden to return a non-negative value if your
   * layer expects some minimum number of bottom blobs.
   */
  virtual inline int MinBottomBlobs() const { return -1; }
  /**
   * @brief Returns the maximum number of bottom blobs required by the layer,
   *        or -1 if no maximum number is required.
   *
   * This method should be overridden to return a non-negative value if your
   * layer expects some maximum number of bottom blobs.
   */
  virtual inline int MaxBottomBlobs() const { return -1; }
  /**
   * @brief Returns the exact number of top blobs required by the layer,
   *        or -1 if no exact number is required.
   *
   * This method should be overridden to return a non-negative value if your
   * layer expects some exact number of top blobs.
   */
  virtual inline int ExactNumTopBlobs() const { return -1; }
  /**
   * @brief Returns the minimum number of top blobs required by the layer,
   *        or -1 if no minimum number is required.
   *
   * This method should be overridden to return a non-negative value if your
   * layer expects some minimum number of top blobs.
   */
  virtual inline int MinTopBlobs() const { return -1; }
  /**
   * @brief Returns the maximum number of top blobs required by the layer,
   *        or -1 if no maximum number is required.
   *
   * This method should be overridden to return a non-negative value if your
   * layer expects some maximum number of top blobs.
   */
  virtual inline int MaxTopBlobs() const { return -1; }
  /**
   * @brief Returns true if the layer requires an equal number of bottom and
   *        top blobs.
   *
   * This method should be overridden to return true if your layer expects an
   * equal number of bottom and top blobs.
   */
  virtual inline bool EqualNumBottomTopBlobs() const { return false; }

  /**
   * @brief Return whether "anonymous" top blobs are created automatically
   *        by the layer.
   *
   * If this method returns true, Net::Init will create enough "anonymous" top
   * blobs to fulfill the requirement specified by ExactNumTopBlobs() or
   * MinTopBlobs().
   */
  virtual inline bool AutoTopBlobs() const { return false; }

  /**
   * @brief Return whether to allow force_backward for a given bottom blob
   *        index.
   *
   * If AllowForceBackward(i) == false, we will ignore the force_backward
   * setting and backpropagate to blob i only if it needs gradient information
   * (as is done when force_backward == false).
   */
  virtual inline bool AllowForceBackward(const int bottom_index) const {
    return true;
  }

  /**
   * @brief Specifies whether the layer should compute gradients w.r.t. a
   *        parameter at a particular index given by param_id.
   *
   * You can safely ignore false values and always compute gradients
   * for all parameters, but possibly with wasteful computation.
   */
  inline bool param_propagate_down(const int param_id) {
    return (param_propagate_down_.size() > param_id) ?
        param_propagate_down_[param_id] : false;
  }
  /**
   * @brief Sets whether the layer should compute gradients w.r.t. a
   *        parameter at a particular index given by param_id.
   */
  inline void set_param_propagate_down(const int param_id, const bool value) {
    if (param_propagate_down_.size() <= param_id) {
      param_propagate_down_.resize(param_id + 1, true);
    }
    param_propagate_down_[param_id] = value;
  }

  virtual DiagonalAffineMap<Dtype> coord_map() {
      NOT_IMPLEMENTED;
      // suppress warnings
      return DiagonalAffineMap<Dtype>(vector<pair<Dtype, Dtype> >());
  }


 protected:
  /** The protobuf that stores the layer parameters */
  LayerParameter layer_param_;
  /** The phase: TRAIN or TEST */
  Phase phase_;
  /** The vector that stores the learnable parameters as a set of blobs. */
  vector<shared_ptr<Blob<Dtype> > > blobs_;
  /** Vector indicating whether to compute the diff of each param blob. */
  vector<bool> param_propagate_down_;

  /** The vector that indicates whether each top blob has a non-zero weight in
   *  the objective function. */
  vector<Dtype> loss_;

  /** @brief Using the CPU device, compute the layer output. */
  virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
      const vector<Blob<Dtype>*>& top) = 0;
  /**
   * @brief Using the GPU device, compute the layer output.
   *        Fall back to Forward_cpu() if unavailable.
   */
  virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
      const vector<Blob<Dtype>*>& top) {
    // LOG(WARNING) << "Using CPU code as backup.";
    return Forward_cpu(bottom, top);
  }

  /**
   * @brief Using the CPU device, compute the gradients for any parameters and
   *        for the bottom blobs if propagate_down is true.
   */
  virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
      const vector<bool>& propagate_down,
      const vector<Blob<Dtype>*>& bottom) = 0;
  /**
   * @brief Using the GPU device, compute the gradients for any parameters and
   *        for the bottom blobs if propagate_down is true.
   *        Fall back to Backward_cpu() if unavailable.
   */
  virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
      const vector<bool>& propagate_down,
      const vector<Blob<Dtype>*>& bottom) {
    // LOG(WARNING) << "Using CPU code as backup.";
    Backward_cpu(top, propagate_down, bottom);
  }

  /**
   * Called by the parent Layer's SetUp to check that the number of bottom
   * and top Blobs provided as input match the expected numbers specified by
   * the {ExactNum,Min,Max}{Bottom,Top}Blobs() functions.
   */
  virtual void CheckBlobCounts(const vector<Blob<Dtype>*>& bottom,
                               const vector<Blob<Dtype>*>& top) {
    if (ExactNumBottomBlobs() >= 0) {
      CHECK_EQ(ExactNumBottomBlobs(), bottom.size())
          << type() << " Layer takes " << ExactNumBottomBlobs()
          << " bottom blob(s) as input.";
    }
    if (MinBottomBlobs() >= 0) {
      CHECK_LE(MinBottomBlobs(), bottom.size())
          << type() << " Layer takes at least " << MinBottomBlobs()
          << " bottom blob(s) as input.";
    }
    if (MaxBottomBlobs() >= 0) {
      CHECK_GE(MaxBottomBlobs(), bottom.size())
          << type() << " Layer takes at most " << MaxBottomBlobs()
          << " bottom blob(s) as input.";
    }
    if (ExactNumTopBlobs() >= 0) {
      CHECK_EQ(ExactNumTopBlobs(), top.size())
          << type() << " Layer produces " << ExactNumTopBlobs()
          << " top blob(s) as output.";
    }
    if (MinTopBlobs() >= 0) {
      CHECK_LE(MinTopBlobs(), top.size())
          << type() << " Layer produces at least " << MinTopBlobs()
          << " top blob(s) as output.";
    }
    if (MaxTopBlobs() >= 0) {
      CHECK_GE(MaxTopBlobs(), top.size())
          << type() << " Layer produces at most " << MaxTopBlobs()
          << " top blob(s) as output.";
    }
    if (EqualNumBottomTopBlobs()) {
      CHECK_EQ(bottom.size(), top.size())
          << type() << " Layer produces one top blob as output for each "
          << "bottom blob input.";
    }
  }

  /**
   * Called by SetUp to initialize the weights associated with any top blobs in
   * the loss function. Store non-zero loss weights in the diff blob.
   */
  inline void SetLossWeights(const vector<Blob<Dtype>*>& top) {
    const int num_loss_weights = layer_param_.loss_weight_size();
    if (num_loss_weights) {
      CHECK_EQ(top.size(), num_loss_weights) << "loss_weight must be "
          "unspecified or specified once per top blob.";
      for (int top_id = 0; top_id < top.size(); ++top_id) {
        const Dtype loss_weight = layer_param_.loss_weight(top_id);
        if (loss_weight == Dtype(0)) { continue; }
        this->set_loss(top_id, loss_weight);
        const int count = top[top_id]->count();
        Dtype* loss_multiplier = top[top_id]->mutable_cpu_diff();
        caffe_set(count, loss_weight, loss_multiplier);
      }
    }
  }

 private:
  /** Whether this layer is actually shared by other nets*/
  bool is_shared_;

  /** The mutex for sequential forward if this layer is shared */
  shared_ptr<boost::mutex> forward_mutex_;

  /** Initialize forward_mutex_ */
  void InitMutex();
  /** Lock forward_mutex_ if this layer is shared */
  void Lock();
  /** Unlock forward_mutex_ if this layer is shared */
  void Unlock();

  DISABLE_COPY_AND_ASSIGN(Layer);
};  // class Layer

// Forward and backward wrappers. You should implement the cpu and
// gpu specific implementations instead, and should not change these
// functions.
template <typename Dtype>
inline Dtype Layer<Dtype>::Forward(const vector<Blob<Dtype>*>& bottom,
    const vector<Blob<Dtype>*>& top) {
  // Lock during forward to ensure sequential forward
  Lock();
  Dtype loss = 0;
  Reshape(bottom, top);
  switch (Caffe::mode()) {
  case Caffe::CPU:
    Forward_cpu(bottom, top);
    for (int top_id = 0; top_id < top.size(); ++top_id) {
      if (!this->loss(top_id)) { continue; }
      const int count = top[top_id]->count();
      const Dtype* data = top[top_id]->cpu_data();
      const Dtype* loss_weights = top[top_id]->cpu_diff();
      loss += caffe_cpu_dot(count, data, loss_weights);
    }
    break;
  case Caffe::GPU:
    Forward_gpu(bottom, top);
#ifndef CPU_ONLY
    for (int top_id = 0; top_id < top.size(); ++top_id) {
      if (!this->loss(top_id)) { continue; }
      const int count = top[top_id]->count();
      const Dtype* data = top[top_id]->gpu_data();
      const Dtype* loss_weights = top[top_id]->gpu_diff();
      Dtype blob_loss = 0;
      caffe_gpu_dot(count, data, loss_weights, &blob_loss);
      loss += blob_loss;
    }
#endif
    break;
  default:
    LOG(FATAL) << "Unknown caffe mode.";
  }
  Unlock();
  return loss;
}

template <typename Dtype>
inline void Layer<Dtype>::Backward(const vector<Blob<Dtype>*>& top,
    const vector<bool>& propagate_down,
    const vector<Blob<Dtype>*>& bottom) {
  switch (Caffe::mode()) {
  case Caffe::CPU:
    Backward_cpu(top, propagate_down, bottom);
    break;
  case Caffe::GPU:
    Backward_gpu(top, propagate_down, bottom);
    break;
  default:
    LOG(FATAL) << "Unknown caffe mode.";
  }
}

// Serialize LayerParameter to protocol buffer
template <typename Dtype>
void Layer<Dtype>::ToProto(LayerParameter* param, bool write_diff) {
  param->Clear();
  param->CopyFrom(layer_param_);
  param->clear_blobs();
  for (int i = 0; i < blobs_.size(); ++i) {
    blobs_[i]->ToProto(param->add_blobs(), write_diff);
  }
}

}  // namespace caffe

#endif  // CAFFE_LAYER_H_





（3）移植CRF层


将caffe/vision_layers.hpp中的MultiStageMeanfieldLayer对应的声明复制到caffe-windows下的caffe/layers/目录下
创建multi_stage_meanfield.hpp

文件的内容如下：

/*!
 * \brief A helper class for {@link MultiStageMeanfieldLayer} class, which is the Caffe layer that implements the
 * CRF-RNN described in the paper: Conditional Random Fields as Recurrent Neural Networks. IEEE ICCV 2015.
 *
 * This class itself is not a proper Caffe layer although it behaves like one to some degree.
 *
 * \authors Sadeep Jayasumana, Bernardino Romera-Paredes, Shuai Zheng, Zhizhong Su.
 * \version 1.0
 * \date 2015
 * \copyright Torr Vision Group, University of Oxford.
 * \details If you use this code, please consider citing the paper:
 * Shuai Zheng, Sadeep Jayasumana, Bernardino Romera-Paredes, Vibhav Vineet, Zhizhong Su, Dalong Du,
 * Chang Huang, Philip H. S. Torr. Conditional Random Fields as Recurrent Neural Networks. IEEE ICCV 2015.
 *
 * For more information about CRF-RNN, please visit the project website http://crfasrnn.torr.vision.
 */

#ifndef CAFFE_MULTI_STAGE_MEANFIELD_LAYER_HPP_
#define CAFFE_MULTI_STAGE_MEANFIELD_LAYER_HPP_

#include <vector>

#include "caffe/blob.hpp"
#include "caffe/layer.hpp"
#include "caffe/proto/caffe.pb.h"
#include "caffe/util/modified_permutohedral.hpp"
#include "caffe/layers/softmax_layer.hpp"
#include "caffe/layers/eltwise_layer.hpp"
#include "caffe/layers/split_layer.hpp"
#include <boost/shared_array.hpp>

namespace caffe {
/*!
 * \brief The Caffe layer that implements the CRF-RNN described in the paper:
 * Conditional Random Fields as Recurrent Neural Networks. IEEE ICCV 2015.
 *
 * \authors Sadeep Jayasumana, Bernardino Romera-Paredes, Shuai Zheng, Zhizhong Su.
 * \version 1.0
 * \date 2015
 * \copyright Torr Vision Group, University of Oxford.
 * \details If you use this code, please consider citing the paper:
 * Shuai Zheng, Sadeep Jayasumana, Bernardino Romera-Paredes, Vibhav Vineet, Zhizhong Su, Dalong Du,
 * Chang Huang, Philip H. S. Torr. Conditional Random Fields as Recurrent Neural Networks. IEEE ICCV 2015.
 *
 * For more information about CRF-RNN, please visit the project website http://crfasrnn.torr.vision.
 */
template <typename Dtype>
class MultiStageMeanfieldLayer : public Layer <Dtype > {

  public :
  explicit MultiStageMeanfieldLayer ( const LayerParameter & param ) : Layer< Dtype >(param ) {}

  virtual void LayerSetUp( const vector < Blob< Dtype >*>& bottom,
  const vector < Blob< Dtype >*>& top);

  virtual void Reshape( const vector < Blob< Dtype >*>& bottom,
  const vector < Blob< Dtype >*>& top);

  virtual inline const char * type () const {
  return "MultiStageMeanfield" ;
  }
  virtual inline int ExactNumBottomBlobs () const { return 3 ; }
  virtual inline int ExactNumTopBlobs () const { return 1 ; }

  protected :
  virtual void Forward_cpu( 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 compute_spatial_kernel (float * const output_kernel );
  virtual void compute_bilateral_kernel (const Blob< Dtype >* const rgb_blob, const int n, float* const output_kernel);

  int count_ ;
  int num_ ;
  int channels_ ;
  int height_ ;
  int width_ ;
  int num_pixels_ ;

  Dtype theta_alpha_ ;
  Dtype theta_beta_ ;
  Dtype theta_gamma_ ;
  int num_iterations_ ;

  boost ::shared_array < Dtype> norm_feed_;
  Blob <Dtype > spatial_norm_ ;
  Blob <Dtype > bilateral_norms_ ;

  vector <Blob < Dtype>*> split_layer_bottom_vec_ ;
  vector <Blob < Dtype>*> split_layer_top_vec_ ;
  vector <shared_ptr < Blob< Dtype > > > split_layer_out_blobs_ ;
  vector <shared_ptr < Blob< Dtype > > > iteration_output_blobs_ ;
  vector <shared_ptr < MeanfieldIteration <Dtype > > > meanfield_iterations_ ;

  shared_ptr <SplitLayer < Dtype> > split_layer_ ;

  shared_ptr <ModifiedPermutohedral > spatial_lattice_ ;
  boost ::shared_array < float> bilateral_kernel_buffer_ ;
  vector <shared_ptr < ModifiedPermutohedral > > bilateral_lattices_ ;
};

} // namespace caffe
#endif //
将caffe/layers/multi_stage_meanfield.cpp都复制到caffe-windows对应的目录下
然后将其中的include的vision_layers.hpp修改为multi_stage_meanfield.hpp
并在multi_stage_meanfield.cpp 文件的末尾修改类的声明方式为：
INSTANTIATE_CLASS(MultiStageMeanfieldLayer);
REGISTER_LAYER_CLASS(MultiStageMeanfield);

修改后的multi_stage_meanfield.cpp文件如下所示

/*!
 * \brief The Caffe layer that implements the CRF-RNN described in the paper:
 * Conditional Random Fields as Recurrent Neural Networks. IEEE ICCV 2015.
 *
 * \authors Sadeep Jayasumana, Bernardino Romera-Paredes, Shuai Zheng, Zhizhong Su.
 * \version 1.0
 * \date 2015
 * \copyright Torr Vision Group, University of Oxford.
 * \details If you use this code, please consider citing the paper:
 * Shuai Zheng, Sadeep Jayasumana, Bernardino Romera-Paredes, Vibhav Vineet, Zhizhong Su, Dalong Du,
 * Chang Huang, Philip H. S. Torr. Conditional Random Fields as Recurrent Neural Networks. IEEE ICCV 2015.
 *
 * For more information about CRF-RNN, please visit the project website http://crfasrnn.torr.vision.
 */
#include <vector>

#include "caffe/filler.hpp"
#include "caffe/layer.hpp"
#include "caffe/util/im2col.hpp"
#include "caffe/layers/multi_stage_meanfield.hpp"

namespace caffe {

template <typename Dtype>
void MultiStageMeanfieldLayer <Dtype >:: LayerSetUp( const vector < Blob< Dtype >*>& bottom ,
  const vector < Blob< Dtype >*>& top ) {

  const caffe :: MultiStageMeanfieldParameter meanfield_param = this -> layer_param_. multi_stage_meanfield_param ();

  num_iterations_ = meanfield_param . num_iterations ();

  CHECK_GT (num_iterations_ , 1 ) << "Number of iterations must be greater than 1." ;

  theta_alpha_ = meanfield_param .theta_alpha ();
  theta_beta_ = meanfield_param .theta_beta ();
  theta_gamma_ = meanfield_param .theta_gamma ();

  count_ = bottom[ 0 ]->count ();
  num_ = bottom[ 0 ]->num ();
  channels_ = bottom[ 0 ]->channels ();
  height_ = bottom[ 0 ]->height ();
  width_ = bottom[ 0 ]->width ();
  num_pixels_ = height_ * width_ ;

  LOG (INFO ) << "This implementation has not been tested batch size > 1." ;

  top [0 ]-> Reshape( num_ , channels_ , height_ , width_ );

  // Initialize the parameters that will updated by backpropagation.
  if ( this ->blobs_ . size() > 0 ) {
  LOG (INFO ) << "Multimeanfield layer skipping parameter initialization." ;
  } else {

  this ->blobs_ . resize( 3 );// blobs_[0] - spatial kernel weights, blobs_[1] - bilateral kernel weights, blobs_[2] - compatability matrix

  // Allocate space for kernel weights.
  this ->blobs_ [ 0]. reset (new Blob< Dtype >(1 , 1 , channels_ , channels_ ));
  this ->blobs_ [ 1]. reset (new Blob< Dtype >(1 , 1 , channels_ , channels_ ));

  caffe_set (channels_ * channels_ , Dtype (0. ), this -> blobs_[ 0 ]->mutable_cpu_data ());
  caffe_set (channels_ * channels_ , Dtype (0. ), this -> blobs_[ 1 ]->mutable_cpu_data ());

