Type Members

1.  final case class AttributeProto(name: Option[String] = _root_.scala.None, refAttrName: Option[String] = _root_.scala.None, docString: Option[String] = _root_.scala.None, type: Option[AttributeType] = _root_.scala.None, f: Option[Float] = _root_.scala.None, i: Option[Long] = _root_.scala.None, s: Option[ByteString] = _root_.scala.None, t: Option[TensorProto] = _root_.scala.None, g: Option[GraphProto] = _root_.scala.None, sparseTensor: Option[SparseTensorProto] = _root_.scala.None, floats: Seq[Float] = _root_.scala.Seq.empty, ints: Seq[Long] = _root_.scala.Seq.empty, strings: Seq[ByteString] = _root_.scala.Seq.empty, tensors: Seq[TensorProto] = _root_.scala.Seq.empty, graphs: Seq[GraphProto] = _root_.scala.Seq.empty, sparseTensors: Seq[SparseTensorProto] = _root_.scala.Seq.empty, unknownFields: UnknownFieldSet = _root_.scalapb.UnknownFieldSet.empty) extends GeneratedMessage with Updatable[AttributeProto] with Product with Serializable

   Attributes

   Attributes

   A named attribute containing either singular float, integer, string, graph, and tensor values, or repeated float, integer, string, graph, and tensor values. An AttributeProto MUST contain the name field, and *only one* of the following content fields, effectively enforcing a C/C++ union equivalent.

   name

   The name field MUST be present for this version of the IR. namespace Attribute

   refAttrName

   if ref_attr_name is not empty, ref_attr_name is the attribute name in parent function. In this case, this AttributeProto does not contain data, and it's a reference of attribute in parent scope. NOTE: This should ONLY be used in function (sub-graph). It's invalid to be used in main graph.

   docString

   A human-readable documentation for this attribute. Markdown is allowed.

   type

   The type field MUST be present for this version of the IR. For 0.0.1 versions of the IR, this field was not defined, and implementations needed to use has_field heuristics to determine which value field was in use. For IR_VERSION 0.0.2 or later, this field MUST be set and match the f|i|s|t|... field in use. This change was made to accommodate proto3 implementations. discriminator that indicates which field below is in use

   f

   Exactly ONE of the following fields must be present for this version of the IR float

   i

   int

   s

   UTF-8 string

   t

   tensor value

   g

   graph

   sparseTensor

   sparse tensor value

   floats

   list of floats

   ints

   list of ints

   strings

   list of UTF-8 strings

   tensors

   list of tensors

   graphs

   list of graph

   sparseTensors

   list of sparse tensors

   Annotations

   @SerialVersionUID()

2.  final case class GraphProto(node: Seq[NodeProto] = _root_.scala.Seq.empty, name: Option[String] = _root_.scala.None, initializer: Seq[TensorProto] = _root_.scala.Seq.empty, sparseInitializer: Seq[SparseTensorProto] = _root_.scala.Seq.empty, docString: Option[String] = _root_.scala.None, input: Seq[ValueInfoProto] = _root_.scala.Seq.empty, output: Seq[ValueInfoProto] = _root_.scala.Seq.empty, valueInfo: Seq[ValueInfoProto] = _root_.scala.Seq.empty, quantizationAnnotation: Seq[TensorAnnotation] = _root_.scala.Seq.empty, unknownFields: UnknownFieldSet = _root_.scalapb.UnknownFieldSet.empty) extends GeneratedMessage with Updatable[GraphProto] with Product with Serializable

   Graphs

   Graphs

   A graph defines the computational logic of a model and is comprised of a parameterized list of nodes that form a directed acyclic graph based on their inputs and outputs. This is the equivalent of the "network" or "graph" in many deep learning frameworks.

   node

   The nodes in the graph, sorted topologically.

   name

   The name of the graph. namespace Graph

   initializer

   A list of named tensor values, used to specify constant inputs of the graph. Each initializer (both TensorProto as well SparseTensorProto) MUST have a name. The name MUST be unique across both initializer and sparse_initializer, but the name MAY also appear in the input list.

