+++ /dev/null
-syntax = "proto2";
-
-package caffe;
-
-// Specifies the shape (dimensions) of a Blob.
-message BlobShape {
- repeated int64 dim = 1 [packed = true];
-}
-
-message BlobProto {
- optional BlobShape shape = 7;
- repeated float data = 5 [packed = true];
- repeated float diff = 6 [packed = true];
- repeated double double_data = 8 [packed = true];
- repeated double double_diff = 9 [packed = true];
-
- // 4D dimensions -- deprecated. Use "shape" instead.
- optional int32 num = 1 [default = 0];
- optional int32 channels = 2 [default = 0];
- optional int32 height = 3 [default = 0];
- optional int32 width = 4 [default = 0];
-}
-
-// The BlobProtoVector is simply a way to pass multiple blobproto 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;
- // If true data contains an encoded image that need to be decoded
- optional bool encoded = 7 [default = false];
-}
-
-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 output weights for a given input in
- // Gaussian filler -- the default -1 means don't perform sparsification.
- optional int32 sparse = 7 [default = -1];
- // Normalize the filler variance by fan_in, fan_out, or their average.
- // Applies to 'xavier' and 'msra' fillers.
- enum VarianceNorm {
- FAN_IN = 0;
- FAN_OUT = 1;
- AVERAGE = 2;
- }
- optional VarianceNorm variance_norm = 8 [default = FAN_IN];
-}
-
-message NetParameter {
- optional string name = 1; // consider giving the network a name
- // DEPRECATED. See InputParameter. The input blobs to the network.
- repeated string input = 3;
- // DEPRECATED. See InputParameter. The shape of the input blobs.
- 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.
- 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];
- // 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.
- optional NetState state = 6;
-
- // Print debugging information about results while running Net::Forward,
- // Net::Backward, and Net::Update.
- 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.
- repeated LayerParameter layer = 100; // ID 100 so layers are printed last.
-
- // DEPRECATED: use 'layer' instead.
- repeated V1LayerParameter layers = 2;
-}
-
-// NOTE
-// Update the next available ID when you add a new SolverParameter field.
-//
-// SolverParameter next available ID: 43 (last added: weights)
-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.
- optional string net = 24;
- // Inline train net param, possibly combined with one or more test nets.
- optional NetParameter net_param = 25;
-
- optional string train_net = 1; // Proto filename for the train net.
- repeated string test_net = 2; // Proto filenames for the test nets.
- optional NetParameter train_net_param = 21; // Inline train net params.
- repeated NetParameter test_net_param = 22; // Inline test net params.
-
- // The states for the train/test nets. Must be unspecified or
- // specified once per net.
- //
- // By default, train_state will have phase = TRAIN,
- // and all test_state's will have phase = TEST.
- // Other defaults are set according to the NetState defaults.
- optional NetState train_state = 26;
- repeated NetState test_state = 27;
-
- // The number of iterations for each test net.
- repeated int32 test_iter = 3;
-
- // The number of iterations between two testing phases.
- optional int32 test_interval = 4 [default = 0];
- 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.
- optional bool test_initialization = 32 [default = true];
- 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;
- // Display the loss averaged over the last average_loss iterations
- optional int32 average_loss = 33 [default = 1];
- optional int32 max_iter = 7; // the maximum number of iterations
- // accumulate gradients over `iter_size` x `batch_size` instances
- 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.
- optional string lr_policy = 8;
- 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.
- // regularization types supported: L1 and L2
- // controlled by weight_decay
- optional string regularization_type = 29 [default = "L2"];
- // the stepsize for learning rate policy "step"
- optional int32 stepsize = 13;
- // the stepsize for learning rate policy "multistep"
- repeated int32 stepvalue = 34;
-
- // Set clip_gradients to >= 0 to clip parameter gradients to that L2 norm,
- // whenever their actual L2 norm is larger.
- optional float clip_gradients = 35 [default = -1];
-
- optional int32 snapshot = 14 [default = 0]; // The snapshot interval
- // The prefix for the snapshot.
- // If not set then is replaced by prototxt file path without extention.
- // If is set to directory then is augmented by prototxt file name
- // without extention.
- optional string snapshot_prefix = 15;
- // 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];
- enum SnapshotFormat {
- HDF5 = 0;
- BINARYPROTO = 1;
- }
- optional SnapshotFormat snapshot_format = 37 [default = BINARYPROTO];
- // the mode solver will use: 0 for CPU and 1 for GPU. Use GPU in default.
- enum SolverMode {
- CPU = 0;
- GPU = 1;
- }
- 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];
-
- // type of the solver
- optional string type = 40 [default = "SGD"];
-
- // numerical stability for RMSProp, AdaGrad and AdaDelta and Adam
- optional float delta = 31 [default = 1e-8];
- // parameters for the Adam solver
- optional float momentum2 = 39 [default = 0.999];
-
- // RMSProp decay value
- // MeanSquare(t) = rms_decay*MeanSquare(t-1) + (1-rms_decay)*SquareGradient(t)
- optional float rms_decay = 38 [default = 0.99];
-
- // If true, print information about the state of the net that may help with
- // debugging learning problems.
