src/armnnSerializer/ArmnnSchema.fbs

//
// Copyright © 2017 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//

namespace armnnSerializer;

file_identifier "ARMN";

file_extension "armnn";

enum ActivationFunction : byte {
    Sigmoid = 0,
    TanH = 1,
    Linear = 2,
    ReLu = 3,
    BoundedReLu = 4,
    SoftReLu = 5,
    LeakyReLu = 6,
    Abs = 7,
    Sqrt = 8,
    Square = 9
}

enum DataType : byte {
    Float16 = 0,
    Float32 = 1,
    QuantisedAsymm8 = 2,
    Signed32 = 3,
    Boolean = 4
}

enum DataLayout : byte {
    NHWC = 0,
    NCHW = 1
}

table TensorInfo {
    dimensions:[uint];
    dataType:DataType;
    quantizationScale:float = 1.0;
    quantizationOffset:int = 0;
}

struct Connection {
    sourceLayerIndex:uint;
    outputSlotIndex:uint;
}

table ByteData {
    data:[byte];
}

table ShortData {
    data:[short];
}

table IntData {
    data:[int];
}

table LongData {
    data:[long];
}

union ConstTensorData { ByteData, ShortData, IntData, LongData }

table ConstTensor {
    info:TensorInfo;
    data:ConstTensorData;
}

table InputSlot {
    index:uint;
    connection:Connection;
}

table OutputSlot {
    index:uint;
    tensorInfo:TensorInfo;
}

enum LayerType : uint {
    Addition = 0,
    Input = 1,
    Multiplication = 2,
    Output = 3,
    Pooling2d = 4,
    Reshape = 5,
    Softmax = 6,
    Convolution2d = 7,
    DepthwiseConvolution2d = 8,
    Activation = 9,
    Permute = 10,
    FullyConnected = 11,
    Constant = 12,
    SpaceToBatchNd = 13,
    BatchToSpaceNd = 14,
    Division = 15,
    Minimum = 16,
    Equal = 17,
    Maximum = 18,
    Normalization = 19,
    Pad = 20,
    Rsqrt = 21,
    Floor = 22,
    BatchNormalization = 23,
    Greater = 24,
    ResizeBilinear = 25,
    Subtraction = 26,
    StridedSlice = 27,
    Gather = 28
}

// Base layer table to be used as part of other layers
table LayerBase {
    index:uint;
    layerName:string;
    layerType:LayerType;
    inputSlots:[InputSlot];
    outputSlots:[OutputSlot];
}

table BindableLayerBase {
    base:LayerBase;
    layerBindingId:int;
}

// Table for each layer defined below
table ActivationLayer {
    base:LayerBase;
    descriptor:ActivationDescriptor;
}

table ActivationDescriptor {
    function:ActivationFunction = Sigmoid;
    a:float;
    b:float;
}

table AdditionLayer {
    base:LayerBase;
}

table ConstantLayer {
    base:LayerBase;
    input:ConstTensor;
}

table Convolution2dLayer {
    base:LayerBase;
    descriptor:Convolution2dDescriptor;
    weights:ConstTensor;
    biases:ConstTensor;
}

table Convolution2dDescriptor {
    padLeft:uint;
    padRight:uint;
    padTop:uint;
    padBottom:uint;
    strideX:uint;
    strideY:uint;
    biasEnabled:bool = false;
    dataLayout:DataLayout = NCHW;
}

table DivisionLayer {
    base:LayerBase;
}

table EqualLayer {
    base:LayerBase;
}

table FloorLayer{
    base:LayerBase;
}

table FullyConnectedLayer {
    base:LayerBase;
    descriptor:FullyConnectedDescriptor;
    weights:ConstTensor;
    biases:ConstTensor;
}

table FullyConnectedDescriptor {
    biasEnabled:bool = false;
    transposeWeightsMatrix:bool = false;
}

table GatherLayer {
    base:LayerBase;
}

table GreaterLayer {
    base:LayerBase;
}

table InputLayer {
    base:BindableLayerBase;
}

table MinimumLayer {
    base:LayerBase;
}

table MaximumLayer {
    base:LayerBase;
}

table MultiplicationLayer {
    base:LayerBase;
}

table Pooling2dLayer {
    base:LayerBase;
    descriptor:Pooling2dDescriptor;
}

enum PoolingAlgorithm : byte {
    Max = 0,
    Average = 1,
    L2 = 2
}

enum OutputShapeRounding : byte {
    Floor = 0,
    Ceiling = 1
}

enum PaddingMethod : byte {
    IgnoreValue = 0,
    Exclude = 1
}

table Pooling2dDescriptor {
    poolType:PoolingAlgorithm;
    padLeft:uint;
    padRight:uint;
    padTop:uint;
    padBottom:uint;
    poolWidth:uint;
    poolHeight:uint;
    strideX:uint;
    strideY:uint;
    outputShapeRounding:OutputShapeRounding;
    paddingMethod:PaddingMethod;
    dataLayout:DataLayout;
}

