text: #include <algorithm>
  #include <map>
  #include <set>
  #include <string>
  #include <utility>
  #include <vector>
  
  #include "hdf5.h"
  
  #include "caffe/common.hpp"
  #include "caffe/layer.hpp"
  #include "caffe/net.hpp"
  #include "caffe/parallel.hpp"
  #include "caffe/proto/caffe.pb.h"
  #include "caffe/util/hdf5.hpp"
  #include "caffe/util/insert_splits.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, const Net* root_net)
      : root_net_(root_net) {
    Init(param);
  }
  
  template <typename Dtype>
  Net<Dtype>::Net(const string& param_file, Phase phase,
      const int level, const vector<string>* stages,
      const Net* root_net)
      : root_net_(root_net) {
    NetParameter param;
    ReadNetParamsFromTextFileOrDie(param_file, &param);
    // Set phase, stages and level
    param.mutable_state()->set_phase(phase);
    if (stages != NULL) {
      for (int i = 0; i < stages->size(); i++) {
        param.mutable_state()->add_stage((*stages)[i]);
      }
    }
    param.mutable_state()->set_level(level);
    Init(param);
  }
  
  template <typename Dtype>
  void Net<Dtype>::Init(const NetParameter& in_param) {
    CHECK(Caffe::root_solver() || root_net_)
        << "root_net_ needs to be set for all non-root solvers";
    // Set phase from the state.
    phase_ = in_param.state().phase();
    // Filter layers based on their include/exclude rules and
    // the current NetState.
    NetParameter filtered_param;
    FilterNet(in_param, &filtered_param);
    DLOG_IF(INFO, Caffe::root_solver())
        << "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 their connections.
    name_ = param.name();
    map<string, int> blob_name_to_idx;
    set<string> available_blobs;
    memory_used_ = 0;
    // For each layer, set up its input and output
    bottom_vecs_.resize(param.layer_size());
    top_vecs_.resize(param.layer_size());
    bottom_id_vecs_.resize(param.layer_size());
    param_id_vecs_.resize(param.layer_size());
    top_id_vecs_.resize(param.layer_size());
    bottom_need_backward_.resize(param.layer_size());
    for (int layer_id = 0; layer_id < param.layer_size(); ++layer_id) {
      // For non-root solvers, whether this layer is shared from root_net_.
      bool share_from_root = !Caffe::root_solver()
          && root_net_->layers_[layer_id]->ShareInParallel();
      // Inherit phase from net if unset.
      if (!param.layer(layer_id).has_phase()) {
        param.mutable_layer(layer_id)->set_phase(phase_);
      }
      // Setup layer.
      const LayerParameter& layer_param = param.layer(layer_id);
      if (layer_param.propagate_down_size() > 0) {
        CHECK_EQ(layer_param.propagate_down_size(),
            layer_param.bottom_size())
            << "propagate_down param must be specified "
            << "either 0 or bottom_size times ";
      }
      if (share_from_root) {
        DLOG(INFO) << "Sharing layer " << layer_param.name() << " from root net";
        layers_.push_back(root_net_->layers_[layer_id]);
        layers_[layer_id]->SetShared(true);
      } else {
        layers_.push_back(LayerRegistry<Dtype>::CreateLayer(layer_param));
      }
      layer_names_.push_back(layer_param.name());
      DLOG_IF(INFO, Caffe::root_solver())
          << "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);
        // Collect Input layer tops as Net inputs.
        if (layer_param.type() == "Input") {
          const int blob_id = blobs_.size() - 1;
          net_input_blob_indices_.push_back(blob_id);
          net_input_blobs_.push_back(blobs_[blob_id].get());
        }
      }
      // 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();
      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.
      if (share_from_root) {
        // Set up size of top blobs using root_net_
        const vector<Blob<Dtype>*>& base_top = root_net_->top_vecs_[layer_id];
