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

#include "caffe/common.hpp"
#include "caffe/layer.hpp"
#include "caffe/net.hpp"
#include "caffe/proto/caffe.pb.h"
#include "caffe/util/insert_splits.hpp"
#include "caffe/util/io.hpp"
#include "caffe/util/math_functions.hpp"
#include "caffe/util/upgrade_proto.hpp"

#include "caffe/test/test_caffe_main.hpp"

namespace caffe {

template <typename Dtype>
Net<Dtype>::Net(const NetParameter& param) {
  Init(param);
}

template <typename Dtype>
Net<Dtype>::Net(const string& param_file) {
  NetParameter param;
  ReadNetParamsFromTextFileOrDie(param_file, &param);
  Init(param);
}

template <typename Dtype>
void Net<Dtype>::Init(const NetParameter& in_param) {

  // Filter layers based on their include/exclude rules and
  // the current NetState.
  NetParameter filtered_param;
  FilterNet(in_param, &filtered_param);
  /*LOG(INFO) << "Initializing net from parameters: " << std::endl
            << filtered_param.DebugString();*/
  // Create a copy of filtered_param with splits added where necessary.
  NetParameter param;
  InsertSplits(filtered_param, &param);

  // Basically, build all the layers and set up its connections.
  name_ = param.name();
  map<string, int> blob_name_to_idx;
  set<string> available_blobs;
  CHECK_EQ(param.input_size() * 4, param.input_dim_size())
      << "Incorrect input blob dimension specifications.";
  memory_used_ = 0;
  // set the input blobs
  for (int input_id = 0; input_id < param.input_size(); ++input_id) {
    const int layer_id = -1;  // inputs have fake layer ID -1
    AppendTop(param, layer_id, input_id, &available_blobs, &blob_name_to_idx);
  }
  DLOG(INFO) << "Memory required for data: " << memory_used_ * sizeof(Dtype);
  // For each layer, set up their input and output
  bottom_vecs_.resize(param.layers_size());
  top_vecs_.resize(param.layers_size());
  bottom_id_vecs_.resize(param.layers_size());
  top_id_vecs_.resize(param.layers_size());
  bottom_need_backward_.resize(param.layers_size());
  for (int layer_id = 0; layer_id < param.layers_size(); ++layer_id) {
    const LayerParameter& layer_param = param.layers(layer_id);
    layers_.push_back(shared_ptr<Layer<Dtype> >(GetLayer<Dtype>(layer_param)));
    layer_names_.push_back(layer_param.name());
    LOG(INFO) << "Creating Layer " << layer_param.name();
    bool need_backward = false;
    // Figure out this layer's input and output
    for (int bottom_id = 0; bottom_id < layer_param.bottom_size();
         ++bottom_id) {
      const int blob_id = AppendBottom(param, layer_id, bottom_id,
                                       &available_blobs, &blob_name_to_idx);
      // If a blob needs backward, this layer should provide it.
      need_backward |= blob_need_backward_[blob_id];
    }
    int num_top = layer_param.top_size();
    for (int top_id = 0; top_id < num_top; ++top_id) {
      AppendTop(param, layer_id, top_id, &available_blobs, &blob_name_to_idx);
    }
    // If the layer specifies that AutoTopBlobs() -> true and the LayerParameter
    // specified fewer than the required number (as specified by
    // ExactNumTopBlobs() or MinTopBlobs()), allocate them here.
    Layer<Dtype>* layer = layers_[layer_id].get();
    LOG(INFO) << "layer name: " << layer->type_name();
    if (layer->AutoTopBlobs()) {
      const int needed_num_top =
          std::max(layer->MinTopBlobs(), layer->ExactNumTopBlobs());
      for (; num_top < needed_num_top; ++num_top) {
        // Add "anonymous" top blobs -- do not modify available_blobs or
        // blob_name_to_idx as we don't want these blobs to be usable as input
        // to other layers.
        AppendTop(param, layer_id, num_top, NULL, NULL);
      }
    }
    // After this layer is connected, set it up.
    LOG(INFO) << "Setting up " << layer_names_[layer_id];
    layers_[layer_id]->SetUp(bottom_vecs_[layer_id], &top_vecs_[layer_id]);
    for (int top_id = 0; top_id < top_vecs_[layer_id].size(); ++top_id) {
      if (blob_loss_weights_.size() <= top_id_vecs_[layer_id][top_id]) {
        blob_loss_weights_.resize(top_id_vecs_[layer_id][top_id] + 1, Dtype(0));
      }
      blob_loss_weights_[top_id_vecs_[layer_id][top_id]] = layer->loss(top_id);
      LOG(INFO) << "Top shape: " << top_vecs_[layer_id][top_id]->num() << " "
          << top_vecs_[layer_id][top_id]->channels() << " "
          << top_vecs_[layer_id][top_id]->height() << " "
          << top_vecs_[layer_id][top_id]->width() << " ("
          << top_vecs_[layer_id][top_id]->count() << ")";
      if (layer->loss(top_id)) {
        LOG(INFO) << "    with loss weight " << layer->loss(top_id);
      }
      memory_used_ += top_vecs_[layer_id][top_id]->count();
    }
    DLOG(INFO) << "Memory required for data: " << memory_used_ * sizeof(Dtype);
    const int blobs_lr_size = layer_param.blobs_lr_size();
    const int num_param_blobs = layers_[layer_id]->blobs().size();
    CHECK(blobs_lr_size == num_param_blobs || blobs_lr_size == 0)
