Caffe源码解析(一) —— caffe.proto

文章作者：Tyan
博客：noahsnail.com  |  CSDN  |  简书

caffe.proto是caffe数据结构定义的主要文件，本文主要是在caffe.proto代码的基础上加上了部分中文注释，其中的内容与caffe的prototxt文件中的结构相对应。

// syntax用来指定protobuf的版本
syntax = "proto2";// package可以看作C++中的namespace，与Caffe C++代码中的namespace caffe对应
// package用来避免名称冲突
package caffe;// 在消息定义中，每个字段都有唯一的一个数字标识符。这些标识符是用来在消息的二进制格式中识别各个字段的，一旦开始使用就不能够再改变。
// 注：[1,15]之内的标识号在编码的时候会占用一个字节。[16,2047]之内的标识号则占用2个字节。所以应该为那些频繁出现的消息元素保留 [1,15]之内的标识号。
// required：一个格式良好的消息一定要含有一个这种字段，表示该值是必须要设置的。
// optional：消息格式中该字段可以有0个或1个值（不超过1个）。
// repeated：在一个格式良好的消息中，这种字段可以重复任意多次（包括0次）。重复的值的顺序会被保留，表示该值可以重复，相当于Java中的List。// Specifies the shape (dimensions) of a Blob.
// 指定Blob的shape，4-D shape
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。 repeated int64 dim = 1 [packed = true];
}// Blob数据块，包括Blob shape，数据和微分
message BlobProto {// Blob的shape, 即numpy中的shapeoptional BlobShape shape = 7;// Blob的数据部分repeated float data = 5 [packed = true];// Blob的微分部分repeated float diff = 6 [packed = true];// Blob中的数据部分(double类型)repeated double double_data = 8 [packed = true];// Blob的微分部分(double类型)repeated double double_diff = 9 [packed = true];// 4D dimensions -- deprecated. Use "shape" instead.// Blob的4个维度，已被Blob shape代替// Blob中数据的个数(例如卷积核的个数)optional int32 num = 1 [default = 0];// Blob中数据的通道数optional int32 channels = 2 [default = 0];// Blob中数据的高度optional int32 height = 3 [default = 0];// Blob中数据的宽度optional int32 width = 4 [default = 0];
}// The BlobProtoVector is simply a way to pass multiple blobproto instances
// around.
// BlobProtoVector, 用来保存多个BlobProb对象的Vector
message BlobProtoVector {repeated BlobProto blobs = 1;
}//图像数据, channel-图像通道数, height-高度, width-宽度, data-图像像素数据, label-图像标签, float_data-图像浮点型数据(0-1之间), encoded-图像编码方式
message Datum {// 图像的通道数optional int32 channels = 1;// 图像的高度optional int32 height = 2;// 图像的宽度optional int32 width = 3;// the actual image data, in bytes// 实际的图像数据，以字节形式(uint8)表示optional bytes data = 4;// 图像对应的标签，必须为整形optional int32 label = 5;// Optionally, the datum could also hold float data.// 可选表示，图像数据表示为float数据，即0-255归一化到0-1之间repeated float float_data = 6;// If true data contains an encoded image that need to be decoded// encoded为true表示图像采用压缩表示，需要解码optional bool encoded = 7 [default = false];
}// Filler参数, filler主要对网络权重进行初始化
// Filler类型分为常量初始化（constant）、高斯分布初始化（gaussian）、positive_unitball初始化、均匀分布初始化（uniform）、xavier初始化、msra初始化、双线性初始化（bilinear）
message FillerParameter {// The filler type.// Filler的类型optional string type = 1 [default = 'constant'];// 常量初始化的值optional float value = 2 [default = 0]; // the value in constant filler// 均匀分布初始化中的最小值optional float min = 3 [default = 0]; // the min value in uniform filler// 均匀分布初始化中的最大值optional float max = 4 [default = 1]; // the max value in uniform filler// 高斯分布初始化中的均值optional float mean = 5 [default = 0]; // the mean value in Gaussian filler// 高斯分布初始化中的标准差optional float std = 6 [default = 1]; // the std value in Gaussian filler// The expected number of non-zero output weights for a given input in// Gaussian filler -- the default -1 means don't perform sparsification.// 在高斯分布初始化中给定输入及权重，期望输出非0值，默认值-1表示不进行稀疏化optional int32 sparse = 7 [default = -1];// Normalize the filler variance by fan_in, fan_out, or their average.// Applies to 'xavier' and 'msra' fillers.// 通过fan_in, fan_out或average来归一化filler方差，主要应用到'xavier'和'msra' filler中enum VarianceNorm { FAN_IN = 0; FAN_OUT = 1; AVERAGE = 2;}// 定义filler方差归一化，默认为FAN_INoptional VarianceNorm variance_norm = 8 [default = FAN_IN];
}//神经网络参数
message NetParameter {// 神经网络名字optional string name = 1; // consider giving the network a name// DEPRECATED. See InputParameter. The input blobs to the network.// 已废弃。网络的输入部分，具体请看InputParameter。repeated string input = 3;// DEPRECATED. See InputParameter. The shape of the input blobs.// 已废弃。输入blob的shape，具体请看InputParameter。repeated BlobShape input_shape = 8;// 4D input dimensions -- deprecated. Use "input_shape" instead.// If specified, for each input blob there should be four// values specifying the num, channels, height and width of the input blob.// Thus, there should be a total of (4 * #input) numbers.// 已废弃。用input_shape代替。