        const vector<Blob<Dtype>*>& this_top = this->top_vecs_[layer_id];
        for (int top_id = 0; top_id < base_top.size(); ++top_id) {
          this_top[top_id]->ReshapeLike(*base_top[top_id]);
          DLOG(INFO) << "Created top blob " << top_id << " (shape: "
              << this_top[top_id]->shape_string() <<  ") for shared layer "
              << layer_param.name();
        }
      } else {
        layers_[layer_id]->SetUp(bottom_vecs_[layer_id], top_vecs_[layer_id]);
      }
      DLOG_IF(INFO, Caffe::root_solver())
          << "Setting up " << layer_names_[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);
        DLOG_IF(INFO, Caffe::root_solver())
            << "Top shape: " << top_vecs_[layer_id][top_id]->shape_string();
        if (layer->loss(top_id)) {
          DLOG_IF(INFO, Caffe::root_solver())
              << "    with loss weight " << layer->loss(top_id);
        }
        memory_used_ += top_vecs_[layer_id][top_id]->count();
      }
      DLOG_IF(INFO, Caffe::root_solver())
          << "Memory required for data: " << memory_used_ * sizeof(Dtype);
      const int param_size = layer_param.param_size();
      const int num_param_blobs = layers_[layer_id]->blobs().size();
      CHECK_LE(param_size, num_param_blobs)
          << "Too many params specified for layer " << layer_param.name();
      ParamSpec default_param_spec;
      for (int param_id = 0; param_id < num_param_blobs; ++param_id) {
        const ParamSpec* param_spec = (param_id < param_size) ?
            &layer_param.param(param_id) : &default_param_spec;
        const bool param_need_backward = param_spec->lr_mult() != 0;
        need_backward |= param_need_backward;
        layers_[layer_id]->set_param_propagate_down(param_id,
                                                    param_need_backward);
      }
      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.
    // Also checks if all bottom blobs don't need backward computation (possible
    // because the skip_propagate_down param) and so we can skip bacward
    // computation for the entire layer
    set<string> blobs_under_loss;
    set<string> blobs_skip_backp;
    for (int layer_id = layers_.size() - 1; layer_id >= 0; --layer_id) {
      bool layer_contributes_loss = false;
      bool layer_skip_propagate_down = true;
      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;
        }
        if (blobs_skip_backp.find(blob_name) == blobs_skip_backp.end()) {
          layer_skip_propagate_down = false;
        }
        if (layer_contributes_loss && !layer_skip_propagate_down)
          break;
      }
      // If this layer can skip backward computation, also all his bottom blobs
      // don't need backpropagation
      if (layer_need_backward_[layer_id] && layer_skip_propagate_down) {
        layer_need_backward_[layer_id] = false;
        for (int bottom_id = 0; bottom_id < bottom_vecs_[layer_id].size();
                 ++bottom_id) {
          bottom_need_backward_[layer_id][bottom_id] = false;
        }
      }
      if (!layer_contributes_loss) { layer_need_backward_[layer_id] = false; }
      if (Caffe::root_solver()) {
        if (layer_need_backward_[layer_id]) {
          DLOG(INFO) << layer_names_[layer_id] << " needs backward computation.";
        } else {
          DLOG(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;
        }
        if (!bottom_need_backward_[layer_id][bottom_id]) {
          const string& blob_name =
                     blob_names_[bottom_id_vecs_[layer_id][bottom_id]];
          blobs_skip_backp.insert(blob_name);
        }
      }
    }
    // 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) {
      DLOG_IF(INFO, Caffe::root_solver())
          << "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;
    }
    ShareWeights();
    debug_info_ = param.debug_info();
    DLOG_IF(INFO, Caffe::root_solver()) << "Network initialization done.";
  }
  