        << "Incorrect blobs lr size: should be either 0 "
        << "or the same as the number of the layer's parameter blobs.";
    if (blobs_lr_size) {
      // Check if this layer needs backward operation itself
      for (int param_id = 0; param_id < blobs_lr_size; ++param_id) {
        const bool param_need_backward = layer_param.blobs_lr(param_id) > 0;
        need_backward |= param_need_backward;
        layers_[layer_id]->set_param_propagate_down(param_id,
                                                    param_need_backward);
      }
    } else if (layers_[layer_id]->blobs().size()) {
      // catch: if a layer param does not specify blobs_lr, we should assume the
      // learning rate to be 1. Thus we will need to perform backward.
      need_backward = true;
      for (int param_id = 0; param_id < blobs_lr_size; ++param_id) {
        layers_[layer_id]->set_param_propagate_down(param_id, true);
      }
    }
    const int param_size = layer_param.param_size();
    CHECK(param_size == num_param_blobs || param_size == 0)
        << "Incorrect param size: should be either 0 or the same as "
           "the number of the layer's parameter blobs: " << num_param_blobs;
    const int blob_share_mode_size = layer_param.blob_share_mode_size();
    CHECK(blob_share_mode_size == num_param_blobs || blob_share_mode_size == 0)
        << "Incorrect blob_share_mode size: should be either 0 or the same as "
           "the number of the layer's parameter blobs: " << num_param_blobs;
    for (int param_id = 0; param_id < num_param_blobs; ++param_id) {
      AppendParam(param, layer_id, param_id);
    }
    // Finally, set the backward flag
    layer_need_backward_.push_back(need_backward);
    if (need_backward) {
      for (int top_id = 0; top_id < top_id_vecs_[layer_id].size(); ++top_id) {
        blob_need_backward_[top_id_vecs_[layer_id][top_id]] = true;
      }
    }
  }
  // Go through the net backwards to determine which blobs contribute to the
  // loss.  We can skip backward computation for blobs that don't contribute
  // to the loss.
  set<string> blobs_under_loss;
  for (int layer_id = layers_.size() - 1; layer_id >= 0; --layer_id) {
    bool layer_contributes_loss = false;
    for (int top_id = 0; top_id < top_vecs_[layer_id].size(); ++top_id) {
      const string& blob_name = blob_names_[top_id_vecs_[layer_id][top_id]];
      if (layers_[layer_id]->loss(top_id) ||
          (blobs_under_loss.find(blob_name) != blobs_under_loss.end())) {
        layer_contributes_loss = true;
        break;
      }
    }
    if (!layer_contributes_loss) { layer_need_backward_[layer_id] = false; }
    if (layer_need_backward_[layer_id]) {
      LOG(INFO) << layer_names_[layer_id] << " needs backward computation.";
    } else {
      LOG(INFO) << layer_names_[layer_id]
                << " does not need backward computation.";
    }
    for (int bottom_id = 0; bottom_id < bottom_vecs_[layer_id].size();
         ++bottom_id) {
      if (layer_contributes_loss) {
        const string& blob_name =
            blob_names_[bottom_id_vecs_[layer_id][bottom_id]];
        blobs_under_loss.insert(blob_name);
      } else {
        bottom_need_backward_[layer_id][bottom_id] = false;
      }
    }
  }
  // Handle force_backward if needed.
  if (param.force_backward()) {
    for (int layer_id = 0; layer_id < layers_.size(); ++layer_id) {
      layer_need_backward_[layer_id] = true;
      for (int bottom_id = 0;
           bottom_id < bottom_need_backward_[layer_id].size(); ++bottom_id) {
        bottom_need_backward_[layer_id][bottom_id] =
            bottom_need_backward_[layer_id][bottom_id] ||
            layers_[layer_id]->AllowForceBackward(bottom_id);
        blob_need_backward_[bottom_id_vecs_[layer_id][bottom_id]] =
            blob_need_backward_[bottom_id_vecs_[layer_id][bottom_id]] ||
            bottom_need_backward_[layer_id][bottom_id];
      }
      for (int param_id = 0; param_id < layers_[layer_id]->blobs().size();
           ++param_id) {
        layers_[layer_id]->set_param_propagate_down(param_id, true);
      }
    }
  }
  // In the end, all remaining blobs are considered output blobs.
  for (set<string>::iterator it = available_blobs.begin();
      it != available_blobs.end(); ++it) {
    LOG(INFO) << "This network produces output " << *it;
    net_output_blobs_.push_back(blobs_[blob_name_to_idx[*it]].get());
    net_output_blob_indices_.push_back(blob_name_to_idx[*it]);
  }
  for (size_t blob_id = 0; blob_id < blob_names_.size(); ++blob_id) {
    blob_names_index_[blob_names_[blob_id]] = blob_id;
  }
  for (size_t layer_id = 0; layer_id < layer_names_.size(); ++layer_id) {
    layer_names_index_[layer_names_[layer_id]] = layer_id;
  }
  GetLearningRateAndWeightDecay();
  LOG(INFO) << "Network initialization done.";
  LOG(INFO) << "Memory required for data: " << memory_used_ * sizeof(Dtype);
  // Don't display debug info by default.
  debug_info_ = false;
}

