caffe的caffe.proto

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经过前面“caffe的protocol buffer使用例子”的学习，对caffe.proto熟悉了。

看caffe源码先从这里开始吧。
它位于…\src\caffe\proto目录下，在这个文件夹下还有一个.pb.cc和一个.pb.h文件，这两个文件都是由caffe.proto编译而来的。
在caffe.proto中定义了很多结构化数据，包括：

BlobProto
Datum
FillerParameter
NetParameter
SolverParameter
SolverState
LayerParameter
ConcatParameter
ConvolutionParameter
DataParameter
DropoutParameter
HDF5DataParameter
HDF5OutputParameter
ImageDataParameter
InfogainLossParameter
InnerProductParameter
LRNParameter
MemoryDataParameter
PoolingParameter
PowerParameter
WindowDataParameter
V0LayerParameter

从caffe.proto编译而来的，无非就是一些关于这些数据结构（类）的标准化操作，比如：
void CopyFrom();//在ByteString中定义实现ByteString和字节数组/字符串互相转换函数
void MergeFrom();//用于合并
void Clear();
bool IsInitialized() const;
int ByteSize() const;

bool MergePartialFromCodedStream();//解码时可以调用C++接口ParseFromArray，编码时可以先调用C++接口ByteSize预先获得编码后的数据大小，让后动态分配内存后调用SerializeToArray进行编码即可。

void SerializeWithCachedSizes() const;//序列化打包
SerializeWithCachedSizesToArray() const;
int GetCachedSize()//打包后出来的大小
void SharedCtor();
void SharedDtor();
void SetCachedSize() const;

这一注释部分是来自人工智能研究网。

[cpp] view plain copy

//////////////////
caffe.proto文件注释，
caffe版本:MS-caffe-master github 2016.8.20
caffe版本:BVLC-caffe-master github 2016.8.20
//////////////////
syntax = "proto2";
package caffe;
// 数据块形状{指定Blob的形状或维度-4D}
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;
}
// 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];
}
//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];
}
//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;
}
// 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
enum SolverType {
SGD = 0;
NESTEROV = 1;
ADAGRAD = 2;
RMSPROP = 3;
ADADELTA = 4;
ADAM = 5;
}
// DEPRECATED: use type instead of solver_type
optional SolverType solver_type = 30 [default = SGD];
}
//对求解器状态进行快照的消息
message SolverState {
optional int32 iter = 1; // The current iteration
optional string learned_net = 2; // The file that stores the learned net.
repeated BlobProto history = 3; // The history for sgd solvers
optional int32 current_step = 4 [default = 0]; // The current step for learning rate
}
enum Phase {
TRAIN = 0;
TEST = 1;
}
//NetState{phase,level,stage}
message NetState {
optional Phase phase = 1 [default = TEST];
optional int32 level = 2 [default = 0];
repeated string stage = 3;
}
//网络状态规则{phases,levels,stages}
message NetStateRule {
//在NetState中设置phase值(TRAIN|TEST)，使其符合此规则
optional Phase phase = 1;
//设置layer中所使用的最小最大levels。使其不定义以满足忽视level的规则。
optional int32 min_level = 2;
optional int32 max_level = 3;
// Customizable sets of stages to include or exclude.
// The net must have ALL of the specified stages and NONE of the specified
// "not_stage"s to meet the rule.
// (Use multiple NetStateRules to specify conjunctions of stages.)
//可定制的stages集合，用于include或exclude在网络中。网络必须包含全
//部制定的"stages"或不包含全部制定的"not_stage"
repeated string stage = 4;
repeated string not_stage = 5;
}
// Specifies training parameters (multipliers on global learning constants,
// and the name and other settings used for weight sharing).
//指定训练参数(乘数及全局学习率常数)和其名称，以及其他用于权值共享的设置。
message ParamSpec {
// 设定参数blobs的名称--用于在层间共享参数，若无此需求则不用设计。
optional string name = 1;
//共享权重时是否需要其形状相同或仅仅数量相同，默认为形状相同
optional DimCheckMode share_mode = 2;
enum DimCheckMode {
// STRICT (default) 形状相同(num, channels, height, width)都匹配.
STRICT = 0;
// PERMISSIVE 数量相同
PERMISSIVE = 1;
}
// The multiplier on the global learning rate for this parameter.
// 全局学习率的乘数
optional float lr_mult = 3 [default = 1.0];
// The multiplier on the global weight decay for this parameter.
// 全局权值衰减系数的乘数
optional float decay_mult = 4 [default = 1.0];
}
//注意：
//当在LayerParameter中新增字段时，需要为其更新下一个可用ID。
//比如，最近新增了smooth_l1_loss_param层，则为其指定层专属ID:149。
//层参数{名称，类型，输入底，输出顶，阶段，损失加权系数，全局乘数，}
message LayerParameter {
optional string name = 1; // 类名称
optional string type = 2; // 类类型
repeated string bottom = 3; // the name of each bottom blob 输入blob名称
repeated string top = 4; // the name of each top blob 输出blob名称
// The train / test phase for computation. //阶段，运行时状态
optional Phase phase = 10;
//每层输出blob在目标损失函数中的加权系数，每层默认为0或1
repeated float loss_weight = 5;
//指定训练参数(全局学习率上的乘数lr_mrlt)
repeated ParamSpec param = 6;
// The blobs containing the numeric parameters of the layer.
//包含每层数值参数的blobs
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控制每层是否被包含在网络中，基于当前的NetState. 可使用非0数规则来
// include或exclude，但不能兼有。如果未指定include或exclude规则，则该层总是
// 被包含在内。
repeated NetStateRule include = 8;
repeated NetStateRule exclude = 9;
// 用于数据预处理的参数
optional TransformationParameter transform_param = 100;
// 由loss层共享的参数.
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.