  // Initialize the kernels weights. The two files spatial.par and bilateral.par should be available.
  FILE * pFile;
  pFile = fopen( "spatial.par" , "r" );
  CHECK (pFile ) << "The file 'spatial.par' is not found. Please create it with initial spatial kernel weights." ;
  for ( int i = 0 ; i < channels_; i++) {
  fscanf ( pFile, "%lf" , & this-> blobs_ [0 ]-> mutable_cpu_data ()[i * channels_ + i]);
  }
  fclose (pFile );

  pFile = fopen( "bilateral.par" , "r" );
  CHECK (pFile ) << "The file 'bilateral.par' is not found. Please create it with initial bilateral kernel weights." ;
  for ( int i = 0 ; i < channels_; i++) {
  fscanf ( pFile, "%lf" , & this-> blobs_ [1 ]-> mutable_cpu_data ()[i * channels_ + i]);
  }
  fclose (pFile );

  // Initialize the compatibility matrix.
  this ->blobs_ [ 2]. reset (new Blob< Dtype >(1 , 1 , channels_ , channels_ ));
  caffe_set (channels_ * channels_ , Dtype (0. ), this -> blobs_[ 2 ]->mutable_cpu_data ());

  // Initialize it to have the Potts model.
  for ( int c = 0 ; c < channels_; ++c ) {
  ( this-> blobs_ [2 ]-> mutable_cpu_data ())[c * channels_ + c] = Dtype (-1. );
  }
  }

  // Initialize the spatial lattice. This does not need to be computed for every image because we use a fixed size.
  float * spatial_kernel = new float [ 2 * num_pixels_ ]; // 声明了要删除！！！！
  compute_spatial_kernel ( spatial_kernel );
  spatial_lattice_ . reset( new ModifiedPermutohedral ());
  spatial_lattice_ -> init( spatial_kernel , 2 , num_pixels_ );
  delete [] spatial_kernel ;

  // Calculate spatial filter normalization factors.
  norm_feed_ .reset ( new Dtype [num_pixels_ ]);
  caffe_set (num_pixels_ , Dtype ( 1.0), norm_feed_. get ());
  spatial_norm_ .Reshape ( 1, 1, height_, width_);
  Dtype * norm_data = spatial_norm_. mutable_cpu_data ();
  spatial_lattice_ -> compute( norm_data , norm_feed_ .get (), 1 );
  for ( int i = 0 ; i < num_pixels_; ++i ) {
  norm_data [i ] = 1.0f / ( norm_data [i ] + 1e-20f);
  }

  // Allocate space for bilateral kernels. This is a temporary buffer used to compute bilateral lattices later.
  // Also allocate space for holding bilateral filter normalization values.
  bilateral_kernel_buffer_ . reset( new float [ 5 * num_pixels_ ]);
  bilateral_norms_ . Reshape( num_ , 1 , height_ , width_ );

  // Configure the split layer that is used to make copies of the unary term. One copy for each iteration.
  // It may be possible to optimize this calculation later.
  split_layer_bottom_vec_ . clear();
  split_layer_bottom_vec_ . push_back( bottom [0 ]);

  split_layer_top_vec_ . clear();

  split_layer_out_blobs_ . resize( num_iterations_ );
  for ( int i = 0 ; i < num_iterations_ ; i ++) {
  split_layer_out_blobs_ [ i]. reset (new Blob< Dtype >());
  split_layer_top_vec_ . push_back( split_layer_out_blobs_ [ i]. get ());
  }

  LayerParameter split_layer_param ;
  split_layer_ .reset ( new SplitLayer <Dtype >( split_layer_param ));
  split_layer_ ->SetUp ( split_layer_bottom_vec_ , split_layer_top_vec_ );

  // Make blobs to store outputs of each meanfield iteration. Output of the last iteration is stored in top[0].
  // So we need only (num_iterations_ - 1) blobs.
  iteration_output_blobs_ . resize( num_iterations_ - 1 );
  for ( int i = 0 ; i < num_iterations_ - 1; ++i ) {
  iteration_output_blobs_ [ i]. reset (new Blob< Dtype >(num_ , channels_ , height_ , width_ ));
  }

  // Make instances of MeanfieldIteration and initialize them.
  meanfield_iterations_ . resize( num_iterations_ );
  for ( int i = 0 ; i < num_iterations_ ; ++ i) {
  meanfield_iterations_ [ i]. reset (new MeanfieldIteration <Dtype >());
  meanfield_iterations_ [ i]-> OneTimeSetUp (
  split_layer_out_blobs_ [ i]. get (), // unary terms
  ( i == 0 ) ? bottom[ 1 ] : iteration_output_blobs_ [ i - 1 ]. get(), // softmax input
  ( i == num_iterations_ - 1 ) ? top [ 0] : iteration_output_blobs_ [ i]. get (), // output blob
  spatial_lattice_ , // spatial lattice
  & spatial_norm_); // spatial normalization factors.
  }

  this ->param_propagate_down_ . resize( this ->blobs_ . size(), true);

  LOG (INFO ) << ("MultiStageMeanfieldLayer initialized." );
}

template <typename Dtype>
void MultiStageMeanfieldLayer <Dtype >:: Reshape( const vector < Blob< Dtype >*>& bottom ,
  const vector < Blob< Dtype >*>& top ) {
  // Do nothing.
}

/**
 * Performs filter-based mean field inference given the image and unaries.
 *
 * bottom[0] - Unary terms
 * bottom[1] - Softmax input/Output from the previous iteration (a copy of the unary terms if this is the first stage).
 * bottom[2] - RGB images
 *
 * top[0] - Output of the mean field inference (not normalized).
 */
template <typename Dtype>
void MultiStageMeanfieldLayer <Dtype >:: Forward_cpu( const vector < Blob< Dtype >*>& bottom ,
  const vector < Blob< Dtype >*>& top ) {

  split_layer_bottom_vec_ [ 0] = bottom [0 ];
  split_layer_ ->Forward ( split_layer_bottom_vec_ , split_layer_top_vec_ );

  // Initialize the bilateral lattices.
  bilateral_lattices_ . resize( num_ );
  for ( int n = 0 ; n < num_; ++n ) {

  compute_bilateral_kernel ( bottom[ 2 ], n , bilateral_kernel_buffer_ . get());
  bilateral_lattices_ [ n]. reset (new ModifiedPermutohedral ());
  bilateral_lattices_ [ n]-> init (bilateral_kernel_buffer_ . get(), 5, num_pixels_);

  // Calculate bilateral filter normalization factors.
  Dtype * norm_output_data = bilateral_norms_ . mutable_cpu_data () + bilateral_norms_ . offset( n );
  bilateral_lattices_ [ n]-> compute (norm_output_data , norm_feed_ . get(), 1);
  for ( int i = 0 ; i < num_pixels_; ++i ) {
  norm_output_data [ i] = 1.f / (norm_output_data [ i] + 1e-20f );
  }
  }

  for ( int i = 0 ; i < num_iterations_ ; ++ i) {

  meanfield_iterations_ [ i]-> PrePass (this -> blobs_, &bilateral_lattices_ , & bilateral_norms_ );

  meanfield_iterations_ [ i]-> Forward_cpu ();
  }
}

/**
 * Backprop through filter-based mean field inference.
 */
template < typename Dtype >
void MultiStageMeanfieldLayer <Dtype >:: Backward_cpu(
  const vector < Blob< Dtype >*>& top , const vector< bool >& propagate_down ,
  const vector < Blob< Dtype >*>& bottom ) {

  for ( int i = ( num_iterations_ - 1); i >= 0 ; -- i ) {
  meanfield_iterations_ [ i]-> Backward_cpu ();
  }

  vector <bool > split_layer_propagate_down ( 1, true);
  split_layer_ ->Backward ( split_layer_top_vec_ , split_layer_propagate_down , split_layer_bottom_vec_ );

  // Accumulate diffs from mean field iterations.
  for ( int blob_id = 0 ; blob_id < this-> blobs_ .size (); ++ blob_id ) {

  Blob <Dtype >* cur_blob = this ->blobs_ [ blob_id]. get ();

  if ( this ->param_propagate_down_ [ blob_id]) {

  caffe_set (cur_blob -> count(), Dtype( 0 ), cur_blob ->mutable_cpu_diff ());

  for ( int i = 0 ; i < num_iterations_ ; ++ i) {
  const Dtype * diffs_to_add = meanfield_iterations_ [ i]-> blobs ()[blob_id ]-> cpu_diff();
  caffe_axpy (cur_blob -> count(), Dtype( 1. ), diffs_to_add , cur_blob ->mutable_cpu_diff ());
  }
  }
  }
}

template < typename Dtype >
void MultiStageMeanfieldLayer <Dtype >:: compute_bilateral_kernel (const Blob< Dtype >* const rgb_blob , const int n ,
  float * const output_kernel ) {

  for ( int p = 0 ; p < num_pixels_; ++p ) {
  output_kernel [5 * p ] = static_cast < float>( p % width_) / theta_alpha_ ;
  output_kernel [5 * p + 1] = static_cast <float >( p / width_ ) / theta_alpha_;

  const Dtype * const rgb_data_start = rgb_blob ->cpu_data () + rgb_blob-> offset (n );
  output_kernel [5 * p + 2] = static_cast <float >( rgb_data_start [p ] / theta_beta_);
  output_kernel [5 * p + 3] = static_cast <float >(( rgb_data_start + num_pixels_)[ p ] / theta_beta_ );
  output_kernel [5 * p + 4] = static_cast <float >(( rgb_data_start + num_pixels_ * 2 )[ p] / theta_beta_ );
  }
}

template <typename Dtype>
void MultiStageMeanfieldLayer <Dtype >:: compute_spatial_kernel (float * const output_kernel) {

  for ( int p = 0 ; p < num_pixels_; ++p ) {
  output_kernel [2 * p] = static_cast <float >( p % width_ ) / theta_gamma_;
  output_kernel [2 * p + 1 ] = static_cast< float >(p / width_ ) / theta_gamma_ ;
  }
}

INSTANTIATE_CLASS ( MultiStageMeanfieldLayer );
REGISTER_LAYER_CLASS ( MultiStageMeanfield );
} // namespace caffe




将caffe/vision_layers.hpp中的MeanfieldIteration 对应的声明复制到caffe-windows下的caffe/layers/目录下
创建meanfield_iteration.hpp

meanfield_iteration.hpp文件如下：
/*!
 * \brief A helper class for {@link MultiStageMeanfieldLayer} class, which is the Caffe layer that implements the
 * CRF-RNN described in the paper: Conditional Random Fields as Recurrent Neural Networks. IEEE ICCV 2015.
 *
 * This class itself is not a proper Caffe layer although it behaves like one to some degree.
 *
 * \authors Sadeep Jayasumana, Bernardino Romera-Paredes, Shuai Zheng, Zhizhong Su.
 * \version 1.0
 * \date 2015
 * \copyright Torr Vision Group, University of Oxford.
 * \details If you use this code, please consider citing the paper:
 * Shuai Zheng, Sadeep Jayasumana, Bernardino Romera-Paredes, Vibhav Vineet, Zhizhong Su, Dalong Du,
 * Chang Huang, Philip H. S. Torr. Conditional Random Fields as Recurrent Neural Networks. IEEE ICCV 2015.
 *
 * For more information about CRF-RNN, please visit the project website http://crfasrnn.torr.vision.
 */

#ifndef CAFFE_MEANFIELD_ITERATION_HPP_
#define CAFFE_MEANFIELD_ITERATION_HPP_

#include <vector>

#include "caffe/blob.hpp"
#include "caffe/layer.hpp"
#include "caffe/proto/caffe.pb.h"

#include "caffe/layers/softmax_layer.hpp"
#include "caffe/layers/eltwise_layer.hpp"
#include "caffe/layers/split_layer.hpp"
#include <boost/shared_array.hpp>

namespace caffe {
template <typename Dtype>
class MeanfieldIteration {

  public :
  /**
  * Must be invoked only once after the construction of the layer.
  */
  void OneTimeSetUp (
  Blob <Dtype >* const unary_terms ,
  Blob <Dtype >* const softmax_input ,
  Blob <Dtype >* const output_blob ,
  const shared_ptr < ModifiedPermutohedral > spatial_lattice ,
  const Blob < Dtype>* const spatial_norm );

  /**
  * Must be invoked before invoking {@link Forward_cpu()}
  */
  virtual void PrePass(
  const vector < shared_ptr< Blob <Dtype > > >& parameters_to_copy_from ,
  const vector < shared_ptr< ModifiedPermutohedral > >* const bilateral_lattices ,
  const Blob < Dtype>* const bilateral_norms );

  /**
  * Forward pass - to be called during inference.
  */
  virtual void Forward_cpu();

  /**
  * Backward pass - to be called during training.
  */
  virtual void Backward_cpu();

  // A quick hack. This should be properly encapsulated.
  vector <shared_ptr < Blob< Dtype > > >& blobs() {
  return blobs_ ;
  }

  protected :
  vector <shared_ptr < Blob< Dtype > > > blobs_;

  int count_ ;
  int num_ ;
  int channels_ ;
  int height_ ;
  int width_ ;
  int num_pixels_ ;

  Blob <Dtype > spatial_out_blob_ ;
  Blob <Dtype > bilateral_out_blob_ ;
  Blob <Dtype > pairwise_ ;
  Blob <Dtype > softmax_input_ ;
  Blob <Dtype > prob_ ;
  Blob <Dtype > message_passing_ ;

  vector <Blob < Dtype>*> softmax_top_vec_ ;
  vector <Blob < Dtype>*> softmax_bottom_vec_ ;
  vector <Blob < Dtype>*> sum_top_vec_;
  vector <Blob < Dtype>*> sum_bottom_vec_ ;

  shared_ptr <SoftmaxLayer < Dtype> > softmax_layer_ ;
  shared_ptr <EltwiseLayer < Dtype> > sum_layer_ ;

  shared_ptr <ModifiedPermutohedral > spatial_lattice_ ;
  const vector < shared_ptr< ModifiedPermutohedral > >* bilateral_lattices_ ;

  const Blob < Dtype>* spatial_norm_;
  const Blob < Dtype>* bilateral_norms_ ;

};

} // namespace caffe
#endif //CAFFE_MEANFIELD_ITERATION_HPP_
将caffe/layers/meanfield_iteration .cpp都复制到caffe-windows对应的目录下
然后将其中的include的vision_layers.hpp修改为meanfield_iteration .hpp

修改后的meanfield_iteration .cpp文件如下所示：
/*!
 * \brief A helper class for {@link MultiStageMeanfieldLayer} class, which is the Caffe layer that implements the
 * CRF-RNN described in the paper: Conditional Random Fields as Recurrent Neural Networks. IEEE ICCV 2015.
 *
 * This class itself is not a proper Caffe layer although it behaves like one to some degree.
 *
 * \authors Sadeep Jayasumana, Bernardino Romera-Paredes, Shuai Zheng, Zhizhong Su.
 * \version 1.0
 * \date 2015
 * \copyright Torr Vision Group, University of Oxford.
 * \details If you use this code, please consider citing the paper:
 * Shuai Zheng, Sadeep Jayasumana, Bernardino Romera-Paredes, Vibhav Vineet, Zhizhong Su, Dalong Du,
 * Chang Huang, Philip H. S. Torr. Conditional Random Fields as Recurrent Neural Networks. IEEE ICCV 2015.
 *
 * For more information about CRF-RNN, please visit the project website http://crfasrnn.torr.vision.
 */
#include <vector>

#include "caffe/filler.hpp"
#include "caffe/layer.hpp"
#include "caffe/layers/meanfield_iteration.hpp"

namespace caffe {

/**
 * To be invoked once only immediately after construction.
 */
template <typename Dtype>
void MeanfieldIteration <Dtype >:: OneTimeSetUp(
  Blob <Dtype >* const unary_terms,
  Blob <Dtype >* const softmax_input,
  Blob <Dtype >* const output_blob,
  const shared_ptr < ModifiedPermutohedral > spatial_lattice ,
  const Blob < Dtype>* const spatial_norm ) {

  spatial_lattice_ = spatial_lattice ;
  spatial_norm_ = spatial_norm;

  count_ = unary_terms-> count ();
  num_ = unary_terms-> num ();
  channels_ = unary_terms-> channels ();
  height_ = unary_terms-> height ();
  width_ = unary_terms-> width ();
  num_pixels_ = height_ * width_ ;

  if ( this ->blobs_ . size() > 0 ) {
  LOG (INFO ) << "Meanfield iteration skipping parameter initialization." ;
  } else {
  blobs_ .resize ( 3);
  blobs_ [0 ]. reset( new Blob < Dtype>( 1 , 1 , channels_ , channels_ )); // spatial kernel weight
  blobs_ [1 ]. reset( new Blob < Dtype>( 1 , 1 , channels_ , channels_ )); // bilateral kernel weight
  blobs_ [2 ]. reset( new Blob < Dtype>( 1 , 1 , channels_ , channels_ )); // compatibility transform matrix
  }

  pairwise_ .Reshape ( num_, channels_, height_, width_);
  spatial_out_blob_ . Reshape( num_ , channels_ , height_ , width_ );
  bilateral_out_blob_ . Reshape( num_ , channels_ , height_ , width_ );
  message_passing_ . Reshape( num_ , channels_ , height_ , width_ );

  // Softmax layer configuration
  softmax_bottom_vec_ . clear();
  softmax_bottom_vec_ . push_back( softmax_input );

  softmax_top_vec_ . clear();
  softmax_top_vec_ . push_back(& prob_ );

  LayerParameter softmax_param;
  softmax_layer_ . reset( new SoftmaxLayer < Dtype>( softmax_param ));
  softmax_layer_ -> SetUp( softmax_bottom_vec_ , softmax_top_vec_ );

  // Sum layer configuration
  sum_bottom_vec_ . clear();
  sum_bottom_vec_ . push_back( unary_terms );
  sum_bottom_vec_ . push_back(& pairwise_ );

  sum_top_vec_ .clear ();
  sum_top_vec_ .push_back ( output_blob);

  LayerParameter sum_param;
  sum_param .mutable_eltwise_param ()-> add_coeff( Dtype (1. ));
  sum_param .mutable_eltwise_param ()-> add_coeff( Dtype (-1. ));
  sum_param .mutable_eltwise_param ()-> set_operation( EltwiseParameter_EltwiseOp_SUM );
  sum_layer_ .reset ( new EltwiseLayer <Dtype >( sum_param));
  sum_layer_ ->SetUp ( sum_bottom_vec_ , sum_top_vec_ );
}

/**
 * To be invoked before every call to the Forward_cpu() method.
 */
template <typename Dtype>
void MeanfieldIteration <Dtype >:: PrePass(
  const vector < shared_ptr< Blob <Dtype > > >& parameters_to_copy_from ,
  const vector < shared_ptr< ModifiedPermutohedral > >* const bilateral_lattices ,
  const Blob < Dtype>* const bilateral_norms ) {

  bilateral_lattices_ = bilateral_lattices ;
  bilateral_norms_ = bilateral_norms ;

  // Get copies of the up-to-date parameters.
  for ( int i = 0 ; i < parameters_to_copy_from .size (); ++ i ) {
  blobs_ [i ]-> CopyFrom(*( parameters_to_copy_from [ i]. get ()));
  }
}

/**
 * Forward pass during the inference.
 */
template <typename Dtype>
void MeanfieldIteration <Dtype >:: Forward_cpu() {

