C++ NetParameter::layer_size方法代码示例

void Net<Dtype>::filterNet(const NetParameter& param, NetParameter* filtered_param){
	NetState state(param.state());
	filtered_param->CopyFrom(param);
	// remove all layer params and then filter
	filtered_param->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();
		//	usually a layer has not any include/exclude rules
		CHECK(layer_param.include_size() == 0 || layer_param.exclude_size() == 0)
			<< "Specify either include or exclude rules.";
		bool layer_included = (layer_param.include_size() == 0);
		//	assume 'included' and check if meet any excluded rules
		for (int j = 0; layer_included&&j < layer_param.exclude_size(); j++){
			if (stateMeetRule(state, layer_param.exclude(j), layer_name)){
				//	cancel 'included'
				layer_included = false;
			}
		}
		//	assume 'excluded' and check if meet any included rules
		for (int j = 0; !layer_included&&j < layer_param.include_size(); j++){
			if (stateMeetRule(state, layer_param.include(j), layer_name)){
				//	cancel 'excluded'
				layer_included = true;
			}
		}
		//	 copy the included layer to filtered_param
		if (layer_included) filtered_param->add_layer()->CopyFrom(layer_param);
	}
}

SolverParameter ModelServer<Dtype>::prepare_model() {
  NetParameter net;
  solver->net()->ToProto(&net);

  for (int i = 0; i < net.layer_size(); ++i) {
    LayerParameter& layer = *net.mutable_layer(i);
    layer.clear_blobs();
    if ((layer.type().find("Data") != std::string::npos)
        && (layer.has_remote_data_param())) {
      layer.set_type("RemoteData");

      for (int j = 0; j < layer.top_size(); ++j) {
        *layer.mutable_remote_data_param()->add_shape()
          = blob_shape_by_name(layer.top(j));
      }
    }
  }

  SolverParameter ret = solver->param();
  ret.clear_net();
  ret.clear_net_param();
  ret.clear_test_net();
  ret.clear_test_net_param();
  ret.clear_train_net();
  *ret.mutable_train_net_param() = net;

  return ret;
}

void NGNet::Init(	) {


	input_layer_top_idx_ = 0;
	output_layer_top_idx_ = 0;
	
	/* Load the network. */
	net_.reset(new Net<float>(model_file_, TEST));
	NetParameter param;
	CHECK(ReadProtoFromTextFile(model_file_, &param))
		<< "Failed to parse NetParameter file: " << model_file_;
	for (int ip = 0; ip < param.layer_size(); ip++) {
		LayerParameter layer_param = param.layer(ip);
		if (layer_param.has_inner_product_param()) {
			InnerProductParameter* inner_product_param = layer_param.mutable_inner_product_param();
			int num_output = inner_product_param->num_output();
			if (num_output > 0) {
				inner_product_param->set_num_output(num_output * 2); 
			}
		}
	}
//	//param.mutable_state()->set_phase(phase);
	Net<float> * new_net = new Net<float>(param);
	
	net_->CopyTrainedLayersFrom(trained_file_);

	
	int input_layer_idx = -1;
	for (size_t layer_id = 0; layer_id < net_->layer_names().size(); ++layer_id) {
		if (net_->layer_names()[layer_id] == input_layer_name_) {
			input_layer_idx = layer_id;
			break;
		}
	}
	if (input_layer_idx == -1) {
		LOG(FATAL) << "Unknown layer name " << input_layer_name_;			
	}

	input_layer_idx_ = input_layer_idx;
	
	input_layer_top_idx_ = 0;

	Blob<float>* input_layer = net_->top_vecs()[input_layer_idx_][input_layer_top_idx_];
	input_layer_dim_ = input_layer->shape(1);

	int output_layer_idx = -1;
	for (size_t layer_id = 0; layer_id < net_->layer_names().size(); ++layer_id) {
		if (net_->layer_names()[layer_id] == output_layer_name_) {
			output_layer_idx = layer_id;
			break;
		}
	}
	if (output_layer_idx == -1) {
		LOG(FATAL) << "Unknown layer name " << output_layer_name_;			
	}
	output_layer_idx_ = output_layer_idx;
	