   sparseInitializer

   Initializers (see above) stored in sparse format.

   docString

   A human-readable documentation for this graph. Markdown is allowed.

   input

   The inputs and outputs of the graph.

   valueInfo

   Information for the values in the graph. The ValueInfoProto.name's must be distinct. It is optional for a value to appear in value_info list.

   quantizationAnnotation

   This field carries information to indicate the mapping among a tensor and its quantization parameter tensors. For example: For tensor 'a', it may have {'SCALE_TENSOR', 'a_scale'} and {'ZERO_POINT_TENSOR', 'a_zero_point'} annotated, which means, tensor 'a_scale' and tensor 'a_zero_point' are scale and zero point of tensor 'a' in the model.

   Annotations

   @SerialVersionUID()

3.  final case class ModelProto(irVersion: Option[Long] = _root_.scala.None, opsetImport: Seq[OperatorSetIdProto] = _root_.scala.Seq.empty, producerName: Option[String] = _root_.scala.None, producerVersion: Option[String] = _root_.scala.None, domain: Option[String] = _root_.scala.None, modelVersion: Option[Long] = _root_.scala.None, docString: Option[String] = _root_.scala.None, graph: Option[GraphProto] = _root_.scala.None, metadataProps: Seq[StringStringEntryProto] = _root_.scala.Seq.empty, trainingInfo: Seq[TrainingInfoProto] = _root_.scala.Seq.empty, unknownFields: UnknownFieldSet = _root_.scalapb.UnknownFieldSet.empty) extends GeneratedMessage with Updatable[ModelProto] with Product with Serializable

   Models

   Models

   ModelProto is a top-level file/container format for bundling a ML model and associating its computation graph with metadata.

   The semantics of the model are described by the associated GraphProto's.

   irVersion

   The version of the IR this model targets. See Version enum above. This field MUST be present.

   opsetImport

   The OperatorSets this model relies on. All ModelProtos MUST have at least one entry that specifies which version of the ONNX OperatorSet is being imported. All nodes in the ModelProto's graph will bind against the operator with the same-domain/same-op_type operator with the HIGHEST version in the referenced operator sets.

   producerName

   The name of the framework or tool used to generate this model. This field SHOULD be present to indicate which implementation/tool/framework emitted the model.

   producerVersion

   The version of the framework or tool used to generate this model. This field SHOULD be present to indicate which implementation/tool/framework emitted the model.

   domain

   Domain name of the model. We use reverse domain names as name space indicators. For example: com.facebook.fair or com.microsoft.cognitiveservices Together with model_version and GraphProto.name, this forms the unique identity of the graph.

   modelVersion

   The version of the graph encoded. See Version enum below.

   docString

   A human-readable documentation for this model. Markdown is allowed.

   graph

   The parameterized graph that is evaluated to execute the model.

   metadataProps

   Named metadata values; keys should be distinct.

   trainingInfo

   Training-specific information. Sequentially executing all stored TrainingInfoProto.algorithms and assigning their outputs following the corresponding TrainingInfoProto.update_bindings is one training iteration. Similarly, to initialize the model (as if training hasn't happened), the user should sequentially execute all stored TrainingInfoProto.initializations and assigns their outputs using TrainingInfoProto.initialization_bindings. If this field is empty, the training behavior of the model is undefined.

   Annotations

   @SerialVersionUID()

4.  final case class NodeProto(input: Seq[String] = _root_.scala.Seq.empty, output: Seq[String] = _root_.scala.Seq.empty, name: Option[String] = _root_.scala.None, opType: Option[String] = _root_.scala.None, domain: Option[String] = _root_.scala.None, attribute: Seq[AttributeProto] = _root_.scala.Seq.empty, docString: Option[String] = _root_.scala.None, unknownFields: UnknownFieldSet = _root_.scalapb.UnknownFieldSet.empty) extends GeneratedMessage with Updatable[NodeProto] with Product with Serializable

   Nodes

   Nodes

   Computation graphs are made up of a DAG of nodes, which represent what is commonly called a "layer" or "pipeline stage" in machine learning frameworks.