  //------------------------------- Softmax normalization--------------------
  softmax_layer_ -> Forward( softmax_bottom_vec_ , softmax_top_vec_ );

  //-----------------------------------Message passing-----------------------
  for ( int n = 0 ; n < num_; ++n ) {

  Dtype * spatial_out_data = spatial_out_blob_ . mutable_cpu_data () + spatial_out_blob_ . offset( n );
  const Dtype * prob_input_data = prob_ .cpu_data () + prob_. offset (n );

  spatial_lattice_ -> compute( spatial_out_data , prob_input_data , channels_ , false );

  // Pixel-wise normalization.
  for ( int channel_id = 0 ; channel_id < channels_; ++channel_id ) {
  caffe_mul (num_pixels_ , spatial_norm_ -> cpu_data(),
  spatial_out_data + channel_id * num_pixels_,
  spatial_out_data + channel_id * num_pixels_);
  }

  Dtype * bilateral_out_data = bilateral_out_blob_ . mutable_cpu_data () + bilateral_out_blob_ . offset( n );

  (* bilateral_lattices_ )[n ]-> compute( bilateral_out_data , prob_input_data , channels_ , false );
  // Pixel-wise normalization.
  for ( int channel_id = 0 ; channel_id < channels_; ++channel_id ) {
  caffe_mul (num_pixels_ , bilateral_norms_ -> cpu_data() + bilateral_norms_ -> offset( n ),
  bilateral_out_data + channel_id * num_pixels_,
  bilateral_out_data + channel_id * num_pixels_);
  }
  }

  caffe_set (count_ , Dtype ( 0.), message_passing_ .mutable_cpu_data ());

  for ( int n = 0 ; n < num_; ++n ) {
  caffe_cpu_gemm < Dtype>( CblasNoTrans , CblasNoTrans , channels_ , num_pixels_ , channels_ , ( Dtype) 1.,
  this ->blobs_ [ 0]-> cpu_data (), spatial_out_blob_ . cpu_data() + spatial_out_blob_ . offset( n ), ( Dtype) 0.,
  message_passing_ . mutable_cpu_data () + message_passing_ . offset( n ));
  }

  for ( int n = 0 ; n < num_; ++n ) {
  caffe_cpu_gemm < Dtype>( CblasNoTrans , CblasNoTrans , channels_ , num_pixels_ , channels_ , ( Dtype) 1.,
  this ->blobs_ [ 1]-> cpu_data (), bilateral_out_blob_ . cpu_data() + bilateral_out_blob_ . offset( n ), ( Dtype) 1.,
  message_passing_ . mutable_cpu_data () + message_passing_ . offset( n ));
  }

  //--------------------------- Compatibility multiplication ----------------
  //Result from message passing needs to be multiplied with compatibility values.
  for ( int n = 0 ; n < num_; ++n ) {
  caffe_cpu_gemm < Dtype>( CblasNoTrans , CblasNoTrans , channels_ , num_pixels_ ,
  channels_ , ( Dtype) 1., this-> blobs_ [2 ]-> cpu_data(),
  message_passing_ . cpu_data() + message_passing_ . offset( n ), ( Dtype) 0.,
  pairwise_ .mutable_cpu_data () + pairwise_. offset (n ));
  }

  //------------------------- Adding unaries, normalization is left to the next iteration --------------
  // Add unary
  sum_layer_ ->Forward ( sum_bottom_vec_ , sum_top_vec_ );
}

template < typename Dtype >
void MeanfieldIteration <Dtype >:: Backward_cpu() {

  //---------------------------- Add unary gradient --------------------------
  vector <bool > eltwise_propagate_down ( 2, true);
  sum_layer_ ->Backward ( sum_top_vec_, eltwise_propagate_down , sum_bottom_vec_ );

  //---------------------------- Update compatibility diffs ------------------
  caffe_set (this -> blobs_[ 2 ]->count (), Dtype ( 0.), this-> blobs_ [2 ]-> mutable_cpu_diff ());

  for ( int n = 0 ; n < num_; ++n ) {
  caffe_cpu_gemm < Dtype>( CblasNoTrans , CblasTrans , channels_ , channels_ ,
  num_pixels_ , ( Dtype) 1., pairwise_. cpu_diff () + pairwise_ . offset( n ),
  message_passing_ . cpu_data() + message_passing_ . offset( n ), ( Dtype) 1.,
  this ->blobs_ [ 2]-> mutable_cpu_diff ());
  }

  //-------------------------- Gradient after compatibility transform--- -----
  for ( int n = 0 ; n < num_; ++n ) {
  caffe_cpu_gemm < Dtype>( CblasTrans , CblasNoTrans , channels_ , num_pixels_ ,
  channels_ , ( Dtype) 1., this-> blobs_ [2 ]-> cpu_data(),
  pairwise_ .cpu_diff () + pairwise_. offset (n ), ( Dtype ) 0. ,
  message_passing_ . mutable_cpu_diff () + message_passing_ . offset( n ));
  }

  // ------------------------- Gradient w.r.t. kernels weights ------------
  caffe_set (this -> blobs_[ 0 ]->count (), Dtype ( 0.), this-> blobs_ [0 ]-> mutable_cpu_diff ());
  caffe_set (this -> blobs_[ 1 ]->count (), Dtype ( 0.), this-> blobs_ [1 ]-> mutable_cpu_diff ());

  for ( int n = 0 ; n < num_; ++n ) {
  caffe_cpu_gemm < Dtype>( CblasNoTrans , CblasTrans , channels_ , channels_ ,
  num_pixels_ , ( Dtype) 1., message_passing_ .cpu_diff () + message_passing_ .offset ( n),
  spatial_out_blob_ . cpu_data() + spatial_out_blob_ . offset( n ), ( Dtype) 1.,
  this ->blobs_ [ 0]-> mutable_cpu_diff ());
  }

  for ( int n = 0 ; n < num_; ++n ) {
  caffe_cpu_gemm < Dtype>( CblasNoTrans , CblasTrans , channels_ , channels_ ,
  num_pixels_ , ( Dtype) 1., message_passing_ .cpu_diff () + message_passing_ .offset ( n),
  bilateral_out_blob_ . cpu_data() + bilateral_out_blob_ . offset( n ), ( Dtype) 1.,
  this ->blobs_ [ 1]-> mutable_cpu_diff ());
  }

  /*Dtype* tmp = new Dtype[count_];
  caffe_mul<Dtype>(count_, message_passing_.cpu_diff(), spatial_out_blob_.cpu_data(), tmp);

  for (int c = 0; c < count_; ++c) {
  (this->blobs_[0]->mutable_cpu_diff())[0] += tmp[c];
  }

  caffe_mul<Dtype>(count_, message_passing_.cpu_diff(), bilateral_out_blob_.cpu_data(), tmp);
  for (int c = 0; c < count_; ++c) {
  (this->blobs_[1]->mutable_cpu_diff())[0] += tmp[c];
  }

  delete[] tmp;*/

  // TODO: Check whether there's a way to improve the accuracy of this calculation.
  for ( int n = 0 ; n < num_; ++n ) {
  caffe_cpu_gemm < Dtype>( CblasTrans , CblasNoTrans , channels_ , num_pixels_ , channels_ , ( Dtype) 1.,
  this ->blobs_ [ 0]-> cpu_data (), message_passing_ . cpu_diff() + message_passing_ . offset( n ),
  ( Dtype) 0.,
  spatial_out_blob_ . mutable_cpu_diff () + spatial_out_blob_ . offset( n ));
  }
  //caffe_cpu_scale<Dtype>(count_, (this->blobs_[0]->cpu_data())[0],
  // message_passing_.cpu_diff(), spatial_out_blob_.mutable_cpu_diff());

  for ( int n = 0 ; n < num_; ++n ) {
  caffe_cpu_gemm < Dtype>( CblasTrans , CblasNoTrans , channels_ , num_pixels_ , channels_ , ( Dtype) 1.,
  this ->blobs_ [ 1]-> cpu_data (), message_passing_ . cpu_diff() + message_passing_ . offset( n ),
  ( Dtype) 0.,
  bilateral_out_blob_ . mutable_cpu_diff () + bilateral_out_blob_ . offset( n ));
  }
  //caffe_cpu_scale<Dtype>(count_, (this->blobs_[1]->cpu_data())[0],
  // message_passing_.cpu_diff(), bilateral_out_blob_.mutable_cpu_diff());

  //---------------------------- BP thru normalization --------------------------
  for ( int n = 0 ; n < num_; ++n ) {

  Dtype * spatial_out_diff = spatial_out_blob_ . mutable_cpu_diff () + spatial_out_blob_ . offset( n );
  for ( int channel_id = 0 ; channel_id < channels_; ++channel_id ) {
  caffe_mul (num_pixels_ , spatial_norm_ -> cpu_data(),
  spatial_out_diff + channel_id * num_pixels_,
  spatial_out_diff + channel_id * num_pixels_);
  }

  Dtype * bilateral_out_diff = bilateral_out_blob_ . mutable_cpu_diff () + bilateral_out_blob_ . offset( n );
  for ( int channel_id = 0 ; channel_id < channels_; ++channel_id ) {
  caffe_mul (num_pixels_ , bilateral_norms_ -> cpu_data() + bilateral_norms_ -> offset( n ),
  bilateral_out_diff + channel_id * num_pixels_,
  bilateral_out_diff + channel_id * num_pixels_);
  }
  }

  //--------------------------- Gradient for message passing ---------------
  for ( int n = 0 ; n < num_; ++n ) {

  spatial_lattice_ -> compute( prob_ .mutable_cpu_diff () + prob_. offset (n ),
  spatial_out_blob_ . cpu_diff() + spatial_out_blob_ . offset( n ), channels_ ,
  true , false );

  (* bilateral_lattices_ )[n ]-> compute( prob_ .mutable_cpu_diff () + prob_. offset (n ),
  bilateral_out_blob_ . cpu_diff() + bilateral_out_blob_ . offset( n ),
  channels_ , true , true );
  }

  //--------------------------------------------------------------------------------
  vector <bool > propagate_down ( 2, true);
  softmax_layer_ -> Backward( softmax_top_vec_ , propagate_down , softmax_bottom_vec_ );
}

INSTANTIATE_CLASS ( MeanfieldIteration );
} // namespace caffe





（4）移植CRF层的参数到caffe.proto文件


修改/src/proto/caffe.proto文件，加入CRF层的参数，并且在LayerParameter中以及V1LayerParameter中加入新的层的参数，由于最新的Caffe有CropLayer，因此也要将这个新层也加入进去。
syntax = "proto2";

package caffe;

// Specifies the shape (dimensions) of a Blob.
message BlobShape {
  repeated int64 dim = 1 [packed = true];
}

message BlobProto {
  optional BlobShape shape = 7;
  repeated float data = 5 [packed = true];
  repeated float diff = 6 [packed = true];
  repeated double double_data = 8 [packed = true];
  repeated double double_diff = 9 [packed = true];

  // 4D dimensions -- deprecated.  Use "shape" instead.
  optional int32 num = 1 [default = 0];
  optional int32 channels = 2 [default = 0];
  optional int32 height = 3 [default = 0];
  optional int32 width = 4 [default = 0];
}

// The BlobProtoVector is simply a way to pass multiple blobproto instances
// around.
message BlobProtoVector {
  repeated BlobProto blobs = 1;
}

message Datum {
  optional int32 channels = 1;
  optional int32 height = 2;
  optional int32 width = 3;
  // the actual image data, in bytes
  optional bytes data = 4;
  optional int32 label = 5;
  // Optionally, the datum could also hold float data.
  repeated float float_data = 6;
  // If true data contains an encoded image that need to be decoded
  optional bool encoded = 7 [default = false];
}

message FillerParameter {
  // The filler type.
  optional string type = 1 [default = 'constant'];
  optional float value = 2 [default = 0]; // the value in constant filler
  optional float min = 3 [default = 0]; // the min value in uniform filler
  optional float max = 4 [default = 1]; // the max value in uniform filler
  optional float mean = 5 [default = 0]; // the mean value in Gaussian filler
  optional float std = 6 [default = 1]; // the std value in Gaussian filler
  // The expected number of non-zero output weights for a given input in
  // Gaussian filler -- the default -1 means don't perform sparsification.
  optional int32 sparse = 7 [default = -1];
  // Normalize the filler variance by fan_in, fan_out, or their average.
  // Applies to 'xavier' and 'msra' fillers.
  enum VarianceNorm {
    FAN_IN = 0;
    FAN_OUT = 1;
    AVERAGE = 2;
  }
  optional VarianceNorm variance_norm = 8 [default = FAN_IN];
}

message NetParameter {
  optional string name = 1; // consider giving the network a name
  // The input blobs to the network.
  repeated string input = 3;
  // The shape of the input blobs.
  repeated BlobShape input_shape = 8;

  // 4D input dimensions -- deprecated.  Use "shape" instead.
  // If specified, for each input blob there should be four
  // values specifying the num, channels, height and width of the input blob.
  // Thus, there should be a total of (4 * #input) numbers.
  repeated int32 input_dim = 4;

  // Whether the network will force every layer to carry out backward operation.
  // If set False, then whether to carry out backward is determined
  // automatically according to the net structure and learning rates.
  optional bool force_backward = 5 [default = false];
  // The current "state" of the network, including the phase, level, and stage.
  // Some layers may be included/excluded depending on this state and the states
  // specified in the layers' include and exclude fields.
  optional NetState state = 6;

  // Print debugging information about results while running Net::Forward,
  // Net::Backward, and Net::Update.
  optional bool debug_info = 7 [default = false];

  // The layers that make up the net.  Each of their configurations, including
  // connectivity and behavior, is specified as a LayerParameter.
  repeated LayerParameter layer = 100;  // ID 100 so layers are printed last.

  // DEPRECATED: use 'layer' instead.
  repeated V1LayerParameter layers = 2;
}

// NOTE
// Update the next available ID when you add a new SolverParameter field.
//
// SolverParameter next available ID: 41 (last added: type)
message SolverParameter {
  //////////////////////////////////////////////////////////////////////////////
  // Specifying the train and test networks
  //
  // Exactly one train net must be specified using one of the following fields:
  //     train_net_param, train_net, net_param, net
  // One or more test nets may be specified using any of the following fields:
  //     test_net_param, test_net, net_param, net
  // If more than one test net field is specified (e.g., both net and
  // test_net are specified), they will be evaluated in the field order given
  // above: (1) test_net_param, (2) test_net, (3) net_param/net.
  // A test_iter must be specified for each test_net.
  // A test_level and/or a test_stage may also be specified for each test_net.
  //////////////////////////////////////////////////////////////////////////////

  // Proto filename for the train net, possibly combined with one or more
  // test nets.
  optional string net = 24;
  // Inline train net param, possibly combined with one or more test nets.
  optional NetParameter net_param = 25;

  optional string train_net = 1; // Proto filename for the train net.
  repeated string test_net = 2; // Proto filenames for the test nets.
  optional NetParameter train_net_param = 21; // Inline train net params.
  repeated NetParameter test_net_param = 22; // Inline test net params.

  // The states for the train/test nets. Must be unspecified or
  // specified once per net.
  //
  // By default, all states will have solver = true;
  // train_state will have phase = TRAIN,
  // and all test_state's will have phase = TEST.
  // Other defaults are set according to the NetState defaults.
  optional NetState train_state = 26;
  repeated NetState test_state = 27;

  // The number of iterations for each test net.
  repeated int32 test_iter = 3;

  // The number of iterations between two testing phases.
  optional int32 test_interval = 4 [default = 0];
  optional bool test_compute_loss = 19 [default = false];
  // If true, run an initial test pass before the first iteration,
  // ensuring memory availability and printing the starting value of the loss.
  optional bool test_initialization = 32 [default = true];
  optional float base_lr = 5; // The base learning rate
  // the number of iterations between displaying info. If display = 0, no info
  // will be displayed.
  optional int32 display = 6;
  // Display the loss averaged over the last average_loss iterations
  optional int32 average_loss = 33 [default = 1];
  optional int32 max_iter = 7; // the maximum number of iterations
  // accumulate gradients over `iter_size` x `batch_size` instances
  optional int32 iter_size = 36 [default = 1];

  // The learning rate decay policy. The currently implemented learning rate
  // policies are as follows:
  //    - fixed: always return base_lr.
  //    - step: return base_lr * gamma ^ (floor(iter / step))
  //    - exp: return base_lr * gamma ^ iter
  //    - inv: return base_lr * (1 + gamma * iter) ^ (- power)
  //    - multistep: similar to step but it allows non uniform steps defined by
  //      stepvalue
  //    - poly: the effective learning rate follows a polynomial decay, to be
  //      zero by the max_iter. return base_lr (1 - iter/max_iter) ^ (power)
  //    - sigmoid: the effective learning rate follows a sigmod decay
  //      return base_lr ( 1/(1 + exp(-gamma * (iter - stepsize))))
  //
  // where base_lr, max_iter, gamma, step, stepvalue and power are defined
  // in the solver parameter protocol buffer, and iter is the current iteration.
  optional string lr_policy = 8;
  optional float gamma = 9; // The parameter to compute the learning rate.
  optional float power = 10; // The parameter to compute the learning rate.
  optional float momentum = 11; // The momentum value.
  optional float weight_decay = 12; // The weight decay.
  // regularization types supported: L1 and L2
  // controlled by weight_decay
  optional string regularization_type = 29 [default = "L2"];
  // the stepsize for learning rate policy "step"
  optional int32 stepsize = 13;
  // the stepsize for learning rate policy "multistep"
  repeated int32 stepvalue = 34;

  // Set clip_gradients to >= 0 to clip parameter gradients to that L2 norm,
  // whenever their actual L2 norm is larger.
  optional float clip_gradients = 35 [default = -1];

  optional int32 snapshot = 14 [default = 0]; // The snapshot interval
  optional string snapshot_prefix = 15; // The prefix for the snapshot.
  // whether to snapshot diff in the results or not. Snapshotting diff will help
  // debugging but the final protocol buffer size will be much larger.
  optional bool snapshot_diff = 16 [default = false];
  enum SnapshotFormat {
    HDF5 = 0;
    BINARYPROTO = 1;
  }
  optional SnapshotFormat snapshot_format = 37 [default = BINARYPROTO];
  // the mode solver will use: 0 for CPU and 1 for GPU. Use GPU in default.
  enum SolverMode {
    CPU = 0;
    GPU = 1;
  }
  optional SolverMode solver_mode = 17 [default = GPU];
  // the device_id will that be used in GPU mode. Use device_id = 0 in default.
  optional int32 device_id = 18 [default = 0];
  // If non-negative, the seed with which the Solver will initialize the Caffe
  // random number generator -- useful for reproducible results. Otherwise,
  // (and by default) initialize using a seed derived from the system clock.
  optional int64 random_seed = 20 [default = -1];

  // type of the solver
  optional string type = 40 [default = "SGD"];

  // numerical stability for RMSProp, AdaGrad and AdaDelta and Adam
  optional float delta = 31 [default = 1e-8];
  // parameters for the Adam solver
  optional float momentum2 = 39 [default = 0.999];