- optional bool debug_info = 23 [default = false];
-
- // If false, don't save a snapshot after training finishes.
- optional bool snapshot_after_train = 28 [default = true];
-
- // DEPRECATED: old solver enum types, use string instead
- enum SolverType {
- SGD = 0;
- NESTEROV = 1;
- ADAGRAD = 2;
- RMSPROP = 3;
- ADADELTA = 4;
- ADAM = 5;
- }
- // DEPRECATED: use type instead of solver_type
- optional SolverType solver_type = 30 [default = SGD];
-
- // Overlap compute and communication for data parallel training
- optional bool layer_wise_reduce = 41 [default = true];
-
- // Path to caffemodel file(s) with pretrained weights to initialize finetuning.
- // Tha same as command line --weights parameter for caffe train command.
- // If command line --weights parameter if specified, it has higher priority
- // and owerwrites this one(s).
- // If --snapshot command line parameter is specified, this one(s) are ignored.
- // If several model files are expected, they can be listed in a one
- // weights parameter separated by ',' (like in a command string) or
- // in repeated weights parameters separately.
- repeated string weights = 42;
-}
-
-// 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
- optional int32 current_step = 4 [default = 0]; // The current step for learning rate
-}
-
-enum Phase {
- TRAIN = 0;
- TEST = 1;
-}
-
-message NetState {
- optional Phase phase = 1 [default = TEST];
- optional int32 level = 2 [default = 0];
- repeated string stage = 3;
-}
-
-message NetStateRule {
- // Set phase to require the NetState have a particular phase (TRAIN or TEST)
- // to meet this rule.
- optional 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.
- optional int32 min_level = 2;
- optional int32 max_level = 3;
-
- // Customizable sets of stages to include or exclude.
- // The net must have ALL of the specified stages and NONE of the specified
- // "not_stage"s to meet the rule.
- // (Use multiple NetStateRules to specify conjunctions of stages.)
- 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).
-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.
- optional string name = 1;
-
- // Whether to require shared weights to have the same shape, or just the same
- // count -- defaults to STRICT if unspecified.
- optional DimCheckMode share_mode = 2;
- 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.
- optional float lr_mult = 3 [default = 1.0];
-
- // The multiplier on the global weight decay for this parameter.
- optional 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: 148 (last added: swish_param)
-message LayerParameter {
- optional string name = 1; // the layer name
- optional string type = 2; // the layer type
- repeated string bottom = 3; // the name of each bottom blob
- repeated string top = 4; // the name of each top blob
-
- // The train / test phase for computation.
- optional 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.
- repeated float loss_weight = 5;
-
- // Specifies training parameters (multipliers on global learning constants,
- // and the name and other settings used for weight sharing).
- repeated ParamSpec param = 6;
-
- // The blobs containing the numeric parameters of the layer.
- repeated BlobProto blobs = 7;
-
- // Specifies whether to backpropagate to each bottom. If unspecified,
- // Caffe will automatically infer whether each input needs backpropagation
- // to compute parameter gradients. If set to true for some inputs,
- // backpropagation to those inputs is forced; if set false for some inputs,
- // backpropagation to those inputs is skipped.
- //
- // The size must be either 0 or equal to the number of bottoms.
- repeated bool propagate_down = 11;
-
- // Rules 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.
- repeated NetStateRule include = 8;
- repeated NetStateRule exclude = 9;
-
- // Parameters for data pre-processing.
- optional TransformationParameter transform_param = 100;
-
- // Parameters shared by loss layers.
- 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.
- 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 SwishParameter swish_param = 147;
- 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
-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.
- optional float scale = 1 [default = 1];
- // Specify if we want to randomly mirror data.
- optional bool mirror = 2 [default = false];
- // Specify if we would like to randomly crop an image.
- optional uint32 crop_size = 3 [default = 0];
- // mean_file and mean_value cannot be specified at the same time
- 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)
- repeated float mean_value = 5;
- // Force the decoded image to have 3 color channels.
- optional bool force_color = 6 [default = false];
- // Force the decoded image to have 1 color channels.
- optional bool force_gray = 7 [default = false];
-}
-
-// Message that stores parameters shared by loss layers
-message LossParameter {
- // If specified, ignore instances with the given label.
- 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.
- 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.
- 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.
- VALID = 1;
- // Divide by the batch size.
- BATCH_SIZE = 2;
- // Do not normalize the loss.
- NONE = 3;
- }
- // For historical reasons, the default normalization for
- // SigmoidCrossEntropyLoss is BATCH_SIZE and *not* VALID.
- 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.
- optional bool normalize = 2;
-}
-
-// Messages that store parameters used by individual layer types follow, in
-// alphabetical order.
