caffe.proto

//Author Binghui Chen
//add these below to your own LayerParameter
message LayerParameter {
optional NormalizationParameter normalization_param = 161;
optional BIERLossParameter bier_loss_param = 162;
optional RankParameter rank_param = 163;
optional NpairLossParameter npair_loss_param = 164;
optional EnergyConfusionLossParameter energy_confusion_loss_param = 165;
}



message RankParameter {//for triplet loss
  optional uint32 neg_num = 1 [default = 1]; //means how many negative pair you want for each positive pair, if it is 4, that means there are 4 triplets
  optional uint32 pair_size = 2 [default = 1]; //just means inputs are pairs of images
  optional float hard_ratio = 3; //
  optional float rand_ratio = 4;//
  optional float margin = 5 [default = 0.5];//
}
message NpairLossParameter {//for npair
  // margin for dissimilar pair
 optional int32  pic_num = 1;//no use
  optional int32 categories = 2;//no use
  optional int32 kcenter = 3;//m*n combination: set to n-1
  optional float margin = 4 [default = 1.0];//no use
  optional int32 kcenter_n = 5;//(m-1)*n
  optional float coeff = 6 [default = 1];//x_norm
  optional float k = 7 [default = 1];//margin constraint strength
}
message BIERLossParameter {//BIER based on binomial deviance loss
  optional float alpha = 1 [default = 2];
  optional float beta = 2 [default = 0.5];
  optional float cost_p = 3 [default = 1];//for positive cost
  optional float cost_n = 4 [default = 25];//for negative cost
  optional float shrinkage =5 [default = 0.06];//for simpler variant of boosting algorithm
}
message NormalizationParameter {
    enum Norm {
      L1 = 1;
      L2 = 2;
    }
    // Specify the Norm to use L1 or L2
    optional Norm norm = 1 [default = L2];
}
message EnergyConfusionLossParameter {//binomial deviance loss
  optional int32 random_num = 1 [default = 10];// for each sample
}
message InnerProductParameter {
  optional uint32 num_output = 1; // The number of outputs for the layer
  optional bool bias_term = 2 [default = true]; // whether to have bias terms
  optional FillerParameter weight_filler = 3; // The filler for the weight
  optional FillerParameter bias_filler = 4; // The filler for the bias

  // The first axis to be lumped into a single inner product computation;
  // all preceding axes are retained in the output.
  // May be negative to index from the end (e.g., -1 for the last axis).
  optional int32 axis = 5 [default = 1];
  // Specify whether to transpose the weight matrix or not.
  // If transpose == true, any operations will be performed on the transpose
  // of the weight matrix. The weight matrix itself is not going to be transposed
  // but rather the transfer flag of operations will be toggled accordingly.
  optional bool transpose = 6 [default = false];
  optional bool bier_init = 7 [default = false];//for iccv17 BIER loss initialization. only .cu version
  optional float lamda = 8 [default = 100.0];//for iccv17 BIER loss initialization. only .cu version
  optional bool param_propagate_down = 9 [default = true];//
}