  // RMSProp decay value
  // MeanSquare(t) = rms_decay*MeanSquare(t-1) + (1-rms_decay)*SquareGradient(t)
  optional float rms_decay = 38;

  // If true, print information about the state of the net that may help with
  // debugging learning problems.
  optional bool debug_info = 23 [default = false];

  // If false, don't save a snapshot after training finishes.
  optional bool snapshot_after_train = 28 [default = true];

  // DEPRECATED: old solver enum types, use string instead
  enum SolverType {
    SGD = 0;
    NESTEROV = 1;
    ADAGRAD = 2;
    RMSPROP = 3;
    ADADELTA = 4;
    ADAM = 5;
  }
  // DEPRECATED: use type instead of solver_type
  optional SolverType solver_type = 30 [default = SGD];
}

// A message that stores the solver snapshots
message SolverState {
  optional int32 iter = 1; // The current iteration
  optional string learned_net = 2; // The file that stores the learned net.
  repeated BlobProto history = 3; // The history for sgd solvers
  optional int32 current_step = 4 [default = 0]; // The current step for learning rate
}

enum Phase {
   TRAIN = 0;
   TEST = 1;
}

message NetState {
  optional Phase phase = 1 [default = TEST];
  optional int32 level = 2 [default = 0];
  repeated string stage = 3;
}

message NetStateRule {
  // Set phase to require the NetState have a particular phase (TRAIN or TEST)
  // to meet this rule.
  optional Phase phase = 1;

  // Set the minimum and/or maximum levels in which the layer should be used.
  // Leave undefined to meet the rule regardless of level.
  optional int32 min_level = 2;
  optional int32 max_level = 3;

  // Customizable sets of stages to include or exclude.
  // The net must have ALL of the specified stages and NONE of the specified
  // "not_stage"s to meet the rule.
  // (Use multiple NetStateRules to specify conjunctions of stages.)
  repeated string stage = 4;
  repeated string not_stage = 5;
}

// Specifies training parameters (multipliers on global learning constants,
// and the name and other settings used for weight sharing).
message ParamSpec {
  // The names of the parameter blobs -- useful for sharing parameters among
  // layers, but never required otherwise.  To share a parameter between two
  // layers, give it a (non-empty) name.
  optional string name = 1;

  // Whether to require shared weights to have the same shape, or just the same
  // count -- defaults to STRICT if unspecified.
  optional DimCheckMode share_mode = 2;
  enum DimCheckMode {
    // STRICT (default) requires that num, channels, height, width each match.
    STRICT = 0;
    // PERMISSIVE requires only the count (num*channels*height*width) to match.
    PERMISSIVE = 1;
  }

  // The multiplier on the global learning rate for this parameter.
  optional float lr_mult = 3 [default = 1.0];

  // The multiplier on the global weight decay for this parameter.
  optional float decay_mult = 4 [default = 1.0];
}

// NOTE
// Update the next available ID when you add a new LayerParameter field.
//
// LayerParameter next available layer-specific ID: 143 (last added: scale_param)
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 = 203;
  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 LogParameter log_param = 134;
  optional LRNParameter lrn_param = 118;
  optional LSTMParameter lstm_param = 201;
   optional MultiStageMeanfieldParameter multi_stage_meanfield_param = 202;
  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 JointImageParameter joint_image_param = 900;
  optional SlideWindowParameter slide_window_param = 901;
  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 MILDataParameter mil_data_param = 0x004d4944; //"MID"
  optional MILParameter mil_param = 0x004d494c; //"MIL"
}

message SlideWindowParameter{
  optional uint32 slidetype = 1 [default=1];
}

// Message that stores parameters used by LSTMParameter
message LSTMParameter {
  optional uint32 num_output = 1; // The number of outputs for the layer
  optional float clipping_threshold = 2 [default = 0.0];
  optional FillerParameter weight_filler = 3; // The filler for weight
  optional FillerParameter bias_filler = 4; // The filler for the bias
  optional uint32 batch_size = 5 [default = 1];
}

// Message that stores parameters used to apply transformation
// to the data layer's data
message TransformationParameter {
  // For data pre-processing, we can do simple scaling and subtracting the
  // data mean, if provided. Note that the mean subtraction is always carried
  // out before scaling.
  optional float scale = 1 [default = 1];
  // Specify if we want to randomly mirror data.
  optional bool mirror = 2 [default = false];
  // Specify if we would like to randomly crop an image.
  optional uint32 crop_size = 3 [default = 0];
  // mean_file and mean_value cannot be specified at the same time
  optional string mean_file = 4;
  // if specified can be repeated once (would substract it from all the channels)
  // or can be repeated the same number of times as channels
  // (would subtract them from the corresponding channel)
  repeated float mean_value = 5;
  // Force the decoded image to have 3 color channels.
  optional bool force_color = 6 [default = false];
  // Force the decoded image to have 1 color channels.
  optional bool force_gray = 7 [default = false];
}

// Message that stores parameters shared by loss layers
message LossParameter {
  // If specified, ignore instances with the given label.
  optional int32 ignore_label = 1;
  // How to normalize the loss for loss layers that aggregate across batches,
  // spatial dimensions, or other dimensions.  Currently only implemented in
  // SoftmaxWithLoss layer.
  enum NormalizationMode {
    // Divide by the number of examples in the batch times spatial dimensions.
    // Outputs that receive the ignore label will NOT be ignored in computing
    // the normalization factor.
    FULL = 0;
    // Divide by the total number of output locations that do not take the
    // ignore_label.  If ignore_label is not set, this behaves like FULL.
    VALID = 1;
    // Divide by the batch size.
    BATCH_SIZE = 2;
    // Do not normalize the loss.
    NONE = 3;
  }
  optional NormalizationMode normalization = 3 [default = VALID];
  // Deprecated.  Ignored if normalization is specified.  If normalization
  // is not specified, then setting this to false will be equivalent to
  // normalization = BATCH_SIZE to be consistent with previous behavior.
  optional bool normalize = 2;
}

// Messages that store parameters used by individual layer types follow, in
// alphabetical order.

message AccuracyParameter {
  // When computing accuracy, count as correct by comparing the true label to
  // the top k scoring classes.  By default, only compare to the top scoring
  // class (i.e. argmax).
  optional uint32 top_k = 1 [default = 1];

  // The "label" axis of the prediction blob, whose argmax corresponds to the
  // predicted label -- may be negative to index from the end (e.g., -1 for the
  // last axis).  For example, if axis == 1 and the predictions are
  // (N x C x H x W), the label blob is expected to contain N*H*W ground truth
  // labels with integer values in {0, 1, ..., C-1}.
  optional int32 axis = 2 [default = 1];

  // If specified, ignore instances with the given label.
  optional int32 ignore_label = 3;
}

message ArgMaxParameter {
  // If true produce pairs (argmax, maxval)
  optional bool out_max_val = 1 [default = false];
  optional uint32 top_k = 2 [default = 1];
  // The axis along which to maximise -- may be negative to index from the
  // end (e.g., -1 for the last axis).
  // By default ArgMaxLayer maximizes over the flattened trailing dimensions
  // for each index of the first / num dimension.
  optional int32 axis = 3;
}

message ConcatParameter {
  // The axis along which to concatenate -- may be negative to index from the
  // end (e.g., -1 for the last axis).  Other axes must have the
  // same dimension for all the bottom blobs.
  // By default, ConcatLayer concatenates blobs along the "channels" axis (1).
  optional int32 axis = 2 [default = 1];

  // DEPRECATED: alias for "axis" -- does not support negative indexing.
  optional uint32 concat_dim = 1 [default = 1];
}

message BatchNormParameter {
  // If false, accumulate global mean/variance values via a moving average. If
  // true, use those accumulated values instead of computing mean/variance
  // across the batch.
  optional bool use_global_stats = 1;
  // How much does the moving average decay each iteration?
  optional float moving_average_fraction = 2 [default = .999];
  // Small value to add to the variance estimate so that we don't divide by
  // zero.
  optional float eps = 3 [default = 1e-5];
}

message BiasParameter {
  // The first axis of bottom[0] (the first input Blob) along which to apply
  // bottom[1] (the second input Blob).  May be negative to index from the end
  // (e.g., -1 for the last axis).
  //
  // For example, if bottom[0] is 4D with shape 100x3x40x60, the output
  // top[0] will have the same shape, and bottom[1] may have any of the
  // following shapes (for the given value of axis):
  //    (axis == 0 == -4) 100; 100x3; 100x3x40; 100x3x40x60
  //    (axis == 1 == -3)          3;     3x40;     3x40x60
  //    (axis == 2 == -2)                   40;       40x60
  //    (axis == 3 == -1)                                60
  // Furthermore, bottom[1] may have the empty shape (regardless of the value of
  // "axis") -- a scalar bias.
  optional int32 axis = 1 [default = 1];

  // (num_axes is ignored unless just one bottom is given and the bias is
  // a learned parameter of the layer.  Otherwise, num_axes is determined by the
  // number of axes by the second bottom.)
  // The number of axes of the input (bottom[0]) covered by the bias
  // parameter, or -1 to cover all axes of bottom[0] starting from `axis`.
  // Set num_axes := 0, to add a zero-axis Blob: a scalar.
  optional int32 num_axes = 2 [default = 1];

  // (filler is ignored unless just one bottom is given and the bias is
  // a learned parameter of the layer.)
  // The initialization for the learned bias parameter.
  // Default is the zero (0) initialization, resulting in the BiasLayer
  // initially performing the identity operation.
  optional FillerParameter filler = 3;
}

message ContrastiveLossParameter {
  // margin for dissimilar pair
  optional float margin = 1 [default = 1.0];
  // The first implementation of this cost did not exactly match the cost of
  // Hadsell et al 2006 -- using (margin - d^2) instead of (margin - d)^2.
  // legacy_version = false (the default) uses (margin - d)^2 as proposed in the
  // Hadsell paper. New models should probably use this version.
  // legacy_version = true uses (margin - d^2). This is kept to support /
  // reproduce existing models and results
  optional bool legacy_version = 2 [default = false];
}

message ConvolutionParameter {
  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 all spatial dimensions, or once per spatial dimension.
  repeated uint32 pad = 3; // The padding size; defaults to 0
  repeated uint32 kernel_size = 4; // The kernel size
  repeated uint32 stride = 6; // The stride; defaults to 1
  // Factor used to dilate the kernel, (implicitly) zero-filling the resulting
  // holes. (Kernel dilation is sometimes referred to by its use in the
  // algorithme à trous from Holschneider et al. 1987.)
  repeated uint32 dilation = 18; // The dilation; defaults to 1

  // For 2D convolution only, the *_h and *_w versions may also be used to
  // specify both spatial dimensions.
  optional uint32 pad_h = 9 [default = 0]; // The padding height (2D only)
  optional uint32 pad_w = 10 [default = 0]; // The padding width (2D only)
  optional uint32 kernel_h = 11; // The kernel height (2D only)
  optional uint32 kernel_w = 12; // The kernel width (2D only)
  optional uint32 stride_h = 13; // The stride height (2D only)
  optional uint32 stride_w = 14; // The stride width (2D only)

  optional uint32 group = 5 [default = 1]; // The group size for group conv

  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];

  // The axis to interpret as "channels" when performing convolution.
  // Preceding dimensions are treated as independent inputs;
  // succeeding dimensions are treated as "spatial".
  // With (N, C, H, W) inputs, and axis == 1 (the default), we perform
  // N independent 2D convolutions, sliding C-channel (or (C/g)-channels, for
  // groups g>1) filters across the spatial axes (H, W) of the input.
  // With (N, C, D, H, W) inputs, and axis == 1, we perform
  // N independent 3D convolutions, sliding (C/g)-channels
  // filters across the spatial axes (D, H, W) of the input.
  optional int32 axis = 16 [default = 1];

  // Whether to force use of the general ND convolution, even if a specific
  // implementation for blobs of the appropriate number of spatial dimensions
  // is available. (Currently, there is only a 2D-specific convolution
  // implementation; for input blobs with num_axes != 2, this option is
  // ignored and the ND implementation will be used.)
  optional bool force_nd_im2col = 17 [default = false];
}

message CropParameter {
  // To crop, elements of the first bottom are selected to fit the dimensions
  // of the second, reference bottom. The crop is configured by
  // - the crop `axis` to pick the dimensions for cropping
  // - the crop `offset` to set the shift for all/each dimension
  // to align the cropped bottom with the reference bottom.
  // All dimensions up to but excluding `axis` are preserved, while
  // the dimensions including and trailing `axis` are cropped.
  // If only one `offset` is set, then all dimensions are offset by this amount.
  // Otherwise, the number of offsets must equal the number of cropped axes to
  // shift the crop in each dimension accordingly.
  // Note: standard dimensions are N,C,H,W so the default is a spatial crop,
  // and `axis` may be negative to index from the end (e.g., -1 for the last
  // axis).
  optional int32 axis = 1 [default = 2];
  repeated uint32 offset = 2;
}

message DataParameter {
  enum DB {
    LEVELDB = 0;
    LMDB = 1;
  }
  // Specify the data source.
  optional string source = 1;
  // Specify the batch size.
  optional uint32 batch_size = 4;
  // The rand_skip variable is for the data layer to skip a few data points
  // to avoid all asynchronous sgd clients to start at the same point. The skip
  // point would be set as rand_skip * rand(0,1). Note that rand_skip should not
  // be larger than the number of keys in the database.
  // DEPRECATED. Each solver accesses a different subset of the database.
  optional uint32 rand_skip = 7 [default = 0];
  optional DB backend = 8 [default = LEVELDB];
  // DEPRECATED. See TransformationParameter. For data pre-processing, we can do
  // simple scaling and subtracting the data mean, if provided. Note that the
  // mean subtraction is always carried out before scaling.
  optional float scale = 2 [default = 1];
  optional string mean_file = 3;
  // DEPRECATED. See TransformationParameter. Specify if we would like to randomly
  // crop an image.
  optional uint32 crop_size = 5 [default = 0];
  // DEPRECATED. See TransformationParameter. Specify if we want to randomly mirror
  // data.
  optional bool mirror = 6 [default = false];
  // Force the encoded image to have 3 color channels
  optional bool force_encoded_color = 9 [default = false];
  // Prefetch queue (Number of batches to prefetch to host memory, increase if
  // data access bandwidth varies).
  optional uint32 prefetch = 10 [default = 4];
}

message DropoutParameter {
  optional float dropout_ratio = 1 [default = 0.5]; // dropout ratio
}

// DummyDataLayer fills any number of arbitrarily shaped blobs with random
// (or constant) data generated by "Fillers" (see "message FillerParameter").
message DummyDataParameter {
  // This layer produces N >= 1 top blobs.  DummyDataParameter must specify 1 or N
  // shape fields, and 0, 1 or N data_fillers.
  //
  // If 0 data_fillers are specified, ConstantFiller with a value of 0 is used.
  // If 1 data_filler is specified, it is applied to all top blobs.  If N are
  // specified, the ith is applied to the ith top blob.
  repeated FillerParameter data_filler = 1;
  repeated BlobShape shape = 6;

  // 4D dimensions -- deprecated.  Use "shape" instead.
  repeated uint32 num = 2;
  repeated uint32 channels = 3;
  repeated uint32 height = 4;
  repeated uint32 width = 5;
}

message EltwiseParameter {
  enum EltwiseOp {
    PROD = 0;
    SUM = 1;
    MAX = 2;
  }
  optional EltwiseOp operation = 1 [default = SUM]; // element-wise operation
  repeated float coeff = 2; // blob-wise coefficient for SUM operation

  // Whether to use an asymptotically slower (for >2 inputs) but stabler method
  // of computing the gradient for the PROD operation. (No effect for SUM op.)
  optional bool stable_prod_grad = 3 [default = true];
}

// Message that stores parameters used by ELULayer
message ELUParameter {
  // Described in:
  // Clevert, D.-A., Unterthiner, T., & Hochreiter, S. (2015). Fast and Accurate
  // Deep Network Learning by Exponential Linear Units (ELUs). arXiv
  optional float alpha = 1 [default = 1];
}

// Message that stores parameters used by EmbedLayer
message EmbedParameter {
  optional uint32 num_output = 1; // The number of outputs for the layer
  // The input is given as integers to be interpreted as one-hot
  // vector indices with dimension num_input.  Hence num_input should be
  // 1 greater than the maximum possible input value.
  optional uint32 input_dim = 2;

  optional bool bias_term = 3 [default = true]; // Whether to use a bias term
  optional FillerParameter weight_filler = 4; // The filler for the weight
  optional FillerParameter bias_filler = 5; // The filler for the bias

}

// Message that stores parameters used by ExpLayer
message ExpParameter {
  // ExpLayer computes outputs y = base ^ (shift + scale * x), for base > 0.
  // Or if base is set to the default (-1), base is set to e,
  // so y = exp(shift + scale * x).
  optional float base = 1 [default = -1.0];
  optional float scale = 2 [default = 1.0];
  optional float shift = 3 [default = 0.0];
}

/// Message that stores parameters used by FlattenLayer
message FlattenParameter {
  // The first axis to flatten: all preceding axes are retained in the output.
  // May be negative to index from the end (e.g., -1 for the last axis).
  optional int32 axis = 1 [default = 1];

  // The last axis to flatten: all following axes are retained in the output.
  // May be negative to index from the end (e.g., the default -1 for the last
  // axis).
  optional int32 end_axis = 2 [default = -1];
}

// Message that stores parameters used by HDF5DataLayer
message HDF5DataParameter {
  // Specify the data source.
  optional string source = 1;
  // Specify the batch size.
  optional uint32 batch_size = 2;

  // Specify whether to shuffle the data.
  // If shuffle == true, the ordering of the HDF5 files is shuffled,
  // and the ordering of data within any given HDF5 file is shuffled,
  // but data between different files are not interleaved; all of a file's
  // data are output (in a random order) before moving onto another file.
  optional bool shuffle = 3 [default = false];
}

message HDF5OutputParameter {
  optional string file_name = 1;
}

message HingeLossParameter {
  enum Norm {
    L1 = 1;
    L2 = 2;
  }
  // Specify the Norm to use L1 or L2
  optional Norm norm = 1 [default = L1];
}

message ImageDataParameter {
  // Specify the data source.
  optional string source = 1;
  // Specify the batch size.
  optional uint32 batch_size = 4 [default = 1];
  // The rand_skip variable is for the data layer to skip a few data points
  // to avoid all asynchronous sgd clients to start at the same point. The skip
  // point would be set as rand_skip * rand(0,1). Note that rand_skip should not
  // be larger than the number of keys in the database.
  optional uint32 rand_skip = 7 [default = 0];
  // Whether or not ImageLayer should shuffle the list of files at every epoch.
  optional bool shuffle = 8 [default = false];
  // It will also resize images if new_height or new_width are not zero.
  optional uint32 new_height = 9 [default = 0];
  optional uint32 new_width = 10 [default = 0];
  // Specify if the images are color or gray
  optional bool is_color = 11 [default = true];
  // DEPRECATED. See TransformationParameter. For data pre-processing, we can do
  // simple scaling and subtracting the data mean, if provided. Note that the
  // mean subtraction is always carried out before scaling.
  optional float scale = 2 [default = 1];
  optional string mean_file = 3;
  // DEPRECATED. See TransformationParameter. Specify if we would like to randomly
  // crop an image.
  optional uint32 crop_size = 5 [default = 0];
  // DEPRECATED. See TransformationParameter. Specify if we want to randomly mirror
  // data.
  optional bool mirror = 6 [default = false];
  optional string root_folder = 12 [default = ""];
}

message InfogainLossParameter {
  // Specify the infogain matrix source.
  optional string source = 1;
}

message InnerProductParameter {
  optional uint32 num_output = 1; // The number of outputs for the layer
  optional bool bias_term = 2 [default = true]; // whether to have bias terms
  optional FillerParameter weight_filler = 3; // The filler for the weight
  optional FillerParameter bias_filler = 4; // The filler for the bias