  template <typename Dtype>
  void Net<Dtype>::SetPhase(Phase phase) {
    // set all layers
    for (int i = 0; i < layers_.size(); ++i) {
      layers_[i]->set_phase(phase);
    }
    // set net phase
    phase_ = phase;
  }
  
  template <typename Dtype>
  void Net<Dtype>::FilterNet(const NetParameter& param,
      NetParameter* param_filtered) {
    NetState net_state(param.state());
    param_filtered->CopyFrom(param);
    param_filtered->clear_layer();
    for (int i = 0; i < param.layer_size(); ++i) {
      const LayerParameter& layer_param = param.layer(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_layer()->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()) {
          DLOG_IF(INFO, Caffe::root_solver())
              << "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()) {
        DLOG_IF(INFO, Caffe::root_solver())
            << "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()) {
        DLOG_IF(INFO, Caffe::root_solver())
            << "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) {
        DLOG_IF(INFO, Caffe::root_solver())
            << "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) {
        DLOG_IF(INFO, Caffe::root_solver())
            << "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 top blob to the net.
  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(
        new LayerParameter(param.layer(layer_id)));
    const string& blob_name = (layer_param->top_size() > top_id) ?
        layer_param->top(top_id) : "(automatic)";
    // Check if we are doing in-place computation
    if (blob_name_to_idx && layer_param->bottom_size() > top_id &&
        blob_name == layer_param->bottom(top_id)) {
      // In-place computation
      DLOG_IF(INFO, Caffe::root_solver())
          << 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) << "Top blob '" << blob_name
                 << "' produced by multiple sources.";
    } else {
      // Normal output.
      if (Caffe::root_solver()) {
        DLOG(INFO) << layer_param->name() << " -> " << 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; }
      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.layer(layer_id);
    const string& blob_name = layer_param.bottom(bottom_id);
    if (available_blobs->find(blob_name) == available_blobs->end()) {
      LOG(FATAL) << "Unknown bottom blob '" << blob_name << "' (layer '"
                 << layer_param.name() << "', bottom index " << bottom_id << ")";
    }
    const int blob_id = (*blob_name_to_idx)[blob_name];
    DLOG_IF(INFO, Caffe::root_solver())
        << 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);
    bool need_backward = blob_need_backward_[blob_id];
    // Check if the backpropagation on bottom_id should be skipped
    if (layer_param.propagate_down_size() > 0) {
      need_backward = layer_param.propagate_down(bottom_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 > param_id) ? layer_param.param(param_id).name() : "";
    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_id_vecs_[layer_id].push_back(net_param_id);
    param_layer_indices_.push_back(make_pair(layer_id, param_id));
    ParamSpec default_param_spec;
    const ParamSpec* param_spec = (layer_param.param_size() > param_id) ?
        &layer_param.param(param_id) : &default_param_spec;
    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_name.size()) {
        param_names_index_[param_name] = net_param_id;
      }
      const int learnable_param_id = learnable_params_.size();
      learnable_params_.push_back(params_[net_param_id].get());
      learnable_param_ids_.push_back(learnable_param_id);
      has_params_lr_.push_back(param_spec->has_lr_mult());
      has_params_decay_.push_back(param_spec->has_decay_mult());
      params_lr_.push_back(param_spec->lr_mult());
      params_weight_decay_.push_back(param_spec->decay_mult());
    } 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;
      DLOG_IF(INFO, Caffe::root_solver()) << "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 param_size = layer_param.param_size();
      if (param_size > param_id && (layer_param.param(param_id).share_mode() ==
                                    ParamSpec_DimCheckMode_PERMISSIVE)) {
        // Permissive dimension checking -- only check counts are the same.
        CHECK_EQ(this_blob->count(), owner_blob->count())
            << "Cannot share param '" << param_name << "' owned by layer '"
            << layer_names_[owner_layer_id] << "' with layer '"
            << layer_names_[layer_id] << "'; count mismatch.  Owner layer param "
            << "shape is " << owner_blob->shape_string() << "; sharing layer "
            << "shape is " << this_blob->shape_string();
      } else {
        // Strict dimension checking -- all dims must be the same.
        CHECK(this_blob->shape() == owner_blob->shape())
            << "Cannot share param '" << param_name << "' owned by layer '"
            << layer_names_[owner_layer_id] << "' with layer '"
            << layer_names_[layer_id] << "'; shape mismatch.  Owner layer param "
            << "shape is " << owner_blob->shape_string() << "; sharing layer "
            << "expects shape " << this_blob->shape_string();
      }
      const int learnable_param_id = learnable_param_ids_[owner_net_param_id];
      learnable_param_ids_.push_back(learnable_param_id);
      if (param_spec->has_lr_mult()) {
        if (has_params_lr_[learnable_param_id]) {
          CHECK_EQ(param_spec->lr_mult(), params_lr_[learnable_param_id])
              << "Shared param '" << param_name << "' has mismatched lr_mult.";
        } else {
          has_params_lr_[learnable_param_id] = true;
          params_lr_[learnable_param_id] = param_spec->lr_mult();
        }
      }
      if (param_spec->has_decay_mult()) {
        if (has_params_decay_[learnable_param_id]) {
          CHECK_EQ(param_spec->decay_mult(),
                   params_weight_decay_[learnable_param_id])
              << "Shared param '" << param_name << "' has mismatched decay_mult.";
        } else {
          has_params_decay_[learnable_param_id] = true;
          params_weight_decay_[learnable_param_id] = param_spec->decay_mult();
        }
      }
    }
  }
  