-
-message AccuracyParameter {
- // When computing accuracy, count as correct by comparing the true label to
- // the top k scoring classes. By default, only compare to the top scoring
- // class (i.e. argmax).
- 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}.
- optional int32 axis = 2 [default = 1];
-
- // If specified, ignore instances with the given label.
- optional int32 ignore_label = 3;
-}
-
-message ArgMaxParameter {
- // If true produce pairs (argmax, maxval)
- optional bool out_max_val = 1 [default = false];
- optional uint32 top_k = 2 [default = 1];
- // The axis along which to maximise -- may be negative to index from the
- // end (e.g., -1 for the last axis).
- // By default ArgMaxLayer maximizes over the flattened trailing dimensions
- // for each index of the first / num dimension.
- optional int32 axis = 3;
-}
-
-message ConcatParameter {
- // The axis along which to concatenate -- may be negative to index from the
- // end (e.g., -1 for the last axis). Other axes must have the
- // same dimension for all the bottom blobs.
- // By default, ConcatLayer concatenates blobs along the "channels" axis (1).
- optional int32 axis = 2 [default = 1];
-
- // DEPRECATED: alias for "axis" -- does not support negative indexing.
- optional uint32 concat_dim = 1 [default = 1];
-}
-
-message BatchNormParameter {
- // If false, normalization is performed over the current mini-batch
- // and global statistics are accumulated (but not yet used) by a moving
- // average.
- // If true, those accumulated mean and variance values are used for the
- // normalization.
- // By default, it is set to false when the network is in the training
- // phase and true when the network is in the testing phase.
- optional bool use_global_stats = 1;
- // What fraction of the moving average remains each iteration?
- // Smaller values make the moving average decay faster, giving more
- // weight to the recent values.
- // Each iteration updates the moving average @f$S_{t-1}@f$ with the
- // current mean @f$ Y_t @f$ by
- // @f$ S_t = (1-\beta)Y_t + \beta \cdot S_{t-1} @f$, where @f$ \beta @f$
- // is the moving_average_fraction parameter.
- optional float moving_average_fraction = 2 [default = .999];
- // Small value to add to the variance estimate so that we don't divide by
- // zero.
- optional float eps = 3 [default = 1e-5];
-}
-
-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;
-}
-
-message ContrastiveLossParameter {
- // margin for dissimilar pair
- optional float margin = 1 [default = 1.0];
- // The first implementation of this cost did not exactly match the cost of
- // Hadsell et al 2006 -- using (margin - d^2) instead of (margin - d)^2.
- // legacy_version = false (the default) uses (margin - d)^2 as proposed in the
- // Hadsell paper. New models should probably use this version.
- // legacy_version = true uses (margin - d^2). This is kept to support /
- // reproduce existing models and results
- optional bool legacy_version = 2 [default = false];
-}
-
-message ConvolutionParameter {
- optional uint32 num_output = 1; // The number of outputs for the layer
- optional bool bias_term = 2 [default = true]; // whether to have bias terms
-
- // Pad, kernel size, and stride are all given as a single value for equal
- // dimensions in all spatial dimensions, or once per spatial dimension.
- repeated uint32 pad = 3; // The padding size; defaults to 0
- repeated uint32 kernel_size = 4; // The kernel size
- repeated uint32 stride = 6; // The stride; defaults to 1
- // Factor used to dilate the kernel, (implicitly) zero-filling the resulting
- // holes. (Kernel dilation is sometimes referred to by its use in the
- // algorithme à trous from Holschneider et al. 1987.)
- repeated uint32 dilation = 18; // The dilation; defaults to 1
-
- // For 2D convolution only, the *_h and *_w versions may also be used to
- // specify both spatial dimensions.
- optional uint32 pad_h = 9 [default = 0]; // The padding height (2D only)
- optional uint32 pad_w = 10 [default = 0]; // The padding width (2D only)
- optional uint32 kernel_h = 11; // The kernel height (2D only)
- optional uint32 kernel_w = 12; // The kernel width (2D only)
- optional uint32 stride_h = 13; // The stride height (2D only)
- optional uint32 stride_w = 14; // The stride width (2D only)
-
- optional uint32 group = 5 [default = 1]; // The group size for group conv
-
- optional FillerParameter weight_filler = 7; // The filler for the weight
- optional FillerParameter bias_filler = 8; // The filler for the bias
- enum Engine {
- DEFAULT = 0;
- CAFFE = 1;
- CUDNN = 2;
- }
- optional Engine engine = 15 [default = DEFAULT];
-
- // The axis to interpret as "channels" when performing convolution.
- // Preceding dimensions are treated as independent inputs;
- // succeeding dimensions are treated as "spatial".
- // With (N, C, H, W) inputs, and axis == 1 (the default), we perform
- // N independent 2D convolutions, sliding C-channel (or (C/g)-channels, for
- // groups g>1) filters across the spatial axes (H, W) of the input.