  // The first axis to be lumped into a single inner product computation;
  // all preceding axes are retained in the output.
  // May be negative to index from the end (e.g., -1 for the last axis).
  optional int32 axis = 5 [default = 1];
}

// Message that stores parameters used by LogLayer
message LogParameter {
  // LogLayer computes outputs y = log_base(shift + scale * x), for base > 0.
  // Or if base is set to the default (-1), base is set to e,
  // so y = ln(shift + scale * x) = log_e(shift + scale * x)
  optional float base = 1 [default = -1.0];
  optional float scale = 2 [default = 1.0];
  optional float shift = 3 [default = 0.0];
}

// Message that stores parameters used by LRNLayer
message LRNParameter {
  optional uint32 local_size = 1 [default = 5];
  optional float alpha = 2 [default = 1.];
  optional float beta = 3 [default = 0.75];
  enum NormRegion {
    ACROSS_CHANNELS = 0;
    WITHIN_CHANNEL = 1;
  }
  optional NormRegion norm_region = 4 [default = ACROSS_CHANNELS];
  optional float k = 5 [default = 1.];
  enum Engine {
    DEFAULT = 0;
    CAFFE = 1;
    CUDNN = 2;
  }
  optional Engine engine = 6 [default = DEFAULT];
}

message MemoryDataParameter {
  optional uint32 batch_size = 1;
  optional uint32 channels = 2;
  optional uint32 height = 3;
  optional uint32 width = 4;
}
// Message that stores parameters used by MultiStageMeanfieldLayer
message MultiStageMeanfieldParameter {
  enum Mode {
    POTTS = 0;
  }
  optional Mode compatibility_mode = 1 [default = POTTS];
  optional float threshold = 2;

  required float theta_alpha = 3 [default = 10.];
  required float theta_beta = 4 [default = 10.];
  required float theta_gamma = 5 [default = 10.];

  required uint32 num_iterations = 6 [default = 1];
  optional float spatial_filter_weight = 7 [default = 1];
  optional float bilateral_filter_weight = 8 [default = 1];

  optional float forced_spatial_filter_weight = 9;
  optional float forced_bilateral_filter_weight = 10;
}

message MVNParameter {
  // This parameter can be set to false to normalize mean only
  optional bool normalize_variance = 1 [default = true];

  // This parameter can be set to true to perform DNN-like MVN
  optional bool across_channels = 2 [default = false];

  // Epsilon for not dividing by zero while normalizing variance
  optional float eps = 3 [default = 1e-9];
}

message PoolingParameter {
  enum PoolMethod {
    MAX = 0;
    AVE = 1;
    STOCHASTIC = 2;
  }
  optional PoolMethod pool = 1 [default = MAX]; // The pooling method
  // 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 = 4 [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 = 2; // The kernel size (square)
  optional uint32 kernel_h = 5; // The kernel height
  optional uint32 kernel_w = 6; // The kernel width
  optional uint32 stride = 3 [default = 1]; // The stride (equal in Y, X)
  optional uint32 stride_h = 7; // The stride height
  optional uint32 stride_w = 8; // The stride width
  enum Engine {
    DEFAULT = 0;
    CAFFE = 1;
    CUDNN = 2;
  }
  optional Engine engine = 11 [default = DEFAULT];
  // If global_pooling then it will pool over the size of the bottom by doing
  // kernel_h = bottom->height and kernel_w = bottom->width
  optional bool global_pooling = 12 [default = false];
}

message PowerParameter {
  // PowerLayer computes outputs y = (shift + scale * x) ^ power.
  optional float power = 1 [default = 1.0];
  optional float scale = 2 [default = 1.0];
  optional float shift = 3 [default = 0.0];
}

message PythonParameter {
  optional string module = 1;
  optional string layer = 2;
  // This value is set to the attribute `param_str` of the `PythonLayer` object
  // in Python before calling the `setup()` method. This could be a number,
  // string, dictionary in Python dict format, JSON, etc. You may parse this
  // string in `setup` method and use it in `forward` and `backward`.
  optional string param_str = 3 [default = ''];
  // Whether this PythonLayer is shared among worker solvers during data parallelism.
  // If true, each worker solver sequentially run forward from this layer.
  // This value should be set true if you are using it as a data layer.
  optional bool share_in_parallel = 4 [default = false];
}

// Message that stores parameters used by ReductionLayer
message ReductionParameter {
  enum ReductionOp {
    SUM = 1;
    ASUM = 2;
    SUMSQ = 3;
    MEAN = 4;
  }

  optional ReductionOp operation = 1 [default = SUM]; // reduction operation

  // The first axis to reduce to a scalar -- may be negative to index from the
  // end (e.g., -1 for the last axis).
  // (Currently, only reduction along ALL "tail" axes is supported; reduction
  // of axis M through N, where N < num_axes - 1, is unsupported.)
  // Suppose we have an n-axis bottom Blob with shape:
  //     (d0, d1, d2, ..., d(m-1), dm, d(m+1), ..., d(n-1)).
  // If axis == m, the output Blob will have shape
  //     (d0, d1, d2, ..., d(m-1)),
  // and the ReductionOp operation is performed (d0 * d1 * d2 * ... * d(m-1))
  // times, each including (dm * d(m+1) * ... * d(n-1)) individual data.
  // If axis == 0 (the default), the output Blob always has the empty shape
  // (count 1), performing reduction across the entire input --
  // often useful for creating new loss functions.
  optional int32 axis = 2 [default = 0];

  optional float coeff = 3 [default = 1.0]; // coefficient for output
}

// Message that stores parameters used by ReLULayer
message ReLUParameter {
  // Allow non-zero slope for negative inputs to speed up optimization
  // Described in:
  // Maas, A. L., Hannun, A. Y., & Ng, A. Y. (2013). Rectifier nonlinearities
  // improve neural network acoustic models. In ICML Workshop on Deep Learning
  // for Audio, Speech, and Language Processing.
  optional float negative_slope = 1 [default = 0];
  enum Engine {
    DEFAULT = 0;
    CAFFE = 1;
    CUDNN = 2;
  }
  optional Engine engine = 2 [default = DEFAULT];
}

message ReshapeParameter {
  // Specify the output dimensions. If some of the dimensions are set to 0,
  // the corresponding dimension from the bottom layer is used (unchanged).
  // Exactly one dimension may be set to -1, in which case its value is
  // inferred from the count of the bottom blob and the remaining dimensions.
  // For example, suppose we want to reshape a 2D blob "input" with shape 2 x 8:
  //
  //   layer {
  //     type: "Reshape" bottom: "input" top: "output"
  //     reshape_param { ... }
  //   }
  //
  // If "input" is 2D with shape 2 x 8, then the following reshape_param
  // specifications are all equivalent, producing a 3D blob "output" with shape
  // 2 x 2 x 4:
  //
  //   reshape_param { shape { dim:  2  dim: 2  dim:  4 } }
  //   reshape_param { shape { dim:  0  dim: 2  dim:  4 } }
  //   reshape_param { shape { dim:  0  dim: 2  dim: -1 } }
  //   reshape_param { shape { dim: -1  dim: 0  dim:  2 } }
  //
  optional BlobShape shape = 1;

  // axis and num_axes control the portion of the bottom blob's shape that are
  // replaced by (included in) the reshape. By default (axis == 0 and
  // num_axes == -1), the entire bottom blob shape is included in the reshape,
  // and hence the shape field must specify the entire output shape.
  //
  // axis may be non-zero to retain some portion of the beginning of the input
  // shape (and may be negative to index from the end; e.g., -1 to begin the
  // reshape after the last axis, including nothing in the reshape,
  // -2 to include only the last axis, etc.).
  //
  // For example, suppose "input" is a 2D blob with shape 2 x 8.
  // Then the following ReshapeLayer specifications are all equivalent,
  // producing a blob "output" with shape 2 x 2 x 4:
  //
  //   reshape_param { shape { dim: 2  dim: 2  dim: 4 } }
  //   reshape_param { shape { dim: 2  dim: 4 } axis:  1 }
  //   reshape_param { shape { dim: 2  dim: 4 } axis: -3 }
  //
  // num_axes specifies the extent of the reshape.
  // If num_axes >= 0 (and axis >= 0), the reshape will be performed only on
  // input axes in the range [axis, axis+num_axes].
  // num_axes may also be -1, the default, to include all remaining axes
  // (starting from axis).
  //
  // For example, suppose "input" is a 2D blob with shape 2 x 8.
  // Then the following ReshapeLayer specifications are equivalent,
  // producing a blob "output" with shape 1 x 2 x 8.
  //
  //   reshape_param { shape { dim:  1  dim: 2  dim:  8 } }
  //   reshape_param { shape { dim:  1  dim: 2  }  num_axes: 1 }
  //   reshape_param { shape { dim:  1  }  num_axes: 0 }
  //
  // On the other hand, these would produce output blob shape 2 x 1 x 8:
  //
  //   reshape_param { shape { dim: 2  dim: 1  dim: 8  }  }
  //   reshape_param { shape { dim: 1 }  axis: 1  num_axes: 0 }
  //
  optional int32 axis = 2 [default = 0];
  optional int32 num_axes = 3 [default = -1];
}

message ScaleParameter {
  // The first axis of bottom[0] (the first input Blob) along which to apply
  // bottom[1] (the second input Blob).  May be negative to index from the end
  // (e.g., -1 for the last axis).
  //
  // For example, if bottom[0] is 4D with shape 100x3x40x60, the output
  // top[0] will have the same shape, and bottom[1] may have any of the
  // following shapes (for the given value of axis):
  //    (axis == 0 == -4) 100; 100x3; 100x3x40; 100x3x40x60
  //    (axis == 1 == -3)          3;     3x40;     3x40x60
  //    (axis == 2 == -2)                   40;       40x60
  //    (axis == 3 == -1)                                60
  // Furthermore, bottom[1] may have the empty shape (regardless of the value of
  // "axis") -- a scalar multiplier.
  optional int32 axis = 1 [default = 1];

  // (num_axes is ignored unless just one bottom is given and the scale is
  // a learned parameter of the layer.  Otherwise, num_axes is determined by the
  // number of axes by the second bottom.)
  // The number of axes of the input (bottom[0]) covered by the scale
  // parameter, or -1 to cover all axes of bottom[0] starting from `axis`.
  // Set num_axes := 0, to multiply with a zero-axis Blob: a scalar.
  optional int32 num_axes = 2 [default = 1];

  // (filler is ignored unless just one bottom is given and the scale is
  // a learned parameter of the layer.)
  // The initialization for the learned scale parameter.
  // Default is the unit (1) initialization, resulting in the ScaleLayer
  // initially performing the identity operation.
  optional FillerParameter filler = 3;

  // Whether to also learn a bias (equivalent to a ScaleLayer+BiasLayer, but
  // may be more efficient).  Initialized with bias_filler (defaults to 0).
  optional bool bias_term = 4 [default = false];
  optional FillerParameter bias_filler = 5;
}

message SigmoidParameter {
  enum Engine {
    DEFAULT = 0;
    CAFFE = 1;
    CUDNN = 2;
  }
  optional Engine engine = 1 [default = DEFAULT];
}

message SliceParameter {
  // The axis along which to slice -- may be negative to index from the end
  // (e.g., -1 for the last axis).
  // By default, SliceLayer concatenates blobs along the "channels" axis (1).
  optional int32 axis = 3 [default = 1];
  repeated uint32 slice_point = 2;

  // DEPRECATED: alias for "axis" -- does not support negative indexing.
  optional uint32 slice_dim = 1 [default = 1];
}

message JointImageParameter {
  optional string root_folder = 1 [default = ""];
  optional string filelistpath = 2 [default = ""];
  optional uint32 batchsize = 3 [default = 300];
  optional uint32 outsize = 4 [default = 227];
  optional uint32 channels = 5 [default = 3];
}

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

  // The axis along which to perform the softmax -- may be negative to index
  // from the end (e.g., -1 for the last axis).
  // Any other axes will be evaluated as independent softmaxes.
  optional int32 axis = 2 [default = 1];
}

message TanHParameter {
  enum Engine {
    DEFAULT = 0;
    CAFFE = 1;
    CUDNN = 2;
  }
  optional Engine engine = 1 [default = DEFAULT];
}

// Message that stores parameters used by TileLayer
message TileParameter {
  // The index of the axis to tile.
  optional int32 axis = 1 [default = 1];

  // The number of copies (tiles) of the blob to output.
  optional int32 tiles = 2;
}

// Message that stores parameters used by ThresholdLayer
message ThresholdParameter {
  optional float threshold = 1 [default = 0]; // Strictly positive values
}

// Message that stores parameters used by MILLayer
message MILParameter {
  enum MILType {
    MAX = 0;
    NOR = 1;
  }
  optional MILType type = 1 [default = MAX]; // The MIL method
}


message WindowDataParameter {
  // Specify the data source.
  optional string source = 1;
  // For data pre-processing, we can do simple scaling and subtracting the
  // data mean, if provided. Note that the mean subtraction is always carried
  // out before scaling.
  optional float scale = 2 [default = 1];
  optional string mean_file = 3;
  // Specify the batch size.
  optional uint32 batch_size = 4;
  // Specify if we would like to randomly crop an image.
  optional uint32 crop_size = 5 [default = 0];
  // Specify if we want to randomly mirror data.
  optional bool mirror = 6 [default = false];
  // Foreground (object) overlap threshold
  optional float fg_threshold = 7 [default = 0.5];
  // Background (non-object) overlap threshold
  optional float bg_threshold = 8 [default = 0.5];
  // Fraction of batch that should be foreground objects
  optional float fg_fraction = 9 [default = 0.25];
  // Amount of contextual padding to add around a window
  // (used only by the window_data_layer)
  optional uint32 context_pad = 10 [default = 0];
  // Mode for cropping out a detection window
  // warp: cropped window is warped to a fixed size and aspect ratio
  // square: the tightest square around the window is cropped
  optional string crop_mode = 11 [default = "warp"];
  // cache_images: will load all images in memory for faster access
  optional bool cache_images = 12 [default = false];
  // append root_folder to locate images
  optional string root_folder = 13 [default = ""];
}

message MILDataParameter {
  // Specify the data source.
  optional string source = 1;

  // Number of scales for each image
  optional uint32 num_scales = 2 [default = 1];

  // Side length ratio between neighbouring scales
  optional float scale_factor = 6 [default = 1];

  // Number of channels in the image
  optional uint32 channels = 4 [default = 3];

  // Specify the number of images per batch
  optional uint32 images_per_batch = 3;
  // Specify the number of classes
  optional uint32 n_classes = 5;
  // specify the box_dir and label_dir
  optional string label_file = 7;

  // Root directory which contains all the images
  optional string root_dir = 11;
  // Extention for the file
  optional string ext = 12;

  // To randomize or not
  optional bool randomize = 13 [default = true];
}



message SPPParameter {
  enum PoolMethod {
    MAX = 0;
    AVE = 1;
    STOCHASTIC = 2;
  }
  optional uint32 pyramid_height = 1;
  optional PoolMethod pool = 2 [default = MAX]; // The pooling method
  enum Engine {
    DEFAULT = 0;
    CAFFE = 1;
    CUDNN = 2;
  }
  optional Engine engine = 6 [default = DEFAULT];
}

// DEPRECATED: use LayerParameter.
message V1LayerParameter {
  repeated string bottom = 2;
  repeated string top = 3;
  optional string name = 4;
  repeated NetStateRule include = 32;
  repeated NetStateRule exclude = 33;
  enum LayerType {
    NONE = 0;
    ABSVAL = 35;
    ACCURACY = 1;
    ARGMAX = 30;
    BNLL = 2;
    CONCAT = 3;
    CONTRASTIVE_LOSS = 37;
    CONVOLUTION = 4;
    CROP = 40;
    DATA = 5;
    DECONVOLUTION = 39;
    DROPOUT = 6;
    DUMMY_DATA = 32;
    EUCLIDEAN_LOSS = 7;
    ELTWISE = 25;
    EXP = 38;
    FLATTEN = 8;
    HDF5_DATA = 9;
    HDF5_OUTPUT = 10;
    HINGE_LOSS = 28;
    IM2COL = 11;
    IMAGE_DATA = 12;
    INFOGAIN_LOSS = 13;
    INNER_PRODUCT = 14;
    LRN = 15;
    MEMORY_DATA = 29;
    MULTI_STAGE_MEANFIELD = 42;
    MULTINOMIAL_LOGISTIC_LOSS = 16;
    MVN = 34;
    POOLING = 17;
    POWER = 26;
    RELU = 18;
    SIGMOID = 19;
    SIGMOID_CROSS_ENTROPY_LOSS = 27;
    SILENCE = 36;
    SOFTMAX = 20;
    SOFTMAX_LOSS = 21;
    SPLIT = 22;
    SLICE = 33;
    TANH = 23;
    WINDOW_DATA = 24;
    THRESHOLD = 31;
  }
  optional LayerType type = 5;
  repeated BlobProto blobs = 6;
  repeated string param = 1001;
  repeated DimCheckMode blob_share_mode = 1002;
  enum DimCheckMode {
    STRICT = 0;
    PERMISSIVE = 1;
  }
  repeated float blobs_lr = 7;
  repeated float weight_decay = 8;
  repeated float loss_weight = 35;
  optional AccuracyParameter accuracy_param = 27;
  optional ArgMaxParameter argmax_param = 23;
  optional ConcatParameter concat_param = 9;
  optional ContrastiveLossParameter contrastive_loss_param = 40;
  optional ConvolutionParameter convolution_param = 10;
  optional CropParameter crop_param = 203;
  optional DataParameter data_param = 11;
  optional DropoutParameter dropout_param = 12;
  optional DummyDataParameter dummy_data_param = 26;
  optional EltwiseParameter eltwise_param = 24;
  optional ExpParameter exp_param = 41;
  optional HDF5DataParameter hdf5_data_param = 13;
  optional HDF5OutputParameter hdf5_output_param = 14;
  optional HingeLossParameter hinge_loss_param = 29;
  optional ImageDataParameter image_data_param = 15;
  optional InfogainLossParameter infogain_loss_param = 16;
  optional InnerProductParameter inner_product_param = 17;
  optional LRNParameter lrn_param = 18;
  optional MultiStageMeanfieldParameter multi_stage_meanfield_param = 202;
  optional MemoryDataParameter memory_data_param = 22;
  optional MVNParameter mvn_param = 34;
  optional PoolingParameter pooling_param = 19;
  optional PowerParameter power_param = 21;
  optional ReLUParameter relu_param = 30;
  optional SigmoidParameter sigmoid_param = 38;
  optional SoftmaxParameter softmax_param = 39;
  optional SliceParameter slice_param = 31;
  optional TanHParameter tanh_param = 37;
  optional ThresholdParameter threshold_param = 25;
  optional WindowDataParameter window_data_param = 20;
  optional TransformationParameter transform_param = 36;
  optional LossParameter loss_param = 42;
  optional V0LayerParameter layer = 1;
}