repeated int32 input_dim = 4;// Whether the network will force every layer to carry out backward operation.// If set False, then whether to carry out backward is determined// automatically according to the net structure and learning rates.// 网络中是否每一层都执行反向传播的标志，如果设为false，反向传播会根据网络结构和学习率自动进行。optional bool force_backward = 5 [default = false];// The current "state" of the network, including the phase, level, and stage.// Some layers may be included/excluded depending on this state and the states// specified in the layers' include and exclude fields.// 网络的当前状态，包括phase, level和stage，(phase应该是对应prototxt文件中的TRAIN,TEST)// 某些层是否included/excluded依赖于层中include，exclue字段指定的state。optional NetState state = 6;// Print debugging information about results while running Net::Forward,// Net::Backward, and Net::Update.// 在执行Net::Forward,Net::Backward, Net::Update时是否打印调试信息。optional bool debug_info = 7 [default = false];// The layers that make up the net. Each of their configurations, including// connectivity and behavior, is specified as a LayerParameter.// 构成网络的layer，每一个layer的配置，包括连接性和行为都在LayerParameter中指定。repeated LayerParameter layer = 100;  // ID 100 so layers are printed last.// DEPRECATED: use 'layer' instead.// 已废弃，用layer代替。repeated V1LayerParameter layers = 2;
}// NOTE
// Update the next available ID when you add a new SolverParameter field.
// 注意：当你添加一个新的SolverParameter字段时，要更新下一个可获取的ID
// SolverParameter next available ID: 41 (last added: type)
// Solver参数
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.//// Proto filename for the train net, possibly combined with one or more test nets.// 训练网络的prototxt文件名，可能结合一个或多个测试网络optional string net = 24;// Inline train net param, possibly combined with one or more test nets.// 内联训练网络参数，可能结合一个或多个测试网络optional NetParameter net_param = 25;// 训练网络的proto文件名optional string train_net = 1; // Proto filename for the train net.// 测试网络的proto文件名repeated string test_net = 2; // Proto filenames for the test nets.// 内联训练网络参数optional NetParameter train_net_param = 21; // Inline train net params.// 内联测试网络参数repeated NetParameter test_net_param = 22; // Inline test net params.// 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.// train/test网络的状态，必须不指定或每个网络指定一次// 默认情况下，所有的状态都有solver = true，train_state的phase = TRAIN，其它默认情况根据NetState默认值设定。// train网络的状态，必须不指定或每个网络指定一次optional NetState train_state = 26;// test网络的状态，必须不指定或每个网络指定一次repeated NetState test_state = 27;// The number of iterations for each test net.// 每个测试网络的迭代次数，即测试数据的迭代次数，测试数据总数=测试迭代次数*测试数据的batch_size。repeated int32 test_iter = 3;// The number of iterations between two testing phases.// 两次测试间隔的迭代次数，即训练数据迭代多少次进行一次测试。optional int32 test_interval = 4 [default = 0];// 测试数据的loss，默认情况下不计算optional bool test_compute_loss = 19 [default = false];// If true, run an initial test pass before the first iteration,// ensuring memory availability and printing the starting value of the loss.// 如果为true，在第一次迭代之前进行一次初始测试，从而确保内存可用性并输出初始损失值。optional bool test_initialization = 32 [default = true];// 基本学习率optional float base_lr = 5; // The base learning rate// the number of iterations between displaying info. If display = 0, no info will be displayed.// 执行多少次迭代显示一次信息，如果display = 0，不输出信息。optional int32 display = 6;// Display the loss averaged over the last average_loss iterations// 输出的平均损失是之前多少次迭代的平均损失。optional int32 average_loss = 33 [default = 1];// 训练的最大迭代次数optional int32 max_iter = 7; // the maximum number of iterations// accumulate gradients over `iter_size` x `batch_size` instances// 累积`iter_size` x `batch_size`个实例的梯度optional int32 iter_size = 36 [default = 1];// 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 but it allows 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))))//// where base_lr, max_iter, gamma, step, stepvalue and power are defined// in the solver parameter protocol buffer, and iter is the current iteration.// 学习率的变化策略optional string lr_policy = 8;// 学习率的计算参数optional float gamma = 9; // The parameter to compute the learning rate.// 学习率的计算参数optional float power = 10; // The parameter to compute the learning rate.// 动量参数optional float momentum = 11; // The momentum value.