【caffe源码研究】第三章：源码篇(6) ：caffe.proto

【caffe源码研究】第三章：源码篇(6) ：caffe.proto

caffe使用protobuf来定义网络结构、参数等。这里介绍一下caffe.proto里面核心的部分。

Blob

先看Blob相关的protobuf

message BlobShape {  
  //数据块形状定义为Num×Channel×Height×Wight原因在于caffe基于容器的多维嵌套  
  //来实现高维数据的封装。即vector(N)>。  
  repeated int64 dim = 1 [packed = true];  
}  

// 数据块{形状，数据，微分}  
message BlobProto {  
  optional BlobShape shape = 7;  
  repeated float data = 5 [packed = true];  
  repeated float diff = 6 [packed = true];  
  repeated double double_data = 8 [packed = true];  
  repeated double double_diff = 9 [packed = true];  

  //数据4D形状 -- 旧版本，已使用"BlobShape shape"代替：  
  optional int32 num = 1 [default = 0]; //样本  
  optional int32 channels = 2 [default = 0];  
  optional int32 height = 3 [default = 0];  
  optional int32 width = 4 [default = 0];  
}  

// 存放多个BlobProto实例的对应Index，易于引用  
message BlobProtoVector {  
  repeated BlobProto blobs = 1;  
}  

 

Datum

// 数据:{C,H,W,data(uchar&float),label} 图像样本  
message Datum {  
  optional int32 channels = 1;  
  optional int32 height = 2;  
  optional int32 width = 3;  
  // the actual image data, in bytes  
  optional bytes data = 4;  
  optional int32 label = 5;  
  // Optionally, the datum could also hold float data.  
  repeated float float_data = 6;  
  // If true data contains an encoded image that need to be decoded  
  optional bool encoded = 7 [default = false];  
}   

 

FillerParameter

//滤波器参数{Type(const|uniform|gauss),}  
message FillerParameter {  
  // The filler type.  
  optional string type = 1 [default = \'constant\'];  
  optional float value = 2 [default = 0]; // the value in constant filler  
  optional float min = 3 [default = 0]; // the min value in uniform filler  
  optional float max = 4 [default = 1]; // the max value in uniform filler  
  optional float mean = 5 [default = 0]; // the mean value in Gaussian filler  
  optional float std = 6 [default = 1]; // the std value in Gaussian filler  
  // 给定输入与权值相乘后应该得到非零输出，默认值-1意为不稀疏化高斯模板。  
  optional int32 sparse = 7 [default = -1];  
  // Normalize the filler variance by fan_in, fan_out, or their average.  
  // Applies to \'xavier\' and \'msra\' fillers.（扇入，扇出）  
  // 通过fanIn,fanOut,及其均值来归一化填充值的方差，有“xavier法”或“msra法”  
  enum VarianceNorm {  
    FAN_IN = 0;  
    FAN_OUT = 1;  
    AVERAGE = 2;  
  }  
  optional VarianceNorm variance_norm = 8 [default = FAN_IN];  
}

 

NetParameter

//网络参数{网名，输入参数，数据块形状，forceBack，NetState,debugInfo,}  
message NetParameter {  
  optional string name = 1; // consider giving the network a name  
  // 旧版--输入网络的数据块Blobs; 改为新版--InputParameter  
  repeated string input = 3;  
  // DEPRECATED. See InputParameter. The shape of the input blobs.  
  // 旧版--输入的Blobs的形状； 改为新版--InputerParameter  
  repeated BlobShape input_shape = 8;  

  // 指定Blobs的4D输入形状 -- 已改为新版：input_shape代替  
  // 如要使用旧版，对每个输入的blob都需要指定4个参数，Num×Cha×H×W  
  // 因此 input_dim需要重复4次  
  repeated int32 input_dim = 4;  

  //确定网络是否要让每个层都强制反向传播。  
  //如果设置为false,将根据网络结构和学习率来自动确定是否需要反向传播。  
  //网络的当前状态"state"包括"phase","level","stage"。
  //某些层需要设置phase属性，使其跳过网络运行时的某些状态.  
  optional NetState state = 6;  

  // 当运行Net::Forward/Backward/Update时，打印调试信息，默认false.  
  optional bool debug_info = 7 [default = false];  

  // 构成net的layers。每个layer的链接和行为通过LayerParameter配置。  
  repeated LayerParameter layer = 100;  // ID 100 so layers are printed last.  