template <typename Dtype>
void Net<Dtype>::FilterNet(const NetParameter& param,
    NetParameter* param_filtered) {
  NetState net_state(param.state());
  // Let the phase of the net be the current global phase provided in the Caffe
  // singleton, unless explicitly provided by the state.
  if (!net_state.has_phase()) {
    switch (Caffe::phase()) {
      case Caffe::TRAIN:
        net_state.set_phase(TRAIN);
        break;
      case Caffe::TEST:
        net_state.set_phase(TEST);
        break;
      default:
        LOG(FATAL) << "Unknown phase: " << Caffe::phase();
    }
  }
  param_filtered->CopyFrom(param);
  param_filtered->clear_layers();
  for (int i = 0; i < param.layers_size(); ++i) {
    const LayerParameter& layer_param = param.layers(i);
    const string& layer_name = layer_param.name();
    CHECK(layer_param.include_size() == 0 || layer_param.exclude_size() == 0)
          << "Specify either include rules or exclude rules; not both.";
    // If no include rules are specified, the layer is included by default and
    // only excluded if it meets one of the exclude rules.
    bool layer_included = (layer_param.include_size() == 0);
    for (int j = 0; layer_included && j < layer_param.exclude_size(); ++j) {
      if (StateMeetsRule(net_state, layer_param.exclude(j), layer_name)) {
        layer_included = false;
      }
    }
    for (int j = 0; !layer_included && j < layer_param.include_size(); ++j) {
      if (StateMeetsRule(net_state, layer_param.include(j), layer_name)) {
        layer_included = true;
      }
    }
    if (layer_included) {
      param_filtered->add_layers()->CopyFrom(layer_param);
    }
  }
}

template <typename Dtype>
bool Net<Dtype>::StateMeetsRule(const NetState& state,
    const NetStateRule& rule, const string& layer_name) {
  // Check whether the rule is broken due to phase.
  if (rule.has_phase()) {
      if (rule.phase() != state.phase()) {
        LOG(INFO) << "The NetState phase (" << state.phase()
          << ") differed from the phase (" << rule.phase()
          << ") specified by a rule in layer " << layer_name;
        return false;
      }
  }
  // Check whether the rule is broken due to min level.
  if (rule.has_min_level()) {
    if (state.level() < rule.min_level()) {
      LOG(INFO) << "The NetState level (" << state.level()
          << ") is above the min_level (" << rule.min_level()
          << ") specified by a rule in layer " << layer_name;
      return false;
    }
  }
  // Check whether the rule is broken due to max level.
  if (rule.has_max_level()) {
    if (state.level() > rule.max_level()) {
      LOG(INFO) << "The NetState level (" << state.level()
          << ") is above the max_level (" << rule.max_level()
          << ") specified by a rule in layer " << layer_name;
      return false;
    }
  }
  // Check whether the rule is broken due to stage. The NetState must
  // contain ALL of the rule's stages to meet it.
  for (int i = 0; i < rule.stage_size(); ++i) {
    // Check that the NetState contains the rule's ith stage.
    bool has_stage = false;
    for (int j = 0; !has_stage && j < state.stage_size(); ++j) {
      if (rule.stage(i) == state.stage(j)) { has_stage = true; }
    }
    if (!has_stage) {
      LOG(INFO) << "The NetState did not contain stage '" << rule.stage(i)
                << "' specified by a rule in layer " << layer_name;
      return false;
    }
  }
  // Check whether the rule is broken due to not_stage. The NetState must
  // contain NONE of the rule's not_stages to meet it.
  for (int i = 0; i < rule.not_stage_size(); ++i) {
    // Check that the NetState contains the rule's ith not_stage.
    bool has_stage = false;
    for (int j = 0; !has_stage && j < state.stage_size(); ++j) {
      if (rule.not_stage(i) == state.stage(j)) { has_stage = true; }
    }
    if (has_stage) {
      LOG(INFO) << "The NetState contained a not_stage '" << rule.not_stage(i)
                << "' specified by a rule in layer " << layer_name;
      return false;
    }
  }
  return true;
}