// 权重衰减，权重衰减主要影响神经网络的正则项，具体可参考Caffe文档optional float weight_decay = 12; // The weight decay.// regularization types supported: L1 and L2, controlled by weight_decay// 正则化类型支持L1和L2，受weight_decay控制。optional string regularization_type = 29 [default = "L2"];// the stepsize for learning rate policy "step"// 学习率方案为step时的参数optional int32 stepsize = 13;// the stepsize for learning rate policy "multistep"// 学习率方案为multistep时的参数repeated int32 stepvalue = 34;// Set clip_gradients to >= 0 to clip parameter gradients to that L2 norm,// whenever their actual L2 norm is larger.// 设置clip_gradients >= 0可以削减L2范数的梯度，当真实L2范数的梯度大于clip_gradients，将L2范数的梯度设为clip_gradientsoptional float clip_gradients = 35 [default = -1];// snapshot的间隔，即迭代多少次保存一次snapshotoptional int32 snapshot = 14 [default = 0]; // The snapshot interval// snapshot的前缀optional string snapshot_prefix = 15; // The prefix for the snapshot.// whether to snapshot diff in the results or not. Snapshotting diff will help// debugging but the final protocol buffer size will be much larger.// 是否在结果中保存snapshot的差分，snapshot diff有助于调试，但snapshot的文件会更大。optional bool snapshot_diff = 16 [default = false];// snapshot的保存格式（hdf5,binaryproto）。enum SnapshotFormat {HDF5 = 0; BINARYPROTO = 1;}// snapshot默认保存为BINARYPROTO。optional SnapshotFormat snapshot_format = 37 [default = BINARYPROTO];// the mode solver will use: 0 for CPU and 1 for GPU. Use GPU in default.// 求解神经网络的方式，0 CPU, 1 GPU。默认使用GPUenum SolverMode { CPU = 0; GPU = 1;}// 求解神经网络的模式，0 CPU, 1 GPU。默认使用GPUoptional SolverMode solver_mode = 17 [default = GPU];// the device_id will that be used in GPU mode. Use device_id = 0 in default.// device_id是GPU模式下GPU的ID。optional int32 device_id = 18 [default = 0];// If non-negative, the seed with which the Solver will initialize the Caffe// random number generator -- useful for reproducible results. Otherwise,// (and by default) initialize using a seed derived from the system clock.// 如果是非负值，seed用来初始化Caffe的随机数生成器，对于再见结果是很有用的，默认情况下，seed的是从系统时钟获取。optional int64 random_seed = 20 [default = -1];// type of the solver// 神经网络求解的类型, 默认为SGDoptional string type = 40 [default = "SGD"];// numerical stability for RMSProp, AdaGrad and AdaDelta and Adam// RMSProp, AdaGrad, AdaDelta, Adam求解类型的参数optional float delta = 31 [default = 1e-8];// parameters for the Adam solver// Adam求解类型的参数optional float momentum2 = 39 [default = 0.999];// RMSProp decay value// MeanSquare(t) = rms_decay*MeanSquare(t-1) + (1-rms_decay)*SquareGradient(t)// RMSProp类型的衰减值optional float rms_decay = 38 [default = 0.99];// If true, print information about the state of the net that may help with// debugging learning problems.// 如果设为true，会输出网络的状态信息，有助于调试optional bool debug_info = 23 [default = false];// If false, don't save a snapshot after training finishes.// 如果设为false，不保存训练结束的snapshot。optional bool snapshot_after_train = 28 [default = true];// DEPRECATED: old solver enum types, use string instead// 已废弃，使用string代替enum SolverType { SGD = 0; NESTEROV = 1; ADAGRAD = 2; RMSPROP = 3; ADADELTA = 4; ADAM = 5;}// DEPRECATED: use type instead of solver_type// 已废弃：使用type代替optional SolverType solver_type = 30 [default = SGD];
}// A message that stores the solver snapshots
// 保存solver snapshots
message SolverState {// 当前的迭代次数optional int32 iter = 1; // The current iteration// 保存学习到的网络optional string learned_net = 2; // The file that stores the learned net.// sgd的求解历史repeated BlobProto history = 3; // The history for sgd solvers// 学习的当前stepoptional int32 current_step = 4 [default = 0]; // The current step for learning rate
}// 定义phase
enum Phase { TRAIN = 0; TEST = 1;
}// 网络状态
message NetState {// 属于哪个phaseoptional Phase phase = 1 [default = TEST];optional int32 level = 2 [default = 0];repeated string stage = 3;
}// 网络状态分类
message NetStateRule {// Set phase to require the NetState have a particular phase (TRAIN or TEST)// to meet this rule.// 设置phaseoptional Phase phase = 1;// Set the minimum and/or maximum levels in which the layer should be used.// Leave undefined to meet the rule regardless of level.// 设置layer的leveloptional 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stage集合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).