// 层类型指定参数
// 注意：
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 ParameterParameter parameter_param = 145;
optional PoolingParameter pooling_param = 121;
optional PowerParameter power_param = 122;
optional PReLUParameter prelu_param = 131;
optional PythonParameter python_param = 130;
optional RecurrentParameter recurrent_param = 146;
optional ReductionParameter reduction_param = 136;
optional ReLUParameter relu_param = 123;
optional ReshapeParameter reshape_param = 133;
optional ROIPoolingParameter roi_pooling_param = 147;
optional ScaleParameter scale_param = 142;
optional SigmoidParameter sigmoid_param = 124;
optional SmoothL1LossParameter smooth_l1_loss_param = 148;
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;
optional MILDataParameter mil_data_param = 0x004d4944; //"MID"
optional MILParameter mil_param = 0x004d494c; //"MIL"
}
// 对数据层进行转换的参数
message TransformationParameter {
// 对data执行预处理，比如简单缩放,去均值。
optional float scale = 1 [default = 1];
// Specify if we want to randomly mirror data.//镜像
optional bool mirror = 2 [default = false];
// Specify if we would like to randomly crop an image.//随机裁剪
optional uint32 crop_size = 3 [default = 0];
// 指定均值文件或均值，二者不可兼有；在对应通道上减去此均值；
optional string mean_file = 4;
repeated float mean_value = 5;
// 强制转换图像为3通道彩色
optional bool force_color = 6 [default = false];
// 强制转换为灰度图
optional bool force_gray = 7 [default = false];
}
// Loss层参数
message LossParameter {
// 如果被指定，则忽略给定label的实例
optional int32 ignore_label = 1;
// 如何对loss层损失归一化，使其跨越"batches,spatial(H*W)"或其他维度。
// 目前仅仅在SoftmaxWithLoss层中实现。
// 归一化模式
enum NormalizationMode {
// 基于batchSize×spatialDim归一化.所设定的忽略标签将不被忽略。
FULL = 0;
// 基于输出位置的总数量(batchSize×H×W)归一化，不包括被忽视的标签。
// 若未设置被忽视标签，则其行为与FULL相同。
VALID = 1;
// Divide by the batch size.基于batchSize进行归一化。
BATCH_SIZE = 2;
// Do not normalize the loss.不归一化损失
NONE = 3;
}
optional NormalizationMode normalization = 3 [default = VALID];
// 旧版--新版如上所述。
// 若"normalization"被指定则忽略此参数；若未被指定，可设置下值为false
// 则基于batchSize归一化。
optional bool normalize = 2;
}
// Messages that store parameters used by individual layer types follow, in
// alphabetical order.
message AccuracyParameter {
// When computing accuracy, count as correct by comparing the true label to
// the top k scoring classes. By default, only compare to the top scoring
// class (i.e. argmax). //Topk正确率计算
optional uint32 top_k = 1 [default = 1];
// The "label" axis of the prediction blob, whose argmax corresponds to the
// predicted label -- may be negative to index from the end (e.g.,-1 for the
// last axis). For example, if axis == 1 and the predictions are
// (N x C x H x W), the label blob is expected to contain N*H*W ground truth
// labels with integer values in {0, 1, ..., C-1}.
// 预测blob的"label"轴--其最大值才对应于预测标签--的索引有可能从负值开始。
// 即: predicted_labels=argmax(predictions blob,label_axis)
// 比如axis==1,其预测blob为(N x C x H x W), 而标签blob被期望包含(N×H×W)个
// 真实标签，且标签值为{0,1,2...C-1}。
optional int32 axis = 2 [default = 1];
// If specified, ignore instances with the given label.
// 如果指定，则忽略给定标签的对应实例
optional int32 ignore_label = 3;
}
//输出最大化参数，对预测标签进行最大化
message ArgMaxParameter {
// If true produce pairs (argmax, maxval)
// 如果真，则产生(argmax,maxval)对
optional bool out_max_val = 1 [default = false];
optional uint32 top_k = 2 [default = 1];
// The axis along which to maximise -- may be negative to index from the
// end (e.g., -1 for the last axis).
// By default ArgMaxLayer maximizes over the flattened trailing dimensions
// for each index of the first / num dimension. ??
//
optional int32 axis = 3;
}
//拼接参数
message ConcatParameter {
// The axis along which to concatenate -- may be negative to index from the
// end (e.g., -1 for the last axis). Other axes must have the
// same dimension for all the bottom blobs.
// By default, ConcatLayer concatenates blobs along the "channels" axis (1).
optional int32 axis = 2 [default = 1];
// DEPRECATED: alias for "axis" -- does not support negative indexing.
optional uint32 concat_dim = 1 [default = 1];
}
//BatchNormParameter参数，源于论文batchNorm
message BatchNormParameter {
// If false, accumulate global mean/variance values via a moving average. If
// true, use those accumulated values instead of computing mean/variance
// across the batch.
optional bool use_global_stats = 1;
// How much does the moving average decay each iteration?
optional float moving_average_fraction = 2 [default = .999];
// Small value to add to the variance estimate so that we don't divide by
// zero.
optional float eps = 3 [default = 1e-5];
}
//偏置参数
message BiasParameter {
// The first axis of bottom[0] (the first input Blob) along which to apply
// bottom[1] (the second input Blob). May be negative to index from the end
// (e.g., -1 for the last axis).
//
// For example, if bottom[0] is 4D with shape 100x3x40x60, the output
// top[0] will have the same shape, and bottom[1] may have any of the
// following shapes (for the given value of axis):
// (axis == 0 == -4) 100; 100x3; 100x3x40; 100x3x40x60
// (axis == 1 == -3) 3; 3x40; 3x40x60
// (axis == 2 == -2) 40; 40x60
// (axis == 3 == -1) 60
// Furthermore, bottom[1] may have the empty shape (regardless of the value of
// "axis") -- a scalar bias.
optional int32 axis = 1 [default = 1];
// (num_axes is ignored unless just one bottom is given and the bias is
// a learned parameter of the layer. Otherwise, num_axes is determined by the
// number of axes by the second bottom.)
// The number of axes of the input (bottom[0]) covered by the bias
// parameter, or -1 to cover all axes of bottom[0] starting from `axis`.
// Set num_axes := 0, to add a zero-axis Blob: a scalar.
optional int32 num_axes = 2 [default = 1];
// (filler is ignored unless just one bottom is given and the bias is
// a learned parameter of the layer.)
