caffe源码解析 — caffe.proto
要看caffe源码，首先应该看的就是caffe.proto。
它位于…\src\caffe\proto目录下，在这个文件夹下还有一个https://www.wendangku.net/doc/a54222450.html,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
caffe.proto中的几个重要数据类型
https://www.wendangku.net/doc/a54222450.html,里面的东西都是从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();
void MergeFrom();
void Clear();
bool IsInitialized() const;
int ByteSize() const;
bool MergePartialFromCodedStream();
void SerializeWithCachedSizes() const;
SerializeWithCachedSizesToArray() const;
int GetCachedSize()
void SharedCtor();
void SharedDtor();
void SetCachedSize() const;
<0> BlobProto
message BlobProto {//blob的属性以及blob中的数据(data\diff)
optional int32 num = 1 [default = 0];
optional int32 channels = 2 [default = 0];
optional int32 height = 3 [default = 0];
optional int32 width = 4 [default = 0];
repeated float data = 5 [packed = true];
repeated float diff = 6 [packed = true];
}
<1> Datum
message Datum {
optional int32 channels = 1;
optional int32 height = 2;
optional int32 width = 3;
optional bytes data = 4;//真实的图像数据，以字节存储(bytes) optional int32 label = 5;
repeated float float_data = 6;//datum也能存float类型的数据
(float)
}
<2> LayerParameter
message LayerParameter {
repeated string bottom = 2; //输入的blob的名字(string)
repeated string top = 3; //输出的blob的名字(string)
optional string name = 4; //层的名字
enum LayerType { //层的枚举（enum，和c++中的enum一样）
NONE = 0;
ACCURACY = 1;
BNLL = 2;
CONCAT = 3;
CONVOLUTION = 4;
DATA = 5;
DROPOUT = 6;
EUCLIDEAN_LOSS = 7;
ELTWISE_PRODUCT = 25;
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;
POOLING = 17;
POWER = 26;
RELU = 18;
SIGMOID = 19;
SIGMOID_CROSS_ENTROPY_LOSS = 27;
SOFTMAX = 20;
SOFTMAX_LOSS = 21;
SPLIT = 22;
TANH = 23;
WINDOW_DATA = 24;
}
optional LayerType type = 5; // 层的类型
repeated BlobProto blobs = 6; //blobs的数值参数
repeated float blobs_lr = 7; //学习速率(repeated)，如果你想设置一个blob的学习速率，你需要设置所有blob的学习速率。
repeated float weight_decay = 8; //权值衰减(repeated)
// 相对于某一特定层的参数(optional)
optional ConcatParameter concat_param = 9;
optional ConvolutionParameter convolution_param = 10;
optional DataParameter data_param = 11;
optional DropoutParameter dropout_param = 12;
optional HDF5DataParameter hdf5_data_param = 13;
optional HDF5OutputParameter hdf5_output_param = 14;
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 PoolingParameter pooling_param = 19;
optional PowerParameter power_param = 21;
optional WindowDataParameter window_data_param = 20;
optional V0LayerParameter layer = 1;
}
<3> NetParameter
message NetParameter {
optional string name = 1;//网络的名字
repeated LayerParameter layers = 2; //repeated类似于数组
repeated string input = 3;//输入层blob的名字
repeated int32 input_dim = 4;//输入层blob的维度，应该等于(4* #input)
optional bool force_backward = 5 [default = false];//网络
是否进行反向传播。如果设置为否，则由网络的结构和学习速率来决定是否进行反向传播。
}
<4> SolverParameter
message SolverParameter {
optional string train_net = 1; // 训练网络的proto file
optional string test_net = 2; // 测试网络的proto file
optional int32 test_iter = 3 [default = 0]; // 每次测试时的迭代次数
optional int32 test_interval = 4 [default = 0]; // 两次测试的间隔迭代次数
optional bool test_compute_loss = 19 [default = false]; optional float base_lr = 5; // 基本学习率
optional int32 display = 6; // 两次显示的间隔迭代次数
optional int32 max_iter = 7; // 最大迭代次数
optional string lr_policy = 8; // 学习速率衰减方式
optional float gamma = 9; // 关于梯度下降的一个参数
optional float power = 10; // 计算学习率的一个参数
optional float momentum = 11; // 动量
optional float weight_decay = 12; // 权值衰减
optional int32 stepsize = 13; // 学习速率的衰减步长
optional int32 snapshot = 14 [default = 0]; // snapshot的间隔
optional string snapshot_prefix = 15; // snapshot的前缀
optional bool snapshot_diff = 16 [default = false]; // 是否对于 diff 进行 snapshot
enum SolverMode {
CPU = 0;
GPU = 1;
}
optional SolverMode solver_mode = 17 [default = GPU]; // solver的模式，默认为GPU
optional int32 device_id = 18 [default = 0]; // GPU的ID optional int64 random_seed = 20 [default = -1]; // 随机数种子
}
caffe.proto源码
// Copyright 2014 BVLC and contributors.