// Helper for Net::Init: add a new input or top blob to the net.  (Inputs have
// layer_id == -1, tops have layer_id >= 0.)
template <typename Dtype>
void Net<Dtype>::AppendTop(const NetParameter& param, const int layer_id,
                           const int top_id, set<string>* available_blobs,
                           map<string, int>* blob_name_to_idx) {
  shared_ptr<LayerParameter> layer_param((layer_id >= 0) ?
    (new LayerParameter(param.layers(layer_id))) : NULL);
  const string& blob_name = layer_param ?
      (layer_param->top_size() > top_id ?
          layer_param->top(top_id) : "(automatic)") : param.input(top_id);
  // Check if we are doing in-place computation
  if (blob_name_to_idx && layer_param && layer_param->bottom_size() > top_id &&
      blob_name == layer_param->bottom(top_id)) {
    // In-place computation
    LOG(INFO) << layer_param->name() << " -> " << blob_name << " (in-place)";
    top_vecs_[layer_id].push_back(blobs_[(*blob_name_to_idx)[blob_name]].get());
    top_id_vecs_[layer_id].push_back((*blob_name_to_idx)[blob_name]);
  } else if (blob_name_to_idx &&
             blob_name_to_idx->find(blob_name) != blob_name_to_idx->end()) {
    // If we are not doing in-place computation but have duplicated blobs,
    // raise an error.
    LOG(FATAL) << "Duplicate blobs produced by multiple sources.";
  } else {
    // Normal output.
    if (layer_param) {
      LOG(INFO) << layer_param->name() << " -> " << blob_name;
    } else {
      LOG(INFO) << "Input " << top_id << " -> " << blob_name;
    }
    shared_ptr<Blob<Dtype> > blob_pointer(new Blob<Dtype>());
    const int blob_id = blobs_.size();
    blobs_.push_back(blob_pointer);
    blob_names_.push_back(blob_name);
    blob_need_backward_.push_back(false);
    if (blob_name_to_idx) { (*blob_name_to_idx)[blob_name] = blob_id; }
    if (layer_id == -1) {
      // Set the (explicitly specified) dimensions of the input blob.
      blob_pointer->Reshape(param.input_dim(top_id * 4),
                            param.input_dim(top_id * 4 + 1),
                            param.input_dim(top_id * 4 + 2),
                            param.input_dim(top_id * 4 + 3));
      net_input_blob_indices_.push_back(blob_id);
      net_input_blobs_.push_back(blob_pointer.get());
    } else {
      top_id_vecs_[layer_id].push_back(blob_id);
      top_vecs_[layer_id].push_back(blob_pointer.get());
    }
  }
  if (available_blobs) { available_blobs->insert(blob_name); }
}

// Helper for Net::Init: add a new bottom blob to the net.
template <typename Dtype>
int Net<Dtype>::AppendBottom(const NetParameter& param,
    const int layer_id, const int bottom_id,
    set<string>* available_blobs, map<string, int>* blob_name_to_idx) {
  const LayerParameter& layer_param = param.layers(layer_id);
  const string& blob_name = layer_param.bottom(bottom_id);
  if (available_blobs->find(blob_name) == available_blobs->end()) {
    LOG(FATAL) << "Unknown blob input " << blob_name
               << " (at index " << bottom_id << ") to layer " << layer_id;
  }
  const int blob_id = (*blob_name_to_idx)[blob_name];
  LOG(INFO) << layer_names_[layer_id] << " <- " << blob_name;
  bottom_vecs_[layer_id].push_back(blobs_[blob_id].get());
  bottom_id_vecs_[layer_id].push_back(blob_id);
  available_blobs->erase(blob_name);
  const bool need_backward = blob_need_backward_[blob_id];
  bottom_need_backward_[layer_id].push_back(need_backward);
  return blob_id;
}

