text: #ifndef CAFFE_LAYER_H_
  #define CAFFE_LAYER_H_
  
  #include <algorithm>
  #include <string>
  #include <utility>
  #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 {
  
  template <typename Dtype> class Net;
  
  /**
   * @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> >());
    }//Seaball
    void set_net(Net<Dtype>* net) { net_ = net; }//Seaball
  
  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_;
    /** The net to which this layer belongs. */
    Net<Dtype>* net_;//Seaball
  
    /** @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_
  