  // DEPRECATED: use \'layer\' instead.  
  repeated V1LayerParameter layers = 2;  
} 

 

SolverParameter

// NOTE：注意  
// Update the next available ID when you add a new SolverParameter field.  
// 当你添加一个新的SolverParameter属性时，需要更新下一个可获得的ID  
// SolverParameter next available ID: 41 (last added: type)  

//求解器参数{网络，}  
message SolverParameter {  
  //////////////////////////////////////////////////////////////////////////////  
  // Specifying the train and test networks  
  //  
  // Exactly one train net must be specified using one of the following fields:  
  //     train_net_param, train_net, net_param, net  
  // One or more test nets may be specified using any of the following fields:  
  //     test_net_param, test_net, net_param, net  
  // If more than one test net field is specified (e.g., both net and  
  // test_net are specified), they will be evaluated in the field order given  
  // above: (1) test_net_param, (2) test_net, (3) net_param/net.  
  // A test_iter must be specified for each test_net.  
  // A test_level and/or a test_stage may also be specified for each test_net.  
  //////////////////////////////////////////////////////////////////////////////  
  //指定网络，可有以下的多种形式  
  // Proto filename for the train net, possibly combined with one or more  
  // test nets.  
  optional string net = 24;  
  // Inline train net param, possibly combined with one or more test nets.  
  optional NetParameter net_param = 25;  

  optional string train_net = 1; // Proto filename for the train net.  
  repeated string test_net = 2; // Proto filenames for the test nets.  
  optional NetParameter train_net_param = 21; // Inline train net params.  
  repeated NetParameter test_net_param = 22; // Inline test net params.  

 // 指定网络状态  
 // The states for the train/test nets. Must be unspecified or  
  // specified once per net.  
  //  
  // By default, all states will have solver = true;  
  // train_state will have phase = TRAIN,  
  // and all test_state\'s will have phase = TEST.  
  // Other defaults are set according to the NetState defaults.  
  optional NetState train_state = 26;  
  repeated NetState test_state = 27;  

  //测试迭代批次数：  
  //合理设置可使得测试遍历完全部测试样本  
  //合理值 = 测试样本总数/每批次测试数 = totalTestSamples/batchSize  
  repeated int32 test_iter = 3;  

  //训练迭代批次数：  
  //两次测试之间所经历的训练迭代次数:合理设置可使得训练遍历完全部训练样本  
  //合理值 = 训练样本总数/每批次训练数 = totalTrainSamples/batchSize  
  optional int32 test_interval = 4 [default = 0];  
  //训练test_interval个批次，再测试test_iter个批次，为一个回合(epoch)  
  //合理设置应使得每个回合内，遍历覆盖到全部训练样本和测试样本  

  //默认不计算测试时损失  
  optional bool test_compute_loss = 19 [default = false];  

  // 如设置为真，则在训练前运行一次测试，以确保内存足够，并打印初始损失值  
  optional bool test_initialization = 32 [default = true];  
  // 基本学习速率  
  optional float base_lr = 5; // The base learning rate  
  // 打印信息的遍历间隔，遍历多少个批次打印一次信息。设置为0则不打印。  
  optional int32 display = 6;  
  // Display the loss averaged over the last average_loss iterations  
  // 打印最后一个迭代批次下的平均损失（？）  
  optional int32 average_loss = 33 [default = 1];  
  // 训练最大迭代次数  
  optional int32 max_iter = 7;  
  // accumulate gradients over `iter_size` x `batch_size` instances  
  // 累积梯度误差基于“iter_size×batchSize”个样本实例  
  // “批次数×批量数”=“遍历的批次数×每批的样本数”个样本实例  
  optional int32 iter_size = 36 [default = 1];  