	
}

bool NetNeedsBatchNormUpgrade(const NetParameter& net_param) {
  for (int i = 0; i < net_param.layer_size(); ++i) {
    // Check if BatchNorm layers declare three parameters, as required by
    // the previous BatchNorm layer definition.
    if (net_param.layer(i).type() == "BatchNorm"
        && net_param.layer(i).param_size() == 3) {
      return true;
    }
  }
  return false;
}

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

bool UpgradeV1Net(const NetParameter& v1_net_param, NetParameter* net_param) {
  if (v1_net_param.layer_size() > 0) {
    LOG(FATAL) << "Refusing to upgrade inconsistent NetParameter input; "
        << "the definition includes both 'layer' and 'layers' fields. "
        << "The current format defines 'layer' fields with string type like "
        << "layer { type: 'Layer' ... } and not layers { type: LAYER ... }. "
        << "Manually switch the definition to 'layer' format to continue.";
  }
  bool is_fully_compatible = true;
  net_param->CopyFrom(v1_net_param);
  net_param->clear_layers();
  net_param->clear_layer();
  for (int i = 0; i < v1_net_param.layers_size(); ++i) {
    if (!UpgradeV1LayerParameter(v1_net_param.layers(i),
                                 net_param->add_layer())) {
      LOG(ERROR) << "Upgrade of input layer " << i << " failed.";
      is_fully_compatible = false;
    }
  }
  return is_fully_compatible;
}

void Net<Dtype>::copyTrainedLayerFrom(const NetParameter& param){
	int num_layers = param.layer_size();
	for (int i = 0; i < num_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()) continue;
		const vector < boost::shared_ptr<Blob<Dtype>>>& target_blobs = layers[target_layer_id]->getBlobs();
		for (int j = 0; j < target_blobs.size(); j++){
			Blob<Dtype> source_blob;
			source_blob.FromProto(source_layer.blobs(j));
			Blob<Dtype>* target_blob = target_blobs[j].get();
			CHECK(source_blob.shape() == target_blob->shape())
				<< "Incompatible shape when sharing trained params.";
			target_blob->FromProto(source_layer.blobs(j), false);
		}
	}
}

void ApolloNet<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();

    if (layers_map_.find(source_layer_name) == layers_map_.end()) {
      LOG(INFO) << "Ignoring source layer " << source_layer_name;
      continue;
    }

    LOG(INFO) << "Copying source layer " << source_layer_name;
    vector<shared_ptr<Blob<Dtype> > >& target_blobs =
        layers_map_[source_layer_name]->blobs();
      
    ASSERT(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) {
      const bool kReshape = false;
      target_blobs[j]->FromProto(source_layer.blobs(j), kReshape);
    }
  }
}