- // With (N, C, D, H, W) inputs, and axis == 1, we perform
- // N independent 3D convolutions, sliding (C/g)-channels
- // filters across the spatial axes (D, H, W) of the input.
- optional int32 axis = 16 [default = 1];
-
- // Whether to force use of the general ND convolution, even if a specific
- // implementation for blobs of the appropriate number of spatial dimensions
- // is available. (Currently, there is only a 2D-specific convolution
- // implementation; for input blobs with num_axes != 2, this option is
- // ignored and the ND implementation will be used.)
- optional bool force_nd_im2col = 17 [default = false];
-}
-
-message CropParameter {
- // To crop, elements of the first bottom are selected to fit the dimensions
- // of the second, reference bottom. The crop is configured by
- // - the crop `axis` to pick the dimensions for cropping
- // - the crop `offset` to set the shift for all/each dimension
- // to align the cropped bottom with the reference bottom.
- // All dimensions up to but excluding `axis` are preserved, while
- // the dimensions including and trailing `axis` are cropped.
- // If only one `offset` is set, then all dimensions are offset by this amount.
- // Otherwise, the number of offsets must equal the number of cropped axes to
- // shift the crop in each dimension accordingly.
- // Note: standard dimensions are N,C,H,W so the default is a spatial crop,
- // and `axis` may be negative to index from the end (e.g., -1 for the last
- // axis).
- optional int32 axis = 1 [default = 2];
- repeated uint32 offset = 2;
-}
-
-message DataParameter {
- enum DB {
- LEVELDB = 0;
- LMDB = 1;
- }
- // Specify the data source.
- optional string source = 1;
- // Specify the batch size.
- optional uint32 batch_size = 4;
- // The rand_skip variable is for the data layer to skip a few data points
- // to avoid all asynchronous sgd clients to start at the same point. The skip
- // point would be set as rand_skip * rand(0,1). Note that rand_skip should not
- // be larger than the number of keys in the database.
- // DEPRECATED. Each solver accesses a different subset of the database.
- optional uint32 rand_skip = 7 [default = 0];
- optional DB backend = 8 [default = LEVELDB];
- // DEPRECATED. See TransformationParameter. For data pre-processing, we can do
- // simple scaling and subtracting the data mean, if provided. Note that the
- // mean subtraction is always carried out before scaling.
- optional float scale = 2 [default = 1];
- optional string mean_file = 3;
- // DEPRECATED. See TransformationParameter. Specify if we would like to randomly
- // crop an image.
- optional uint32 crop_size = 5 [default = 0];
- // DEPRECATED. See TransformationParameter. Specify if we want to randomly mirror
- // data.
- optional bool mirror = 6 [default = false];
- // Force the encoded image to have 3 color channels
- optional bool force_encoded_color = 9 [default = false];
- // Prefetch queue (Increase if data feeding bandwidth varies, within the
- // limit of device memory for GPU training)
- optional uint32 prefetch = 10 [default = 4];
-}
-
-message DropoutParameter {
- 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").
-message DummyDataParameter {
- // This layer produces N >= 1 top blobs. DummyDataParameter must specify 1 or N
- // shape fields, and 0, 1 or N data_fillers.
- //
- // If 0 data_fillers are specified, ConstantFiller with a value of 0 is used.
- // If 1 data_filler is specified, it is applied to all top blobs. If N are
- // specified, the ith is applied to the ith top blob.
- repeated FillerParameter data_filler = 1;
- repeated BlobShape shape = 6;
-
- // 4D dimensions -- deprecated. Use "shape" instead.
- repeated uint32 num = 2;
- repeated uint32 channels = 3;
- repeated uint32 height = 4;
- repeated uint32 width = 5;
-}
-
-message EltwiseParameter {
- enum EltwiseOp {
- PROD = 0;
- SUM = 1;
- MAX = 2;
- }
- optional EltwiseOp operation = 1 [default = SUM]; // element-wise operation
- repeated float coeff = 2; // blob-wise coefficient for SUM operation
-
- // Whether to use an asymptotically slower (for >2 inputs) but stabler method
- // of computing the gradient for the PROD operation. (No effect for SUM op.)
- optional bool stable_prod_grad = 3 [default = true];
-}
-
-// Message that stores parameters used by ELULayer
-message ELUParameter {
- // Described in:
- // Clevert, D.-A., Unterthiner, T., & Hochreiter, S. (2015). Fast and Accurate
- // Deep Network Learning by Exponential Linear Units (ELUs). arXiv
- optional float alpha = 1 [default = 1];
-}
-
-// Message that stores parameters used by EmbedLayer
-message EmbedParameter {
- optional uint32 num_output = 1; // The number of outputs for the layer
- // The input is given as integers to be interpreted as one-hot
- // vector indices with dimension num_input. Hence num_input should be
- // 1 greater than the maximum possible input value.