// DEPRECATED: V0LayerParameter is the old way of specifying layer parameters
// in Caffe.  We keep this message type around for legacy support.
message V0LayerParameter {
  optional string name = 1; // the layer name
  optional string type = 2; // the string to specify the layer type

  // Parameters to specify layers with inner products.
  optional uint32 num_output = 3; // The number of outputs for the layer
  optional bool biasterm = 4 [default = true]; // whether to have bias terms
  optional FillerParameter weight_filler = 5; // The filler for the weight
  optional FillerParameter bias_filler = 6; // The filler for the bias

  optional uint32 pad = 7 [default = 0]; // The padding size
  optional uint32 kernelsize = 8; // The kernel size
  optional uint32 group = 9 [default = 1]; // The group size for group conv
  optional uint32 stride = 10 [default = 1]; // The stride
  enum PoolMethod {
    MAX = 0;
    AVE = 1;
    STOCHASTIC = 2;
  }
  optional PoolMethod pool = 11 [default = MAX]; // The pooling method
  optional float dropout_ratio = 12 [default = 0.5]; // dropout ratio

  optional uint32 local_size = 13 [default = 5]; // for local response norm
  optional float alpha = 14 [default = 1.]; // for local response norm
  optional float beta = 15 [default = 0.75]; // for local response norm
  optional float k = 22 [default = 1.];

  // For data layers, specify the data source
  optional string source = 16;
  // For data pre-processing, we can do simple scaling and subtracting the
  // data mean, if provided. Note that the mean subtraction is always carried
  // out before scaling.
  optional float scale = 17 [default = 1];
  optional string meanfile = 18;
  // For data layers, specify the batch size.
  optional uint32 batchsize = 19;
  // For data layers, specify if we would like to randomly crop an image.
  optional uint32 cropsize = 20 [default = 0];
  // For data layers, specify if we want to randomly mirror data.
  optional bool mirror = 21 [default = false];

  // The blobs containing the numeric parameters of the layer
  repeated BlobProto blobs = 50;
  // The ratio that is multiplied on the global learning rate. If you want to
  // set the learning ratio for one blob, you need to set it for all blobs.
  repeated float blobs_lr = 51;
  // The weight decay that is multiplied on the global weight decay.
  repeated float weight_decay = 52;

  // The rand_skip variable is for the data layer to skip a few data points
  // to avoid all asynchronous sgd clients to start at the same point. The skip
  // point would be set as rand_skip * rand(0,1). Note that rand_skip should not
  // be larger than the number of keys in the database.
  optional uint32 rand_skip = 53 [default = 0];

  // Fields related to detection (det_*)
  // foreground (object) overlap threshold
  optional float det_fg_threshold = 54 [default = 0.5];
  // background (non-object) overlap threshold
  optional float det_bg_threshold = 55 [default = 0.5];
  // Fraction of batch that should be foreground objects
  optional float det_fg_fraction = 56 [default = 0.25];

  // optional bool OBSOLETE_can_clobber = 57 [default = true];

  // Amount of contextual padding to add around a window
  // (used only by the window_data_layer)
  optional uint32 det_context_pad = 58 [default = 0];

  // Mode for cropping out a detection window
  // warp: cropped window is warped to a fixed size and aspect ratio
  // square: the tightest square around the window is cropped
  optional string det_crop_mode = 59 [default = "warp"];

  // For ReshapeLayer, one needs to specify the new dimensions.
  optional int32 new_num = 60 [default = 0];
  optional int32 new_channels = 61 [default = 0];
  optional int32 new_height = 62 [default = 0];
  optional int32 new_width = 63 [default = 0];

  // Whether or not ImageLayer should shuffle the list of files at every epoch.
  // It will also resize images if new_height or new_width are not zero.
  optional bool shuffle_images = 64 [default = false];

  // For ConcatLayer, one needs to specify the dimension for concatenation, and
  // the other dimensions must be the same for all the bottom blobs.
  // By default it will concatenate blobs along the channels dimension.
  optional uint32 concat_dim = 65 [default = 1];

  optional HDF5OutputParameter hdf5_output_param = 1001;
}

message PReLUParameter {
  // Parametric ReLU described in K. He et al, Delving Deep into Rectifiers:
  // Surpassing Human-Level Performance on ImageNet Classification, 2015.

  // Initial value of a_i. Default is a_i=0.25 for all i.
  optional FillerParameter filler = 1;
  // Whether or not slope paramters are shared across channels.
  optional bool channel_shared = 2 [default = false];
}



（5）修改/src/caffe/util/upgrade_proto.cpp


为啥要修改升级proto，因为CRFasRNN的作者提供的是旧的格式的proto，因此需要这个来升级一下网络的配置文件。


#include <google/protobuf/io/coded_stream.h>
#include <google/protobuf/io/zero_copy_stream_impl.h>
#include <google/protobuf/text_format.h>

#include <map>
#include <string>

#include "caffe/common.hpp"
#include "caffe/proto/caffe.pb.h"
#include "caffe/util/io.hpp"
#include "caffe/util/upgrade_proto.hpp"

namespace caffe {

bool NetNeedsUpgrade(const NetParameter& net_param) {
  return NetNeedsV0ToV1Upgrade(net_param) || NetNeedsV1ToV2Upgrade(net_param);
}

bool UpgradeNetAsNeeded(const string& param_file, NetParameter* param) {
  bool success = true;
  if (NetNeedsV0ToV1Upgrade(*param)) {
    // NetParameter was specified using the old style (V0LayerParameter); try to
    // upgrade it.
    LOG(INFO) << "Attempting to upgrade input file specified using deprecated "
              << "V0LayerParameter: " << param_file;
    NetParameter original_param(*param);
    if (!UpgradeV0Net(original_param, param)) {
      success = false;
      LOG(ERROR) << "Warning: had one or more problems upgrading "
          << "V0NetParameter to NetParameter (see above); continuing anyway.";
    } else {
      LOG(INFO) << "Successfully upgraded file specified using deprecated "
                << "V0LayerParameter";
    }
    LOG(WARNING) << "Note that future Caffe releases will not support "
        << "V0NetParameter; use ./build/tools/upgrade_net_proto_text for "
        << "prototxt and ./build/tools/upgrade_net_proto_binary for model "
        << "weights upgrade this and any other net protos to the new format.";
  }
  // NetParameter uses old style data transformation fields; try to upgrade it.
  if (NetNeedsDataUpgrade(*param)) {
    LOG(INFO) << "Attempting to upgrade input file specified using deprecated "
              << "transformation parameters: " << param_file;
    UpgradeNetDataTransformation(param);
    LOG(INFO) << "Successfully upgraded file specified using deprecated "
              << "data transformation parameters.";
    LOG(WARNING) << "Note that future Caffe releases will only support "
                 << "transform_param messages for transformation fields.";
  }
  if (NetNeedsV1ToV2Upgrade(*param)) {
    LOG(INFO) << "Attempting to upgrade input file specified using deprecated "
              << "V1LayerParameter: " << param_file;
    NetParameter original_param(*param);
    if (!UpgradeV1Net(original_param, param)) {
      success = false;
      LOG(ERROR) << "Warning: had one or more problems upgrading "
                 << "V1LayerParameter (see above); continuing anyway.";
    } else {
      LOG(INFO) << "Successfully upgraded file specified using deprecated "
                << "V1LayerParameter";
    }
  }
  return success;
}

void ReadNetParamsFromTextFileOrDie(const string& param_file,
                                    NetParameter* param) {
  CHECK(ReadProtoFromTextFile(param_file, param))
      << "Failed to parse NetParameter file: " << param_file;
  UpgradeNetAsNeeded(param_file, param);
}

void ReadNetParamsFromBinaryFileOrDie(const string& param_file,
                                      NetParameter* param) {
  CHECK(ReadProtoFromBinaryFile(param_file, param))
      << "Failed to parse NetParameter file: " << param_file;
  UpgradeNetAsNeeded(param_file, param);
}

bool NetNeedsV0ToV1Upgrade(const NetParameter& net_param) {
  for (int i = 0; i < net_param.layers_size(); ++i) {
    if (net_param.layers(i).has_layer()) {
      return true;
    }
  }
  return false;
}

bool NetNeedsV1ToV2Upgrade(const NetParameter& net_param) {
  return net_param.layers_size() > 0;
}

bool UpgradeV0Net(const NetParameter& v0_net_param_padding_layers,
                  NetParameter* net_param) {
  // First upgrade padding layers to padded conv layers.
  NetParameter v0_net_param;
  UpgradeV0PaddingLayers(v0_net_param_padding_layers, &v0_net_param);
  // Now upgrade layer parameters.
  bool is_fully_compatible = true;
  net_param->Clear();
  if (v0_net_param.has_name()) {
    net_param->set_name(v0_net_param.name());
  }
  for (int i = 0; i < v0_net_param.layers_size(); ++i) {
    is_fully_compatible &= UpgradeV0LayerParameter(v0_net_param.layers(i),
                                                   net_param->add_layers());
  }
  for (int i = 0; i < v0_net_param.input_size(); ++i) {
    net_param->add_input(v0_net_param.input(i));
  }
  for (int i = 0; i < v0_net_param.input_dim_size(); ++i) {
    net_param->add_input_dim(v0_net_param.input_dim(i));
  }
  if (v0_net_param.has_force_backward()) {
    net_param->set_force_backward(v0_net_param.force_backward());
  }
  return is_fully_compatible;
}

void UpgradeV0PaddingLayers(const NetParameter& param,
                            NetParameter* param_upgraded_pad) {
  // Copy everything other than the layers from the original param.
  param_upgraded_pad->Clear();
  param_upgraded_pad->CopyFrom(param);
  param_upgraded_pad->clear_layers();
  // Figure out which layer each bottom blob comes from.
  map<string, int> blob_name_to_last_top_idx;
  for (int i = 0; i < param.input_size(); ++i) {
    const string& blob_name = param.input(i);
    blob_name_to_last_top_idx[blob_name] = -1;
  }
  for (int i = 0; i < param.layers_size(); ++i) {
    const V1LayerParameter& layer_connection = param.layers(i);
    const V0LayerParameter& layer_param = layer_connection.layer();
    // Add the layer to the new net, unless it's a padding layer.
    if (layer_param.type() != "padding") {
      param_upgraded_pad->add_layers()->CopyFrom(layer_connection);
    }
    for (int j = 0; j < layer_connection.bottom_size(); ++j) {
      const string& blob_name = layer_connection.bottom(j);
      if (blob_name_to_last_top_idx.find(blob_name) ==
          blob_name_to_last_top_idx.end()) {
        LOG(FATAL) << "Unknown blob input " << blob_name << " to layer " << j;
      }
      const int top_idx = blob_name_to_last_top_idx[blob_name];
      if (top_idx == -1) {
        continue;
      }
      const V1LayerParameter& source_layer = param.layers(top_idx);
      if (source_layer.layer().type() == "padding") {
        // This layer has a padding layer as input -- check that it is a conv
        // layer or a pooling layer and takes only one input.  Also check that
        // the padding layer input has only one input and one output.  Other
        // cases have undefined behavior in Caffe.
        CHECK((layer_param.type() == "conv") || (layer_param.type() == "pool"))
            << "Padding layer input to "
            "non-convolutional / non-pooling layer type "
            << layer_param.type();
        CHECK_EQ(layer_connection.bottom_size(), 1)
            << "Conv Layer takes a single blob as input.";
        CHECK_EQ(source_layer.bottom_size(), 1)
            << "Padding Layer takes a single blob as input.";
        CHECK_EQ(source_layer.top_size(), 1)
            << "Padding Layer produces a single blob as output.";
        int layer_index = param_upgraded_pad->layers_size() - 1;
        param_upgraded_pad->mutable_layers(layer_index)->mutable_layer()
            ->set_pad(source_layer.layer().pad());
        param_upgraded_pad->mutable_layers(layer_index)
            ->set_bottom(j, source_layer.bottom(0));
      }
    }
    for (int j = 0; j < layer_connection.top_size(); ++j) {
      const string& blob_name = layer_connection.top(j);
      blob_name_to_last_top_idx[blob_name] = i;
    }
  }
}