  //学习率衰减策略(7种)  
  // The learning rate decay policy. The currently implemented learning rate  
  // policies are as follows:  
  //    - fixed: always return base_lr.  
  //    - step: return base_lr * gamma ^ (floor(iter / step))  
  //    - exp: return base_lr * gamma ^ iter  
  //    - inv: return base_lr * (1 + gamma * iter) ^ (- power)  
  //    - multistep: similar to step butallows non uniform steps defined by  
  //      stepvalue  
  //    - poly: the effective learning rate follows a polynomial decay, to be  
  //      zero by the max_iter. return base_lr (1 - iter/max_iter) ^ (power)  
  //    - sigmoid: the effective learning rate follows a sigmod decay  
  //      return base_lr ( 1/(1 + exp(-gamma * (iter - stepsize))))  
  //  
  // 在上述参数中，base_lr, max_iter, gamma, step, stepvalue and power 被定义  
  // 在solver.prototxt文件中，iter是当前迭代次数。  
  optional string lr_policy = 8; //学习率调节策略  
  optional float gamma = 9; // The parameter to compute the learning rate.  
  optional float power = 10; // The parameter to compute the learning rate.  
  optional float momentum = 11; // The momentum value.动量  
  optional float weight_decay = 12; // The weight decay.权值衰减系数  
  //由权值衰减系数所控制的正则化类型：L1或L2范数，默认L2  
  optional string regularization_type = 29 [default = "L2"];  
  //"step"策略下，学习率的步长值  
  optional int32 stepsize = 13;  
  //"multistep"策略下的步长值  
  repeated int32 stepvalue = 34;  

  //设置梯度裁剪阈值为>=0，当其实际L2范数超出此值时(?)  
  optional float clip_gradients = 35 [default = -1];  

  //快照间隔，遍历多少次对模型和求解器状态保存一次  
  optional int32 snapshot = 14 [default = 0]; // The snapshot interval  
  optional string snapshot_prefix = 15; // The prefix for the snapshot.  
  //是否对diff快照，有助调试，但最终的protocol buffer尺寸会很大  
  optional bool snapshot_diff = 16 [default = false];  
  //快照数据保存格式{hdf5,binaryproto(默认)}  
  enum SnapshotFormat {  
    HDF5 = 0;  
    BINARYPROTO = 1;  
  }  
  optional SnapshotFormat snapshot_format = 37 [default = BINARYPROTO];  
  // the mode solver will use: 0 for CPU and 1 for GPU. Use GPU in default.  
  enum SolverMode {  
    CPU = 0;  
    GPU = 1;  
  }  
  求解模式{GPU(device_id),CPU}  
  optional SolverMode solver_mode = 17 [default = GPU];  
  optional int32 device_id = 18 [default = 0];  
  //随机数种子，设为正则表示Solver会以此为随机数初始化caffe,可产生重复随机  
  //数，易于重复试验；设为默认-1代表使用系统时钟作为种子。  
  optional int64 random_seed = 20 [default = -1];  

  //求解器类型=SGD(默认)  
  optional string type = 40 [default = "SGD"];  

  // numerical stability for RMSProp, AdaGrad and AdaDelta and Adam  
  optional float delta = 31 [default = 1e-8];  
  // parameters for the Adam solver  
  optional float momentum2 = 39 [default = 0.999];  

  // RMSProp decay value  
  // MeanSquare(t) = rms_decay*MeanSquare(t-1) + (1-rms_decay)*SquareGradient(t)  
  optional float rms_decay = 38;  

  //若真，则打印网络状态信息，有助于调试问题  
  optional bool debug_info = 23 [default = false];  

  //若假，则不会在训练后保存快照  
  optional bool snapshot_after_train = 28 [default = true];  

  // DEPRECATED: old solver enum types, use string instead