/*
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Copyright (c) 2014, 2015, The Regents of the University of California (Regents)
All rights reserved.

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Copyright (c) 2014, 2015, the respective contributors
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For the list of contributors go to https://github.com/BVLC/caffe/blob/master/CONTRIBUTORS.md


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modification, are permitted provided that the following conditions are met:

    * Redistributions of source code must retain the above copyright notice,
      this list of conditions and the following disclaimer.
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      may be used to endorse or promote products derived from this software
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AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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*/

#ifndef CAFFE_NET_HPP_
#define CAFFE_NET_HPP_

#include <map>
#include <set>
#include <string>
#include <utility>
#include <vector>

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

#include "caffe/util/benchmark.hpp"


namespace caffe {

/**
 * @brief Connects Layer%s together into a directed acyclic graph (DAG)
 *        specified by a NetParameter.
 *
 * TODO(dox): more thorough description.
 */
template <typename Dtype>
class Net {
 public:
  explicit Net(const NetParameter& param, const Net* root_net = NULL);
  explicit Net(const string& param_file, Phase phase,
      const int level = 0, const vector<string>* stages = NULL,
      const Net* root_net = NULL, std::string engine = "");
  virtual ~Net() {}

  /// @brief Initialize a network with a NetParameter.
  void Init(const NetParameter& param);

  /**
   * @brief Run Forward and return the result.
   *
   */
  const vector<Blob<Dtype>*>& Forward(Dtype* loss = NULL);
  /// @brief DEPRECATED; use Forward() instead.
  const vector<Blob<Dtype>*>& ForwardPrefilled(Dtype* loss = NULL) {
    LOG_EVERY_N(WARNING, 1000) << "DEPRECATED: ForwardPrefilled() "
        << "will be removed in a future version. Use Forward().";
    return Forward(loss);
  }

  /**
   * The From and To variants of Forward and Backward operate on the
   * (topological) ordering by which the net is specified. For general DAG
   * networks, note that (1) computing from one layer to another might entail
   * extra computation on unrelated branches, and (2) computation starting in
   * the middle may be incorrect if all of the layers of a fan-in are not
   * included.
   */
  Dtype ForwardFromTo(int start, int end);
  Dtype ForwardFrom(int start);
  Dtype ForwardTo(int end);
  /// @brief DEPRECATED; set input blobs then use Forward() instead.
  const vector<Blob<Dtype>*>& Forward(const vector<Blob<Dtype>* > & bottom,
      Dtype* loss = NULL);

  /**
   * @brief Zeroes out the diffs of all net parameters.
   *        Should be run before Backward.
   */
  void ClearParamDiffs(int learnable_param_id);
  void ClearParamDiffs();

  /**
   * The network backward should take no input and output, since it solely
   * computes the gradient w.r.t the parameters, and the data has already been
   * provided during the forward pass.
   */
  void Backward();
  void BackwardFromTo(int start, int end);
  void BackwardFrom(int start);
  void BackwardTo(int end);

  /**
   * @brief Reshape all layers from bottom to top.
   *
   * This is useful to propagate changes to layer sizes without running
   * a forward pass, e.g. to compute output feature size.
   */
  void Reshape();

  Dtype ForwardBackward() {
    Dtype loss;
    Forward(&loss);
    Backward();
    return loss;
  }

  /// @brief Updates the network weights based on the diff values computed.
  void Update();
  /**
   * @brief Shares weight data of owner blobs with shared blobs.
   *
   * Note: this is called by Net::Init, and thus should normally not be
   * called manually.
   */
  void ShareWeights();

  /**
   * @brief For an already initialized net, implicitly copies (i.e., using no
   *        additional memory) the pre-trained layers from another Net.
   */
  void ShareTrainedLayersWith(const Net* other);
  // For an already initialized net, CopyTrainedLayersFrom() copies the already
  // trained layers from another net parameter instance.
  /**
   * @brief For an already initialized net, copies the pre-trained layers from
   *        another Net.
   */
  void CopyTrainedLayersFrom(const NetParameter& param);
  void CopyTrainedLayersFrom(const string trained_filename);
  void CopyTrainedLayersFromBinaryProto(const string trained_filename);
  void CopyTrainedLayersFromHDF5(const string trained_filename);
  /// @brief Writes the net to a proto.
  void ToProto(NetParameter* param, bool write_diff = false) const;
  /// @brief Writes the net to an HDF5 file.
  void ToHDF5(const string& filename, bool write_diff = false) const;