// The initialization for the learned bias parameter.
// Default is the zero (0) initialization, resulting in the BiasLayer
// initially performing the identity operation.
optional FillerParameter filler = 3;
}
//对比度损失参数
message ContrastiveLossParameter {
// margin for dissimilar pair
optional float margin = 1 [default = 1.0];
// The first implementation of this cost did not exactly match the cost of
// Hadsell et al 2006 -- using (margin - d^2) instead of (margin - d)^2.
// legacy_version = false (the default) uses (margin - d)^2 as proposed in the
// Hadsell paper. New models should probably use this version.
// legacy_version = true uses (margin - d^2). This is kept to support /
// reproduce existing models and results
optional bool legacy_version = 2 [default = false];
}
//卷积参数
message ConvolutionParameter {
optional uint32 num_output = 1; // The number of outputs for the layer
optional bool bias_term = 2 [default = true]; // whether to have bias terms
// Pad, kernel size, and stride are all given as a single value for equal
// dimensions in all spatial dimensions, or once per spatial dimension.
repeated uint32 pad = 3; // The padding size; defaults to 0
repeated uint32 kernel_size = 4; // The kernel size
repeated uint32 stride = 6; // The stride; defaults to 1
// Factor used to dilate the kernel, (implicitly) zero-filling the resulting
// holes. (Kernel dilation is sometimes referred to by its use in the
// algorithme 脿 trous from Holschneider et al. 1987.)
repeated uint32 dilation = 18; // The dilation; defaults to 1
// For 2D convolution only, the *_h and *_w versions may also be used to
// specify both spatial dimensions.
optional uint32 pad_h = 9 [default = 0]; // The padding height (2D only)
optional uint32 pad_w = 10 [default = 0]; // The padding width (2D only)
optional uint32 kernel_h = 11; // The kernel height (2D only)
optional uint32 kernel_w = 12; // The kernel width (2D only)
optional uint32 stride_h = 13; // The stride height (2D only)
optional uint32 stride_w = 14; // The stride width (2D only)
optional uint32 group = 5 [default = 1]; // The group size for group conv
optional FillerParameter weight_filler = 7; // The filler for the weight
optional FillerParameter bias_filler = 8; // The filler for the bias
enum Engine {
DEFAULT = 0; //CPU
CAFFE = 1; //GPU-CUDA
CUDNN = 2; //GPU-CUDA-CUDNN
}
optional Engine engine = 15 [default = DEFAULT];
// The axis to interpret as "channels" when performing convolution.
// Preceding dimensions are treated as independent inputs;
// succeeding dimensions are treated as "spatial".
// With (N, C, H, W) inputs, and axis == 1 (the default), we perform
// N independent 2D convolutions, sliding C-channel (or (C/g)-channels, for
// groups g>1) filters across the spatial axes (H, W) of the input.
// With (N, C, D, H, W) inputs, and axis == 1, we perform
// N independent 3D convolutions, sliding (C/g)-channels
// filters across the spatial axes (D, H, W) of the input.
optional int32 axis = 16 [default = 1];
// Whether to force use of the general ND convolution, even if a specific
// implementation for blobs of the appropriate number of spatial dimensions
// is available. (Currently, there is only a 2D-specific convolution
// implementation; for input blobs with num_axes != 2, this option is
// ignored and the ND implementation will be used.)
optional bool force_nd_im2col = 17 [default = false];
}
//裁剪参数
message CropParameter {
// To crop, elements of the first bottom are selected to fit the dimensions
// of the second, reference bottom. The crop is configured by
// - the crop `axis` to pick the dimensions for cropping
// - the crop `offset` to set the shift for all/each dimension
// to align the cropped bottom with the reference bottom.
// All dimensions up to but excluding `axis` are preserved, while
// the dimensions including and trailing `axis` are cropped.
// If only one `offset` is set, then all dimensions are offset by this amount.
// Otherwise, the number of offsets must equal the number of cropped axes to
// shift the crop in each dimension accordingly.
// Note: standard dimensions are N,C,H,W so the default is a spatial crop,
// and `axis` may be negative to index from the end (e.g., -1 for the last
// axis).
optional int32 axis = 1 [default = 2];
repeated uint32 offset = 2;
}
//数据参数
message DataParameter {
enum DB {
LEVELDB = 0;
LMDB = 1;
}
// Specify the data source.
optional string source = 1;
// Specify the batch size.
optional uint32 batch_size = 4;
// The rand_skip variable is for the data layer to skip a few data points
// to avoid all asynchronous sgd clients to start at the same point. The skip
// point would be set as rand_skip * rand(0,1). Note that rand_skip should not
// be larger than the number of keys in the database.
// DEPRECATED. Each solver accesses a different subset of the database.
optional uint32 rand_skip = 7 [default = 0];
optional DB backend = 8 [default = LEVELDB];
// DEPRECATED. See TransformationParameter. For data pre-processing, we can do
// simple scaling and subtracting the data mean, if provided. Note that the
// mean subtraction is always carried out before scaling.
optional float scale = 2 [default = 1];
optional string mean_file = 3;
// DEPRECATED. See TransformationParameter. Specify if we would like to randomly
// crop an image.
optional uint32 crop_size = 5 [default = 0];
// DEPRECATED. See TransformationParameter. Specify if we want to randomly mirror
// data.
optional bool mirror = 6 [default = false];
// Force the encoded image to have 3 color channels
optional bool force_encoded_color = 9 [default = false];
// Prefetch queue (Number of batches to prefetch to host memory, increase if
// data access bandwidth varies).
optional uint32 prefetch = 10 [default = 4];
}
//DropoutParameter参数
message DropoutParameter {
optional float dropout_ratio = 1 [default = 0.5]; // dropout ratio
optional bool scale_train = 2 [default = true]; // scale train or test phase
}
// DummyDataLayer fills any number of arbitrarily shaped blobs with random
// (or constant) data generated by "Fillers" (see "message FillerParameter").
message DummyDataParameter {
// This layer produces N >= 1 top blobs. DummyDataParameter must specify 1 or N
// shape fields, and 0, 1 or N data_fillers.