  template <typename Dtype>
  Dtype Net<Dtype>::ForwardFromTo(int start, int end) {
    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];
      Dtype layer_loss = layers_[i]->Forward(bottom_vecs_[i], top_vecs_[i]);
      loss += layer_loss;
      if (debug_info_) { ForwardDebugInfo(i); }
    }
    return loss;
  }
  
  template <typename Dtype>
  Dtype Net<Dtype>::ForwardFrom(int start) {
    return ForwardFromTo(start, layers_.size() - 1);
  }
  
  template <typename Dtype>
  Dtype Net<Dtype>::ForwardTo(int end) {
    return ForwardFromTo(0, end);
  }
  
  template <typename Dtype>
  const vector<Blob<Dtype>*>& Net<Dtype>::Forward(Dtype* loss) {
    if (loss != NULL) {
      *loss = ForwardFromTo(0, layers_.size() - 1);
    } else {
      ForwardFromTo(0, layers_.size() - 1);
    }
    return net_output_blobs_;
  }
  
  template <typename Dtype>
  const vector<Blob<Dtype>*>& Net<Dtype>::Forward(
      const vector<Blob<Dtype>*> & bottom, Dtype* loss) {
    LOG_EVERY_N(WARNING, 1000) << "DEPRECATED: Forward(bottom, loss) "
        << "will be removed in a future version. Use Forward(loss).";
    // Copy bottom to net bottoms
    for (int i = 0; i < bottom.size(); ++i) {
      net_input_blobs_[i]->CopyFrom(*bottom[i]);
    }
    return Forward(loss);
  }
  
  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();
      DLOG_IF(INFO, Caffe::root_solver())
          << "    [Forward] "
          << "Layer " << layer_names_[layer_id]
          << ", top blob " << blob_name
          << " data: " << data_abs_val_mean;
    }
    for (int param_id = 0; param_id < layers_[layer_id]->blobs().size();
         ++param_id) {
      const Blob<Dtype>& blob = *layers_[layer_id]->blobs()[param_id];
      const int net_param_id = param_id_vecs_[layer_id][param_id];
      const string& blob_name = param_display_names_[net_param_id];
      const Dtype data_abs_val_mean = blob.asum_data() / blob.count();
      DLOG_IF(INFO, Caffe::root_solver())
          << "    [Forward] "
          << "Layer " << layer_names_[layer_id]
          << ", param 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();
      DLOG_IF(INFO, Caffe::root_solver())
          << "    [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();
      DLOG_IF(INFO, Caffe::root_solver())
          << "    [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();
      DLOG_IF(INFO, Caffe::root_solver())
          << "    [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];
      DLOG_IF(INFO, Caffe::root_solver())
          << "    [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(const 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()) {
        LOG(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(target_blobs[j]->shape() == source_blob->shape())
            << "Cannot share param " << j << " weights from layer '"
            << source_layer_name << "'; shape mismatch.  Source param shape is "
            << source_blob->shape_string() << "; target param shape is "
            << target_blobs[j]->shape_string();
        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);
    if (debug_info_) {
      Dtype asum_data = 0, asum_diff = 0, sumsq_data = 0, sumsq_diff = 0;
      for (int i = 0; i < learnable_params_.size(); ++i) {
        asum_data += learnable_params_[i]->asum_data();
        asum_diff += learnable_params_[i]->asum_diff();
        sumsq_data += learnable_params_[i]->sumsq_data();
        sumsq_diff += learnable_params_[i]->sumsq_diff();
      }
      const Dtype l2norm_data = std::sqrt(sumsq_data);
      const Dtype l2norm_diff = std::sqrt(sumsq_diff);
      LOG(ERROR) << "    [Backward] All net params (data, diff): "
                 << "L1 norm = (" << asum_data << ", " << asum_diff << "); "
                 << "L2 norm = (" << l2norm_data << ", " << l2norm_diff << ")";
    }
  }
  