package caffe;
message BlobProto {
optional int32 num = 1 [default = 0];
optional int32 channels = 2 [default = 0];
optional int32 height = 3 [default = 0];
optional int32 width = 4 [default = 0];
repeated float data = 5 [packed = true];
repeated float diff = 6 [packed = true];
}
// The BlobProtoVector is simply a way to pass multiple blo bproto instances
// around.
message BlobProtoVector {
repeated BlobProto blobs = 1;
}
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;
}
message FillerParameter {
// The filler type.
optional string type = 1 [default = 'constant'];
optional float value = 2 [default = 0]; // the value in co nstant 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 i n Gaussian filler
optional float std = 6 [default = 1]; // the std value in Gaussian filler
// The expected number of non-zero input weights for a gi ven output in
// Gaussian filler -- the default -1 means don't perform sparsification.
optional int32 sparse = 7 [default = -1];
}
message NetParameter {
optional string name = 1; // consider giving the network a name
repeated LayerParameter layers = 2; // a bunch of layers. // The input blobs to the network.
repeated string input = 3;
// The dim of the input blobs. For each input blob there s hould be four
// values specifying the num, channels, height and width of the input blob.
// Thus, there should be a total of (4 * #input) numbers. repeated int32 input_dim = 4;
// Whether the network will force every layer to carry ou t backward operation.
// If set False, then whether to carry out backward is de termined
// automatically according to the net structure and learn ing rates.
optional bool force_backward = 5 [default = false];
}
message SolverParameter {
optional string train_net = 1; // The proto file for the training net.
optional string test_net = 2; // The proto file for the t esting net.
// The number of iterations for each testing phase.
optional int32 test_iter = 3 [default = 0];
// The number of iterations between two testing phases. optional int32 test_interval = 4 [default = 0];
optional bool test_compute_loss = 19 [default = false]; optional float base_lr = 5; // The base learning rate
// the number of iterations between displaying info. If d isplay = 0, no info
// will be displayed.
optional int32 display = 6;
optional int32 max_iter = 7; // the maximum number of ite rations
optional string lr_policy = 8; // The learning rate decay policy.
optional float gamma = 9; // The parameter to compute the learning rate.
optional float power = 10; // The parameter to compute th e learning rate.
optional float momentum = 11; // The momentum value.
optional float weight_decay = 12; // The weight decay.
optional int32 stepsize = 13; // the stepsize for learnin g rate policy "step"
optional int32 snapshot = 14 [default = 0]; // The snapsh ot interval
optional string snapshot_prefix = 15; // The prefix for t he snapshot.
// whether to snapshot diff in the results or not. Snapsh otting diff will help
// debugging but the final protocol buffer size will be m uch larger.
optional bool snapshot_diff = 16 [default = false];
// the mode solver will use: 0 for CPU and 1 for GPU. Use GPU in default.
enum SolverMode {
CPU = 0;
GPU = 1;
}
optional SolverMode solver_mode = 17 [default = GPU];
// the device_id will that be used in GPU mode. Use devic e_id = 0 in default.
optional int32 device_id = 18 [default = 0];
// If non-negative, the seed with which the Solver will i nitialize the Caffe
// random number generator -- useful for reproducible res ults. Otherwise,
// (and by default) initialize using a seed derived from the system clock.
optional int64 random_seed = 20 [default = -1];
}
// A message that stores the solver snapshots
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 so lvers
}
// Update the next available ID when you add a new LayerPar ameter field.