//
// If 0 data_fillers are specified, ConstantFiller with a value of 0 is used.
// If 1 data_filler is specified, it is applied to all top blobs. If N are
// specified, the ith is applied to the ith top blob.
repeated FillerParameter data_filler = 1;
repeated BlobShape shape = 6;
// 4D dimensions -- deprecated. Use "shape" instead.
repeated uint32 num = 2;
repeated uint32 channels = 3;
repeated uint32 height = 4;
repeated uint32 width = 5;
}
message EltwiseParameter {
enum EltwiseOp {
PROD = 0;
SUM = 1;
MAX = 2;
}
optional EltwiseOp operation = 1 [default = SUM]; // element-wise operation
repeated float coeff = 2; // blob-wise coefficient for SUM operation
// Whether to use an asymptotically slower (for >2 inputs) but stabler method
// of computing the gradient for the PROD operation. (No effect for SUM op.)
optional bool stable_prod_grad = 3 [default = true];
}
// Message that stores parameters used by ELULayer
message ELUParameter {
// Described in:
// Clevert, D.-A., Unterthiner, T., & Hochreiter, S. (2015). Fast and Accurate
// Deep Network Learning by Exponential Linear Units (ELUs). arXiv
optional float alpha = 1 [default = 1];
}
// Message that stores parameters used by EmbedLayer
message EmbedParameter {
optional uint32 num_output = 1; // The number of outputs for the layer
// The input is given as integers to be interpreted as one-hot
// vector indices with dimension num_input. Hence num_input should be
// 1 greater than the maximum possible input value.
optional uint32 input_dim = 2;
optional bool bias_term = 3 [default = true]; // Whether to use a bias term
optional FillerParameter weight_filler = 4; // The filler for the weight
optional FillerParameter bias_filler = 5; // The filler for the bias
}
// Message that stores parameters used by ExpLayer
message ExpParameter {
// ExpLayer computes outputs y = base ^ (shift + scale * x), for base > 0.
// Or if base is set to the default (-1), base is set to e,
// so y = exp(shift + scale * x).
optional float base = 1 [default = -1.0];
optional float scale = 2 [default = 1.0];
optional float shift = 3 [default = 0.0];
}
/// Message that stores parameters used by FlattenLayer
message FlattenParameter {
// The first axis to flatten: 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 = 1 [default = 1];
// The last axis to flatten: all following axes are retained in the output.
// May be negative to index from the end (e.g., the default -1 for the last
// axis).
optional int32 end_axis = 2 [default = -1];
}
// Message that stores parameters used by HDF5DataLayer
message HDF5DataParameter {
// Specify the data source.
optional string source = 1;
// Specify the batch size.
optional uint32 batch_size = 2;
// Specify whether to shuffle the data.
// If shuffle == true, the ordering of the HDF5 files is shuffled,
// and the ordering of data within any given HDF5 file is shuffled,
// but data between different files are not interleaved; all of a file's
// data are output (in a random order) before moving onto another file.
optional bool shuffle = 3 [default = false];
}
message HDF5OutputParameter {
optional string file_name = 1;
}
message HingeLossParameter {
enum Norm {
L1 = 1;
L2 = 2;
}
// Specify the Norm to use L1 or L2
optional Norm norm = 1 [default = L1];
}
//数据集参数
message ImageDataParameter {
// 指定数据源文件
optional string source = 1;
// 指定批量大小batchSize
optional uint32 batch_size = 4 [default = 1];
// The rand_skip variable is for the data layer to skip a few data points
// to avoid all asynchronous sgd clients to start at the same point. The skip
// point would be set as rand_skip * rand(0,1). Note that rand_skip should not
// be larger than the number of keys in the database.
// 随机跳过rand_skip * rand(0,1)个样本，以使得SGD从不同状态点启动
optional uint32 rand_skip = 7 [default = 0];
// Whether or not ImageLayer should shuffle the list of files at every epoch.是否在每个回合都混排图片，默认否
optional bool shuffle = 8 [default = false];
// It will also resize images if new_height or new_width are not zero.
// 若以下2个值不为0，则将图片缩放为下面的形状
optional uint32 new_height = 9 [default = 0];
optional uint32 new_width = 10 [default = 0];
// Specify if the images are color or gray指明是彩色还是灰度图
optional bool is_color = 11 [default = true];
// DEPRECATED. See TransformationParameter. For data pre-processing, we can do
// simple scaling and subtracting the data mean, if provided. Note that the
// mean subtraction is always carried out before scaling.
// 旧版--图片预处理参数，新版用TransformationParameter
optional float scale = 2 [default = 1];
optional string mean_file = 3;
// DEPRECATED. See TransformationParameter. Specify if we would like to randomly
// crop an image.
optional uint32 crop_size = 5 [default = 0];
// DEPRECATED. See TransformationParameter. Specify if we want to randomly mirror
// data.
optional bool mirror = 6 [default = false];
optional string root_folder = 12 [default = ""];
}
//信息增益损失参数
message InfogainLossParameter {
// Specify the infogain matrix source.
optional string source = 1;
}
//内积参数
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];
}
//输入参数
message InputParameter {
// This layer produces N >= 1 top blob(s) to be assigned manually.
// Define N shapes to set a shape for each top.
// Define 1 shape to set the same shape for every top.
// Define no shape to defer to reshaping manually.
// 此层管理输入(top)blobs，当输入blob个数N≥1，可使其自动分配。
// 设定N个shapes为N个输入blob；设定1个shape使得全部输入blob形状相同；
// 不设定，可手动调整。
// 可查看.\models\bvlc_reference_caffenet\deploy.prototxt中指定1个shape
repeated BlobShape shape = 1;
}
// LogLayer的参数
message LogParameter {
// LogLayer computes outputs y = log_base(shift + scale * x), for base > 0.