  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.layer_size();
    for (int i = 0; i < num_source_layers; ++i) {
      const LayerParameter& source_layer = param.layer(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()) {
        LOG(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) {
        if (!target_blobs[j]->ShapeEquals(source_layer.blobs(j))) {
          Blob<Dtype> source_blob;
          const bool kReshape = true;
          source_blob.FromProto(source_layer.blobs(j), kReshape);
          LOG(FATAL) << "Cannot copy param " << j << " weights from layer '"
              << source_layer_name << "'; shape mismatch.  Source param shape is "
              << source_blob.shape_string() << "; target param shape is "
              << target_blobs[j]->shape_string() << ". "
              << "To learn this layer's parameters from scratch rather than "
              << "copying from a saved net, rename the layer.";
        }
        const bool kReshape = false;
        target_blobs[j]->FromProto(source_layer.blobs(j), kReshape);
      }
    }
  }
  
  template <typename Dtype>
  void Net<Dtype>::CopyTrainedLayersFrom(const string trained_filename) {
    if (trained_filename.size() >= 3 &&
        trained_filename.compare(trained_filename.size() - 3, 3, ".h5") == 0) {
      CopyTrainedLayersFromHDF5(trained_filename);
    } else {
      CopyTrainedLayersFromBinaryProto(trained_filename);
    }
  }
  
  template <typename Dtype>
  void Net<Dtype>::CopyTrainedLayersFromBinaryProto(
      const string trained_filename) {
    NetParameter param;
    ReadNetParamsFromBinaryFileOrDie(trained_filename, &param);
    CopyTrainedLayersFrom(param);
  }
  
  template <typename Dtype>
  void Net<Dtype>::CopyTrainedLayersFromHDF5(const string trained_filename) {
    hid_t file_hid = H5Fopen(trained_filename.c_str(), H5F_ACC_RDONLY,
                             H5P_DEFAULT);
    CHECK_GE(file_hid, 0) << "Couldn't open " << trained_filename;
    hid_t data_hid = H5Gopen2(file_hid, "data", H5P_DEFAULT);
    CHECK_GE(data_hid, 0) << "Error reading weights from " << trained_filename;
    int num_layers = hdf5_get_num_links(data_hid);
    for (int i = 0; i < num_layers; ++i) {
      string source_layer_name = hdf5_get_name_by_idx(data_hid, i);
      if (!layer_names_index_.count(source_layer_name)) {
        LOG(INFO) << "Ignoring source layer " << source_layer_name;
        continue;
      }
      int target_layer_id = layer_names_index_[source_layer_name];
      DLOG(INFO) << "Copying source layer " << source_layer_name;
      vector<shared_ptr<Blob<Dtype> > >& target_blobs =
          layers_[target_layer_id]->blobs();
      hid_t layer_hid = H5Gopen2(data_hid, source_layer_name.c_str(),
          H5P_DEFAULT);
      CHECK_GE(layer_hid, 0)
          << "Error reading weights from " << trained_filename;
      // Check that source layer doesn't have more params than target layer
      int num_source_params = hdf5_get_num_links(layer_hid);
      CHECK_LE(num_source_params, target_blobs.size())
          << "Incompatible number of blobs for layer " << source_layer_name;
      for (int j = 0; j < target_blobs.size(); ++j) {
        ostringstream oss;
        oss << j;
        string dataset_name = oss.str();
        int target_net_param_id = param_id_vecs_[target_layer_id][j];
        if (!H5Lexists(layer_hid, dataset_name.c_str(), H5P_DEFAULT)) {
          // Target param doesn't exist in source weights...
          if (param_owners_[target_net_param_id] != -1) {
            // ...but it's weight-shared in target, so that's fine.
            continue;
          } else {
            LOG(FATAL) << "Incompatible number of blobs for layer "
                << source_layer_name;
          }
        }
        hdf5_load_nd_dataset(layer_hid, dataset_name.c_str(), 0, kMaxBlobAxes,
            target_blobs[j].get());
      }
      H5Gclose(layer_hid);
    }
    H5Gclose(data_hid);
    H5Fclose(file_hid);
  }
  
  template <typename Dtype>
  void Net<Dtype>::ToProto(NetParameter* param, bool write_diff) const {
    param->Clear();
    param->set_name(name_);
    // Add bottom and top
    DLOG(INFO) << "Serializing " << layers_.size() << " layers";
    for (int i = 0; i < layers_.size(); ++i) {
      LayerParameter* layer_param = param->add_layer();
      layers_[i]->ToProto(layer_param, write_diff);
    }
  }
  