//
// LayerParameter next available ID: 23 (last added: memory _data_param)
message LayerParameter {
repeated string bottom = 2; // the name of the bottom blo bs
repeated string top = 3; // the name of the top blobs
optional string name = 4; // the layer name
// Add new LayerTypes to the enum below in lexicographica l order (other than
// starting with NONE), starting with the next available ID in the comment
// line above the enum. Update the next available ID when you add a new
// LayerType.
//
// LayerType next available ID: 30 (last added: MEMORY_DA TA)
enum LayerType {
// "NONE" layer type is 0th enum element so that we don' t cause confusion
// by defaulting to an existent LayerType (instead, sho uld usually error if
// the type is unspecified).
NONE = 0;
ACCURACY = 1;
BNLL = 2;
CONCAT = 3;
CONVOLUTION = 4;
DATA = 5;
DROPOUT = 6;
EUCLIDEAN_LOSS = 7;
ELTWISE_PRODUCT = 25;
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;
POOLING = 17;
POWER = 26;
RELU = 18;
SIGMOID = 19;
SIGMOID_CROSS_ENTROPY_LOSS = 27;
SOFTMAX = 20;
SOFTMAX_LOSS = 21;
SPLIT = 22;
TANH = 23;
WINDOW_DATA = 24;
}
optional LayerType type = 5; // the layer type from the e num above
// The blobs containing the numeric parameters of the lay er
repeated BlobProto blobs = 6;
// The ratio that is multiplied on the global learning ra te. If you want to
// set the learning ratio for one blob, you need to set it for all blobs.
repeated float blobs_lr = 7;
// The weight decay that is multiplied on the global weig ht decay.
repeated float weight_decay = 8;
// Parameters for particular layer types.
optional ConcatParameter concat_param = 9;
optional ConvolutionParameter convolution_param = 10;
optional DataParameter data_param = 11;
optional DropoutParameter dropout_param = 12;
optional HDF5DataParameter hdf5_data_param = 13;
optional HDF5OutputParameter hdf5_output_param = 14;
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 PoolingParameter pooling_param = 19;
optional PowerParameter power_param = 21;
optional WindowDataParameter window_data_param = 20;
// DEPRECATED: The layer parameters specified as a V0Laye rParameter.
// This should never be used by any code except to upgrad e to the new
// LayerParameter specification.
optional V0LayerParameter layer = 1;
}
// Message that stores parameters used by ConcatLayer message ConcatParameter {
// Concat Layer needs to specify the dimension along the concat will happen,
// the other dimensions must be the same for all the bott om blobs
// By default it will concatenate blobs along channels di mension
optional uint32 concat_dim = 1 [default = 1];
}
// Message that stores parameters used by ConvolutionLayer 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
optional uint32 pad = 3 [default = 0]; // The padding siz e
optional uint32 kernel_size = 4; // The kernel size
optional uint32 group = 5 [default = 1]; // The group siz e for group conv
optional uint32 stride = 6 [default = 1]; // The stride
optional FillerParameter weight_filler = 7; // The filler for the weight
optional FillerParameter bias_filler = 8; // The filler f or the bias
}
// Message that stores parameters used by DataLayer message DataParameter {
// Specify the data source.
optional string source = 1;
// 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;
// Specify the batch size.
optional uint32 batch_size = 4;
// Specify if we would like to randomly crop an image.
optional uint32 crop_size = 5 [default = 0];
// Specify if we want to randomly mirror data.
optional bool mirror = 6 [default = false];
// 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 leveldb.
optional uint32 rand_skip = 7 [default = 0];
}
// Message that stores parameters used by DropoutLayer message DropoutParameter {
optional float dropout_ratio = 1 [default = 0.5]; // drop out ratio
}
// 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;
}
// Message that stores parameters used by HDF5OutputLayer message HDF5OutputParameter {
optional string file_name = 1;
}
// Message that stores parameters used by ImageDataLayer message ImageDataParameter {
// Specify the data source.
optional string source = 1;
// 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;
// Specify the batch size.
optional uint32 batch_size = 4;
// Specify if we would like to randomly crop an image.
optional uint32 crop_size = 5 [default = 0];
// Specify if we want to randomly mirror data.