bool UpgradeV0LayerParameter(const V1LayerParameter& v0_layer_connection,
                             V1LayerParameter* layer_param) {
  bool is_fully_compatible = true;
  layer_param->Clear();
  for (int i = 0; i < v0_layer_connection.bottom_size(); ++i) {
    layer_param->add_bottom(v0_layer_connection.bottom(i));
  }
  for (int i = 0; i < v0_layer_connection.top_size(); ++i) {
    layer_param->add_top(v0_layer_connection.top(i));
  }
  if (v0_layer_connection.has_layer()) {
    const V0LayerParameter& v0_layer_param = v0_layer_connection.layer();
    if (v0_layer_param.has_name()) {
      layer_param->set_name(v0_layer_param.name());
    }
    const string& type = v0_layer_param.type();
    if (v0_layer_param.has_type()) {
      layer_param->set_type(UpgradeV0LayerType(type));
    }
    for (int i = 0; i < v0_layer_param.blobs_size(); ++i) {
      layer_param->add_blobs()->CopyFrom(v0_layer_param.blobs(i));
    }
    for (int i = 0; i < v0_layer_param.blobs_lr_size(); ++i) {
      layer_param->add_blobs_lr(v0_layer_param.blobs_lr(i));
    }
    for (int i = 0; i < v0_layer_param.weight_decay_size(); ++i) {
      layer_param->add_weight_decay(v0_layer_param.weight_decay(i));
    }
    if (v0_layer_param.has_num_output()) {
      if (type == "conv") {
        layer_param->mutable_convolution_param()->set_num_output(
            v0_layer_param.num_output());
      } else if (type == "innerproduct") {
        layer_param->mutable_inner_product_param()->set_num_output(
            v0_layer_param.num_output());
      } else {
        LOG(ERROR) << "Unknown parameter num_output for layer type " << type;
        is_fully_compatible = false;
      }
    }
    if (v0_layer_param.has_biasterm()) {
      if (type == "conv") {
        layer_param->mutable_convolution_param()->set_bias_term(
            v0_layer_param.biasterm());
      } else if (type == "innerproduct") {
        layer_param->mutable_inner_product_param()->set_bias_term(
            v0_layer_param.biasterm());
      } else {
        LOG(ERROR) << "Unknown parameter biasterm for layer type " << type;
        is_fully_compatible = false;
      }
    }
    if (v0_layer_param.has_weight_filler()) {
      if (type == "conv") {
        layer_param->mutable_convolution_param()->
            mutable_weight_filler()->CopyFrom(v0_layer_param.weight_filler());
      } else if (type == "innerproduct") {
        layer_param->mutable_inner_product_param()->
            mutable_weight_filler()->CopyFrom(v0_layer_param.weight_filler());
      } else {
        LOG(ERROR) << "Unknown parameter weight_filler for layer type " << type;
        is_fully_compatible = false;
      }
    }
    if (v0_layer_param.has_bias_filler()) {
      if (type == "conv") {
        layer_param->mutable_convolution_param()->
            mutable_bias_filler()->CopyFrom(v0_layer_param.bias_filler());
      } else if (type == "innerproduct") {
        layer_param->mutable_inner_product_param()->
            mutable_bias_filler()->CopyFrom(v0_layer_param.bias_filler());
      } else {
        LOG(ERROR) << "Unknown parameter bias_filler for layer type " << type;
        is_fully_compatible = false;
      }
    }
    if (v0_layer_param.has_pad()) {
      if (type == "conv") {
        layer_param->mutable_convolution_param()->add_pad(v0_layer_param.pad());
      } else if (type == "pool") {
        layer_param->mutable_pooling_param()->set_pad(v0_layer_param.pad());
      } else {
        LOG(ERROR) << "Unknown parameter pad for layer type " << type;
        is_fully_compatible = false;
      }
    }
    if (v0_layer_param.has_kernelsize()) {
      if (type == "conv") {
        layer_param->mutable_convolution_param()->add_kernel_size(
            v0_layer_param.kernelsize());
      } else if (type == "pool") {
        layer_param->mutable_pooling_param()->set_kernel_size(
            v0_layer_param.kernelsize());
      } else {
        LOG(ERROR) << "Unknown parameter kernelsize for layer type " << type;
        is_fully_compatible = false;
      }
    }
    if (v0_layer_param.has_group()) {
      if (type == "conv") {
        layer_param->mutable_convolution_param()->set_group(
            v0_layer_param.group());
      } else {
        LOG(ERROR) << "Unknown parameter group for layer type " << type;
        is_fully_compatible = false;
      }
    }
    if (v0_layer_param.has_stride()) {
      if (type == "conv") {
        layer_param->mutable_convolution_param()->add_stride(
            v0_layer_param.stride());
      } else if (type == "pool") {
        layer_param->mutable_pooling_param()->set_stride(
            v0_layer_param.stride());
      } else {
        LOG(ERROR) << "Unknown parameter stride for layer type " << type;
        is_fully_compatible = false;
      }
    }
    if (v0_layer_param.has_pool()) {
      if (type == "pool") {
        V0LayerParameter_PoolMethod pool = v0_layer_param.pool();
        switch (pool) {
        case V0LayerParameter_PoolMethod_MAX:
          layer_param->mutable_pooling_param()->set_pool(
              PoolingParameter_PoolMethod_MAX);
          break;
        case V0LayerParameter_PoolMethod_AVE:
          layer_param->mutable_pooling_param()->set_pool(
              PoolingParameter_PoolMethod_AVE);
          break;
        case V0LayerParameter_PoolMethod_STOCHASTIC:
          layer_param->mutable_pooling_param()->set_pool(
              PoolingParameter_PoolMethod_STOCHASTIC);
          break;
        default:
          LOG(ERROR) << "Unknown pool method " << pool;
          is_fully_compatible = false;
        }
      } else {
        LOG(ERROR) << "Unknown parameter pool for layer type " << type;
        is_fully_compatible = false;
      }
    }
    if (v0_layer_param.has_dropout_ratio()) {
      if (type == "dropout") {
        layer_param->mutable_dropout_param()->set_dropout_ratio(
            v0_layer_param.dropout_ratio());
      } else {
        LOG(ERROR) << "Unknown parameter dropout_ratio for layer type " << type;
        is_fully_compatible = false;
      }
    }
    if (v0_layer_param.has_local_size()) {
      if (type == "lrn") {
        layer_param->mutable_lrn_param()->set_local_size(
            v0_layer_param.local_size());
      } else {
        LOG(ERROR) << "Unknown parameter local_size for layer type " << type;
        is_fully_compatible = false;
      }
    }
    if (v0_layer_param.has_alpha()) {
      if (type == "lrn") {
        layer_param->mutable_lrn_param()->set_alpha(v0_layer_param.alpha());
      } else {
        LOG(ERROR) << "Unknown parameter alpha for layer type " << type;
        is_fully_compatible = false;
      }
    }
    if (v0_layer_param.has_beta()) {
      if (type == "lrn") {
        layer_param->mutable_lrn_param()->set_beta(v0_layer_param.beta());
      } else {
        LOG(ERROR) << "Unknown parameter beta for layer type " << type;
        is_fully_compatible = false;
      }
    }
    if (v0_layer_param.has_k()) {
      if (type == "lrn") {
        layer_param->mutable_lrn_param()->set_k(v0_layer_param.k());
      } else {
        LOG(ERROR) << "Unknown parameter k for layer type " << type;
        is_fully_compatible = false;
      }
    }
    if (v0_layer_param.has_source()) {
      if (type == "data") {
        layer_param->mutable_data_param()->set_source(v0_layer_param.source());
      } else if (type == "hdf5_data") {
        layer_param->mutable_hdf5_data_param()->set_source(
            v0_layer_param.source());
      } else if (type == "images") {
        layer_param->mutable_image_data_param()->set_source(
            v0_layer_param.source());
      } else if (type == "window_data") {
        layer_param->mutable_window_data_param()->set_source(
            v0_layer_param.source());
      } else if (type == "infogain_loss") {
        layer_param->mutable_infogain_loss_param()->set_source(
            v0_layer_param.source());
      } else {
        LOG(ERROR) << "Unknown parameter source for layer type " << type;
        is_fully_compatible = false;
      }
    }
    if (v0_layer_param.has_scale()) {
      layer_param->mutable_transform_param()->
          set_scale(v0_layer_param.scale());
    }
    if (v0_layer_param.has_meanfile()) {
      layer_param->mutable_transform_param()->
          set_mean_file(v0_layer_param.meanfile());
    }
    if (v0_layer_param.has_batchsize()) {
      if (type == "data") {
        layer_param->mutable_data_param()->set_batch_size(
            v0_layer_param.batchsize());
      } else if (type == "hdf5_data") {
        layer_param->mutable_hdf5_data_param()->set_batch_size(
            v0_layer_param.batchsize());
      } else if (type == "images") {
        layer_param->mutable_image_data_param()->set_batch_size(
            v0_layer_param.batchsize());
      } else if (type == "window_data") {
        layer_param->mutable_window_data_param()->set_batch_size(
            v0_layer_param.batchsize());
      } else {
        LOG(ERROR) << "Unknown parameter batchsize for layer type " << type;
        is_fully_compatible = false;
      }
    }
    if (v0_layer_param.has_cropsize()) {
      layer_param->mutable_transform_param()->
          set_crop_size(v0_layer_param.cropsize());
    }
    if (v0_layer_param.has_mirror()) {
      layer_param->mutable_transform_param()->
          set_mirror(v0_layer_param.mirror());
    }
    if (v0_layer_param.has_rand_skip()) {
      if (type == "data") {
        layer_param->mutable_data_param()->set_rand_skip(
            v0_layer_param.rand_skip());
      } else if (type == "images") {
        layer_param->mutable_image_data_param()->set_rand_skip(
            v0_layer_param.rand_skip());
      } else {
        LOG(ERROR) << "Unknown parameter rand_skip for layer type " << type;
        is_fully_compatible = false;
      }
    }
    if (v0_layer_param.has_shuffle_images()) {
      if (type == "images") {
        layer_param->mutable_image_data_param()->set_shuffle(
            v0_layer_param.shuffle_images());
      } else {
        LOG(ERROR) << "Unknown parameter shuffle for layer type " << type;
        is_fully_compatible = false;
      }
    }
    if (v0_layer_param.has_new_height()) {
      if (type == "images") {
        layer_param->mutable_image_data_param()->set_new_height(
            v0_layer_param.new_height());
      } else {
        LOG(ERROR) << "Unknown parameter new_height for layer type " << type;
        is_fully_compatible = false;
      }
    }
    if (v0_layer_param.has_new_width()) {
      if (type == "images") {
        layer_param->mutable_image_data_param()->set_new_width(
            v0_layer_param.new_width());
      } else {
        LOG(ERROR) << "Unknown parameter new_width for layer type " << type;
        is_fully_compatible = false;
      }
    }
    if (v0_layer_param.has_concat_dim()) {
      if (type == "concat") {
        layer_param->mutable_concat_param()->set_concat_dim(
            v0_layer_param.concat_dim());
      } else {
        LOG(ERROR) << "Unknown parameter concat_dim for layer type " << type;
        is_fully_compatible = false;
      }
    }
    if (v0_layer_param.has_det_fg_threshold()) {
      if (type == "window_data") {
        layer_param->mutable_window_data_param()->set_fg_threshold(
            v0_layer_param.det_fg_threshold());
      } else {
        LOG(ERROR) << "Unknown parameter det_fg_threshold for layer type "
                   << type;
        is_fully_compatible = false;
      }
    }
    if (v0_layer_param.has_det_bg_threshold()) {
      if (type == "window_data") {
        layer_param->mutable_window_data_param()->set_bg_threshold(
            v0_layer_param.det_bg_threshold());
      } else {
        LOG(ERROR) << "Unknown parameter det_bg_threshold for layer type "
                   << type;
        is_fully_compatible = false;
      }
    }
    if (v0_layer_param.has_det_fg_fraction()) {
      if (type == "window_data") {
        layer_param->mutable_window_data_param()->set_fg_fraction(
            v0_layer_param.det_fg_fraction());
      } else {
        LOG(ERROR) << "Unknown parameter det_fg_fraction for layer type "
                   << type;
        is_fully_compatible = false;
      }
    }
    if (v0_layer_param.has_det_context_pad()) {
      if (type == "window_data") {
        layer_param->mutable_window_data_param()->set_context_pad(
            v0_layer_param.det_context_pad());
      } else {
        LOG(ERROR) << "Unknown parameter det_context_pad for layer type "
                   << type;
        is_fully_compatible = false;
      }
    }
    if (v0_layer_param.has_det_crop_mode()) {
      if (type == "window_data") {
        layer_param->mutable_window_data_param()->set_crop_mode(
            v0_layer_param.det_crop_mode());
      } else {
        LOG(ERROR) << "Unknown parameter det_crop_mode for layer type "
                   << type;
        is_fully_compatible = false;
      }
    }
    if (v0_layer_param.has_hdf5_output_param()) {
      if (type == "hdf5_output") {
        layer_param->mutable_hdf5_output_param()->CopyFrom(
            v0_layer_param.hdf5_output_param());
      } else {
        LOG(ERROR) << "Unknown parameter hdf5_output_param for layer type "
                   << type;
        is_fully_compatible = false;
      }
    }
  }
  return is_fully_compatible;
}

V1LayerParameter_LayerType UpgradeV0LayerType(const string& type) {
  if (type == "accuracy") {
    return V1LayerParameter_LayerType_ACCURACY;
  } else if (type == "bnll") {
    return V1LayerParameter_LayerType_BNLL;
  } else if (type == "concat") {
    return V1LayerParameter_LayerType_CONCAT;
  } else if (type == "conv") {
    return V1LayerParameter_LayerType_CONVOLUTION;
  } else if (type == "data") {
    return V1LayerParameter_LayerType_DATA;
  } else if (type == "dropout") {
    return V1LayerParameter_LayerType_DROPOUT;
  } else if (type == "euclidean_loss") {
    return V1LayerParameter_LayerType_EUCLIDEAN_LOSS;
  } else if (type == "flatten") {
    return V1LayerParameter_LayerType_FLATTEN;
  } else if (type == "hdf5_data") {
    return V1LayerParameter_LayerType_HDF5_DATA;
  } else if (type == "hdf5_output") {
    return V1LayerParameter_LayerType_HDF5_OUTPUT;
  } else if (type == "im2col") {
    return V1LayerParameter_LayerType_IM2COL;
  } else if (type == "images") {
    return V1LayerParameter_LayerType_IMAGE_DATA;
  } else if (type == "infogain_loss") {
    return V1LayerParameter_LayerType_INFOGAIN_LOSS;
  } else if (type == "innerproduct") {
    return V1LayerParameter_LayerType_INNER_PRODUCT;
  } else if (type == "lrn") {
    return V1LayerParameter_LayerType_LRN;
  } else if (type == "multinomial_logistic_loss") {
    return V1LayerParameter_LayerType_MULTINOMIAL_LOGISTIC_LOSS;
  } else if (type == "pool") {
    return V1LayerParameter_LayerType_POOLING;
  } else if (type == "relu") {
    return V1LayerParameter_LayerType_RELU;
  } else if (type == "sigmoid") {
    return V1LayerParameter_LayerType_SIGMOID;
  } else if (type == "softmax") {
    return V1LayerParameter_LayerType_SOFTMAX;
  } else if (type == "softmax_loss") {
    return V1LayerParameter_LayerType_SOFTMAX_LOSS;
  } else if (type == "split") {
    return V1LayerParameter_LayerType_SPLIT;
  } else if (type == "tanh") {
    return V1LayerParameter_LayerType_TANH;
  } else if (type == "window_data") {
    return V1LayerParameter_LayerType_WINDOW_DATA;
  } else {
    LOG(FATAL) << "Unknown layer name: " << type;
    return V1LayerParameter_LayerType_NONE;
  }
}

bool NetNeedsDataUpgrade(const NetParameter& net_param) {
  for (int i = 0; i < net_param.layers_size(); ++i) {
    if (net_param.layers(i).type() == V1LayerParameter_LayerType_DATA) {
      DataParameter layer_param = net_param.layers(i).data_param();
      if (layer_param.has_scale()) { return true; }
      if (layer_param.has_mean_file()) { return true; }
      if (layer_param.has_crop_size()) { return true; }
      if (layer_param.has_mirror()) { return true; }
    }
    if (net_param.layers(i).type() == V1LayerParameter_LayerType_IMAGE_DATA) {
      ImageDataParameter layer_param = net_param.layers(i).image_data_param();
      if (layer_param.has_scale()) { return true; }
      if (layer_param.has_mean_file()) { return true; }
      if (layer_param.has_crop_size()) { return true; }
      if (layer_param.has_mirror()) { return true; }
    }
    if (net_param.layers(i).type() == V1LayerParameter_LayerType_WINDOW_DATA) {
      WindowDataParameter layer_param = net_param.layers(i).window_data_param();
      if (layer_param.has_scale()) { return true; }
      if (layer_param.has_mean_file()) { return true; }
      if (layer_param.has_crop_size()) { return true; }
      if (layer_param.has_mirror()) { return true; }
    }
  }
  return false;
}

#define CONVERT_LAYER_TRANSFORM_PARAM(TYPE, Name, param_name) \
  do { \
    if (net_param->layers(i).type() == V1LayerParameter_LayerType_##TYPE) { \
      Name##Parameter* layer_param = \
          net_param->mutable_layers(i)->mutable_##param_name##_param(); \
      TransformationParameter* transform_param = \
          net_param->mutable_layers(i)->mutable_transform_param(); \
      if (layer_param->has_scale()) { \
        transform_param->set_scale(layer_param->scale()); \
        layer_param->clear_scale(); \
      } \
      if (layer_param->has_mean_file()) { \
        transform_param->set_mean_file(layer_param->mean_file()); \
        layer_param->clear_mean_file(); \
      } \
      if (layer_param->has_crop_size()) { \
        transform_param->set_crop_size(layer_param->crop_size()); \
        layer_param->clear_crop_size(); \
      } \
      if (layer_param->has_mirror()) { \
        transform_param->set_mirror(layer_param->mirror()); \
        layer_param->clear_mirror(); \
      } \
    } \
  } while (0)

void UpgradeNetDataTransformation(NetParameter* net_param) {
  for (int i = 0; i < net_param->layers_size(); ++i) {
    CONVERT_LAYER_TRANSFORM_PARAM(DATA, Data, data);
    CONVERT_LAYER_TRANSFORM_PARAM(IMAGE_DATA, ImageData, image_data);
    CONVERT_LAYER_TRANSFORM_PARAM(WINDOW_DATA, WindowData, window_data);
  }
}

bool UpgradeV1Net(const NetParameter& v1_net_param, NetParameter* net_param) {
  bool is_fully_compatible = true;
  if (v1_net_param.layer_size() > 0) {
    LOG(ERROR) << "Input NetParameter to be upgraded already specifies 'layer' "
               << "fields; these will be ignored for the upgrade.";
    is_fully_compatible = false;
  }
  net_param->CopyFrom(v1_net_param);
  net_param->clear_layers();
  net_param->clear_layer();
  for (int i = 0; i < v1_net_param.layers_size(); ++i) {
    if (!UpgradeV1LayerParameter(v1_net_param.layers(i),
                                 net_param->add_layer())) {
      LOG(ERROR) << "Upgrade of input layer " << i << " failed.";
      is_fully_compatible = false;
    }
  }
  return is_fully_compatible;
}

bool UpgradeV1LayerParameter(const V1LayerParameter& v1_layer_param,
                             LayerParameter* layer_param) {
  layer_param->Clear();
  bool is_fully_compatible = true;
  for (int i = 0; i < v1_layer_param.bottom_size(); ++i) {
    layer_param->add_bottom(v1_layer_param.bottom(i));
  }
  for (int i = 0; i < v1_layer_param.top_size(); ++i) {
    layer_param->add_top(v1_layer_param.top(i));
  }
  if (v1_layer_param.has_name()) {
    layer_param->set_name(v1_layer_param.name());
  }
  for (int i = 0; i < v1_layer_param.include_size(); ++i) {
    layer_param->add_include()->CopyFrom(v1_layer_param.include(i));
  }
  for (int i = 0; i < v1_layer_param.exclude_size(); ++i) {
    layer_param->add_exclude()->CopyFrom(v1_layer_param.exclude(i));
  }
  if (v1_layer_param.has_type()) {
    layer_param->set_type(UpgradeV1LayerType(v1_layer_param.type()));
  }
  for (int i = 0; i < v1_layer_param.blobs_size(); ++i) {
    layer_param->add_blobs()->CopyFrom(v1_layer_param.blobs(i));
  }
  for (int i = 0; i < v1_layer_param.param_size(); ++i) {
    while (layer_param->param_size() <= i) { layer_param->add_param(); }
    layer_param->mutable_param(i)->set_name(v1_layer_param.param(i));
  }
  ParamSpec_DimCheckMode mode;
  for (int i = 0; i < v1_layer_param.blob_share_mode_size(); ++i) {
    while (layer_param->param_size() <= i) { layer_param->add_param(); }
    switch (v1_layer_param.blob_share_mode(i)) {
    case V1LayerParameter_DimCheckMode_STRICT:
      mode = ParamSpec_DimCheckMode_STRICT;
      break;
    case V1LayerParameter_DimCheckMode_PERMISSIVE:
      mode = ParamSpec_DimCheckMode_PERMISSIVE;
      break;
    default:
      LOG(FATAL) << "Unknown blob_share_mode: "
                 << v1_layer_param.blob_share_mode(i);
      break;
    }
    layer_param->mutable_param(i)->set_share_mode(mode);
  }
  for (int i = 0; i < v1_layer_param.blobs_lr_size(); ++i) {
    while (layer_param->param_size() <= i) { layer_param->add_param(); }
    layer_param->mutable_param(i)->set_lr_mult(v1_layer_param.blobs_lr(i));
  }
  for (int i = 0; i < v1_layer_param.weight_decay_size(); ++i) {
    while (layer_param->param_size() <= i) { layer_param->add_param(); }
    layer_param->mutable_param(i)->set_decay_mult(
        v1_layer_param.weight_decay(i));
  }
  for (int i = 0; i < v1_layer_param.loss_weight_size(); ++i) {
    layer_param->add_loss_weight(v1_layer_param.loss_weight(i));
  }
  if (v1_layer_param.has_accuracy_param()) {
    layer_param->mutable_accuracy_param()->CopyFrom(
        v1_layer_param.accuracy_param());
  }
  if (v1_layer_param.has_argmax_param()) {
    layer_param->mutable_argmax_param()->CopyFrom(
        v1_layer_param.argmax_param());
  }
  if (v1_layer_param.has_concat_param()) {
    layer_param->mutable_concat_param()->CopyFrom(
        v1_layer_param.concat_param());
  }
  if (v1_layer_param.has_contrastive_loss_param()) {
    layer_param->mutable_contrastive_loss_param()->CopyFrom(
        v1_layer_param.contrastive_loss_param());
  }
  if (v1_layer_param.has_convolution_param()) {
    layer_param->mutable_convolution_param()->CopyFrom(
        v1_layer_param.convolution_param());
  }
  if (v1_layer_param.has_data_param()) {
    layer_param->mutable_data_param()->CopyFrom(
        v1_layer_param.data_param());
  }
  if (v1_layer_param.has_dropout_param()) {
    layer_param->mutable_dropout_param()->CopyFrom(
        v1_layer_param.dropout_param());
  }
  if (v1_layer_param.has_dummy_data_param()) {
    layer_param->mutable_dummy_data_param()->CopyFrom(
        v1_layer_param.dummy_data_param());
  }
  if (v1_layer_param.has_eltwise_param()) {
    layer_param->mutable_eltwise_param()->CopyFrom(
        v1_layer_param.eltwise_param());
  }
  if (v1_layer_param.has_exp_param()) {
    layer_param->mutable_exp_param()->CopyFrom(
        v1_layer_param.exp_param());
  }
  if (v1_layer_param.has_hdf5_data_param()) {
    layer_param->mutable_hdf5_data_param()->CopyFrom(
        v1_layer_param.hdf5_data_param());
  }
  if (v1_layer_param.has_hdf5_output_param()) {
    layer_param->mutable_hdf5_output_param()->CopyFrom(
        v1_layer_param.hdf5_output_param());
  }
  if (v1_layer_param.has_hinge_loss_param()) {
    layer_param->mutable_hinge_loss_param()->CopyFrom(
        v1_layer_param.hinge_loss_param());
  }
  if (v1_layer_param.has_image_data_param()) {
    layer_param->mutable_image_data_param()->CopyFrom(
        v1_layer_param.image_data_param());
  }
  if (v1_layer_param.has_infogain_loss_param()) {
    layer_param->mutable_infogain_loss_param()->CopyFrom(
        v1_layer_param.infogain_loss_param());
  }
  if (v1_layer_param.has_inner_product_param()) {
    layer_param->mutable_inner_product_param()->CopyFrom(
        v1_layer_param.inner_product_param());
  }
  if (v1_layer_param.has_lrn_param()) {
    layer_param->mutable_lrn_param()->CopyFrom(
        v1_layer_param.lrn_param());
  }
  if (v1_layer_param.has_memory_data_param()) {
    layer_param->mutable_memory_data_param()->CopyFrom(
        v1_layer_param.memory_data_param());
  }
  if (v1_layer_param.has_mvn_param()) {
    layer_param->mutable_mvn_param()->CopyFrom(
        v1_layer_param.mvn_param());
  }
  if (v1_layer_param.has_pooling_param()) {
    layer_param->mutable_pooling_param()->CopyFrom(
        v1_layer_param.pooling_param());
  }
  if (v1_layer_param.has_power_param()) {
    layer_param->mutable_power_param()->CopyFrom(
        v1_layer_param.power_param());
  }
  if (v1_layer_param.has_relu_param()) {
    layer_param->mutable_relu_param()->CopyFrom(
        v1_layer_param.relu_param());
  }
  if (v1_layer_param.has_sigmoid_param()) {
    layer_param->mutable_sigmoid_param()->CopyFrom(
        v1_layer_param.sigmoid_param());
  }
  if (v1_layer_param.has_softmax_param()) {
    layer_param->mutable_softmax_param()->CopyFrom(
        v1_layer_param.softmax_param());
  }
  if (v1_layer_param.has_slice_param()) {
    layer_param->mutable_slice_param()->CopyFrom(
        v1_layer_param.slice_param());
  }
  if (v1_layer_param.has_tanh_param()) {
    layer_param->mutable_tanh_param()->CopyFrom(
        v1_layer_param.tanh_param());
  }
  if (v1_layer_param.has_threshold_param()) {
    layer_param->mutable_threshold_param()->CopyFrom(
        v1_layer_param.threshold_param());
  }
  if (v1_layer_param.has_window_data_param()) {
    layer_param->mutable_window_data_param()->CopyFrom(
        v1_layer_param.window_data_param());
  }
  if (v1_layer_param.has_transform_param()) {
    layer_param->mutable_transform_param()->CopyFrom(
        v1_layer_param.transform_param());
  }
  if (v1_layer_param.has_loss_param()) {
    layer_param->mutable_loss_param()->CopyFrom(
        v1_layer_param.loss_param());
  }
  if (v1_layer_param.has_layer()) {
    LOG(ERROR) << "Input NetParameter has V0 layer -- ignoring.";
    is_fully_compatible = false;
  }
  return is_fully_compatible;
}