template <typename Dtype>
void Net<Dtype>::AppendParam(const NetParameter& param, const int layer_id,
                             const int param_id) {
  const LayerParameter& layer_param = layers_[layer_id]->layer_param();
  const int param_size = layer_param.param_size();
  string param_name = param_size ? layer_param.param(param_id) : "";
  if (param_name.size()) {
    param_display_names_.push_back(param_name);
  } else {
    ostringstream param_display_name;
    param_display_name << param_id;
    param_display_names_.push_back(param_display_name.str());
  }
  const int net_param_id = params_.size();
  params_.push_back(layers_[layer_id]->blobs()[param_id]);
  param_layer_indices_.push_back(make_pair(layer_id, param_id));
  if (!param_size || !param_name.size() || (param_name.size() &&
      param_names_index_.find(param_name) == param_names_index_.end())) {
    // This layer "owns" this parameter blob -- it is either anonymous
    // (i.e., not given a param_name) or explicitly given a name that we
    // haven't already seen.
    param_owners_.push_back(-1);
    if (param_size) {
      param_names_index_[param_name] = net_param_id;
    }
  } else {
    // Named param blob with name we've seen before: share params
    const int owner_net_param_id = param_names_index_[param_name];
    param_owners_.push_back(owner_net_param_id);
    const pair<int, int>& owner_index =
        param_layer_indices_[owner_net_param_id];
    const int owner_layer_id = owner_index.first;
    const int owner_param_id = owner_index.second;
    LOG(INFO) << "Sharing parameters '" << param_name << "' owned by "
              << "layer '" << layer_names_[owner_layer_id] << "', param "
              << "index " << owner_param_id;
    Blob<Dtype>* this_blob = layers_[layer_id]->blobs()[param_id].get();
    Blob<Dtype>* owner_blob =
        layers_[owner_layer_id]->blobs()[owner_param_id].get();
    const int blob_share_mode_size = layer_param.blob_share_mode_size();
    if (blob_share_mode_size > param_id &&
        (layer_param.blob_share_mode(param_id) ==
         LayerParameter_DimCheckMode_PERMISSIVE)) {
      // Permissive dimension checking -- only check counts are the same.
      CHECK_EQ(this_blob->count(), owner_blob->count())
          << "Shared parameter blobs must have the same count.";
    } else {
      // Strict dimension checking -- all dims must be the same.
      CHECK_EQ(this_blob->num(), owner_blob->num())
          << "Shared parameter blobs must have the same num.";
      CHECK_EQ(this_blob->channels(), owner_blob->channels())
          << "Shared parameter blobs must have the same channels.";
      CHECK_EQ(this_blob->height(), owner_blob->height())
          << "Shared parameter blobs must have the same height.";
      CHECK_EQ(this_blob->width(), owner_blob->width())
          << "Shared parameter blobs must have the same width.";
    }
    layers_[layer_id]->blobs()[param_id]->ShareData(
        *layers_[owner_layer_id]->blobs()[owner_param_id]);
  }
}

template <typename Dtype>
void Net<Dtype>::GetLearningRateAndWeightDecay() {
  LOG(INFO) << "Collecting Learning Rate and Weight Decay.";
  for (int i = 0; i < layers_.size(); ++i) {
    vector<shared_ptr<Blob<Dtype> > >& layer_blobs = layers_[i]->blobs();
    // push the learning rate mutlipliers
    if (layers_[i]->layer_param().blobs_lr_size()) {
      CHECK_EQ(layers_[i]->layer_param().blobs_lr_size(), layer_blobs.size());
      for (int j = 0; j < layer_blobs.size(); ++j) {
        float local_lr = layers_[i]->layer_param().blobs_lr(j);
        CHECK_GE(local_lr, 0.);
        params_lr_.push_back(local_lr);
      }
    } else {
      for (int j = 0; j < layer_blobs.size(); ++j) {
        params_lr_.push_back(1.);
      }
    }
    // push the weight decay multipliers
    if (layers_[i]->layer_param().weight_decay_size()) {
      CHECK_EQ(layers_[i]->layer_param().weight_decay_size(),
          layer_blobs.size());
      for (int j = 0; j < layer_blobs.size(); ++j) {
        float local_decay = layers_[i]->layer_param().weight_decay(j);
        CHECK_GE(local_decay, 0.);
        params_weight_decay_.push_back(local_decay);
      }
    } else {
      for (int j = 0; j < layer_blobs.size(); ++j) {
        params_weight_decay_.push_back(1.);
      }
    }
  }
}

template <typename Dtype>
Dtype Net<Dtype>::ForwardFromTo(int start, int end, Dtype * layerloss) {
  CHECK_GE(start, 0);
  CHECK_LT(end, layers_.size());
  Dtype loss = 0;

