C++ NetParameter::layer方法代码示例

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;
}

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);
	}
}

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;
	
	
}

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->count(blob_name))
		LOG(FATAL) << "Unknown bottom blob: " << blob_name<< " at layer: " << layer_param.name() << ".";
	//	a bottom blob must share a top blob
	const int blob_id = (*blob_name_to_idx)[blob_name];
	LOG_IF(INFO, Dragon::get_root_solver())
		<< layer_param.name() << "[Layer-Accept] <- " << blob_name << " [Blob-Name]";
	bottom_vecs[layer_id].push_back(blobs[blob_id].get());
	bottom_id_vecs[layer_id].push_back(blob_id);
	//	ensure that a top blob must specify only one bottom blob
	//	SplitLayer can be used to shadow a top blob into several top blobs
	available_blobs->erase(blob_name);
	bool need_bp = true;
	//	default(TEST) is false
	bottoms_need_backward[layer_id].push_back(need_bp & blobs_need_backward[blob_id]);
	return blob_id;
}

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 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){
	boost::shared_ptr<LayerParameter> layer_param(
		layer_id >= 0 ? new LayerParameter(param.layer(layer_id)) : NULL);
	//	use (layer_id//top_id) or (-1//top_id) to get a blob name
	const string& blob_name = layer_param ?
		(top_id<layer_param->top_size() ? layer_param->top(top_id) : "(automatic)") : param.input(top_id);
	//	in-place case (e.g:
	//	I0721 10:38 : 16.722070  4692 net.cpp : 84] relu1 <-conv1
	//	I0721 10:38 : 16.722082  4692 net.cpp : 98] relu1->conv1(in-place)
	//	check a blob whether at the same postion in both bottom and top
	if (blob_name_to_idx && layer_param && top_id < layer_param->bottom_size()
		&& blob_name == layer_param->bottom(top_id)){
		LOG_IF(INFO, Dragon::get_root_solver())
			<< layer_param->name() << "[Layer-Produce]->" << blob_name << " [Blob-Name] (in-place)";
		//	add into this layer's top blob using blob_name
		top_vecs[layer_id].push_back(blobs[(*blob_name_to_idx)[blob_name]].get());
		//	log the id
		top_id_vecs[layer_id].push_back((*blob_name_to_idx)[blob_name]);
	}
	else if (blob_name_to_idx && (*blob_name_to_idx).count(blob_name) ){
		LOG(FATAL) << "Top blob:" << blob_name << " propogate from multiple sources.";
	}
	// normal top blob stuffing
	else{
		//	debug info
		if (Dragon::get_root_solver()){
			if (layer_param) LOG(INFO) << layer_param->name() << "[Layer-Produce] ->" << blob_name << " [Blob-Name]";
			//	special case and only used when viewing a Net's structure
			//	because they need not specify data source and can not train or test
			//	virtual data input blobs do not belong to any layers
			//	see more in insert_splits.cpp/void InsertSplits()
			else LOG(INFO) << "Input " << top_id << "[Blob-Code] -> " << blob_name << "[Blob - Name]";
		}
		//	allocate a null blob at first
		boost::shared_ptr<Blob<Dtype>> ptr_blob(new Blob<Dtype>());
		//	store global blob infos
		const int blob_id = blobs.size();
		blobs.push_back(ptr_blob);
		blobs_name.push_back(blob_name);
		blobs_need_backward.push_back(false);
		//	encode index number for a name
		//	which also represent this top blob is binded from a bottom
		//	check it before can know whether a top blob has multiple sources(Forbidden)
		if (blob_name_to_idx) (*blob_name_to_idx)[blob_name] = blob_id;
		//	reshape for virtual input blobs solely
		//	becaude they do not exist into a DataLayer(provide reshape/transfrom service)
		if (layer_id == -1){
			ptr_blob->reshape(param.input_shape(top_id));
			//	store solely for virtual input blobs
			net_input_blobs.push_back(ptr_blob.get());
			net_input_blob_indices.push_back(blob_id);
		}
		else{
			top_vecs[layer_id].push_back(ptr_blob.get());
			top_id_vecs[layer_id].push_back(blob_id);
		}
	}
	//	a set used for listing all exsiting top blobs
	if (available_blobs) available_blobs->insert(blob_name);
}