table SoftmaxLayer {
    base:LayerBase;
    descriptor:SoftmaxDescriptor;
}

table SoftmaxDescriptor {
    beta:float;
}

table DepthwiseConvolution2dLayer {
    base:LayerBase;
    descriptor:DepthwiseConvolution2dDescriptor;
    weights:ConstTensor;
    biases:ConstTensor;
}

table DepthwiseConvolution2dDescriptor {
    padLeft:uint;
    padRight:uint;
    padTop:uint;
    padBottom:uint;
    strideX:uint;
    strideY:uint;
    biasEnabled:bool = false;
    dataLayout:DataLayout = NCHW;
}

table OutputLayer {
    base:BindableLayerBase;
}

table ReshapeLayer {
    base:LayerBase;
    descriptor:ReshapeDescriptor;
}

table ReshapeDescriptor {
  targetShape:[uint];
}

table PermuteLayer {
    base:LayerBase;
    descriptor:PermuteDescriptor;
}

table PermuteDescriptor {
    dimMappings:[uint];
}

table SpaceToBatchNdLayer {
    base:LayerBase;
    descriptor:SpaceToBatchNdDescriptor;
}

table SpaceToBatchNdDescriptor {
    blockShape:[uint];
    padList:[uint];
    dataLayout:DataLayout;
}

table SubtractionLayer {
    base:LayerBase;
}

table BatchToSpaceNdLayer {
    base:LayerBase;
    descriptor:BatchToSpaceNdDescriptor;
}

table BatchToSpaceNdDescriptor {
    blockShape:[uint];
    crops:[uint];
    dataLayout:DataLayout;
}

enum NormalizationAlgorithmChannel : byte {
    Across = 0,
    Within = 1
}

enum NormalizationAlgorithmMethod : byte {
    LocalBrightness = 0,
    LocalContrast = 1
}

table NormalizationLayer {
    base:LayerBase;
    descriptor:NormalizationDescriptor;
}

table NormalizationDescriptor {
    normChannelType:NormalizationAlgorithmChannel = Across;
    normMethodType:NormalizationAlgorithmMethod = LocalBrightness;
    normSize:uint;
    alpha:float;
    beta:float;
    k:float;
    dataLayout:DataLayout = NCHW;
}

table PadLayer {
    base:LayerBase;
    descriptor:PadDescriptor;
}

table PadDescriptor {
    padList:[uint];
}

table RsqrtLayer {
    base:LayerBase;
}

table BatchNormalizationLayer {
    base:LayerBase;
    descriptor:BatchNormalizationDescriptor;
    mean:ConstTensor;
    variance:ConstTensor;
    beta:ConstTensor;
    gamma:ConstTensor;
}

table BatchNormalizationDescriptor {
    eps:float;
    dataLayout:DataLayout;
}

table ResizeBilinearLayer {
    base:LayerBase;
    descriptor:ResizeBilinearDescriptor;
}

table ResizeBilinearDescriptor {
    targetWidth:uint;
    targetHeight:uint;
    dataLayout:DataLayout;
}

table StridedSliceLayer {
    base:LayerBase;
    descriptor:StridedSliceDescriptor;
}

table StridedSliceDescriptor {
    begin:[int];
    end:[int];
    stride:[int];
    beginMask:int;
    endMask:int;
    shrinkAxisMask:int;
    ellipsisMask:int;
    newAxisMask:int;
    dataLayout:DataLayout;
}

union Layer {
    ActivationLayer,
    AdditionLayer,
    BatchToSpaceNdLayer,
    BatchNormalizationLayer,
    ConstantLayer,
    Convolution2dLayer,
    DepthwiseConvolution2dLayer,
    FullyConnectedLayer,
    InputLayer,
    MultiplicationLayer,
    OutputLayer,
    PermuteLayer,
    Pooling2dLayer,
    ReshapeLayer,
    SoftmaxLayer,
    SpaceToBatchNdLayer,
    DivisionLayer,
    MinimumLayer,
    EqualLayer,
    MaximumLayer,
    NormalizationLayer,
    PadLayer,
    RsqrtLayer,
    FloorLayer,
    GreaterLayer,
    ResizeBilinearLayer,
    SubtractionLayer,
    StridedSliceLayer,
    GatherLayer
}

table AnyLayer {
    layer:Layer;
}

// Root type for serialized data is the graph of the network
table SerializedGraph {
    layers:[AnyLayer];
    inputIds:[uint];
    outputIds:[uint];
}

root_type SerializedGraph;