// Or if base is set to the default (-1), base is set to e,
// so y = ln(shift + scale * x) = log_e(shift + scale * x)
optional float base = 1 [default = -1.0];
optional float scale = 2 [default = 1.0];
optional float shift = 3 [default = 0.0];
}
// LRNLayer层参数
message LRNParameter {
optional uint32 local_size = 1 [default = 5];
optional float alpha = 2 [default = 1.];
optional float beta = 3 [default = 0.75];
enum NormRegion {
ACROSS_CHANNELS = 0;
WITHIN_CHANNEL = 1;
}
optional NormRegion norm_region = 4 [default = ACROSS_CHANNELS];
optional float k = 5 [default = 1.];
enum Engine {
DEFAULT = 0;
CAFFE = 1;
CUDNN = 2;
}
optional Engine engine = 6 [default = DEFAULT];
}
//数据内存占用参数
message MemoryDataParameter {
optional uint32 batch_size = 1;
optional uint32 channels = 2;
optional uint32 height = 3;
optional uint32 width = 4;
}
//MVN参数{均值，方差，跨通道}(mean-varance-normalization)
message MVNParameter {
// This parameter can be set to false to normalize mean only
// 设定为false时仅归一化均值，否则包括方差
optional bool normalize_variance = 1 [default = true];
// This parameter can be set to true to perform DNN-like MVN
// 执行跨通道归一化,类似于DNN的MVN；默认否，只执行Spatial内归一化。
optional bool across_channels = 2 [default = false];
// Epsilon for not dividing by zero while normalizing variance
// 防止除0的极小数
optional float eps = 3 [default = 1e-9];
}
//？？
message ParameterParameter {
optional BlobShape shape = 1;
}
//池化层参数
message PoolingParameter {
enum PoolMethod {
MAX = 0;
AVE = 1;
STOCHASTIC = 2;
}
optional PoolMethod pool = 1 [default = MAX]; // The pooling method
// Pad, kernel size, and stride are all given as a single value for equal
// dimensions in height and width or as Y, X pairs.
optional uint32 pad = 4 [default = 0]; // The padding size (equal in Y, X)
optional uint32 pad_h = 9 [default = 0]; // The padding height
optional uint32 pad_w = 10 [default = 0]; // The padding width
optional uint32 kernel_size = 2; // The kernel size (square)
optional uint32 kernel_h = 5; // The kernel height
optional uint32 kernel_w = 6; // The kernel width
optional uint32 stride = 3 [default = 1]; // The stride (equal in Y, X)
optional uint32 stride_h = 7; // The stride height
optional uint32 stride_w = 8; // The stride width
enum Engine {
DEFAULT = 0;
CAFFE = 1;
CUDNN = 2;
}
optional Engine engine = 11 [default = DEFAULT];
// If global_pooling then it will pool over the size of the bottom by doing
// kernel_h = bottom->height and kernel_w = bottom->width
optional bool global_pooling = 12 [default = false];
}
message PowerParameter {
// PowerLayer computes outputs y = (shift + scale * x) ^ power.
optional float power = 1 [default = 1.0];
optional float scale = 2 [default = 1.0];
optional float shift = 3 [default = 0.0];
}
//Python参数
message PythonParameter {
optional string module = 1;
optional string layer = 2;
// This value is set to the attribute `param_str` of the `PythonLayer` object
// in Python before calling the `setup()` method. This could be a number,
// string, dictionary in Python dict format, JSON, etc. You may parse this
// string in `setup` method and use it in `forward` and `backward`.
optional string param_str = 3 [default = ''];
// Whether this PythonLayer is shared among worker solvers during data parallelism.
// If true, each worker solver sequentially run forward from this layer.
// This value should be set true if you are using it as a data layer.
optional bool share_in_parallel = 4 [default = false];
}
// RecurrentLayer参数
message RecurrentParameter {
// 输出表示的维度必须是非0的
optional uint32 num_output = 1 [default = 0];
optional FillerParameter weight_filler = 2; //weight权值参数
optional FillerParameter bias_filler = 3; //bias偏置参数
// Whether to enable displaying debug_info in the unrolled recurrent net.
// 在展开RCNN时是否打印deuginfo
optional bool debug_info = 4 [default = false];
// Whether to add as additional inputs (bottoms) the initial hidden state
// blobs, and add as additional outputs (tops) the final timestep hidden state
// blobs. The number of additional bottom/top blobs required depends on the
// recurrent architecture -- e.g., 1 for RNNs, 2 for LSTMs.
// 是否添加初始化的隐藏blobs作为额外输入(bottoms)，以及添加最终的timestep隐
// 藏blobs作为额外输出（tops）。
optional bool expose_hidden = 5 [default = false];
}
// ReductionLayer参数
message ReductionParameter {
enum ReductionOp {
SUM = 1;
ASUM = 2;
SUMSQ = 3;
MEAN = 4;
}
optional ReductionOp operation = 1 [default = SUM]; // reduction operation
// The first axis to reduce to a scalar -- may be negative to index from the
// end (e.g., -1 for the last axis).
// (Currently, only reduction along ALL "tail" axes is supported; reduction
// of axis M through N, where N < num_axes - 1, is unsupported.)
// Suppose we have an n-axis bottom Blob with shape:
// (d0, d1, d2, ..., d(m-1), dm, d(m+1), ..., d(n-1)).
// If axis == m, the output Blob will have shape
// (d0, d1, d2, ..., d(m-1)),
// and the ReductionOp operation is performed (d0 * d1 * d2 * ... * d(m-1))
// times, each including (dm * d(m+1) * ... * d(n-1)) individual data.
// If axis == 0 (the default), the output Blob always has the empty shape
// (count 1), performing reduction across the entire input --
// often useful for creating new loss functions.
optional int32 axis = 2 [default = 0];
optional float coeff = 3 [default = 1.0]; // coefficient for output
}
// ReLULayer参数
message ReLUParameter {
// 允许非0斜率可以加速优化:
// Maas, A. L., Hannun, A. Y., & Ng, A. Y. (2013). Rectifier nonlinearities
// improve neural network acoustic models. In ICML Workshop on Deep Learning
// for Audio, Speech, and Language Processing.
optional float negative_slope = 1 [default = 0];
enum Engine {
DEFAULT = 0;
CAFFE = 1;
CUDNN = 2;
}
optional Engine engine = 2 [default = DEFAULT];
}
message ReshapeParameter {
// Specify the output dimensions. If some of the dimensions are set to 0,
// the corresponding dimension from the bottom layer is used (unchanged).