  for (int i = start; i <= end; ++i) {
    // LOG(ERROR) << "Forwarding " << layer_names_[i];
	  //LOG(INFO) << "Forwarding " << layer_names_[i];
    layers_[i]->Reshape(bottom_vecs_[i], &top_vecs_[i]);
    Dtype layer_loss = layers_[i]->Forward(bottom_vecs_[i], &top_vecs_[i]);
    loss += layer_loss;
    if (layerloss!=NULL) {;
    	layerloss[i-start] = layer_loss;
    }
    /*if ((layer_loss>0) && (Caffe::phase() == Caffe::TEST))
    {
    	LOG(INFO) << "Loss (" << layer_names_[i] << "): " << layer_loss;
    }*/
    //if (debug_info_) { ForwardDebugInfo(i); }
    if (debug_info_) { ForwardDebugInfo(i); }
  }
  return loss;
}

template <typename Dtype>
Dtype Net<Dtype>::ForwardFrom(int start) {
	LOG(INFO) << "Call ForwardFrom! Please remember to delete the return value!";
  return ForwardFromTo(start, layers_.size() - 1, NULL);
}

template <typename Dtype>
Dtype Net<Dtype>::ForwardTo(int end) {
	LOG(INFO) << "Call ForwardTo! Please remember to delete the return value!";
  return ForwardFromTo(0, end, NULL);
}

template <typename Dtype>
const vector<Blob<Dtype>*>& Net<Dtype>::ForwardPrefilled(Dtype* loss, Dtype * layerloss) {
  if (loss != NULL) {
    *loss = ForwardFromTo(0, layers_.size() - 1, layerloss);
  } else {
    ForwardFromTo(0, layers_.size() - 1, layerloss);
  }
  return net_output_blobs_;
}

template <typename Dtype>
const vector<Blob<Dtype>*>& Net<Dtype>::Forward(
    const vector<Blob<Dtype>*> & bottom, Dtype* loss, Dtype * layerloss) {
  // Copy bottom to internal bottom
  for (int i = 0; i < bottom.size(); ++i) {
    net_input_blobs_[i]->CopyFrom(*bottom[i]);
  }
  return ForwardPrefilled(loss, layerloss);
}

template <typename Dtype>
string Net<Dtype>::Forward(const string& input_blob_protos, Dtype* loss, Dtype * layerloss) {
  BlobProtoVector blob_proto_vec;
  if (net_input_blobs_.size()) {
    blob_proto_vec.ParseFromString(input_blob_protos);
    CHECK_EQ(blob_proto_vec.blobs_size(), net_input_blobs_.size())
        << "Incorrect input size.";
    for (int i = 0; i < blob_proto_vec.blobs_size(); ++i) {
      net_input_blobs_[i]->FromProto(blob_proto_vec.blobs(i));
    }
  }
  ForwardPrefilled(loss, layerloss);
  blob_proto_vec.Clear();
  for (int i = 0; i < net_output_blobs_.size(); ++i) {
    net_output_blobs_[i]->ToProto(blob_proto_vec.add_blobs());
  }
  string output;
  blob_proto_vec.SerializeToString(&output);
  return output;
}

template <typename Dtype>
void Net<Dtype>::BackwardFromTo(int start, int end) {
  CHECK_GE(end, 0);
  CHECK_LT(start, layers_.size());
  for (int i = start; i >= end; --i) {
    if (layer_need_backward_[i]) {
      layers_[i]->Backward(
          top_vecs_[i], bottom_need_backward_[i], &bottom_vecs_[i]);
      if (debug_info_) { BackwardDebugInfo(i); }
    }
  }
}

template <typename Dtype>
void Net<Dtype>::ForwardDebugInfo(const int layer_id) {
  for (int top_id = 0; top_id < top_vecs_[layer_id].size(); ++top_id) {
    const Blob<Dtype>& blob = *top_vecs_[layer_id][top_id];
    const string& blob_name = blob_names_[top_id_vecs_[layer_id][top_id]];
    const Dtype data_abs_val_mean = blob.asum_data() / blob.count();
    LOG(INFO) << "    [Forward] "
       << "Layer " << layer_names_[layer_id] << ", top blob " << blob_name
       << " data: " << data_abs_val_mean;
  }
}

template <typename Dtype>
void Net<Dtype>::BackwardDebugInfo(const int layer_id) {
  const vector<Blob<Dtype>*>& bottom_vec = bottom_vecs_[layer_id];
  for (int bottom_id = 0; bottom_id < bottom_vec.size(); ++bottom_id) {
    if (!bottom_need_backward_[layer_id][bottom_id]) { continue; }
    const Blob<Dtype>& blob = *bottom_vec[bottom_id];
    const string& blob_name = blob_names_[bottom_id_vecs_[layer_id][bottom_id]];
    const Dtype diff_abs_val_mean = blob.asum_diff() / blob.count();
    LOG(INFO) << "    [Backward] "
        << "Layer " << layer_names_[layer_id] << ", bottom blob " << blob_name
        << " diff: " << diff_abs_val_mean;
  }
  for (int param_id = 0; param_id < layers_[layer_id]->blobs().size();
       ++param_id) {
    if (!layers_[layer_id]->param_propagate_down(param_id)) { continue; }
    const Blob<Dtype>& blob = *layers_[layer_id]->blobs()[param_id];
    const Dtype diff_abs_val_mean = blob.asum_diff() / blob.count();
    LOG(INFO) << "    [Backward] "
        << "Layer " << layer_names_[layer_id] << ", param blob " << param_id
        << " diff: " << diff_abs_val_mean;
  }
}