void InsertSplits(const NetParameter& param, NetParameter* param_split) {
  // Initialize by copying from the input NetParameter.
  param_split->CopyFrom(param);
  param_split->clear_layer();
  map<string, pair<int, int> > blob_name_to_last_top_idx;
  map<pair<int, int>, pair<int, int> > bottom_idx_to_source_top_idx;
  map<pair<int, int>, int> top_idx_to_bottom_count;
  map<pair<int, int>, float> top_idx_to_loss_weight;
  map<pair<int, int>, int> top_idx_to_bottom_split_idx;
  map<int, string> layer_idx_to_layer_name;
  for (int i = 0; i < param.layer_size(); ++i) {
    const LayerParameter& layer_param = param.layer(i);
    layer_idx_to_layer_name[i] = layer_param.name();
    for (int j = 0; j < layer_param.bottom_size(); ++j) {
      const string& blob_name = layer_param.bottom(j);
      if (blob_name_to_last_top_idx.find(blob_name) ==
          blob_name_to_last_top_idx.end()) {
        LOG(FATAL) << "Unknown bottom blob '" << blob_name << "' (layer '"
                   << layer_param.name() << "', bottom index " << j << ")";
      }
      const pair<int, int>& bottom_idx = make_pair(i, j);
      const pair<int, int>& top_idx = blob_name_to_last_top_idx[blob_name];
      bottom_idx_to_source_top_idx[bottom_idx] = top_idx;
      ++top_idx_to_bottom_count[top_idx];
    }
    for (int j = 0; j < layer_param.top_size(); ++j) {
      const string& blob_name = layer_param.top(j);
      blob_name_to_last_top_idx[blob_name] = make_pair(i, j);
    }
    // A use of a top blob as a loss should be handled similarly to the use of
    // a top blob as a bottom blob to another layer.
    const int last_loss =
        std::min(layer_param.loss_weight_size(), layer_param.top_size());
    for (int j = 0; j < last_loss; ++j) {
      const string& blob_name = layer_param.top(j);
      const pair<int, int>& top_idx = blob_name_to_last_top_idx[blob_name];
      top_idx_to_loss_weight[top_idx] = layer_param.loss_weight(j);
      if (top_idx_to_loss_weight[top_idx]) {
        ++top_idx_to_bottom_count[top_idx];
      }
    }
  }
  for (int i = 0; i < param.layer_size(); ++i) {
    LayerParameter* layer_param = param_split->add_layer();
    layer_param->CopyFrom(param.layer(i));
    // Replace any shared bottom blobs with split layer outputs.
    for (int j = 0; j < layer_param->bottom_size(); ++j) {
      const pair<int, int>& top_idx =
          bottom_idx_to_source_top_idx[make_pair(i, j)];
      const int split_count = top_idx_to_bottom_count[top_idx];
      if (split_count > 1) {
        const string& layer_name = layer_idx_to_layer_name[top_idx.first];
        const string& blob_name = layer_param->bottom(j);
        layer_param->set_bottom(j, SplitBlobName(layer_name,
            blob_name, top_idx.second, top_idx_to_bottom_split_idx[top_idx]++));
      }
    }
    // Create split layer for any top blobs used by other layer as bottom
    // blobs more than once.
    for (int j = 0; j < layer_param->top_size(); ++j) {
      const pair<int, int>& top_idx = make_pair(i, j);
      const int split_count = top_idx_to_bottom_count[top_idx];
      if (split_count > 1) {
        const string& layer_name = layer_idx_to_layer_name[i];
        const string& blob_name = layer_param->top(j);
        LayerParameter* split_layer_param = param_split->add_layer();
        const float loss_weight = top_idx_to_loss_weight[top_idx];
        ConfigureSplitLayer(layer_name, blob_name, j, split_count,
            loss_weight, split_layer_param);
        if (loss_weight) {
          layer_param->clear_loss_weight();
          top_idx_to_bottom_split_idx[top_idx]++;
        }
      }
    }
  }
}