// Exactly one dimension may be set to -1, in which case its value is
// inferred from the count of the bottom blob and the remaining dimensions.
// For example, suppose we want to reshape a 2D blob "input" with shape 2 x 8:
//
// layer {
// type: "Reshape" bottom: "input" top: "output"
// reshape_param { ... }
// }
//
// If "input" is 2D with shape 2 x 8, then the following reshape_param
// specifications are all equivalent, producing a 3D blob "output" with shape
// 2 x 2 x 4:
//
// reshape_param { shape { dim: 2 dim: 2 dim: 4 } }
// reshape_param { shape { dim: 0 dim: 2 dim: 4 } }
// reshape_param { shape { dim: 0 dim: 2 dim: -1 } }
// reshape_param { shape { dim: 0 dim:-1 dim: 4 } }
//
optional BlobShape shape = 1;
// axis and num_axes control the portion of the bottom blob's shape that are
// replaced by (included in) the reshape. By default (axis == 0 and
// num_axes == -1), the entire bottom blob shape is included in the reshape,
// and hence the shape field must specify the entire output shape.
//
// axis may be non-zero to retain some portion of the beginning of the input
// shape (and may be negative to index from the end; e.g., -1 to begin the
// reshape after the last axis, including nothing in the reshape,
// -2 to include only the last axis, etc.).
//
// For example, suppose "input" is a 2D blob with shape 2 x 8.
// Then the following ReshapeLayer specifications are all equivalent,
// producing a blob "output" with shape 2 x 2 x 4:
//
// reshape_param { shape { dim: 2 dim: 2 dim: 4 } }
// reshape_param { shape { dim: 2 dim: 4 } axis: 1 }
// reshape_param { shape { dim: 2 dim: 4 } axis: -3 }
//
// num_axes specifies the extent of the reshape.
// If num_axes >= 0 (and axis >= 0), the reshape will be performed only on
// input axes in the range [axis, axis+num_axes].
// num_axes may also be -1, the default, to include all remaining axes
// (starting from axis).
//
// For example, suppose "input" is a 2D blob with shape 2 x 8.
// Then the following ReshapeLayer specifications are equivalent,
// producing a blob "output" with shape 1 x 2 x 8.
//
// reshape_param { shape { dim: 1 dim: 2 dim: 8 } }
// reshape_param { shape { dim: 1 dim: 2 } num_axes: 1 }
// reshape_param { shape { dim: 1 } num_axes: 0 }
//
// On the other hand, these would produce output blob shape 2 x 1 x 8:
//
// reshape_param { shape { dim: 2 dim: 1 dim: 8 } }
// reshape_param { shape { dim: 1 } axis: 1 num_axes: 0 }
//
optional int32 axis = 2 [default = 0];
optional int32 num_axes = 3 [default = -1];
}
// ROIPoolingLayer参数
message ROIPoolingParameter {
// Pad, kernel size, and stride are all given as a single value for equal
// dimensions in height and width or as Y, X pairs.
optional uint32 pooled_h = 1 [default = 0]; // The pooled output height
optional uint32 pooled_w = 2 [default = 0]; // The pooled output width
// Multiplicative spatial scale factor to translate ROI coords from their
// input scale to the scale used when pooling
optional float spatial_scale = 3 [default = 1];
}
//ScaleParameter参数
message ScaleParameter {
// The first axis of bottom[0] (the first input Blob) along which to apply
// bottom[1] (the second input Blob). May be negative to index from the end
// (e.g., -1 for the last axis).
// ？？？？？？？？？？？？？？？？？？？？？？？？？？？？？？？
// 第一个输入Blob的首axis，被应用到第二个输入Blob。但第2个Blob的形状可能不同
// For example, if bottom[0] is 4D with shape 100x3x40x60, the output
// top[0] will have the same shape, and bottom[1] may have any of the
// following shapes (for the given value of axis):
// (axis == 0 == -4) 100; 100x3; 100x3x40; 100x3x40x60
// (axis == 1 == -3) 3; 3x40; 3x40x60
// (axis == 2 == -2) 40; 40x60
// (axis == 3 == -1) 60
// Furthermore,bottom[1]may have the empty shape (regardless of the value of
// "axis") -- a scalar multiplier.
optional int32 axis = 1 [default = 1];
// (num_axes is ignored unless just one bottom is given and the scale is
// a learned parameter of the layer. Otherwise, num_axes is determined by the
// number of axes by the second bottom.)
// The number of axes of the input (bottom[0]) covered by the scale
// parameter, or -1 to cover all axes of bottom[0] starting from `axis`.
// Set num_axes := 0, to multiply with a zero-axis Blob: a scalar.
optional int32 num_axes = 2 [default = 1];
// (filler is ignored unless just one bottom is given and the scale is
// a learned parameter of the layer.)
// The initialization for the learned scale parameter.
// Default is the unit (1) initialization, resulting in the ScaleLayer
// initially performing the identity operation.
optional FillerParameter filler = 3;
// Whether to also learn a bias (equivalent to a ScaleLayer+BiasLayer, but
// may be more efficient). Initialized with bias_filler (defaults to 0).
optional bool bias_term = 4 [default = false];
optional FillerParameter bias_filler = 5;
}
//SigmoidParameter参数
message SigmoidParameter {
enum Engine {
DEFAULT = 0;
CAFFE = 1;
CUDNN = 2;
}
optional Engine engine = 1 [default = DEFAULT];
}
//SliceParameter参数
message SliceParameter {
// The axis along which to slice -- may be negative to index from the end
// (e.g., -1 for the last axis).
// By default, SliceLayer concatenates blobs along the "channels" axis (1).
optional int32 axis = 3 [default = 1];
repeated uint32 slice_point = 2;
// DEPRECATED: alias for "axis" -- does not support negative indexing.
optional uint32 slice_dim = 1 [default = 1];
}
//SmoothL1LossParameter参数
message SmoothL1LossParameter {
// SmoothL1Loss(x) =
// 0.5 * (sigma * x) ** 2 -- if x < 1.0 / sigma / sigma
// |x| - 0.5 / sigma / sigma -- otherwise
optional float sigma = 1 [default = 1];
}
//SoftmaxLayer, SoftmaxWithLossLayer的参数
message SoftmaxParameter {
enum Engine {
DEFAULT = 0;
CAFFE = 1;
CUDNN = 2;
}
optional Engine engine = 1 [default = DEFAULT];
// The axis along which to perform the softmax -- may be negative to index
// from the end (e.g., -1 for the last axis).