template <typename Dtype>
void Net<Dtype>::UpdateDebugInfo(const int param_id) {
  const Blob<Dtype>& blob = *params_[param_id];
  const int param_owner = param_owners_[param_id];
  const string& layer_name = layer_names_[param_layer_indices_[param_id].first];
  const string& param_display_name = param_display_names_[param_id];
  const Dtype diff_abs_val_mean = blob.asum_diff() / blob.count();
  if (param_owner < 0) {
    const Dtype data_abs_val_mean = blob.asum_data() / blob.count();
    Dtype data_std = blob.std_data();
    LOG(INFO) << "    [Update] Layer " << layer_name
        << ", param " << param_display_name
        << " data: " << data_abs_val_mean << "; diff: " << diff_abs_val_mean;
  } else {
    const string& owner_layer_name =
        layer_names_[param_layer_indices_[param_owner].first];
    LOG(INFO) << "    [Update] Layer " << layer_name
        << ", param blob " << param_display_name
        << " (owned by layer " << owner_layer_name << ", "
        << "param " << param_display_names_[param_owners_[param_id]] << ")"
        << " diff: " << diff_abs_val_mean;
  }
}

template <typename Dtype>
void Net<Dtype>::ShareTrainedLayersWith(Net* other) {
  int num_source_layers = other->layers().size();
  for (int i = 0; i < num_source_layers; ++i) {
    Layer<Dtype>* source_layer = other->layers()[i].get();
    const string& source_layer_name = other->layer_names()[i];
    int target_layer_id = 0;
    while (target_layer_id != layer_names_.size() &&
        layer_names_[target_layer_id] != source_layer_name) {
      ++target_layer_id;
    }
    if (target_layer_id == layer_names_.size()) {
      DLOG(INFO) << "Ignoring source layer " << source_layer_name;
      continue;
    }
    DLOG(INFO) << "Copying source layer " << source_layer_name;
    vector<shared_ptr<Blob<Dtype> > >& target_blobs =
        layers_[target_layer_id]->blobs();
    CHECK_EQ(target_blobs.size(), source_layer->blobs().size())
        << "Incompatible number of blobs for layer " << source_layer_name;
    for (int j = 0; j < target_blobs.size(); ++j) {
      Blob<Dtype>* source_blob = source_layer->blobs()[j].get();
      CHECK_EQ(target_blobs[j]->num(), source_blob->num());
      CHECK_EQ(target_blobs[j]->channels(), source_blob->channels());
      CHECK_EQ(target_blobs[j]->height(), source_blob->height());
      CHECK_EQ(target_blobs[j]->width(), source_blob->width());
      target_blobs[j]->ShareData(*source_blob);
    }
  }
}

template <typename Dtype>
void Net<Dtype>::BackwardFrom(int start) {
  BackwardFromTo(start, 0);
}

template <typename Dtype>
void Net<Dtype>::BackwardTo(int end) {
  BackwardFromTo(layers_.size() - 1, end);
}

template <typename Dtype>
void Net<Dtype>::Backward() {
  BackwardFromTo(layers_.size() - 1, 0);
}

template <typename Dtype>
void Net<Dtype>::Reshape() {
  for (int i = 0; i < layers_.size(); ++i) {
    layers_[i]->Reshape(bottom_vecs_[i], &top_vecs_[i]);
  }
}

template <typename Dtype>
void Net<Dtype>::CopyTrainedLayersFrom(const NetParameter& param) {
  int num_source_layers = param.layers_size();
  for (int i = 0; i < num_source_layers; ++i) {
    const LayerParameter& source_layer = param.layers(i);
    const string& source_layer_name = source_layer.name();
    int target_layer_id = 0;
    while (target_layer_id != layer_names_.size() &&
        layer_names_[target_layer_id] != source_layer_name) {
      ++target_layer_id;
    }
    if (target_layer_id == layer_names_.size()) {
      DLOG(INFO) << "Ignoring source layer " << source_layer_name;
      continue;
    }
    DLOG(INFO) << "Copying source layer " << source_layer_name;
    vector<shared_ptr<Blob<Dtype> > >& target_blobs =
        layers_[target_layer_id]->blobs();
    CHECK_EQ(target_blobs.size(), source_layer.blobs_size())
        << "Incompatible number of blobs for layer " << source_layer_name;
    for (int j = 0; j < target_blobs.size(); ++j) {
      CHECK_EQ(target_blobs[j]->num(), source_layer.blobs(j).num());
      CHECK_EQ(target_blobs[j]->channels(), source_layer.blobs(j).channels());
      CHECK_EQ(target_blobs[j]->height(), source_layer.blobs(j).height());
      CHECK_EQ(target_blobs[j]->width(), source_layer.blobs(j).width());
      target_blobs[j]->FromProto(source_layer.blobs(j));
    }
  }
}