  /// @brief returns the network name.
  inline const string& name() const { return name_; }
  /// @brief returns the layer names
  inline const vector<string>& layer_names() const { return layer_names_; }
  /// @brief returns the blob names
  inline const vector<string>& blob_names() const { return blob_names_; }
  /// @brief returns the blobs
  inline const vector<shared_ptr<Blob<Dtype> > >& blobs() const {
    return blobs_;
  }
  /// @brief returns the layers
  inline const vector<shared_ptr<Layer<Dtype> > >& layers() const {
    return layers_;
  }
  /// @brief returns the phase: TRAIN or TEST
  inline Phase phase() const { return phase_; }
  /**
   * @brief returns the bottom vecs for each layer -- usually you won't
   *        need this unless you do per-layer checks such as gradients.
   */
  inline const vector<vector<Blob<Dtype>*> >& bottom_vecs() const {
    return bottom_vecs_;
  }
  /**
   * @brief returns the top vecs for each layer -- usually you won't
   *        need this unless you do per-layer checks such as gradients.
   */
  inline const vector<vector<Blob<Dtype>*> >& top_vecs() const {
    return top_vecs_;
  }
  /// @brief returns the ids of the top blobs of layer i
  inline const vector<int> & top_ids(int i) const {
    CHECK_GE(i, 0) << "Invalid layer id";
    CHECK_LT(i, top_id_vecs_.size()) << "Invalid layer id";
    return top_id_vecs_[i];
  }
  /// @brief returns the ids of the bottom blobs of layer i
  inline const vector<int> & bottom_ids(int i) const {
    CHECK_GE(i, 0) << "Invalid layer id";
    CHECK_LT(i, bottom_id_vecs_.size()) << "Invalid layer id";
    return bottom_id_vecs_[i];
  }
  inline const vector<vector<bool> >& bottom_need_backward() const {
    return bottom_need_backward_;
  }
  inline const vector<Dtype>& blob_loss_weights() const {
    return blob_loss_weights_;
  }
  inline const vector<bool>& layer_need_backward() const {
    return layer_need_backward_;
  }
  /// @brief returns the parameters
  inline const vector<shared_ptr<Blob<Dtype> > >& params() const {
    return params_;
  }
  inline const vector<Blob<Dtype>*>& learnable_params() const {
    return learnable_params_;
  }

  vector<int> get_layer_learnable_param_ids(int layer_id) const;

  /// @brief returns the learnable parameter learning rate multipliers
  inline const vector<float>& params_lr() const { return params_lr_; }
  inline const vector<bool>& has_params_lr() const { return has_params_lr_; }
  /// @brief returns the learnable parameter decay multipliers
  inline const vector<float>& params_weight_decay() const {
    return params_weight_decay_;
  }
  inline const vector<bool>& has_params_decay() const {
    return has_params_decay_;
  }
  const map<string, int>& param_names_index() const {
    return param_names_index_;
  }
  inline const vector<int>& param_owners() const { return param_owners_; }
  inline const vector<string>& param_display_names() const {
    return param_display_names_;
  }

  inline const pair<int, int>& param_layer_indices(int param_id) {
    return param_layer_indices_[param_id];
  }

  /// @brief Input and output blob numbers
  inline int num_inputs() const { return net_input_blobs_.size(); }
  inline int num_outputs() const { return net_output_blobs_.size(); }
  inline const vector<Blob<Dtype>*>& input_blobs() const {
    return net_input_blobs_;
  }
  inline const vector<Blob<Dtype>*>& output_blobs() const {
    return net_output_blobs_;
  }
  inline const vector<int>& input_blob_indices() const {
    return net_input_blob_indices_;
  }
  inline const vector<int>& output_blob_indices() const {
    return net_output_blob_indices_;
  }
  bool has_blob(const string& blob_name) const;
  const shared_ptr<Blob<Dtype> > blob_by_name(const string& blob_name) const;
  bool has_layer(const string& layer_name) const;
  const shared_ptr<Layer<Dtype> > layer_by_name(const string& layer_name) const;

  void set_debug_info(const bool value) { debug_info_ = value; }

  // Helpers for Init.
  /**
   * @brief Remove layers that the user specified should be excluded given the current
   *        phase, level, and stage.
   */
  static void FilterNet(const NetParameter& param,
      NetParameter* param_filtered);

  /**
   * @brief Remove or Replace layers that the user specified should be excluded to increase
   *        computational performance.
   */
  static void CompileNet(const NetParameter& param,
    NetParameter* param_compiled);

  /**
  * @brief This is rule that analyze layer if it is of type Scale and if that is the case
  *        and previous layer which serves as input layer to Scale Layer is MKLBatchNorm
  *        then scale layer can be dropped
  */
  // TODO: Make it decent C++ anonymous function etc.
  static void CompilationRuleRemoveScale(const NetParameter& param,
                                 NetParameter* param_compiled);
  /**
  * @brief This is rule that analyze layer if it is of type MKLDNNReLU and if that is the case
  *        and previous layer which serves as input layer to MKLDNNReLU Layer is MKLDNNConvolution
  *        then MKLDNNReLU layer can be dropped
  */


  static void CompilationRuleConvReluFusion(const NetParameter& param,
                                 NetParameter* param_compiled);