- optional uint32 input_dim = 2;
-
- optional bool bias_term = 3 [default = true]; // Whether to use a bias term
- optional FillerParameter weight_filler = 4; // The filler for the weight
- optional FillerParameter bias_filler = 5; // The filler for the bias
-
-}
-
-// Message that stores parameters used by ExpLayer
-message ExpParameter {
- // ExpLayer computes outputs y = base ^ (shift + scale * x), for base > 0.
- // Or if base is set to the default (-1), base is set to e,
- // so y = exp(shift + scale * x).
- optional float base = 1 [default = -1.0];
- optional float scale = 2 [default = 1.0];
- optional float shift = 3 [default = 0.0];
-}
-
-/// Message that stores parameters used by FlattenLayer
-message FlattenParameter {
- // The first axis to flatten: all preceding axes are retained in the output.
- // May be negative to index from the end (e.g., -1 for the last axis).
- optional int32 axis = 1 [default = 1];
-
- // The last axis to flatten: all following axes are retained in the output.
- // May be negative to index from the end (e.g., the default -1 for the last
- // axis).
- optional int32 end_axis = 2 [default = -1];
-}
-
-// Message that stores parameters used by HDF5DataLayer
-message HDF5DataParameter {
- // Specify the data source.
- optional string source = 1;
- // Specify the batch size.
- optional uint32 batch_size = 2;
-
- // Specify whether to shuffle the data.
- // If shuffle == true, the ordering of the HDF5 files is shuffled,
- // and the ordering of data within any given HDF5 file is shuffled,
- // but data between different files are not interleaved; all of a file's
- // data are output (in a random order) before moving onto another file.
- optional bool shuffle = 3 [default = false];
-}
-
-message HDF5OutputParameter {
- optional string file_name = 1;
-}
-
-message HingeLossParameter {
- enum Norm {
- L1 = 1;
- L2 = 2;
- }
- // Specify the Norm to use L1 or L2
- optional Norm norm = 1 [default = L1];
-}
-
-message ImageDataParameter {
- // Specify the data source.
- optional string source = 1;
- // Specify the batch size.
- optional uint32 batch_size = 4 [default = 1];
- // The rand_skip variable is for the data layer to skip a few data points
- // to avoid all asynchronous sgd clients to start at the same point. The skip
- // point would be set as rand_skip * rand(0,1). Note that rand_skip should not
- // be larger than the number of keys in the database.
- 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];
- // Specify if the images are color or gray
- 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.
- optional float scale = 2 [default = 1];
- optional string mean_file = 3;
- // DEPRECATED. See TransformationParameter. Specify if we would like to randomly
- // crop an image.
- optional uint32 crop_size = 5 [default = 0];
- // DEPRECATED. See TransformationParameter. Specify if we want to randomly mirror
- // data.
- optional bool mirror = 6 [default = false];
- optional string root_folder = 12 [default = ""];
-}
-
-message InfogainLossParameter {
- // Specify the infogain matrix source.
- optional string source = 1;
- optional int32 axis = 2 [default = 1]; // axis of prob
-}
-
-message InnerProductParameter {
- optional uint32 num_output = 1; // The number of outputs for the layer
- optional bool bias_term = 2 [default = true]; // whether to have bias terms
- optional FillerParameter weight_filler = 3; // The filler for the weight
- optional FillerParameter bias_filler = 4; // The filler for the bias
-
- // The first axis to be lumped into a single inner product computation;
- // all preceding axes are retained in the output.
- // May be negative to index from the end (e.g., -1 for the last axis).
- optional int32 axis = 5 [default = 1];
- // Specify whether to transpose the weight matrix or not.
- // If transpose == true, any operations will be performed on the transpose
- // of the weight matrix. The weight matrix itself is not going to be transposed
- // but rather the transfer flag of operations will be toggled accordingly.
- optional bool transpose = 6 [default = false];
-}
-
-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.
- repeated BlobShape shape = 1;
-}
-
-// Message that stores parameters used by LogLayer
-message LogParameter {
- // LogLayer computes outputs y = log_base(shift + scale * x), for base > 0.
- // Or if base is set to the default (-1), base is set to e,
- // so y = ln(shift + scale * x) = log_e(shift + scale * x)
- optional float base = 1 [default = -1.0];
- optional float scale = 2 [default = 1.0];
- optional float shift = 3 [default = 0.0];
-}
-
-// 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];
- optional float k = 5 [default = 1.];
- enum Engine {
- DEFAULT = 0;
- CAFFE = 1;
- CUDNN = 2;
- }
- optional Engine engine = 6 [default = DEFAULT];
-}
-
-message MemoryDataParameter {
- optional uint32 batch_size = 1;
- optional uint32 channels = 2;
- optional uint32 height = 3;
- optional uint32 width = 4;
-}
-
-message MVNParameter {
- // This parameter can be set to false to normalize mean only
- optional bool normalize_variance = 1 [default = true];
-
- // This parameter can be set to true to perform DNN-like MVN
- optional bool across_channels = 2 [default = false];
-
- // Epsilon for not dividing by zero while normalizing variance
- optional float eps = 3 [default = 1e-9];
-}
-
-message ParameterParameter {
- optional BlobShape shape = 1;
-}
-
-message PoolingParameter {
- enum PoolMethod {
- MAX = 0;
- AVE = 1;
- STOCHASTIC = 2;
- }
- optional PoolMethod pool = 1 [default = MAX]; // The pooling method
- // Pad, kernel size, and stride are all given as a single value for equal
- // dimensions in height and width or as Y, X pairs.