// 指定训练参数及名称以及权重共享的其它设置
message ParamSpec {// The names of the parameter blobs -- useful for sharing parameters among// layers, but never required otherwise. To share a parameter between two// layers, give it a (non-empty) name.// 两个layer之间进行参数共享的blob名字optional string name = 1;// Whether to require shared weights to have the same shape, or just the same// count -- defaults to STRICT if unspecified.// 参数共享时是否需要具有相同的shape，默认情况下需要有相同的shapeoptional DimCheckMode share_mode = 2;// 参数共享时的维度检查enum DimCheckMode {// STRICT (default) requires that num, channels, height, width each match. STRICT = 0;// PERMISSIVE requires only the count (num*channels*height*width) to match. PERMISSIVE = 1;}// The multiplier on the global learning rate for this parameter.// 学习率参数, learning rate = base_lr * lr_multoptional float lr_mult = 3 [default = 1.0];// The multiplier on the global weight decay for this parameter.// 权重衰减参数, weight = weight_decay * decay_multoptional float decay_mult = 4 [default = 1.0];
}// NOTE
// Update the next available ID when you add a new LayerParameter field.
// LayerParameter next available layer-specific ID: 147 (last added: recurrent_param)
// 注意：当你添加一个新的LayerParameter字段时，要更新下一个可获取的ID
message LayerParameter {// layer名称optional string name = 1; // the layer name// layer类型optional string type = 2; // the layer type// layer的输入repeated string bottom = 3; // the name of each bottom blob// layer的输出repeated string top = 4; // the name of each top blob// The train / test phase for computation.// layer用在train/test phaseoptional 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.// layer对最终的loss损失值的贡献率repeated float loss_weight = 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repeated ParamSpec param = 6;// The blobs containing the numeric parameters of the layer.// layer的blobsrepeated 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.// 指定反向传播是否传播到每一个bottom，如果不指定，caffe会自动检查推断是否每一个输入都需要反向传播来计算梯度。如果一些输入设为true,// 则这些layer强制进行反向传播，如果设为false，这些layer将跳过反向传播。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.// 控制layer included/excludedrepeated NetStateRule include = 8;repeated NetStateRule exclude = 9;// Parameters for data pre-processing.// 数据预处理参数optional TransformationParameter transform_param = 100;// Parameters shared by loss layers.// loss layer的参数共享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.// 特定layer的参数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 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;
}// Message that stores parameters used to apply transformation to the data layer's data
// 用来进行数据层（图像）变换的参数
message 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.// 像素归一化，归一化之前会减去均值optional float scale = 1 [default = 1];// Specify if we want to randomly mirror data.// 图像进行随机mirror操作optional bool mirror = 2 [default = false];// Specify if we would like to randomly crop an image.// 图像随机crop操作optional uint32 crop_size = 3 [default = 0];// mean_file and mean_value cannot be specified at the same time// 图像的均值文件optional string mean_file = 4;// if specified can be repeated once (would subtract it from all the channels)// or can be repeated the same number of times as channels// (would subtract them from the corresponding channel)// 图像的均值，手动指定，通常是三个repeated float mean_value = 5;// Force the decoded image to have 3 color channels.// 强制图像必须有三个颜色通道optional bool force_color = 6 [default = false];// Force the decoded image to have 1 color channels.// 强制图像为灰度图像optional bool force_gray = 7 [default = false];
}// Message that stores parameters shared by loss layers
// loss层参数
message LossParameter {// If specified, ignore instances with the given label.// 如果指定，则label等于ignore_label的样本将不参与Loss计算，并且反向传播时梯度直接置0。optional int32 ignore_label = 1;// How to normalize the loss for loss layers that aggregate across batches,// spatial dimensions, or other dimensions. Currently only implemented in// SoftmaxWithLoss and SigmoidCrossEntropyLoss layers.// 指定loss归一化的方式enum NormalizationMode {// Divide by the number of examples in the batch times spatial dimensions.// Outputs that receive the ignore label will NOT be ignored in computing// the normalization factor.// 所有样本都参与计算，包括ignore label FULL = 0;// Divide by the total number of output locations that do not take the// ignore_label. If ignore_label is not set, this behaves like FULL.// 所有样本都参与计算，不包括ignore label VALID = 1;// Divide by the batch size.// 除以给定的batch size。 BATCH_SIZE = 2;// Do not normalize the loss.// 不归一化loss NONE = 3;}// For historical reasons, the default normalization for// SigmoidCrossEntropyLoss is BATCH_SIZE and *not* VALID.// loss归一化方式optional NormalizationMode normalization = 3 [default = VALID];// Deprecated. Ignored if normalization is specified. If normalization// is not specified, then setting this to false will be equivalent to// normalization = BATCH_SIZE to be consistent with previous behavior.// 已废弃。Loss会除以参与计算的样本总数；否则Loss等于直接求和optional bool normalize = 2;
}// Messages that store parameters used by individual layer types follow, in
// alphabetical order.