optional bool mirror = 6 [default = false];
// 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 leveldb.
optional uint32 rand_skip = 7 [default = 0];
// Whether or not ImageLayer should shuffle the list of f iles at every epoch.
optional bool shuffle = 8 [default = false];
// It will also resize images if new_height or new_width are not zero.
optional uint32 new_height = 9 [default = 0];
optional uint32 new_width = 10 [default = 0];
}
// Message that stores parameters InfogainLossLayer message InfogainLossParameter {
// Specify the infogain matrix source.
optional string source = 1;
}
// Message that stores parameters used by InnerProductLaye r
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 f or the bias
}
// Message that stores parameters used by LRNLayer 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_CHA NNELS];
}
// Message that stores parameters used by MemoryDataLayer message MemoryDataParameter {
optional uint32 batch_size = 1;
optional uint32 channels = 2;
optional uint32 height = 3;
optional uint32 width = 4;
}
// Message that stores parameters used by PoolingLayer message PoolingParameter {
enum PoolMethod {
MAX = 0;
AVE = 1;
STOCHASTIC = 2;
}
optional PoolMethod pool = 1 [default = MAX]; // The pool ing method
optional uint32 kernel_size = 2; // The kernel size
optional uint32 stride = 3 [default = 1]; // The stride
// The padding size -- currently implemented only for ave rage pooling.
optional uint32 pad = 4 [default = 0];
}
// Message that stores parameters used by PowerLayer message PowerParameter {
// PowerLayer computes outputs y = (shift + scale * x) ^ p ower.
optional float power = 1 [default = 1.0];
optional float scale = 2 [default = 1.0];
optional float shift = 3 [default = 0.0];
}
// Message that stores parameters used by WindowDataLayer message WindowDataParameter {
// Specify the data source.
optional string source = 1;
// 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;
// Specify the batch size.
optional uint32 batch_size = 4;
// Specify if we would like to randomly crop an image.
optional uint32 crop_size = 5 [default = 0];
// Specify if we want to randomly mirror data.
optional bool mirror = 6 [default = false];
// Foreground (object) overlap threshold
optional float fg_threshold = 7 [default = 0.5];
// Background (non-object) overlap threshold
optional float bg_threshold = 8 [default = 0.5];
// Fraction of batch that should be foreground objects
optional float fg_fraction = 9 [default = 0.25];
// Amount of contextual padding to add around a window
// (used only by the window_data_layer)
optional uint32 context_pad = 10 [default = 0];
// Mode for cropping out a detection window
// warp: cropped window is warped to a fixed size and asp ect ratio
// square: the tightest square around the window is cropp ed
optional string crop_mode = 11 [default = "warp"];
}
// DEPRECATED: V0LayerParameter is the old way of specifyin g layer parameters
// in Caffe. We keep this message type around for legacy s upport.
message V0LayerParameter {
optional string name = 1; // the layer name
optional string type = 2; // the string to specify the la yer 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 t o have bias terms
optional FillerParameter weight_filler = 5; // The filler for the weight
optional FillerParameter bias_filler = 6; // The filler f or the bias
optional uint32 pad = 7 [default = 0]; // The padding siz e
optional uint32 kernelsize = 8; // The kernel size
optional uint32 group = 9 [default = 1]; // The group siz e 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 poo ling method
optional float dropout_ratio = 12 [default = 0.5]; // dro pout ratio
optional uint32 local_size = 13 [default = 5]; // for loc al response norm
optional float alpha = 14 [default = 1.]; // for local re sponse norm
optional float beta = 15 [default = 0.75]; // for local r esponse norm
// 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 lay er
repeated BlobProto blobs = 50;
// The ratio that is multiplied on the global learning ra te. 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 weig ht 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 leveldb.
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 = tru e];
// 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 asp ect ratio
// square: the tightest square around the window is cropp ed
optional string det_crop_mode = 59 [default = "warp"];
// For ReshapeLayer, one needs to specify the new dimensi ons.
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 f iles 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 fo r concatenation, and
// the other dimensions must be the same for all the bott om blobs.
// By default it will concatenate blobs along the channel s dimension.
optional uint32 concat_dim = 65 [default = 1];
optional HDF5OutputParameter hdf5_output_param = 1001; }