- optional uint32 pad = 4 [default = 0]; // The padding size (equal in Y, X)
- optional uint32 pad_h = 9 [default = 0]; // The padding height
- optional uint32 pad_w = 10 [default = 0]; // The padding width
- optional uint32 kernel_size = 2; // The kernel size (square)
- optional uint32 kernel_h = 5; // The kernel height
- optional uint32 kernel_w = 6; // The kernel width
- optional uint32 stride = 3 [default = 1]; // The stride (equal in Y, X)
- optional uint32 stride_h = 7; // The stride height
- optional uint32 stride_w = 8; // The stride width
- enum Engine {
- DEFAULT = 0;
- CAFFE = 1;
- CUDNN = 2;
- }
- optional Engine engine = 11 [default = DEFAULT];
- // If global_pooling then it will pool over the size of the bottom by doing
- // kernel_h = bottom->height and kernel_w = bottom->width
- optional bool global_pooling = 12 [default = false];
-}
-
-message PowerParameter {
- // PowerLayer computes outputs y = (shift + scale * x) ^ power.
- optional float power = 1 [default = 1.0];
- optional float scale = 2 [default = 1.0];
- optional float shift = 3 [default = 0.0];
-}
-
-message PythonParameter {
- optional string module = 1;
- optional string layer = 2;
- // This value is set to the attribute `param_str` of the `PythonLayer` object
- // in Python before calling the `setup()` method. This could be a number,
- // string, dictionary in Python dict format, JSON, etc. You may parse this
- // string in `setup` method and use it in `forward` and `backward`.
- optional string param_str = 3 [default = ''];
- // DEPRECATED
- optional bool share_in_parallel = 4 [default = false];
-}
-
-// Message that stores parameters used by RecurrentLayer
-message RecurrentParameter {
- // The dimension of the output (and usually hidden state) representation --
- // must be explicitly set to non-zero.
- optional uint32 num_output = 1 [default = 0];
-
- optional FillerParameter weight_filler = 2; // The filler for the weight
- optional FillerParameter bias_filler = 3; // The filler for the bias
-
- // Whether to enable displaying debug_info in the unrolled recurrent net.
- 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.
- optional bool expose_hidden = 5 [default = false];
-}
-
-// Message that stores parameters used by ReductionLayer
-message ReductionParameter {
- enum ReductionOp {
- SUM = 1;
- ASUM = 2;
- SUMSQ = 3;
- MEAN = 4;
- }
-
- optional ReductionOp operation = 1 [default = SUM]; // reduction operation
-
- // The first axis to reduce to a scalar -- may be negative to index from the
- // end (e.g., -1 for the last axis).
- // (Currently, only reduction along ALL "tail" axes is supported; reduction
- // of axis M through N, where N < num_axes - 1, is unsupported.)
- // Suppose we have an n-axis bottom Blob with shape:
- // (d0, d1, d2, ..., d(m-1), dm, d(m+1), ..., d(n-1)).
- // If axis == m, the output Blob will have shape
- // (d0, d1, d2, ..., d(m-1)),
- // and the ReductionOp operation is performed (d0 * d1 * d2 * ... * d(m-1))
- // times, each including (dm * d(m+1) * ... * d(n-1)) individual data.
- // If axis == 0 (the default), the output Blob always has the empty shape
- // (count 1), performing reduction across the entire input --
- // often useful for creating new loss functions.
- optional int32 axis = 2 [default = 0];
-
- optional float coeff = 3 [default = 1.0]; // coefficient for output
-}
-
-// Message that stores parameters used by ReLULayer
-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.
- optional float negative_slope = 1 [default = 0];
- enum Engine {
- DEFAULT = 0;
- CAFFE = 1;
- CUDNN = 2;
- }
- optional Engine engine = 2 [default = DEFAULT];
-}
-
-message ReshapeParameter {
- // Specify the output dimensions. If some of the dimensions are set to 0,
- // the corresponding dimension from the bottom layer is used (unchanged).
- // Exactly one dimension may be set to -1, in which case its value is
- // inferred from the count of the bottom blob and the remaining dimensions.
