caffe.proto

// Copyright 2014 BVLC and contributors.

package caffe;

message BlobProto {
  optional int32 num = 1 [default = 0];
  optional int32 channels = 2 [default = 0];
  optional int32 length = 3 [default = 0];
  optional int32 height = 4 [default = 0];
  optional int32 width = 5 [default = 0];
  repeated float data = 6 [packed = true];
  repeated float diff = 7 [packed = true];
}

// The BlobProtoVector is simply a way to pass multiple blobproto instances
// around.
message BlobProtoVector {
  repeated BlobProto blobs = 1;
}

message VolumeDatum {
  optional int32 channels = 1;
  optional int32 length = 2 [default = 1];
  optional int32 height = 3;
  optional int32 width = 4;
  // the actual image data, in bytes
  optional bytes data = 5;
  optional int32 label = 6;
  // Optionally, the datum could also hold float data.
  repeated float float_data = 7;
}

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 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
  // The expected number of non-zero input weights for a given 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 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.
  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.
  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 testing 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 display = 0, no info
  // will be displayed.
  optional int32 display = 6;
  optional int32 max_iter = 7; // the maximum number of iterations
  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 the learning rate.
  optional float momentum = 11; // The momentum value.
  optional float weight_decay = 12; // The weight decay.
  optional int32 stepsize = 13; // the stepsize for learning rate policy "step"
  optional int32 snapshot = 14 [default = 0]; // The snapshot interval
  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.
  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 device_id = 0 in default.
  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.
  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 solvers
}

// Update the next available ID when you add a new LayerParameter field.
//
// LayerParameter next available ID: 23 (last added: memory_data_param)
message LayerParameter {
  repeated string bottom = 2; // the name of the bottom blobs
  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 lexicographical 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: 40 (last added: DOWN_SAMPLING)
  enum LayerType {
    // "NONE" layer type is 0th enum element so that we don't cause confusion
    // by defaulting to an existent LayerType (instead, should 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;
    CONVOLUTION3D = 30;
    POOLING3D = 31;
    VOLUME_DATA = 32;
    VIDEO_DATA = 33;
    DECONVOLUTION3D = 34;
    VIDEO_SEGMENTATION_DATA = 35;
    VOXEL_SOFTMAX = 36;
    VOXEL_SOFTMAX_LOSS = 37;
    VIDEO_WITH_VOXEL_TRUTH_DATA = 38;
    VOXEL_CUSTOM_LOSS = 39;
    DOWN_SAMPLING = 40;
  }
  optional LayerType type = 5; // the layer type from the enum above

  // The blobs containing the numeric parameters of the layer
  repeated BlobProto blobs = 6;
  // 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 = 7;
  // The weight decay that is multiplied on the global weight 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;
  optional DownSamplingParameter down_sampling_param = 23;

  // DEPRECATED: The layer parameters specified as a V0LayerParameter.
  // This should never be used by any code except to upgrade 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 bottom blobs
  // By default it will concatenate blobs along channels dimension
  optional uint32 concat_dim = 1 [default = 1];
}

// Message that stores parameters used by DownSamplingLayer
message DownSamplingParameter {
  enum DownSamplingType {
  	NONE = 0;
  	VOTE = 1;
  	AVERAGE = 2;
  }
  optional DownSamplingType type = 1 [default = VOTE];
  optional int32 spatial_factor = 2;
  optional int32 temporal_factor = 3;
}

// 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 size
//  optional uint32 kernel_size = 4; // The kernel size
//  optional uint32 group = 5 [default = 1]; // The group size 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 for the bias
//}

// Message that stores parameters used by Convolution3DLayer
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 size
  optional uint32 kernel_size = 4; // The kernel size
  optional uint32 group = 5 [default = 1]; // The group size for group conv
  optional uint32 kernel_depth = 6; // The kernel size
  optional uint32 stride = 7 [default = 1]; // The stride
  optional uint32 temporal_stride = 8 [default = 1]; // The stride for temporal
  optional FillerParameter weight_filler = 9; // The filler for the weight
  optional FillerParameter bias_filler = 10; // The filler for the bias
  optional uint32 filter_group = 11 [default = 1]; // divide filters into groups to reduce memory consumption
  optional uint32 temporal_pad = 12 [default = 0]; // padding size for temporal
}

// 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];
  optional int32 show_data = 8 [default = 0];  
}

// Message that stores parameters used by DropoutLayer
message DropoutParameter {
  optional float dropout_ratio = 1 [default = 0.5]; // dropout 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 files 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];
  optional uint32 new_length = 11 [default = 0];
  optional int32 show_data = 12 [default = 0];
  optional bool use_image = 13 [default = false];
  optional int32 sampling_rate = 14 [default = 1];
  optional bool use_label = 15 [default = true];
  optional bool use_temporal_jitter = 16 [default = false];
  optional float mean_value = 17 [default = 0];
  optional uint32 num_truth_channels = 18 [default = 1];
  optional bool use_byte_input = 19 [default = false];
  optional bool is_flow = 20 [default = false];
  optional float truth_scale = 21 [default = 1];
  optional float clip_min = 22;
  optional float clip_max = 23;
}


// Message that stores parameters InfogainLossLayer
message InfogainLossParameter {
  // Specify the infogain matrix source.
  optional string source = 1;
}

// Message that stores parameters used by InnerProductLayer
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
}

// 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_CHANNELS];
}

// 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 pooling method
  optional uint32 kernel_size = 2; // The kernel size
  optional uint32 stride = 3 [default = 1]; // The stride
  // The padding size -- currently implemented only for average pooling.
  optional uint32 pad = 4 [default = 0];
  optional uint32 kernel_depth = 5;
  optional uint32 temporal_stride = 6 [default = 1]; // The stride
}

// Message that stores parameters used by PoolingLayer
message AggregationParameter {

  optional float aggregation_param_r = 1 [default = 1]; // The stride
}

// Message that stores parameters used by PowerLayer
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];
}

// 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 aspect ratio
  // square: the tightest square around the window is cropped
  optional string crop_mode = 11 [default = "warp"];
}

// DEPRECATED: V0LayerParameter is the old way of specifying layer parameters
// in Caffe.  We keep this message type around for legacy support.
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

  // 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 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 = 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;
}