// Any other axes will be evaluated as independent softmaxes.
// 沿着哪一个轴运用softmax，该轴上必须是相互独立的分量。
// eg.预测时对类标签运用，计算损失时对每个类的对数损失运用。
optional int32 axis = 2 [default = 1];
}
//TanHParameter参数
message TanHParameter {
enum Engine {
DEFAULT = 0;
CAFFE = 1;
CUDNN = 2;
}
optional Engine engine = 1 [default = DEFAULT];
}
// TileLayer参数
message TileParameter {
// The index of the axis to tile.
optional int32 axis = 1 [default = 1];
// The number of copies (tiles) of the blob to output.
optional int32 tiles = 2;
}
// ThresholdLayer参数
message ThresholdParameter {
optional float threshold = 1 [default = 0]; // Strictly positive values
}
// MILLayer参数
message MILParameter {
enum MILType {
MAX = 0;
NOR = 1;
}
optional MILType type = 1 [default = MAX]; // The MIL method
}
//窗口数据参数：专用于目标检测或分割
message WindowDataParameter {
// Specify the data source.指定数据源
optional string source = 1;
// 数据预处理：尺度缩放，去均值等。去均值应在缩放前执行。
optional float scale = 2 [default = 1];
optional string mean_file = 3;
// 指定批处理的数据量
optional uint32 batch_size = 4;
// 是否随机裁剪
optional uint32 crop_size = 5 [default = 0];
// 是否镜像变换
optional bool mirror = 6 [default = false];
// Foreground (object) overlap threshold 前景目标重合阈值
optional float fg_threshold = 7 [default = 0.5];
// Background (non-object) overlap threshold背景重合阈值
optional float bg_threshold = 8 [default = 0.5];
// Fraction of batch that should be foreground objects
// 前景目标在batch中的比例
optional float fg_fraction = 9 [default = 0.25];
// Amount of contextual padding to add around a window
// (used only by the window_data_layer)
// 窗口周边需要添加的上下文padding
optional uint32 context_pad = 10 [default = 0];
// Mode for cropping out a detection window
// warp: cropped window is warped to a fixed size and aspect ratio
// square: the tightest square around the window is cropped
// mode:裁剪出一个检测窗口的模式
// warp:裁剪窗口被扭曲为某个固定尺寸和形状
// square:裁剪窗口周边最紧？的方框
optional string crop_mode = 11 [default = "warp"];
// cache_images: will load all images in memory for faster access
//将全部图像(裁剪得到的小图像)放入内存以便快速存取
optional bool cache_images = 12 [default = false];
// append root_folder to locate images
// 添加根文件夹以定位文件
optional string root_folder = 13 [default = ""];
}
//MILDataParameter参数
message MILDataParameter {
// Specify the data source.
optional string source = 1;
// Number of scales for each image
optional uint32 num_scales = 2 [default = 1];
// Side length ratio between neighbouring scales
optional float scale_factor = 6 [default = 1];
// Number of channels in the image
optional uint32 channels = 4 [default = 3];
// Specify the number of images per batch
optional uint32 images_per_batch = 3;
// Specify the number of classes
optional uint32 n_classes = 5;
// specify the box_dir and label_dir
optional string label_file = 7;
// Root directory which contains all the images
optional string root_dir = 11;
// Extention for the file
optional string ext = 12;
// To randomize or not
optional bool randomize = 13 [default = true];
}
//SPP参数,源于论文SPPNet
message SPPParameter {
enum PoolMethod {
MAX = 0;
AVE = 1;
STOCHASTIC = 2;
} //池化方法，获得金字塔的方法，最大/平均/随机
optional uint32 pyramid_height = 1; //金字塔高度
optional PoolMethod pool = 2 [default = MAX]; // The pooling method
enum Engine {
DEFAULT = 0;
CAFFE = 1;
CUDNN = 2;
}
optional Engine engine = 6 [default = DEFAULT];
}
// DEPRECATED: use LayerParameter.