- // For example, suppose we want to reshape a 2D blob "input" with shape 2 x 8:
- //
- // layer {
- // type: "Reshape" bottom: "input" top: "output"
- // reshape_param { ... }
- // }
- //
- // If "input" is 2D with shape 2 x 8, then the following reshape_param
- // specifications are all equivalent, producing a 3D blob "output" with shape
- // 2 x 2 x 4:
- //
- // reshape_param { shape { dim: 2 dim: 2 dim: 4 } }
- // reshape_param { shape { dim: 0 dim: 2 dim: 4 } }
- // reshape_param { shape { dim: 0 dim: 2 dim: -1 } }
- // reshape_param { shape { dim: 0 dim:-1 dim: 4 } }
- //
- optional BlobShape shape = 1;
-
- // axis and num_axes control the portion of the bottom blob's shape that are
- // replaced by (included in) the reshape. By default (axis == 0 and
- // num_axes == -1), the entire bottom blob shape is included in the reshape,
- // and hence the shape field must specify the entire output shape.
- //
- // axis may be non-zero to retain some portion of the beginning of the input
- // shape (and may be negative to index from the end; e.g., -1 to begin the
- // reshape after the last axis, including nothing in the reshape,
- // -2 to include only the last axis, etc.).
- //
- // For example, suppose "input" is a 2D blob with shape 2 x 8.
- // Then the following ReshapeLayer specifications are all equivalent,
- // producing a blob "output" with shape 2 x 2 x 4:
- //
- // reshape_param { shape { dim: 2 dim: 2 dim: 4 } }
- // reshape_param { shape { dim: 2 dim: 4 } axis: 1 }
- // reshape_param { shape { dim: 2 dim: 4 } axis: -3 }
- //
- // num_axes specifies the extent of the reshape.
- // If num_axes >= 0 (and axis >= 0), the reshape will be performed only on
- // input axes in the range [axis, axis+num_axes].
- // num_axes may also be -1, the default, to include all remaining axes
- // (starting from axis).
- //
- // For example, suppose "input" is a 2D blob with shape 2 x 8.
- // Then the following ReshapeLayer specifications are equivalent,
- // producing a blob "output" with shape 1 x 2 x 8.
- //
- // reshape_param { shape { dim: 1 dim: 2 dim: 8 } }
- // reshape_param { shape { dim: 1 dim: 2 } num_axes: 1 }
- // reshape_param { shape { dim: 1 } num_axes: 0 }
- //
- // On the other hand, these would produce output blob shape 2 x 1 x 8:
- //
- // reshape_param { shape { dim: 2 dim: 1 dim: 8 } }
- // reshape_param { shape { dim: 1 } axis: 1 num_axes: 0 }
- //
- optional int32 axis = 2 [default = 0];
- optional int32 num_axes = 3 [default = -1];
-}
-
-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).
- optional bool bias_term = 4 [default = false];
- optional FillerParameter bias_filler = 5;
-}
-
-message SigmoidParameter {
- enum Engine {
- DEFAULT = 0;
- CAFFE = 1;
- CUDNN = 2;
- }
- optional Engine engine = 1 [default = DEFAULT];
-}
-
-message SliceParameter {
- // The axis along which to slice -- may be negative to index from the end
- // (e.g., -1 for the last axis).
- // By default, SliceLayer concatenates blobs along the "channels" axis (1).
- optional int32 axis = 3 [default = 1];
- repeated uint32 slice_point = 2;
-
- // DEPRECATED: alias for "axis" -- does not support negative indexing.
- optional uint32 slice_dim = 1 [default = 1];
-}
-
-// Message that stores parameters used by SoftmaxLayer, SoftmaxWithLossLayer
-message SoftmaxParameter {
- enum Engine {
- DEFAULT = 0;
- CAFFE = 1;
- CUDNN = 2;
- }
- optional Engine engine = 1 [default = DEFAULT];
-
- // The axis along which to perform the softmax -- may be negative to index
- // from the end (e.g., -1 for the last axis).
- // Any other axes will be evaluated as independent softmaxes.
- optional int32 axis = 2 [default = 1];
-}
-
-// Message that stores parameters used by SwishLayer
-message SwishParameter {
- // Beta parameter for the Swish activation function
- // Described in:
- // Prajit Ramachandran, Barret Zoph, Quoc V. Le. (2017). Searching for
- // Activation Functions. https://arxiv.org/abs/1710.05941v2
- optional float beta = 1 [default = 1];
-}
-
-message TanHParameter {
- enum Engine {
- DEFAULT = 0;
- CAFFE = 1;
- CUDNN = 2;
- }
- optional Engine engine = 1 [default = DEFAULT];
-}
-
-// Message that stores parameters used by TileLayer
-message TileParameter {
- // The index of the axis to tile.
- optional int32 axis = 1 [default = 1];
-
- // The number of copies (tiles) of the blob to output.
- optional int32 tiles = 2;
-}
-
-// Message that stores parameters used by ThresholdLayer
-message ThresholdParameter {
- optional float threshold = 1 [default = 0]; // Strictly positive values
-}
-
-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"];
- // cache_images: will load all images in memory for faster access
- optional bool cache_images = 12 [default = false];
- // append root_folder to locate images
- optional string root_folder = 13 [default = ""];
-}
-
-message SPPParameter {
- enum PoolMethod {
- MAX = 0;
- AVE = 1;
- STOCHASTIC = 2;
- }
- optional uint32 pyramid_height = 1;
- optional PoolMethod pool = 2 [default = MAX]; // The pooling method
- enum Engine {
- DEFAULT = 0;
- CAFFE = 1;
- CUDNN = 2;
- }
- optional Engine engine = 6 [default = DEFAULT];
-}
-
-// DEPRECATED: use LayerParameter.