  /**
  * @brief This is rule analyze if layer is of type MKLBatchNorm
  *        and is to perform in place computation 
  *        if positive then make it doing out-ofplace computation
  */
  static void CompilationRuleBNInplace(const NetParameter& param,
                             NetParameter* param_compiled);

  /**
  * @brief This is rule analyze for conv/elt/relu fusion.
  */
  static void CompilationRuleConvSumFusion(const NetParameter& param,
                             NetParameter* param_compiled);

   /**
  * @brief This is rule analyze for general sparse.
  */
  static void CompilationRuleSparse(const NetParameter& param,
                             NetParameter* param_compiled);

  /**
  * @brief This is rule that analyze layer if it is of type MKLDNNReLU and if that is the case
  *        and previous layer which serves as input layer to MKLDNNReLU Layer is MKLDNNBN
  *        then MKLDNNReLU layer can be dropped
  */
  static void CompilationRuleFuseBnRelu(const NetParameter& param,
                                 NetParameter* param_compiled);

 
   /**
   * @brief If find "Conv--BN--Scale" in current network, merge BN and Scale layer into Convolution
   * layers, this optimization only works in caffe TEST phase now.
   */

  static void GetBlobConsumers(std::vector<const LayerParameter*> &cnsmer_blobs,
                                                const string& blob_name_to_find,
                                                const NetParameter& param,
                                                int layer_id);

  static void GetNeedToCancelInplaceLayers(
      vector<vector<const LayerParameter*>>& layer_pairs,    
      std::map<string, int>& specified_layer_blob_name_to_index,      
      std::map<string, int>& inplace_blob_name_to_index,
      vector<string>& each_blob_list,
      const NetParameter& param);

  static void ParseNetInplaceStatus(
      std::map<string, int>& inplace_blob_name_to_index,
      std::map<string, int>& specified_layer_blob_name_to_index,      
      vector<vector<string>>& specified_layer_input_blob_names,
      NetParameter* param, const string& specified_layer_type);

  /// @brief return whether NetState state meets NetStateRule rule
  static bool StateMeetsRule(const NetState& state, const NetStateRule& rule,
      const string& layer_name);
  /**
   * @brief Look at the layer activations and parameters to find the maximum
   * absolute values. The following layers are considered: Convolution,
   * InnerProduct.
   *
   * @param layer_name The layers that should be quantized to fixed point.
   * @param max_in The highest layer input.
   * @param max_out The highest layer output.
   * @param max_param The highest layer parameter.
   *
   * For layer parameters, the biases are ignored.
   */
  void RangeInLayers(vector<string>* layer_name, vector<Dtype>* max_in,
      vector<Dtype>* max_out, vector<vector<Dtype>>* max_param, string scaling);
  /**
   * @brief Find the maximum value in a blob.
   */
  vector<Dtype> FindMax(Blob<Dtype>* blob, bool is_single=true);
  inline const map<string,int>& blob_names_index() const {
    return blob_names_index_;
  }

#ifdef CAFFE_PER_LAYER_TIMINGS
  /* Timers for performance measurements */
  Timer timer;
#ifdef FW_OVERLAP_OPT
  Timer wait_timer;
#endif
  std::vector<double> forward_time_per_layer;
  std::vector<double> backward_time_per_layer;
  std::vector<double> update_time_per_layer;
  double cleardiffs_time_per_iter;
#ifdef USE_MLSL
  std::vector<double> startcomm_time_per_layer;
  std::vector<double> waitcomm_time_per_layer;

  std::vector<double> startcomm_start_time_per_layer;
  std::vector<double> waitcomm_start_time_per_layer;
  std::vector<double> startcomm_stop_time_per_layer;
  std::vector<double> waitcomm_stop_time_per_layer;

#ifdef FW_OVERLAP_OPT
  std::vector<double> first_waitcomm_start_time_per_layer;
  std::vector<double> first_waitcomm_stop_time_per_layer;
  std::vector<double> first_update_start_time_per_layer;
  std::vector<double> first_update_stop_time_per_layer;
#endif

