caffe layer loss_weight_size

Question

私はcaffe層に関するコードを読んでいたときに、私はloss_weight_size()とadd_loss_weight()のような機能を見つけました。私はプロジェクト全体を検索し、これらの関数の定義を見つけませんでした。では、これらの機能は何ですか？

template <typename Dtype> 
void LossLayer<Dtype>::LayerSetUp(
    const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) { 
    // LossLayers have a non-zero (1) loss by default. 
    if (this->layer_param_.loss_weight_size() == 0) { 
    this->layer_param_.add_loss_weight(Dtype(1)); 
    } 
} 

layer_param現在のクラスのメンバーである、それは、プロト・ファイルで定義されています。あなたがよくadd_loss_weight()機能がcaffe.protoで定義されてrepeated float loss_weight;句に関連しているに気づいたよう

message LayerParameter { 
    optional string name = 1; // the layer name 
    optional string type = 2; // the layer type 
    repeated string bottom = 3; // the name of each bottom blob 
    repeated string top = 4; // the name of each top blob 

    // The train/test phase for computation. 
    optional Phase phase = 10; 

    // The amount of weight to assign each top blob in the objective. 
    // Each layer assigns a default value, usually of either 0 or 1, 
    // to each top blob. 
    repeated float loss_weight = 5; 

    // Specifies training parameters (multipliers on global learning constants, 
    // and the name and other settings used for weight sharing). 
    repeated ParamSpec param = 6; 

    // The blobs containing the numeric parameters of the layer. 
    repeated BlobProto blobs = 7; 

    // Specifies whether to backpropagate to each bottom. If unspecified, 
    // Caffe will automatically infer whether each input needs backpropagation 
    // to compute parameter gradients. If set to true for some inputs, 
    // backpropagation to those inputs is forced; if set false for some inputs, 
    // backpropagation to those inputs is skipped. 
    // 
    // The size must be either 0 or equal to the number of bottoms. 
    repeated bool propagate_down = 11; 

    // Rules controlling whether and when a layer is included in the network, 
    // based on the current NetState. You may specify a non-zero number of rules 
    // to include OR exclude, but not both. If no include or exclude rules are 
    // specified, the layer is always included. If the current NetState meets 
    // ANY (i.e., one or more) of the specified rules, the layer is 
    // included/excluded. 
    repeated NetStateRule include = 8; 
    repeated NetStateRule exclude = 9; 

    // Parameters for data pre-processing. 
    optional TransformationParameter transform_param = 100; 

    // Parameters shared by loss layers. 
    optional LossParameter loss_param = 101; 

    // Layer type-specific parameters. 
    // 
    // Note: certain layers may have more than one computational engine 
    // for their implementation. These layers include an Engine type and 
    // engine parameter for selecting the implementation. 
    // The default for the engine is set by the ENGINE switch at compile-time. 
    optional AccuracyParameter accuracy_param = 102; 
    optional ArgMaxParameter argmax_param = 103; 
    optional BatchNormParameter batch_norm_param = 139; 
    optional BiasParameter bias_param = 141; 
    optional ConcatParameter concat_param = 104; 
    optional ContrastiveLossParameter contrastive_loss_param = 105; 
    optional ConvolutionParameter convolution_param = 106; 
    optional CropParameter crop_param = 144; 
    optional DataParameter data_param = 107; 
    optional DropoutParameter dropout_param = 108; 
    optional DummyDataParameter dummy_data_param = 109; 
    optional EltwiseParameter eltwise_param = 110; 
    optional ELUParameter elu_param = 140; 
    optional EmbedParameter embed_param = 137; 
    optional ExpParameter exp_param = 111; 
    optional FlattenParameter flatten_param = 135; 
    optional HDF5DataParameter hdf5_data_param = 112; 
    optional HDF5OutputParameter hdf5_output_param = 113; 
    optional HingeLossParameter hinge_loss_param = 114; 
    optional ImageDataParameter image_data_param = 115; 
    optional InfogainLossParameter infogain_loss_param = 116; 
    optional InnerProductParameter inner_product_param = 117; 
    optional InputParameter input_param = 143; 
    optional LogParameter log_param = 134; 
    optional LRNParameter lrn_param = 118; 
    optional MemoryDataParameter memory_data_param = 119; 
    optional MVNParameter mvn_param = 120; 
    optional PoolingParameter pooling_param = 121; 
    optional PowerParameter power_param = 122; 
    optional PReLUParameter prelu_param = 131; 
    optional PythonParameter python_param = 130; 
    optional ReductionParameter reduction_param = 136; 
    optional ReLUParameter relu_param = 123; 
    optional ReshapeParameter reshape_param = 133; 
    optional ScaleParameter scale_param = 142; 
    optional SigmoidParameter sigmoid_param = 124; 
    optional SoftmaxParameter softmax_param = 125; 
    optional SPPParameter spp_param = 132; 
    optional SliceParameter slice_param = 126; 
    optional TanHParameter tanh_param = 127; 
    optional ThresholdParameter threshold_param = 128; 
    optional TileParameter tile_param = 138; 
    optional WindowDataParameter window_data_param = 129; 
} 

Answer 1

。
Googleプロトコルバッファはcaffe.protoをコンパイルし、protobufメッセージを処理するためのC++呼び出しを生成します。 protobuf C++インターフェイスhereについての詳しい情報があります。

loss_weightについては、caffe.protoを見ると、floatのフィールドがrepeatedと定義されています。 reference for Repeated Numeric FieldsあなたがそのいるProtobufが仮に

int loss_weight_size() const; // Returns the number of elements currently in the field 
void add_foo(int32 value); // Appends a new element to the field with the given value.