-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.
-message V0LayerParameter {
- optional string name = 1; // the layer name
- optional string type = 2; // the string to specify the layer type
-
- // Parameters to specify layers with inner products.
- optional uint32 num_output = 3; // The number of outputs for the layer
- optional bool biasterm = 4 [default = true]; // whether to have bias terms
- optional FillerParameter weight_filler = 5; // The filler for the weight
- optional FillerParameter bias_filler = 6; // The filler for the bias
-
- optional uint32 pad = 7 [default = 0]; // The padding size
- optional uint32 kernelsize = 8; // The kernel size
- optional uint32 group = 9 [default = 1]; // The group size for group conv
- optional uint32 stride = 10 [default = 1]; // The stride
- enum PoolMethod {
- MAX = 0;
- AVE = 1;
- STOCHASTIC = 2;
- }
- optional PoolMethod pool = 11 [default = MAX]; // The pooling method
- optional float dropout_ratio = 12 [default = 0.5]; // dropout ratio
-
- optional uint32 local_size = 13 [default = 5]; // for local response norm
- optional float alpha = 14 [default = 1.]; // for local response norm
- optional float beta = 15 [default = 0.75]; // for local response norm
- optional float k = 22 [default = 1.];
-
- // For data layers, specify the data source
- optional string source = 16;
- // For data pre-processing, we can do simple scaling and subtracting the
- // data mean, if provided. Note that the mean subtraction is always carried
- // out before scaling.
- optional float scale = 17 [default = 1];
- optional string meanfile = 18;
- // For data layers, specify the batch size.
- optional uint32 batchsize = 19;
- // For data layers, specify if we would like to randomly crop an image.
- optional uint32 cropsize = 20 [default = 0];
- // For data layers, specify if we want to randomly mirror data.
- optional bool mirror = 21 [default = false];
-
- // The blobs containing the numeric parameters of the layer
- repeated BlobProto blobs = 50;
- // The ratio that is multiplied on the global learning rate. If you want to
- // set the learning ratio for one blob, you need to set it for all blobs.
- repeated float blobs_lr = 51;
- // The weight decay that is multiplied on the global weight decay.
- repeated float weight_decay = 52;
-
- // The rand_skip variable is for the data layer to skip a few data points
- // to avoid all asynchronous sgd clients to start at the same point. The skip
- // point would be set as rand_skip * rand(0,1). Note that rand_skip should not
- // be larger than the number of keys in the database.
- optional uint32 rand_skip = 53 [default = 0];
-
- // Fields related to detection (det_*)
- // foreground (object) overlap threshold
- optional float det_fg_threshold = 54 [default = 0.5];
- // background (non-object) overlap threshold
- optional float det_bg_threshold = 55 [default = 0.5];
- // Fraction of batch that should be foreground objects
- optional float det_fg_fraction = 56 [default = 0.25];
-
- // optional bool OBSOLETE_can_clobber = 57 [default = true];
-
- // Amount of contextual padding to add around a window
- // (used only by the window_data_layer)
- optional uint32 det_context_pad = 58 [default = 0];
-
- // Mode for cropping out a detection window
- // warp: cropped window is warped to a fixed size and aspect ratio
- // square: the tightest square around the window is cropped
- optional string det_crop_mode = 59 [default = "warp"];
-
- // For ReshapeLayer, one needs to specify the new dimensions.
- optional int32 new_num = 60 [default = 0];
- optional int32 new_channels = 61 [default = 0];
- optional int32 new_height = 62 [default = 0];
- optional int32 new_width = 63 [default = 0];
-
- // Whether or not ImageLayer should shuffle the list of files at every epoch.
- // It will also resize images if new_height or new_width are not zero.
- optional bool shuffle_images = 64 [default = false];
-
- // For ConcatLayer, one needs to specify the dimension for concatenation, and
- // the other dimensions must be the same for all the bottom blobs.
- // By default it will concatenate blobs along the channels dimension.
- optional uint32 concat_dim = 65 [default = 1];
-
- optional HDF5OutputParameter hdf5_output_param = 1001;
-}
-
-message PReLUParameter {
- // Parametric ReLU described in K. He et al, Delving Deep into Rectifiers:
- // Surpassing Human-Level Performance on ImageNet Classification, 2015.
-
- // Initial value of a_i. Default is a_i=0.25 for all i.
- optional FillerParameter filler = 1;
- // Whether or not slope parameters are shared across channels.
- optional bool channel_shared = 2 [default = false];
-}
projects / platform / core / ml / nnfw.git / commitdiff