// accuracy层参数
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).// 计算前top-k的准确率，默认计算top-1准确率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}.// 指定在哪个维度上计算labeloptional int32 axis = 2 [default = 1];// If specified, ignore instances with the given label.// 如果指定，则忽略给定标签的实例optional int32 ignore_label = 3;
}// 标签最大化参数，标签最大化即确定概率最大的label
message ArgMaxParameter {// If true produce pairs (argmax, maxval)// 如果为真，则生成(argmax, maxval)optional bool out_max_val = 1 [default = false];// 类别的top-koptional 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.// 根据axis进行标签最大化optional int32 axis = 3;
}// 参数拼接，在deconv的prototxt文件中见过
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).// 参数拼接时的维度，按axis进行拼接optional int32 axis = 2 [default = 1];// DEPRECATED: alias for "axis" -- does not support negative indexing.// 已废弃。与axis一样。optional uint32 concat_dim = 1 [default = 1];
}// batch norm层的相关参数, batch norm layer通常配与scale layer一起使用，具体用法可参考Resnet结构
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.// 如果设为false，累计全部的mean/variance，如果为true，使用累计值代替batch上mean/variance的计算// true是使用了caffe内部的均值和方差，false是使用了每个Batch里的数据的均值和方差optional bool use_global_stats = 1;// How much does the moving average decay each iteration?// 每次迭代平均值衰减比例optional float moving_average_fraction = 2 [default = .999];// Small value to add to the variance estimate so that we don't divide by// zero.// variance估计时为了使除数不为0，需要加上epsoptional float eps = 3 [default = 1e-5];
}// bias层参数，没找到实际的应用例子
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;
}// 对比损失层，siamese network中使用了对比损失
message ContrastiveLossParameter {// margin for dissimilar pair// 不相似的样本对的距离保持在margin以上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// 第一版对比损失没有完全按论文写，如果为false，则按照论文原来的公式计算optional bool legacy_version = 2 [default = false];
}// 卷积层参数
message ConvolutionParameter {// 输出数据的个数optional uint32 num_output = 1; // The number of outputs for the layer// 是否有偏置项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.// 卷积padding的大小repeated uint32 pad = 3; // The padding size; defaults to 0// 卷积核的大小repeated uint32 kernel_size = 4; // The kernel size// 卷积的步长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 à trous from Holschneider et al. 1987.)// 卷积膨胀，在卷积的时候可以skip一定长度的像素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.// padding, kernel, stride的宽度和高度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)// 来自于AlexNet论文optional uint32 group = 5 [default = 1]; // The group size for group conv// 权重初始化optional FillerParameter weight_filler = 7; // The filler for the weight// 偏置初始化optional FillerParameter bias_filler = 8; // The filler for the biasenum Engine { DEFAULT = 0; CAFFE = 1; CUDNN = 2;}// 卷积的方式的选择，default是正常的卷积，caffe是矩阵乘法的卷积，cudnn是cuda库流并行式的卷积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.// 通道channel所在的维度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.)// 如果输入数据维度等于2，则执行通用的ND卷积，否则正常执行卷积optional bool force_nd_im2col = 17 [default = false];
}// 图像裁剪参数
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).// axis是在哪个维度上进行裁剪，会裁剪轴2及之后的所有轴optional int32 axis = 1 [default = 2];// offset设置是每个维度进行裁剪时的偏移量repeated uint32 offset = 2;
}// 数据层参数
message DataParameter {enum DB { LEVELDB = 0; LMDB = 1;}// Specify the data source.// 设定数据源路径optional string source = 1;// Specify the batch size.// 指定一次处理的图片数量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.// rand_skip跳过指定的数据点，避免异步的sgd从同一个数据点开始optional uint32 rand_skip = 7 [default = 0];// 使用的数据库类型，LMDB or LEVELDBoptional 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.// 已废弃。图像归一化，在TransformationParameter中。optional float scale = 2 [default = 1];// 已废弃。均值文件，在TransformationParameter中。optional string mean_file = 3;// DEPRECATED. See TransformationParameter. Specify if we would like to randomly// crop an image.// 已废弃。图像裁剪，在TransformationParameter中。optional uint32 crop_size = 5 [default = 0];// DEPRECATED. See TransformationParameter. Specify if we want to randomly mirror// data.// 已废弃。图像翻转，在TransformationParameter中。optional bool mirror = 6 [default = false];// Force the encoded image to have 3 color channels// 强制图像数据有三个颜色通道optional bool force_encoded_color = 9 [default = false];// Prefetch queue (Number of batches to prefetch to host memory, increase if// data access bandwidth varies).// 预先拉取batch的数目optional uint32 prefetch = 10 [default = 4];
}// dropout层参数
message DropoutParameter {// 为了避免过拟合，参数随机失活的比例optional float dropout_ratio = 1 [default = 0.5]; // dropout ratio
}// DummyDataLayer fills any number of arbitrarily shaped blobs with random
// (or constant) data generated by "Fillers" (see "message FillerParameter").
