STT-tensorflow/tensorflow/lite/schema/schema_v0.fbs

// Copyright 2017 The TensorFlow Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
//     http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.

namespace tflite;

// The type of data stored in a tensor.
enum TensorType : byte {
  FLOAT32 = 0,
  FLOAT16 = 1,
  INT32 = 2,
  UINT8 = 3,
  INT64 = 4,
}

// Parameters for converting a quantized tensor back to float. Given a
// quantized value q, the corresponding float value f should be:
//   f = scale * (q - zero_point)
table QuantizationParameters {
  min:[float];  // For importing back into tensorflow.
  max:[float];  // For importing back into tensorflow.
  scale:[float];
  zero_point:[long];
}

table Tensor {
  // The tensor shape. The meaning of each entry is operator-specific but
  // builtin ops use: [batch size, height, width, number of channels] (That's
  // Tensorflow's NHWC).
  shape:[int];
  type:TensorType;
  // The data_buffer is an opaque container, with the assumption that the
  // target device is little-endian. In addition, all builtin operators assume
  // the memory is ordered such that if `shape` is [4, 3, 2], then index
  // [i, j, k] maps to data_buffer[i*4*3 + j*3 + k].
  data_buffer:[ubyte];
  name:string;  // For debugging and importing back into tensorflow.
  quantization:QuantizationParameters;  // Optional.
}

// A list of builtin operators. Builtin operators are slightly faster than custom
// ones, but not by much. Moreover, while custom operators accept an opaque
// object containing configuration parameters, builtins have a predetermined
// set of acceptable options.
enum BuiltinOperator : byte {
  CUSTOM = 0,
  CONVOLUTION = 1,
  DEPTHWISE_CONVOLUTION = 2,
  CONCAT_EMBEDDINGS = 3,
  LSH_PROJECTION = 4,
  TANH = 5,
  RELU = 6,
  AVERAGE_POOL = 7,
  MAX_POOL = 8,
  L2_POOL = 9,
  SIGMOID = 10,
  SVDF = 11,
  BasicRNN = 12,
  RELU6 = 13,
  EMBEDDING_LOOKUP = 14,
  FULLY_CONNECTED = 15,
  HASHTABLE_LOOKUP = 16,
  SOFTMAX = 17,
  CONCATENATION = 18,
  LSTM = 19,
  ADD = 20,
  L2NORM = 21,
  LOCAL_RESPONSE_NORM = 22,
  RESIZE_BILINEAR = 23,
}

// Options for the builtin operators.
union BuiltinOptions {
  ConvolutionOptions,
  DepthwiseConvolutionOptions,
  ConcatEmbeddingsOptions,
  LSHProjectionOptions,
  PoolOptions,
  SVDFOptions,
  BasicRNNOptions,
  FullyConnectedOptions,
  SoftmaxOptions,
  ConcatenationOptions,
  AddOptions,
  L2NormOptions,
  LocalResponseNormOptions,
  LSTMOptions,
  ResizeBilinearOptions,
}

enum Padding : byte { SAME, VALID }

enum ActivationFunctionType : byte {
  NONE = 0,
  RELU = 1,
  RELU1 = 2,
  RELU6 = 3,
  TANH = 4,
  SIGN_BIT = 5,
}

table ConvolutionOptions {
  padding:Padding;
  stride_w:int;
  stride_h:int;
  fused_activation_function:ActivationFunctionType;
}

table PoolOptions {
  padding:Padding;
  stride_w:int;
  stride_h:int;
  filter_width:int;
  filter_height:int;
  fused_activation_function:ActivationFunctionType;
}

table DepthwiseConvolutionOptions {
  padding:Padding;
  stride_w:int;
  stride_h:int;
  depth_multiplier:int;
  fused_activation_function:ActivationFunctionType;
}

table ConcatEmbeddingsOptions {
  num_channels:int;
  num_columns_per_channel:[int];
  embedding_dim_per_channel:[int]; // This could be inferred from parameters.
}

enum LSHProjectionType: byte {
  UNKNOWN = 0,
  SPARSE = 1,
  DENSE = 2,
}

table LSHProjectionOptions {
  type: LSHProjectionType;
}

table SVDFOptions {
  rank:int;
  fused_activation_function:ActivationFunctionType;
}

// An implementation of TensorFlow BasicRNNCell.
table BasicRNNOptions {
  fused_activation_function:ActivationFunctionType;
}

// An implementation of TensorFlow fully_connected (a.k.a Dense) layer.
table FullyConnectedOptions {
  fused_activation_function:ActivationFunctionType;
}

table SoftmaxOptions {
  beta: float;
}

// An implementation of TensorFlow concat.
table ConcatenationOptions {
  axis:int;
  fused_activation_function:ActivationFunctionType;
}

table AddOptions {
  fused_activation_function:ActivationFunctionType;
}

table L2NormOptions {
  fused_activation_function:ActivationFunctionType;
}

table LocalResponseNormOptions {
  radius:int;
  bias:float;
  alpha:float;
  beta:float;
}

// An implementation of TensorFlow LSTMCell and CoupledInputForgetGateLSTMCell
table LSTMOptions {
  fused_activation_function:ActivationFunctionType;
  cell_clip: float; // Optional, 0.0 means no clipping
  proj_clip: float; // Optional, 0.0 means no clipping
}

table ResizeBilinearOptions {
  new_height:int;
  new_width:int;
}

// An OperatorCode can be an enum value (BuiltinOperator) if the operator is a
// builtin, or a string if the operator is custom.
table OperatorCode {
  builtin_code:BuiltinOperator;
  custom_code:string;
}

// An operator takes tensors as inputs and outputs. The type of operation being
// performed is determined by an index into the list of valid OperatorCodes,
// while the specifics of each operations is configured using builtin_options
// or custom_options.
table Operator {
  // Index into the operator_codes array. Using an integer here avoids
  // complicate map lookups.
  opcode_index:int;

  inputs:[int];
  outputs:[int];

  builtin_options:BuiltinOptions;
  custom_options:[ubyte];
}

// The root type, defining a model.
table Model {
  // A list of all tensors used in this model.
  tensors:[Tensor];

  // Indices of the input tensors.
  inputs:[int];

  // Indices of the output tensors.
  outputs:[int];

  // A list of all operator codes used in this model. This is
  // kept in order because operators carry an index into this
  // vector.
  operator_codes:[OperatorCode];

  // All operators, in execution order.
  operators:[Operator];
}

root_type Model;