void insertSplits(const NetParameter& param, NetParameter* splitted_param){
	splitted_param->CopyFrom(param);
	splitted_param->clear_layer();
	//	pair<layer_idx,blob_idx>
	map<string, pair<int, int> > blob_name_to_last_top_idx;
	map<pair<int, int>, pair<int, int> > bottom_idx_to_source_top_idx;
	map<pair<int, int>, int> top_idx_to_bottom_count;
	map<pair<int, int>, float> top_idx_to_loss_weight;
	map<pair<int, int>, int> top_idx_to_bottom_split_idx;
	map<int, string> layer_idx_to_layer_name;
	layer_idx_to_layer_name[-1] = "input";
	//	scan and stuff all input blobs into a virtual layer named as "input" at -1
	//  input blobs do not belong to any layers and we stuff them into a virtual layer
	//	usually use for viewing a Net(e.g: examples\cifar10\cifar10_full.prototxt
	//	input: "data"      ***  ¡û_¡û  specify it as a temporary data blob ***
	//	input_shape{	   ***  ¡û_¡û  specify it as shape***
	//		dim: 1
	//		dim : 3
	//		dim : 32
	//		dim : 32
	//	}
	//	pay attention: input blobs should not use in train/test prototxt
	//	because they are not specified vaild data sources
	//	you can regard them as viewing toys
	for (int i = 0; i < param.input_size(); i++){
		const string& blob_name = param.input(i);
		blob_name_to_last_top_idx[blob_name] = make_pair(-1, i);
	}
	for (int i = 0; i < param.layer_size(); i++){
		const LayerParameter& layer_param = param.layer(i);
		//	bind layer idx to layer name
		layer_idx_to_layer_name[i] = layer_param.name();
		//	a layer has several bottom blobs(e.g DataLayer)
		for (int j = 0; j < layer_param.bottom_size(); j++){
			const string& blob_name = layer_param.bottom(j);
			//	ensure that all bottom blobs must have the same name as one top blob
			if (!blob_name_to_last_top_idx.count(blob_name)){
				LOG(FATAL) << "Unknown bottom blob: " << blob_name
					<< " at layer: " << layer_param.name() << ".";
			}
			const pair<int, int>& bottom_idx = make_pair(i, j);
			const pair<int, int>& top_idx = blob_name_to_last_top_idx[blob_name];
			//	a bottom's name must be as same as one top's name
			//	find a bottom's parent top (<- backward direction)
			//	note that top name must declare before bottom name
			//	or a bottom will bind to layer_{-1}
			bottom_idx_to_source_top_idx[bottom_idx] = top_idx;
			top_idx_to_bottom_count[top_idx]++;
		}
		// update top name's position for following bottom names
		for (int j = 0; j < layer_param.top_size(); j++){
			const string& blob_name = layer_param.top(j);
			blob_name_to_last_top_idx[blob_name] = make_pair(i, j);
		}
		const int last_loss = min(layer_param.loss_weight_size(), layer_param.top_size());
		//	only work in LossLayer
		for (int j = 0; j < last_loss; j++){
			const string& blob_name = layer_param.top(j);
			//	updated before
			const pair<int, int>& top_idx = blob_name_to_last_top_idx[blob_name];
			top_idx_to_loss_weight[top_idx] = layer_param.loss_weight(j);
			//	from loss(top) backward to bottom 
			if (top_idx_to_loss_weight[top_idx]) top_idx_to_bottom_count[top_idx]++;
		}
	}
	//	special case: data blob shared by other blobs in the virtual layer
	//	split it also
	for (int i = 0; i < param.input_size(); i++){
		const int split_count = top_idx_to_bottom_count[make_pair(-1, i)];
		if (split_count > 1){
			//	"input"
			const string& layer_name = layer_idx_to_layer_name[-1];
			const string& blob_name = param.input(i);
			//	push_back a new param
			LayerParameter* split_layer_param = splitted_param->add_layer();
			const float kZeroLossWeight = 0;
			configureSplitLayer(layer_name, blob_name, i, split_count, kZeroLossWeight, split_layer_param);
		}
	}
	for (int i = 0; i < param.layer_size(); i++){
		//	push_back a new param
		LayerParameter* layer_param = splitted_param->add_layer();
		layer_param->CopyFrom(param.layer(i));
		for (int j = 0; j < layer_param->bottom_size(); j++){
			//  call the top before bottom
			const pair<int, int>& top_idx = bottom_idx_to_source_top_idx[make_pair(i, j)];
			//	check top's count
			const int split_count = top_idx_to_bottom_count[top_idx];
			if (split_count > 1){
				// previous layer_name
				const string& layer_name = layer_idx_to_layer_name[top_idx.first];
				const string& blob_name = layer_param->bottom(j);
				// e.g: conv1 => conv1_conv1_0_split_0
				//	once used then ++ for next
				layer_param->set_bottom(j, splitBlobName(layer_name, blob_name, top_idx.second, 
					top_idx_to_bottom_split_idx[top_idx]++));
			}
		}
		for (int j = 0; j < layer_param->top_size(); j++){
			const pair<int, int>& top_idx = make_pair(i, j);
//.........这里部分代码省略.........