// DummyData层的参数
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.// blob数据的生成方式repeated FillerParameter data_filler = 1;// 数据的维度repeated BlobShape shape = 6;// 4D dimensions -- deprecated. Use "shape" instead.// 已废弃。使用shape代替。repeated uint32 num = 2;repeated uint32 channels = 3;repeated uint32 height = 4;repeated uint32 width = 5;
}//Eltwise层的参数
message EltwiseParameter {// 操作的类型enum EltwiseOp { PROD = 0; SUM = 1; MAX = 2;}// 数据操作分三种：点乘，相加，取最大值optional EltwiseOp operation = 1 [default = SUM]; // element-wise operation// SUM操作时各个blob对应的系数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.)// 在进行PROD操作，即乘法时是否使用异步操作来计算梯度，更慢但更稳定。optional bool stable_prod_grad = 3 [default = true];
}// Message that stores parameters used by ELULayer
// ELU层的参数，具体看论文
message ELUParameter {// Described in:// Clevert, D.-A., Unterthiner, T., & Hochreiter, S. (2015). Fast and Accurate// Deep Network Learning by Exponential Linear Units (ELUs). arXivoptional float alpha = 1 [default = 1];
}// Message that stores parameters used by EmbedLayer
// Embed层的参数，主要用于LSTM等翻译网络
message EmbedParameter {// Embed层的输出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.// Embed层的输入optional uint32 input_dim = 2;// 是否使用偏置项optional bool bias_term = 3 [default = true]; // Whether to use a bias term// 权重生成optional FillerParameter weight_filler = 4; // The filler for the weight// 偏置生成optional FillerParameter bias_filler = 5; // The filler for the bias}// Message that stores parameters used by ExpLayer
// Exp层的参数，即指数层参数
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).// 指数层的计算是y = base ^ (shift + scale * x)，下面分别是公式中的三个参数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
// Flatten层的参数，主要是按某个轴展开（平铺），mnist demo的mnist_autoencode就使用了Flatten层
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).// 从哪一层开始展开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).// 展开到哪一层结束optional int32 end_axis = 2 [default = -1];
}// Message that stores parameters used by HDF5DataLayer
// HDF5数据层的参数
message HDF5DataParameter {// Specify the data source.// HDF5层输入数据的数据源optional string source = 1;// Specify the batch size.// 训练的batch_sizeoptional 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.// 是否对HDF5的输入数据进行shuffleoptional bool shuffle = 3 [default = false];
}// HDF5输出层参数
message HDF5OutputParameter {// 输出的HDF5文件的文件名optional string file_name = 1;
}// HingeLoss层参数
message HingeLossParameter {enum Norm {L1 = 1;L2 = 2;}// Specify the Norm to use L1 or L2// 指定HingeLoss的类型optional Norm norm = 1 [default = L1];
}// ImageData层参数，网络中直接输入原图
message ImageDataParameter {// Specify the data source.// 描述图像路径及标签的文件optional string source = 1;// Specify the batch size.// 训练的batch sizeoptional 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.// rand_skip跳过指定的数据点，避免异步的sgd从同一个数据点开始，与Data层中是一样的optional uint32 rand_skip = 7 [default = 0];// Whether or not ImageLayer should shuffle the list of files at every epoch.// 是否对图像顺序进行shuffleoptional bool shuffle = 8 [default = false];// It will also resize images if new_height or new_width are not zero.// 图像resize的高度optional uint32 new_height = 9 [default = 0];// 图像resize的宽度optional uint32 new_width = 10 [default = 0];// Specify if the images are color or gray// 指定图像彩色图像还是灰度图像，默认彩色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.// 已废弃。参考TransformationParameter中的scaleoptional float scale = 2 [default = 1];// 指定均值文件optional string mean_file = 3;// DEPRECATED. See TransformationParameter. Specify if we would like to randomly// crop an image.// 已废弃。参考TransformationParameter中的crop_sizeoptional uint32 crop_size = 5 [default = 0];// DEPRECATED. See TransformationParameter. Specify if we want to randomly mirror// data.// 已废弃，参考TransformationParameter的mirror。optional bool mirror = 6 [default = false];// 不太清楚root_folder具体是什么optional string root_folder = 12 [default = ""];
}// 信息增益损失层参数
message InfogainLossParameter {// Specify the infogain matrix source.// 指定存储信息增益矩阵的源文件optional string source = 1;
}// InnerProduct层的参数
message InnerProductParameter {// InnerProduct层的输出optional uint32 num_output = 1; // The number of outputs for the layer// 是否有偏置项optional bool bias_term = 2 [default = true]; // whether to have bias terms// 权重初始化，随机生成optional FillerParameter weight_filler = 3; // The filler for the weight// 偏置初始化，随机生成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).// 从某一维度开始进行内积计算，前面的维度保留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.// 是否对权重矩阵进行转置optional bool transpose = 6 [default = false];
}// Input参数，caffe网络部署时会用到
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.// 输入数据的shaperepeated BlobShape shape = 1;
}// Message that stores parameters used by LogLayer
// Log层参数，对数据进行Log运算
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)// Log层计算公式为y = log_base(shift + scale * x)，下面分别是公式中的三个参数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 LRNLayer
// LRN层的参数，局部归一化，AlexNet中的LRN