void Net<Dtype>::appendParam(const NetParameter& param, const int layer_id, const int param_id){
	const LayerParameter& layer_param = param.layer(layer_id);
	Layer<Dtype>* layer = layers[layer_id].get();
	const int param_size = layer_param.param_size();
	//	default name="" (not set)
	string param_name = param_id<param_size? layer_param.param(param_id).name() : "";
	//	has name
	if (param_name.size()) param_display_names.push_back(param_name);
	//	set param_id as name
	else{
		ostringstream display_name;
		display_name << param_id;
		param_display_names.push_back(display_name.str());
	}
	//	each param blob has a net id(both weight and bias)
	const int net_param_id = param_blobs.size();
	//	add param blob which can be used by a net id
	param_blobs.push_back(layer->getBlobs()[param_id]);
	//	store a net id
	//	param_id_vecs[layer_id][param_id] can get the net_param_id
	param_id_vecs[layer_id].push_back(net_param_id);
	//	store orginal id ( x_th layer/ y_th param )
	//	param_layer_indices[net_param_id] can get layer_id/param_id
	param_layer_indices.push_back(make_pair(layer_id, param_id));
	ParamSpec default_hyperparameter;
	const ParamSpec* hyperparameter = param_id < param_size ?
		&layer_param.param(param_id) : &default_hyperparameter;
	//	do not have a name or
	if (!param_size || !param_name.size() ||
		(param_name.size() && !param_names_index.count(param_name))){
		param_owners.push_back(-1);
		//	has a name(non-empty) but has not logged before
		if (param_name.size()) param_names_index[param_name] = net_param_id;
		const int learnable_param_id = learnable_params.size();
		learnable_params.push_back(param_blobs[net_param_id].get());
		learnable_param_ids.push_back(learnable_param_id);
		has_params_lr.push_back(hyperparameter->has_lr_mult());
		has_params_decay.push_back(hyperparameter->has_decay_mult());
		params_lr.push_back(hyperparameter->lr_mult());
		params_decay.push_back(hyperparameter->decay_mult());
	}
	else{
		//	has a name(non-empty) and has logged before
		//	it means to share this param and we need get the owner id
		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_IF(INFO, Dragon::get_root_solver())
			<< "Share parameter: " << param_name << " ownd by layer: "
			<< layer_names[owner_layer_id] << " param index: " << owner_layer_id;
		Blob<Dtype>* this_blob = param_blobs[net_param_id].get();
		Blob<Dtype>* owner_blob = param_blobs[owner_net_param_id].get();
		CHECK(this_blob);CHECK(owner_blob);
		//	check before sharing
		if (layer_param.param(param_id).share_mode() == ParamSpec_DimCheckMode_PERMISSIVE_MODE)
			CHECK_EQ(this_blob->count(), owner_blob->count());
		else CHECK(this_blob->shape() == owner_blob->shape());
		//	note that learnable_param_id = owner_net_param_id
		const int learnable_param_id = learnable_param_ids[owner_net_param_id];
		//	store parent id
		learnable_param_ids.push_back(learnable_param_id);
		//	check lr_mult
		if (hyperparameter->has_lr_mult()){
			if (has_params_lr[learnable_param_id])
				CHECK_EQ(hyperparameter->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] = hyperparameter->lr_mult();
			}
		}
		//	check decay_mult
		if (hyperparameter->has_decay_mult()){
			if (has_params_decay[learnable_param_id])
				CHECK_EQ(hyperparameter->decay_mult(), params_decay[learnable_param_id])
				<< "Shared param: " << param_name << " has mismatched decay_mult.";
			else{
				has_params_decay[learnable_param_id] = true;
				params_decay[learnable_param_id] = hyperparameter->decay_mult();
			}
		}
	}
}

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.........