   For example, it can be a node of type "Conv" that takes in an image, a filter tensor and a bias tensor, and produces the convolved output.

   input

   namespace Value

   output

   namespace Value

   name

   An optional identifier for this node in a graph. This field MAY be absent in ths version of the IR. namespace Node

   opType

   The symbolic identifier of the Operator to execute. namespace Operator

   domain

   The domain of the OperatorSet that specifies the operator named by op_type. namespace Domain

   attribute

   Additional named attributes.

   docString

   A human-readable documentation for this node. Markdown is allowed.

   Annotations

   @SerialVersionUID()

5.  final case class OperatorSetIdProto(domain: Option[String] = _root_.scala.None, version: Option[Long] = _root_.scala.None, unknownFields: UnknownFieldSet = _root_.scalapb.UnknownFieldSet.empty) extends GeneratedMessage with Updatable[OperatorSetIdProto] with Product with Serializable

   Operator Sets

   Operator Sets

   OperatorSets are uniquely identified by a (domain, opset_version) pair.

   domain

   The domain of the operator set being identified. The empty string ("") or absence of this field implies the operator set that is defined as part of the ONNX specification. This field MUST be present in this version of the IR when referring to any other operator set.

   version

   The version of the operator set being identified. This field MUST be present in this version of the IR.

   Annotations

   @SerialVersionUID()

6.  final case class SparseTensorProto(values: Option[TensorProto] = _root_.scala.None, indices: Option[TensorProto] = _root_.scala.None, dims: Seq[Long] = _root_.scala.Seq.empty, unknownFields: UnknownFieldSet = _root_.scalapb.UnknownFieldSet.empty) extends GeneratedMessage with Updatable[SparseTensorProto] with Product with Serializable

   A serialized sparse-tensor value

   A serialized sparse-tensor value

   values

   The sequence of non-default values are encoded as a tensor of shape [NNZ]. The default-value is zero for numeric tensors, and empty-string for string tensors. values must have a non-empty name present which serves as a name for SparseTensorProto when used in sparse_initializer list.

   indices

   The indices of the non-default values, which may be stored in one of two formats. (a) Indices can be a tensor of shape [NNZ, rank] with the [i,j]-th value corresponding to the j-th index of the i-th value (in the values tensor). (b) Indices can be a tensor of shape [NNZ], in which case the i-th value must be the linearized-index of the i-th value (in the values tensor). The linearized-index can be converted into an index tuple (k_1,...,k_rank) using the shape provided below. The indices must appear in ascending order without duplication. In the first format, the ordering is lexicographic-ordering: e.g., index-value [1,4] must appear before [2,1]

   dims

   The shape of the underlying dense-tensor: [dim_1, dim_2, ... dim_rank]

   Annotations

   @SerialVersionUID()

7.  final case class StringStringEntryProto(key: Option[String] = _root_.scala.None, value: Option[String] = _root_.scala.None, unknownFields: UnknownFieldSet = _root_.scalapb.UnknownFieldSet.empty) extends GeneratedMessage with Updatable[StringStringEntryProto] with Product with Serializable

   StringStringEntryProto follows the pattern for cross-proto-version maps.

   StringStringEntryProto follows the pattern for cross-proto-version maps. See https://developers.google.com/protocol-buffers/docs/proto3#maps

   Annotations

   @SerialVersionUID()

8.  final case class TensorAnnotation(tensorName: Option[String] = _root_.scala.None, quantParameterTensorNames: Seq[StringStringEntryProto] = _root_.scala.Seq.empty, unknownFields: UnknownFieldSet = _root_.scalapb.UnknownFieldSet.empty) extends GeneratedMessage with Updatable[TensorAnnotation] with Product with Serializable
   quantParameterTensorNames

   <key, value> pairs to annotate tensor specified by <tensor_name> above. The keys used in the mapping below must be pre-defined in ONNX spec. For example, for 8-bit linear quantization case, 'SCALE_TENSOR', 'ZERO_POINT_TENSOR' will be pre-defined as quantization parameter keys.