message LRNParameter {// 如果是跨通道LRN，则表示求和的通道数；如果是在通道内LRN，则表示求和的正方形区域长度。optional uint32 local_size = 1 [default = 5];// 归一化公式中的参数optional float alpha = 2 [default = 1.];optional float beta = 3 [default = 0.75];enum NormRegion { ACROSS_CHANNELS = 0; WITHIN_CHANNEL = 1;}// 归一化的区域，分为通道内和跨通道两种optional NormRegion norm_region = 4 [default = ACROSS_CHANNELS];optional float k = 5 [default = 1.];enum Engine { DEFAULT = 0; CAFFE = 1; CUDNN = 2;}// 与前面的engine是一样的optional Engine engine = 6 [default = DEFAULT];
}// 内存数据层参数
message MemoryDataParameter {// 训练的batch_sizeoptional uint32 batch_size = 1;// 图像通道数optional uint32 channels = 2;// 图像高度optional uint32 height = 3;// 图像宽度optional uint32 width = 4;
}// mean-variance normalization层参数
message MVNParameter {// This parameter can be set to false to normalize mean only// 是否对方差进行归一化optional bool normalize_variance = 1 [default = true];// This parameter can be set to true to perform DNN-like MVN// 是否进行跨通道的MVNoptional bool across_channels = 2 [default = false];// Epsilon for not dividing by zero while normalizing variance// 避免除数为0，与前面的一样optional float eps = 3 [default = 1e-9];
}// 参数层参数
message ParameterParameter {// 用户自己定义的shapeoptional BlobShape shape = 1;
}// 池化层参数
message PoolingParameter {enum PoolMethod { MAX = 0; AVE = 1; STOCHASTIC = 2;}// 池化的方式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.// padding的大小optional uint32 pad = 4 [default = 0]; // The padding size (equal in Y, X)// padding的高度optional uint32 pad_h = 9 [default = 0]; // The padding height// padding的宽度optional uint32 pad_w = 10 [default = 0]; // The padding width// 池化的核大小optional uint32 kernel_size = 2; // The kernel size (square)// 核高度optional uint32 kernel_h = 5; // The kernel height// 核宽度optional uint32 kernel_w = 6; // The kernel width// 池化的步长optional uint32 stride = 3 [default = 1]; // The stride (equal in Y, X)// 步长的高度optional uint32 stride_h = 7; // The stride height// 步长的宽度optional uint32 stride_w = 8; // The stride widthenum Engine { DEFAULT = 0; CAFFE = 1; CUDNN = 2;}// 执行池化操作的类型，与前面的一样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// global_pooling是对多个通道进行pooling，例如从三通道pooling为单通道optional bool global_pooling = 12 [default = false];
}// Power层参数
message PowerParameter {// PowerLayer computes outputs y = (shift + scale * x) ^ power.// Power的计算公式为y = (shift + scale * x) ^ power，下面是公式中的参数optional float power = 1 [default = 1.0];optional float scale = 2 [default = 1.0];optional float shift = 3 [default = 0.0];
}// python layer参数，在faster rcnn中有应用
message PythonParameter {// python模块名称optional string module = 1;// python模块中层的名字，即类名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`.// 可以用来设置参数，key-value形式，可以参考faster rcnn中模型的train.prototxtoptional 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.// 是否需要在并行时共享layeroptional bool share_in_parallel = 4 [default = false];
}// Message that stores parameters used by RecurrentLayer
// Recurrent层参数
message RecurrentParameter {// The dimension of the output (and usually hidden state) representation --// must be explicitly set to non-zero.// Recurrent层的输出——必须非零optional uint32 num_output = 1 [default = 0];// 权重初始化，随机生成初始化optional FillerParameter weight_filler = 2; // The filler for the weight// 偏置初始化，随机生成optional FillerParameter bias_filler = 3; // The filler for the bias// Whether to enable displaying debug_info in the unrolled recurrent net.// 是否输出调试信息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.// 是否添加额外的输入optional bool expose_hidden = 5 [default = false];
}// Message that stores parameters used by ReductionLayer
// Reduction层参数
message ReductionParameter {enum ReductionOp { SUM = 1; ASUM = 2; SUMSQ = 3; MEAN = 4;}// 通过reduction操作来将数据减少到一维，可以通过上面的四种方式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.// 在哪个轴上执行reduction操作optional int32 axis = 2 [default = 0];// 输出系数optional float coeff = 3 [default = 1.0]; // coefficient for output
}// Message that stores parameters used by ReLULayer
// ReLU层参数
message ReLUParameter {// Allow non-zero slope for negative inputs to speed up optimization// Described in:// 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.// ReLUU操作的阈值optional float negative_slope = 1 [default = 0];enum Engine { DEFAULT = 0; CAFFE = 1; CUDNN = 2;}// 执行ReLU操作的类型，与前面的一样optional Engine engine = 2 [default = DEFAULT];
}// Reshape层参数，与numpy中的Reshape作用是一样的
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 } }// reshape之后输出的维度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];
}// Scale层参数，与batch norm layer配合使用，可参考Resnet结构
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).//// 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).// 是否使用偏置项optional bool bias_term = 4 [default = false];// 偏置项初始化optional FillerParameter bias_filler = 5;
}// Sigmoid层参数
message SigmoidParameter {enum Engine { DEFAULT = 0; CAFFE = 1; CUDNN = 2;}// 使用哪种sigmoid实现optional Engine engine = 1 [default = DEFAULT];
}// Slice层参数