// 旧版：使用层参数。 V1可能是第一版version1的意思
message V1LayerParameter {
repeated string bottom = 2; //输入
repeated string top = 3; //输出
optional string name = 4; //层名称
repeated NetStateRule include = 32; //运行时状态：包含
repeated NetStateRule exclude = 33; //运行时状态：不包含
enum LayerType { //层类型
NONE = 0;
ABSVAL = 35;
ACCURACY = 1;
ARGMAX = 30;
BNLL = 2;
CONCAT = 3;
CONTRASTIVE_LOSS = 37;
CONVOLUTION = 4;
DATA = 5;
DECONVOLUTION = 39;
DROPOUT = 6;
DUMMY_DATA = 32;
EUCLIDEAN_LOSS = 7;
ELTWISE = 25;
EXP = 38;
FLATTEN = 8;
HDF5_DATA = 9;
HDF5_OUTPUT = 10;
HINGE_LOSS = 28;
IM2COL = 11;
IMAGE_DATA = 12;
INFOGAIN_LOSS = 13;
INNER_PRODUCT = 14;
LRN = 15;
MEMORY_DATA = 29;
MULTINOMIAL_LOGISTIC_LOSS = 16;
MVN = 34;
POOLING = 17;
POWER = 26;
RELU = 18;
SIGMOID = 19;
SIGMOID_CROSS_ENTROPY_LOSS = 27;
SILENCE = 36;
SOFTMAX = 20;
SOFTMAX_LOSS = 21;
SPLIT = 22;
SLICE = 33;
TANH = 23;
WINDOW_DATA = 24;
THRESHOLD = 31;
}
optional LayerType type = 5;
repeated BlobProto blobs = 6;
repeated string param = 1001;
repeated DimCheckMode blob_share_mode = 1002;
enum DimCheckMode {
STRICT = 0;
PERMISSIVE = 1;
}
repeated float blobs_lr = 7;
repeated float weight_decay = 8;
repeated float loss_weight = 35;
optional AccuracyParameter accuracy_param = 27;
optional ArgMaxParameter argmax_param = 23;
optional ConcatParameter concat_param = 9;
optional ContrastiveLossParameter contrastive_loss_param = 40;
optional ConvolutionParameter convolution_param = 10;
optional DataParameter data_param = 11;
optional DropoutParameter dropout_param = 12;
optional DummyDataParameter dummy_data_param = 26;
optional EltwiseParameter eltwise_param = 24;
optional ExpParameter exp_param = 41;
optional HDF5DataParameter hdf5_data_param = 13;
optional HDF5OutputParameter hdf5_output_param = 14;
optional HingeLossParameter hinge_loss_param = 29;
optional ImageDataParameter image_data_param = 15;
optional InfogainLossParameter infogain_loss_param = 16;
optional InnerProductParameter inner_product_param = 17;
optional LRNParameter lrn_param = 18;
optional MemoryDataParameter memory_data_param = 22;
optional MVNParameter mvn_param = 34;
optional PoolingParameter pooling_param = 19;
optional PowerParameter power_param = 21;
optional ReLUParameter relu_param = 30;
optional SigmoidParameter sigmoid_param = 38;
optional SoftmaxParameter softmax_param = 39;
optional SliceParameter slice_param = 31;
optional TanHParameter tanh_param = 37;
optional ThresholdParameter threshold_param = 25;
optional WindowDataParameter window_data_param = 20;
optional TransformationParameter transform_param = 36;
optional LossParameter loss_param = 42;
optional V0LayerParameter layer = 1;
}
// DEPRECATED: V0LayerParameter is the old way of specifying layer parameters
// in Caffe. We keep this message type around for legacy support.
// 旧版本：V0LayerParameter version-0版
message V0LayerParameter {
optional string name = 1; // the layer name
optional string type = 2; // the string to specify the layer type
// Parameters to specify layers with inner products.
optional uint32 num_output = 3; // The number of outputs for the layer
optional bool biasterm = 4 [default = true]; // whether to have bias terms
optional FillerParameter weight_filler = 5; // The filler for the weight
optional FillerParameter bias_filler = 6; // The filler for the bias
optional uint32 pad = 7 [default = 0]; // The padding size
optional uint32 kernelsize = 8; // The kernel size
optional uint32 group = 9 [default = 1]; // The group size for group conv
optional uint32 stride = 10 [default = 1]; // The stride
enum PoolMethod {
MAX = 0;
AVE = 1;
STOCHASTIC = 2;
}
optional PoolMethod pool = 11 [default = MAX]; // The pooling method
optional float dropout_ratio = 12 [default = 0.5]; // dropout ratio
optional uint32 local_size = 13 [default = 5]; // for local response norm
optional float alpha = 14 [default = 1.]; // for local response norm
optional float beta = 15 [default = 0.75]; // for local response norm
optional float k = 22 [default = 1.];
// For data layers, specify the data source
optional string source = 16;
// For data pre-processing, we can do simple scaling and subtracting the
// data mean, if provided. Note that the mean subtraction is always carried
// out before scaling.
optional float scale = 17 [default = 1];
optional string meanfile = 18;
// For data layers, specify the batch size.
optional uint32 batchsize = 19;
// For data layers, specify if we would like to randomly crop an image.
optional uint32 cropsize = 20 [default = 0];
// For data layers, specify if we want to randomly mirror data.
optional bool mirror = 21 [default = false];
// The blobs containing the numeric parameters of the layer
repeated BlobProto blobs = 50;
// The ratio that is multiplied on the global learning rate. If you want to
// set the learning ratio for one blob, you need to set it for all blobs.
repeated float blobs_lr = 51;
// The weight decay that is multiplied on the global weight decay.
repeated float weight_decay = 52;
// The rand_skip variable is for the data layer to skip a few data points
// to avoid all asynchronous sgd clients to start at the same point. The skip
// point would be set as rand_skip * rand(0,1). Note that rand_skip should not
// be larger than the number of keys in the database.
optional uint32 rand_skip = 53 [default = 0];
// Fields related to detection (det_*)
// foreground (object) overlap threshold
optional float det_fg_threshold = 54 [default = 0.5];
// background (non-object) overlap threshold
optional float det_bg_threshold = 55 [default = 0.5];
// Fraction of batch that should be foreground objects
optional float det_fg_fraction = 56 [default = 0.25];
// optional bool OBSOLETE_can_clobber = 57 [default = true];
// Amount of contextual padding to add around a window
// (used only by the window_data_layer)
optional uint32 det_context_pad = 58 [default = 0];
// Mode for cropping out a detection window
// warp: cropped window is warped to a fixed size and aspect ratio
// square: the tightest square around the window is cropped
optional string det_crop_mode = 59 [default = "warp"];
// For ReshapeLayer, one needs to specify the new dimensions.
optional int32 new_num = 60 [default = 0];
optional int32 new_channels = 61 [default = 0];
optional int32 new_height = 62 [default = 0];
optional int32 new_width = 63 [default = 0];
// Whether or not ImageLayer should shuffle the list of files at every epoch.
// It will also resize images if new_height or new_width are not zero.
optional bool shuffle_images = 64 [default = false];
// For ConcatLayer, one needs to specify the dimension for concatenation, and
// the other dimensions must be the same for all the bottom blobs.
// By default it will concatenate blobs along the channels dimension.
optional uint32 concat_dim = 65 [default = 1];
optional HDF5OutputParameter hdf5_output_param = 1001;
}
//PReLUParameter,源于论文
message PReLUParameter {
// Parametric ReLU described in K. He et al, Delving Deep into Rectifiers:
// Surpassing Human-Level Performance on ImageNet Classification, 2015.
// Initial value of a_i. Default is a_i=0.25 for all i.
optional FillerParameter filler = 1;
// Whether or not slope paramters are shared across channels.
optional bool channel_shared = 2 [default = false];
}

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