const char* UpgradeV1LayerType(const V1LayerParameter_LayerType type) {
  switch (type) {
  case V1LayerParameter_LayerType_NONE:
    return "";
  case V1LayerParameter_LayerType_ABSVAL:
    return "AbsVal";
  case V1LayerParameter_LayerType_ACCURACY:
    return "Accuracy";
  case V1LayerParameter_LayerType_ARGMAX:
    return "ArgMax";
  case V1LayerParameter_LayerType_BNLL:
    return "BNLL";
  case V1LayerParameter_LayerType_CONCAT:
    return "Concat";
  case V1LayerParameter_LayerType_CONTRASTIVE_LOSS:
    return "ContrastiveLoss";
  case V1LayerParameter_LayerType_CONVOLUTION:
    return "Convolution";
  case V1LayerParameter_LayerType_CROP:
      return "Crop";
  case V1LayerParameter_LayerType_DECONVOLUTION:
    return "Deconvolution";
  case V1LayerParameter_LayerType_DATA:
    return "Data";
  case V1LayerParameter_LayerType_DROPOUT:
    return "Dropout";
  case V1LayerParameter_LayerType_DUMMY_DATA:
    return "DummyData";
  case V1LayerParameter_LayerType_EUCLIDEAN_LOSS:
    return "EuclideanLoss";
  case V1LayerParameter_LayerType_ELTWISE:
    return "Eltwise";
  case V1LayerParameter_LayerType_EXP:
    return "Exp";
  case V1LayerParameter_LayerType_FLATTEN:
    return "Flatten";
  case V1LayerParameter_LayerType_HDF5_DATA:
    return "HDF5Data";
  case V1LayerParameter_LayerType_HDF5_OUTPUT:
    return "HDF5Output";
  case V1LayerParameter_LayerType_HINGE_LOSS:
    return "HingeLoss";
  case V1LayerParameter_LayerType_IM2COL:
    return "Im2col";
  case V1LayerParameter_LayerType_IMAGE_DATA:
    return "ImageData";
  case V1LayerParameter_LayerType_INFOGAIN_LOSS:
    return "InfogainLoss";
  case V1LayerParameter_LayerType_INNER_PRODUCT:
    return "InnerProduct";
  case V1LayerParameter_LayerType_LRN:
    return "LRN";
  case V1LayerParameter_LayerType_MEMORY_DATA:
    return "MemoryData";
  case V1LayerParameter_LayerType_MULTI_STAGE_MEANFIELD:
    return "MultiStageMeanfield";
  case V1LayerParameter_LayerType_MULTINOMIAL_LOGISTIC_LOSS:
    return "MultinomialLogisticLoss";
  case V1LayerParameter_LayerType_MVN:
    return "MVN";
  case V1LayerParameter_LayerType_POOLING:
    return "Pooling";
  case V1LayerParameter_LayerType_POWER:
    return "Power";
  case V1LayerParameter_LayerType_RELU:
    return "ReLU";
  case V1LayerParameter_LayerType_SIGMOID:
    return "Sigmoid";
  case V1LayerParameter_LayerType_SIGMOID_CROSS_ENTROPY_LOSS:
    return "SigmoidCrossEntropyLoss";
  case V1LayerParameter_LayerType_SILENCE:
    return "Silence";
  case V1LayerParameter_LayerType_SOFTMAX:
    return "Softmax";
  case V1LayerParameter_LayerType_SOFTMAX_LOSS:
    return "SoftmaxWithLoss";
  case V1LayerParameter_LayerType_SPLIT:
    return "Split";
  case V1LayerParameter_LayerType_SLICE:
    return "Slice";
  case V1LayerParameter_LayerType_TANH:
    return "TanH";
  case V1LayerParameter_LayerType_WINDOW_DATA:
    return "WindowData";
  case V1LayerParameter_LayerType_THRESHOLD:
    return "Threshold";
  default:
    LOG(FATAL) << "Unknown V1LayerParameter layer type: " << type;
    return "";
  }
}

// Return true iff the solver contains any old solver_type specified as enums
bool SolverNeedsTypeUpgrade(const SolverParameter& solver_param) {
  if (solver_param.has_solver_type()) {
    return true;
  }
  return false;
}

bool UpgradeSolverType(SolverParameter* solver_param) {
  CHECK(!solver_param->has_solver_type() || !solver_param->has_type())
      << "Failed to upgrade solver: old solver_type field (enum) and new type "
      << "field (string) cannot be both specified in solver proto text.";
  if (solver_param->has_solver_type()) {
    string type;
    switch (solver_param->solver_type()) {
    case SolverParameter_SolverType_SGD:
      type = "SGD";
      break;
    case SolverParameter_SolverType_NESTEROV:
      type = "Nesterov";
      break;
    case SolverParameter_SolverType_ADAGRAD:
      type = "AdaGrad";
      break;
    case SolverParameter_SolverType_RMSPROP:
      type = "RMSProp";
      break;
    case SolverParameter_SolverType_ADADELTA:
      type = "AdaDelta";
      break;
    case SolverParameter_SolverType_ADAM:
      type = "Adam";
      break;
    default:
      LOG(FATAL) << "Unknown SolverParameter solver_type: " << type;
    }
    solver_param->set_type(type);
    solver_param->clear_solver_type();
  } else {
    LOG(ERROR) << "Warning: solver type already up to date. ";
    return false;
  }
  return true;
}

// Check for deprecations and upgrade the SolverParameter as needed.
bool UpgradeSolverAsNeeded(const string& param_file, SolverParameter* param) {
  bool success = true;
  // Try to upgrade old style solver_type enum fields into new string type
  if (SolverNeedsTypeUpgrade(*param)) {
    LOG(INFO) << "Attempting to upgrade input file specified using deprecated "
              << "'solver_type' field (enum)': " << param_file;
    if (!UpgradeSolverType(param)) {
      success = false;
      LOG(ERROR) << "Warning: had one or more problems upgrading "
                 << "SolverType (see above).";
    } else {
      LOG(INFO) << "Successfully upgraded file specified using deprecated "
                << "'solver_type' field (enum) to 'type' field (string).";
      LOG(WARNING) << "Note that future Caffe releases will only support "
                   << "'type' field (string) for a solver's type.";
    }
  }
  return success;
}

// Read parameters from a file into a SolverParameter proto message.
void ReadSolverParamsFromTextFileOrDie(const string& param_file,
                                       SolverParameter* param) {
  CHECK(ReadProtoFromTextFile(param_file, param))
      << "Failed to parse SolverParameter file: " << param_file;
  UpgradeSolverAsNeeded(param_file, param);
}

}  // namespace caffe

升级之后的网络定义TVG_CRF_RNN_COCO_VOC.prototxt如下：

name: "TVG_CRF_RNN_COCO_VOC"
input: "data"
input_dim: 1
input_dim: 3
input_dim: 500
input_dim: 500
force_backward: true
layer {
  name: "conv1_1"
  type: "Convolution"
  bottom: "data"
  top: "conv1_1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    pad: 100
    kernel_size: 3
    engine: CAFFE
  }
}
layer {
  name: "relu1_1"
  type: "ReLU"
  bottom: "conv1_1"
  top: "conv1_1"
}
layer {
  name: "conv1_2"
  type: "Convolution"
  bottom: "conv1_1"
  top: "conv1_2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    pad: 1
    kernel_size: 3
    engine: CAFFE
  }
}
layer {
  name: "relu1_2"
  type: "ReLU"
  bottom: "conv1_2"
  top: "conv1_2"
}
layer {
  name: "pool1"
  type: "Pooling"
  bottom: "conv1_2"
  top: "pool1"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "conv2_1"
  type: "Convolution"
  bottom: "pool1"
  top: "conv2_1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 1
    kernel_size: 3
    engine: CAFFE
  }
}
layer {
  name: "relu2_1"
  type: "ReLU"
  bottom: "conv2_1"
  top: "conv2_1"
}
layer {
  name: "conv2_2"
  type: "Convolution"
  bottom: "conv2_1"
  top: "conv2_2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 1
    kernel_size: 3
    engine: CAFFE
  }
}
layer {
  name: "relu2_2"
  type: "ReLU"
  bottom: "conv2_2"
  top: "conv2_2"
}
layer {
  name: "pool2"
  type: "Pooling"
  bottom: "conv2_2"
  top: "pool2"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "conv3_1"
  type: "Convolution"
  bottom: "pool2"
  top: "conv3_1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
    engine: CAFFE
  }
}
layer {
  name: "relu3_1"
  type: "ReLU"
  bottom: "conv3_1"
  top: "conv3_1"
}
layer {
  name: "conv3_2"
  type: "Convolution"
  bottom: "conv3_1"
  top: "conv3_2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
    engine: CAFFE
  }
}
layer {
  name: "relu3_2"
  type: "ReLU"
  bottom: "conv3_2"
  top: "conv3_2"
}
layer {
  name: "conv3_3"
  type: "Convolution"
  bottom: "conv3_2"
  top: "conv3_3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
    engine: CAFFE
  }
}
layer {
  name: "relu3_3"
  type: "ReLU"
  bottom: "conv3_3"
  top: "conv3_3"
}
layer {
  name: "pool3"
  type: "Pooling"
  bottom: "conv3_3"
  top: "pool3"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "conv4_1"
  type: "Convolution"
  bottom: "pool3"
  top: "conv4_1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
    engine: CAFFE
  }
}
layer {
  name: "relu4_1"
  type: "ReLU"
  bottom: "conv4_1"
  top: "conv4_1"
}
layer {
  name: "conv4_2"
  type: "Convolution"
  bottom: "conv4_1"
  top: "conv4_2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
    engine: CAFFE
  }
}
layer {
  name: "relu4_2"
  type: "ReLU"
  bottom: "conv4_2"
  top: "conv4_2"
}
layer {
  name: "conv4_3"
  type: "Convolution"
  bottom: "conv4_2"
  top: "conv4_3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
    engine: CAFFE
  }
}
layer {
  name: "relu4_3"
  type: "ReLU"
  bottom: "conv4_3"
  top: "conv4_3"
}
layer {
  name: "pool4"
  type: "Pooling"
  bottom: "conv4_3"
  top: "pool4"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "conv5_1"
  type: "Convolution"
  bottom: "pool4"
  top: "conv5_1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
    engine: CAFFE
  }
}
layer {
  name: "relu5_1"
  type: "ReLU"
  bottom: "conv5_1"
  top: "conv5_1"
}
layer {
  name: "conv5_2"
  type: "Convolution"
  bottom: "conv5_1"
  top: "conv5_2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
    engine: CAFFE
  }
}
layer {
  name: "relu5_2"
  type: "ReLU"
  bottom: "conv5_2"
  top: "conv5_2"
}
layer {
  name: "conv5_3"
  type: "Convolution"
  bottom: "conv5_2"
  top: "conv5_3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
    engine: CAFFE
  }
}
layer {
  name: "relu5_3"
  type: "ReLU"
  bottom: "conv5_3"
  top: "conv5_3"
}
layer {
  name: "pool5"
  type: "Pooling"
  bottom: "conv5_3"
  top: "pool5"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "fc6"
  type: "Convolution"
  bottom: "pool5"
  top: "fc6"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 4096
    kernel_size: 7
    engine: CAFFE
  }
}
layer {
  name: "relu6"
  type: "ReLU"
  bottom: "fc6"
  top: "fc6"
}
layer {
  name: "drop6"
  type: "Dropout"
  bottom: "fc6"
  top: "fc6"
  dropout_param {
    dropout_ratio: 0.5
  }
}
layer {
  name: "fc7"
  type: "Convolution"
  bottom: "fc6"
  top: "fc7"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 4096
    kernel_size: 1
    engine: CAFFE
  }
}
layer {
  name: "relu7"
  type: "ReLU"
  bottom: "fc7"
  top: "fc7"
}
layer {
  name: "drop7"
  type: "Dropout"
  bottom: "fc7"
  top: "fc7"
  dropout_param {
    dropout_ratio: 0.5
  }
}
layer {
  name: "score-fr"
  type: "Convolution"
  bottom: "fc7"
  top: "score"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 21
    kernel_size: 1
    engine: CAFFE
  }
}
layer {
  name: "score2"
  type: "Deconvolution"
  bottom: "score"
  top: "score2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 21
    kernel_size: 4
    stride: 2
  }
}
layer {
  name: "score-pool4"
  type: "Convolution"
  bottom: "pool4"
  top: "score-pool4"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 21
    kernel_size: 1
    engine: CAFFE
  }
}
layer {
  name: "crop"
  type: "Crop"
  bottom: "score-pool4"
  bottom: "score2"
  top: "score-pool4c"
}
layer {
  name: "fuse"
  type: "Eltwise"
  bottom: "score2"
  bottom: "score-pool4c"
  top: "score-fused"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "score4"
  type: "Deconvolution"
  bottom: "score-fused"
  top: "score4"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  convolution_param {
    num_output: 21
    bias_term: false
    kernel_size: 4
    stride: 2
  }
}
layer {
  name: "score-pool3"
  type: "Convolution"
  bottom: "pool3"
  top: "score-pool3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 21
    kernel_size: 1
    engine: CAFFE
  }
}
layer {
  name: "crop"
  type: "Crop"
  bottom: "score-pool3"
  bottom: "score4"
  top: "score-pool3c"
}
layer {
  name: "fuse"
  type: "Eltwise"
  bottom: "score4"
  bottom: "score-pool3c"
  top: "score-final"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "upsample"
  type: "Deconvolution"
  bottom: "score-final"
  top: "bigscore"
  param {
    lr_mult: 0
  }
  convolution_param {
    num_output: 21
    bias_term: false
    kernel_size: 16
    stride: 8
  }
}
layer {
  name: "crop"
  type: "Crop"
  bottom: "bigscore"
  bottom: "data"
  top: "coarse"
}
layer {
  name: "splitting"
  type: "Split"
  bottom: "coarse"
  top: "unary"
  top: "Q0"
}
layer {
  name: "inference1"
  type: "MultiStageMeanfield"
  bottom: "unary"
  bottom: "Q0"
  bottom: "data"
  top: "pred"
  param {
    lr_mult: 0.001
  }
  param {
    lr_mult: 0.001
  }
  param {
    lr_mult: 0.01
  }
  multi_stage_meanfield_param {
   num_iterations: 10
   compatibility_mode: POTTS
   threshold: 2
   theta_alpha: 160
   theta_beta: 3
   theta_gamma: 3
   spatial_filter_weight: 3
   bilateral_filter_weight: 5
  }
}



（6）将旧的matlab程序进行适配，以适应新的caffe的版本



crfrnn_demo.m文件如下：
% This package contains code for the "CRF-RNN" semantic image segmentation method, published in the
% ICCV 2015 paper Conditional Random Fields as Recurrent Neural Networks. Our software is built on
% top of the Caffe deep learning library.
%
% Contact:
% Shuai Zheng ([email protected]), Sadeep Jayasumana ([email protected]), Bernardino Romera-Paredes ([email protected])
%
% Supervisor:
% Philip Torr ([email protected])
%
% For more information about CRF-RNN please vist the project website http://crfasrnn.torr.vision.
%



model_def_file = 'TVG_CRFRNN_COCO_VOC.prototxt';
model_file = 'TVG_CRFRNN_COCO_VOC.caffemodel';
phase = 'test';
use_gpu = 0; % Set this to 0 if you don't have a GPU.

if exist(model_file, 'file') ~= 2
    error('You need a network model file. Please download our default model by running ./download_trained_model.sh');
end



if exist('use_gpu', 'var') && use_gpu
  caffe.set_mode_gpu();
  gpu_id = 0;  % we will use the first gpu in this demo
  caffe.set_device(gpu_id);
else
  caffe.set_mode_cpu();
end



net = caffe.Net(model_def_file, model_file, phase);


im = imread('input.jpg');

[h, w, d] = size(im);

if (d ~= 3)
   error('Error! Wrong depth.\n');
end

if (h > 500 || w > 500)
   error('Error! Wrong image size.\n');
end

prepared_im = tvg_prepare_image_fixed(im);

inputData = {prepared_im};


scores = net.forward(inputData);


Q = scores{1};       

[dumb, pred] = max(Q, [], 3);
pred = pred';
pred = pred(1:h, 1:w);

load map.mat
imwrite(pred, map, 'output.png', 'png');   




总结一下：



本次移植主要涉及到以下文件：
include和src下的util文件夹下
coords.hpp

modified_permutohedral.hpp
modified_permutohedral.cpp

upgrade_proto.cpp


include和src下的layers文件夹下
meanfield_iteration.hpp
meanfield_iteration.cpp

multi_stage_meanfield.hpp
multi_stage_meanfield.cpp

crop_layer.hpp(从官方的master去下载即可)
crop_layer.cpp(从官方的master去下载即可)



caffe/include下的
layer.hpp


还有matlab下的
crfrnn_demo.m



CRFAsRNN可以从
http://www.robots.ox.ac.uk/~szheng/crfasrnndemo/下载


另外windows下的Caffe我博客里面有写怎么下载和安装的

移植到windows下的文件的打包可以下载
http://download.csdn.net/detail/xizero00/9493870

移植之后的结果貌似跟原来的结果不大一样，可能是因为用了caffe官方的CropLayer的缘故，所以结果出了偏差。
原图：

这里windows下我移植的效果
Caffe实战系列：如何将CRFAsRNN移植到caffe-windows上去_第1张图片
linux下跑的结果

Caffe实战系列：如何将CRFAsRNN移植到caffe-windows上去_第2张图片

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