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).// 在哪个维度上进行拆分optional int32 axis = 3 [default = 1];// 指定拆分点repeated uint32 slice_point = 2;// DEPRECATED: alias for "axis" -- does not support negative indexing.// 已废弃。optional uint32 slice_dim = 1 [default = 1];
}// Message that stores parameters used by SoftmaxLayer, SoftmaxWithLossLayer
// Softmax层参数
message SoftmaxParameter {enum Engine { DEFAULT = 0; CAFFE = 1; CUDNN = 2;}// 使用哪种softmax实现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.// 在哪个维度上进行softmaxoptional int32 axis = 2 [default = 1];
}// TanH层参数
message TanHParameter {enum Engine { DEFAULT = 0; CAFFE = 1; CUDNN = 2;}// 执行tanh激活函数的类型optional Engine engine = 1 [default = DEFAULT];
}// Message that stores parameters used by TileLayer
// Tile层参数，扩大某一维度
message TileParameter {// The index of the axis to tile.// 扩大哪个维度optional int32 axis = 1 [default = 1];// The number of copies (tiles) of the blob to output.// 创建多少个副本optional int32 tiles = 2;
}// Message that stores parameters used by ThresholdLayer
// Threshold层参数，主要用来测试输入是否超过阈值
message ThresholdParameter {// 设置阈值optional float threshold = 1 [default = 0]; // Strictly positive values
}// WindowData层参数
message WindowDataParameter {// Specify the data source.// 指定数据源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.// 是否归一化optional float scale = 2 [default = 1];// 图像均值文件optional string mean_file = 3;// Specify the batch size.// 训练的batch_sizeoptional uint32 batch_size = 4;// Specify if we would like to randomly crop an image.// 是否随机cropoptional uint32 crop_size = 5 [default = 0];// Specify if we want to randomly mirror data.// 是否随机mirroroptional bool mirror = 6 [default = false];// Foreground (object) overlap threshold// 前景重叠阈值optional float fg_threshold = 7 [default = 0.5];// Background (non-object) overlap threshold// 背景重叠阈值optional float bg_threshold = 8 [default = 0.5];// Fraction of batch that should be foreground objects// 前景比例optional float fg_fraction = 9 [default = 0.25];// Amount of contextual padding to add around a window// (used only by the window_data_layer)// 是否paddingoptional 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// crop的方式optional string crop_mode = 11 [default = "warp"];// cache_images: will load all images in memory for faster access// 是否缓存图像，即将图像都转入内存optional bool cache_images = 12 [default = false];// append root_folder to locate images// 图像文件的根目录optional string root_folder = 13 [default = ""];
}// SPP层参数，SPP是spatial pyramid pooling，空间金字塔池化，具体可参考何凯明论文Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
message SPPParameter {enum PoolMethod { MAX = 0; AVE = 1; STOCHASTIC = 2;}// 空间金字塔高度optional uint32 pyramid_height = 1;// 池化方法optional PoolMethod pool = 2 [default = MAX]; // The pooling methodenum Engine { DEFAULT = 0; CAFFE = 1; CUDNN = 2;}// 执行SPP的方式optional Engine engine = 6 [default = DEFAULT];
}// DEPRECATED: use LayerParameter.
// 已废弃，使用LayerParameter。
message V1LayerParameter {repeated string bottom = 2;repeated string top = 3;optional string name = 4;repeated NetStateRule include = 32;repeated NetStateRule exclude = 33;enum LayerType { 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.
// 已废弃。
message V0LayerParameter {optional string name = 1; // the layer nameoptional 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 layeroptional bool biasterm = 4 [default = true]; // whether to have bias termsoptional FillerParameter weight_filler = 5; // The filler for the weightoptional FillerParameter bias_filler = 6; // The filler for the biasoptional uint32 pad = 7 [default = 0]; // The padding sizeoptional uint32 kernelsize = 8; // The kernel sizeoptional uint32 group = 9 [default = 1]; // The group size for group convoptional uint32 stride = 10 [default = 1]; // The strideenum PoolMethod { MAX = 0; AVE = 1; STOCHASTIC = 2;}optional PoolMethod pool = 11 [default = MAX]; // The pooling methodoptional float dropout_ratio = 12 [default = 0.5]; // dropout ratiooptional uint32 local_size = 13 [default = 5]; // for local response normoptional float alpha = 14 [default = 1.]; // for local response normoptional float beta = 15 [default = 0.75]; // for local response normoptional float k = 22 [default = 1.];// For data layers, specify the data sourceoptional 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 layerrepeated 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 thresholdoptional float det_fg_threshold = 54 [default = 0.5];// background (non-object) overlap thresholdoptional float det_bg_threshold = 55 [default = 0.5];// Fraction of batch that should be foreground objectsoptional 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 croppedoptional 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;
}// PReLU层参数，ReLU的进化版本
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.// 参数初始化optional FillerParameter filler = 1;// Whether or not slope parameters are shared across channels.// 是否在各通道共享参数optional bool channel_shared = 2 [default = false];
}