template <typename Dtype>
void Net<Dtype>::CopyTrainedLayersFrom(const string trained_filename) {
  NetParameter param;
  ReadNetParamsFromBinaryFileOrDie(trained_filename, &param);
  CopyTrainedLayersFrom(param);
}

template <typename Dtype>
void Net<Dtype>::ToProto(NetParameter* param, bool write_diff) {
  param->Clear();
  param->set_name(name_);
  // Add bottom and top
  for (int i = 0; i < net_input_blob_indices_.size(); ++i) {
    param->add_input(blob_names_[net_input_blob_indices_[i]]);
  }
  DLOG(INFO) << "Serializing " << layers_.size() << " layers";
  for (int i = 0; i < layers_.size(); ++i) {
    LayerParameter* layer_param = param->add_layers();
    for (int j = 0; j < bottom_id_vecs_[i].size(); ++j) {
      layer_param->add_bottom(blob_names_[bottom_id_vecs_[i][j]]);
    }
    for (int j = 0; j < top_id_vecs_[i].size(); ++j) {
      layer_param->add_top(blob_names_[top_id_vecs_[i][j]]);
    }
    layers_[i]->ToProto(layer_param, write_diff);
  }
}

template <typename Dtype>
void Net<Dtype>::Update() {
  // First, accumulate the diffs of any shared parameters into their owner's
  // diff. (Assumes that the learning rate, weight decay, etc. have already been
  // accounted for in the current diff.)
  for (int i = 0; i < params_.size(); ++i) {
    if (param_owners_[i] < 0) { continue; }
    if (debug_info_) { UpdateDebugInfo(i); }
    const int count = params_[i]->count();
    const Dtype* this_diff;
    Dtype* this_diff_mutable;
    Dtype* owner_diff;
    switch (Caffe::mode()) {
    case Caffe::CPU:
      this_diff = params_[i]->cpu_diff();
      owner_diff = params_[param_owners_[i]]->mutable_cpu_diff();
      caffe_add(count, this_diff, owner_diff, owner_diff);
      break;
#ifndef CPU_ONLY
    case Caffe::GPU:
      this_diff = params_[i]->gpu_diff();
      owner_diff = params_[param_owners_[i]]->mutable_gpu_diff();
      caffe_gpu_add(count, this_diff, owner_diff, owner_diff);
      break;
#else
      NO_GPU;
#endif
    default:
      LOG(FATAL) << "Unknown caffe mode: " << Caffe::mode();
    }
  }
  // Now, update the owned parameters.
  for (int i = 0; i < params_.size(); ++i) {
    if (param_owners_[i] >= 0) { continue; }
    if (debug_info_) { UpdateDebugInfo(i); }
    params_[i]->Update();
  }
}

template <typename Dtype>
bool Net<Dtype>::has_blob(const string& blob_name) {
  return blob_names_index_.find(blob_name) != blob_names_index_.end();
}

template <typename Dtype>
const shared_ptr<Blob<Dtype> > Net<Dtype>::blob_by_name(
    const string& blob_name) {
  shared_ptr<Blob<Dtype> > blob_ptr;
  if (has_blob(blob_name)) {
    blob_ptr = blobs_[blob_names_index_[blob_name]];
  } else {
    blob_ptr.reset((Blob<Dtype>*)(NULL));
    LOG(WARNING) << "Unknown blob name " << blob_name;
  }
  return blob_ptr;
}

template <typename Dtype>
bool Net<Dtype>::has_layer(const string& layer_name) {
  return layer_names_index_.find(layer_name) != layer_names_index_.end();
}

template <typename Dtype>
const shared_ptr<Layer<Dtype> > Net<Dtype>::layer_by_name(
    const string& layer_name) {
  shared_ptr<Layer<Dtype> > layer_ptr;
  if (has_layer(layer_name)) {
    layer_ptr = layers_[layer_names_index_[layer_name]];
  } else {
    layer_ptr.reset((Layer<Dtype>*)(NULL));
    LOG(WARNING) << "Unknown layer name " << layer_name;
  }
  return layer_ptr;
}

INSTANTIATE_CLASS(Net);

}  // namespace caffe