#endif

  std::vector<double> forward_time_per_layer_total;
  std::vector<double> backward_time_per_layer_total;
  std::vector<double> update_time_per_layer_total;
  double cleardiffs_time_per_iter_total;
#ifdef USE_MLSL
  std::vector<double> startcomm_time_per_layer_total;
  std::vector<double> waitcomm_time_per_layer_total;
#endif

  std::vector<double> forward_start_time_per_layer;
  std::vector<double> backward_start_time_per_layer;
  std::vector<double> update_start_time_per_layer;

  std::vector<double> forward_stop_time_per_layer;
  std::vector<double> backward_stop_time_per_layer;
  std::vector<double> update_stop_time_per_layer;

  void InitTimers();
  void ResetTimers();
  void PrintTimers(bool printTotal);

  void PrintPayloadSize();
  void SaveTimeline();

#endif /* CAFFE_PER_LAYER_TIMINGS */

 protected:
  // Helpers for Init.
  /// @brief Append a new top blob to the net.
  void AppendTop(const NetParameter& param, const int layer_id,
                 const int top_id, set<string>* available_blobs,
                 map<string, int>* blob_name_to_idx);
  /// @brief Append a new bottom blob to the net.
  int AppendBottom(const NetParameter& param, const int layer_id,
                   const int bottom_id, set<string>* available_blobs,
                   map<string, int>* blob_name_to_idx);
  /// @brief Append a new parameter blob to the net.
  void AppendParam(const NetParameter& param, const int layer_id,
                   const int param_id);

  /// @brief Helper for displaying debug info in Forward.
  void ForwardDebugInfo(const int layer_id);
  /// @brief Helper for displaying debug info in Backward.
  void BackwardDebugInfo(const int layer_id);
  /// @brief Helper for displaying debug info in Update.
  void UpdateDebugInfo(const int param_id);
  bool bn_scale_remove_;
  bool bn_scale_merge_;
  vector<string> kept_bn_layers_;

  vector<Dtype> first_conv_quantized_shift_;
  vector<string> first_conv_names_;
  /// @brief The network name
  string name_;
  /// @brief The engine name
  string engine_name_;
  /// @brief The phase: TRAIN or TEST
  Phase phase_;
  /// @brief Individual layers in the net
  vector<shared_ptr<Layer<Dtype> > > layers_;
  vector<string> layer_names_;
  map<string, int> layer_names_index_;
  vector<bool> layer_need_backward_;
  /// @brief the blobs storing intermediate results between the layer.
  vector<shared_ptr<Blob<Dtype> > > blobs_;
  vector<string> blob_names_;
  map<string, int> blob_names_index_;
  vector<bool> blob_need_backward_;
  /// bottom_vecs stores the vectors containing the input for each layer.
  /// They don't actually host the blobs (blobs_ does), so we simply store
  /// pointers.
  vector<vector<Blob<Dtype>*> > bottom_vecs_;
  vector<vector<int> > bottom_id_vecs_;
  vector<vector<bool> > bottom_need_backward_;
  /// top_vecs stores the vectors containing the output for each layer
  vector<vector<Blob<Dtype>*> > top_vecs_;
  vector<vector<int> > top_id_vecs_;
  /// Vector of weight in the loss (or objective) function of each net blob,
  /// indexed by blob_id.
  vector<Dtype> blob_loss_weights_;
  vector<vector<int> > param_id_vecs_;
  vector<int> param_owners_;
  vector<string> param_display_names_;
  vector<pair<int, int> > param_layer_indices_;
  map<string, int> param_names_index_;
  /// blob indices for the input and the output of the net
  vector<int> net_input_blob_indices_;
  vector<int> net_output_blob_indices_;
  vector<Blob<Dtype>*> net_input_blobs_;
  vector<Blob<Dtype>*> net_output_blobs_;
  /// The parameters in the network.
  vector<shared_ptr<Blob<Dtype> > > params_;
  vector<Blob<Dtype>*> learnable_params_;
  /**
   * The mapping from params_ -> learnable_params_: we have
   * learnable_param_ids_.size() == params_.size(),
   * and learnable_params_[learnable_param_ids_[i]] == params_[i].get()
   * if and only if params_[i] is an "owner"; otherwise, params_[i] is a sharer
   * and learnable_params_[learnable_param_ids_[i]] gives its owner.
   */
  vector<int> learnable_param_ids_;
  /// the learning rate multipliers for learnable_params_
  vector<float> params_lr_;
  vector<bool> has_params_lr_;
  /// the weight decay multipliers for learnable_params_
  vector<float> params_weight_decay_;
  vector<bool> has_params_decay_;
  /// The bytes of memory used by this net
  size_t memory_used_;
  /// Whether to compute and display debug info for the net.
  bool debug_info_;
  /// The root net that actually holds the shared layers in data parallelism
  const Net* const root_net_;
  DISABLE_COPY_AND_ASSIGN(Net);
};


}  // namespace caffe

#endif  // CAFFE_NET_HPP_