  template <typename Dtype>
  void Net<Dtype>::ToHDF5(const string& filename, bool write_diff) const {
    hid_t file_hid = H5Fcreate(filename.c_str(), H5F_ACC_TRUNC, H5P_DEFAULT,
        H5P_DEFAULT);
    CHECK_GE(file_hid, 0)
        << "Couldn't open " << filename << " to save weights.";
    hid_t data_hid = H5Gcreate2(file_hid, "data", H5P_DEFAULT, H5P_DEFAULT,
        H5P_DEFAULT);
    CHECK_GE(data_hid, 0) << "Error saving weights to " << filename << ".";
    hid_t diff_hid = -1;
    if (write_diff) {
      diff_hid = H5Gcreate2(file_hid, "diff", H5P_DEFAULT, H5P_DEFAULT,
          H5P_DEFAULT);
      CHECK_GE(diff_hid, 0) << "Error saving weights to " << filename << ".";
    }
    for (int layer_id = 0; layer_id < layers_.size(); ++layer_id) {
      const LayerParameter& layer_param = layers_[layer_id]->layer_param();
      string layer_name = layer_param.name();
      hid_t layer_data_hid = H5Gcreate2(data_hid, layer_name.c_str(),
          H5P_DEFAULT, H5P_DEFAULT, H5P_DEFAULT);
      CHECK_GE(layer_data_hid, 0)
          << "Error saving weights to " << filename << ".";
      hid_t layer_diff_hid = -1;
      if (write_diff) {
        layer_diff_hid = H5Gcreate2(diff_hid, layer_name.c_str(),
            H5P_DEFAULT, H5P_DEFAULT, H5P_DEFAULT);
        CHECK_GE(layer_diff_hid, 0)
            << "Error saving weights to " << filename << ".";
      }
      int num_params = layers_[layer_id]->blobs().size();
      for (int param_id = 0; param_id < num_params; ++param_id) {
        ostringstream dataset_name;
        dataset_name << param_id;
        const int net_param_id = param_id_vecs_[layer_id][param_id];
        if (param_owners_[net_param_id] == -1) {
          // Only save params that own themselves
          hdf5_save_nd_dataset<Dtype>(layer_data_hid, dataset_name.str(),
              *params_[net_param_id]);
        }
        if (write_diff) {
          // Write diffs regardless of weight-sharing
          hdf5_save_nd_dataset<Dtype>(layer_diff_hid, dataset_name.str(),
              *params_[net_param_id], true);
        }
      }
      H5Gclose(layer_data_hid);
      if (write_diff) {
        H5Gclose(layer_diff_hid);
      }
    }
    H5Gclose(data_hid);
    if (write_diff) {
      H5Gclose(diff_hid);
    }
    H5Fclose(file_hid);
  }
  
  template <typename Dtype>
  void Net<Dtype>::Update() {
    for (int i = 0; i < learnable_params_.size(); ++i) {
      learnable_params_[i]->Update();
    }
  }
  
  template <typename Dtype>
  void Net<Dtype>::ClearParamDiffs() {
    for (int i = 0; i < learnable_params_.size(); ++i) {
      Blob<Dtype>* blob = learnable_params_[i];
      switch (Caffe::mode()) {
      case Caffe::CPU:
        caffe_set(blob->count(), static_cast<Dtype>(0),
                  blob->mutable_cpu_diff());
        break;
      case Caffe::GPU:
  #ifndef CPU_ONLY
        caffe_gpu_set(blob->count(), static_cast<Dtype>(0),
                      blob->mutable_gpu_diff());
  #else
        NO_GPU;
  #endif
        break;
      }
    }
  }
  
  template <typename Dtype>
  void Net<Dtype>::ShareWeights() {
    for (int i = 0; i < params_.size(); ++i) {
      if (param_owners_[i] < 0) { continue; }
      params_[i]->ShareData(*params_[param_owners_[i]]);
      params_[i]->ShareDiff(*params_[param_owners_[i]]);
    }
  }
  
  template <typename Dtype>
  bool Net<Dtype>::has_blob(const string& blob_name) const {
    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) const {
    shared_ptr<Blob<Dtype> > blob_ptr;
    if (has_blob(blob_name)) {
      blob_ptr = blobs_[blob_names_index_.find(blob_name)->second];
    } 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) const {
    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) const {
    shared_ptr<Layer<Dtype> > layer_ptr;
    if (has_layer(layer_name)) {
      layer_ptr = layers_[layer_names_index_.find(layer_name)->second];
    } else {
      layer_ptr.reset((Layer<Dtype>*)(NULL));
      LOG(WARNING) << "Unknown layer name " << layer_name;
    }
    return layer_ptr;
  }
  
  INSTANTIATE_CLASS(Net);
  
  }  // namespace caffe
  