   Annotations

   @SerialVersionUID()

9.  final case class TensorProto(dims: Seq[Long] = _root_.scala.Seq.empty, dataType: Option[Int] = _root_.scala.None, segment: Option[Segment] = _root_.scala.None, floatData: Seq[Float] = _root_.scala.Seq.empty, int32Data: Seq[Int] = _root_.scala.Seq.empty, stringData: Seq[ByteString] = _root_.scala.Seq.empty, int64Data: Seq[Long] = _root_.scala.Seq.empty, name: Option[String] = _root_.scala.None, docString: Option[String] = _root_.scala.None, rawData: Option[ByteString] = _root_.scala.None, externalData: Seq[StringStringEntryProto] = _root_.scala.Seq.empty, dataLocation: Option[DataLocation] = _root_.scala.None, doubleData: Seq[Double] = _root_.scala.Seq.empty, uint64Data: Seq[Long] = _root_.scala.Seq.empty, unknownFields: UnknownFieldSet = _root_.scalapb.UnknownFieldSet.empty) extends GeneratedMessage with Updatable[TensorProto] with Product with Serializable

   Tensors

   Tensors

   A serialized tensor value.

   dims

   The shape of the tensor.

   dataType

   The data type of the tensor. This field MUST have a valid TensorProto.DataType value

   floatData

   For float and complex64 values Complex64 tensors are encoded as a single array of floats, with the real components appearing in odd numbered positions, and the corresponding imaginary component appearing in the subsequent even numbered position. (e.g., [1.0 + 2.0i, 3.0 + 4.0i] is encoded as [1.0, 2.0 ,3.0 ,4.0] When this field is present, the data_type field MUST be FLOAT or COMPLEX64.

   int32Data

   For int32, uint8, int8, uint16, int16, bool, and float16 values float16 values must be bit-wise converted to an uint16_t prior to writing to the buffer. When this field is present, the data_type field MUST be INT32, INT16, INT8, UINT16, UINT8, BOOL, or FLOAT16

   stringData

   For strings. Each element of string_data is a UTF-8 encoded Unicode string. No trailing null, no leading BOM. The protobuf "string" scalar type is not used to match ML community conventions. When this field is present, the data_type field MUST be STRING

   int64Data

   For int64. When this field is present, the data_type field MUST be INT64

   name

   Optionally, a name for the tensor. namespace Value

   docString

   A human-readable documentation for this tensor. Markdown is allowed.

   rawData

   Serializations can either use one of the fields above, or use this raw bytes field. The only exception is the string case, where one is required to store the content in the repeated bytes string_data field. When this raw_data field is used to store tensor value, elements MUST be stored in as fixed-width, little-endian order. Floating-point data types MUST be stored in IEEE 754 format. Complex64 elements must be written as two consecutive FLOAT values, real component first. Complex128 elements must be written as two consecutive DOUBLE values, real component first. Boolean type MUST be written one byte per tensor element (00000001 for true, 00000000 for false). Note: the advantage of specific field rather than the raw_data field is that in some cases (e.g. int data), protobuf does a better packing via variable length storage, and may lead to smaller binary footprint. When this field is present, the data_type field MUST NOT be STRING or UNDEFINED

   externalData

   Data can be stored inside the protobuf file using type-specific fields or raw_data. Alternatively, raw bytes data can be stored in an external file, using the external_data field. external_data stores key-value pairs describing data location. Recognized keys are:

   • "location" (required) - POSIX filesystem path relative to the directory where the ONNX protobuf model was stored
   • "offset" (optional) - position of byte at which stored data begins. Integer stored as string. Offset values SHOULD be multiples 4096 (page size) to enable mmap support.
   • "length" (optional) - number of bytes containing data. Integer stored as string.
   • "checksum" (optional) - SHA1 digest of file specified in under 'location' key.

   dataLocation

   If value not set, data is stored in raw_data (if set) otherwise in type-specified field.

   doubleData

   For double Complex128 tensors are encoded as a single array of doubles, with the real components appearing in odd numbered positions, and the corresponding imaginary component appearing in the subsequent even numbered position. (e.g., [1.0 + 2.0i, 3.0 + 4.0i] is encoded as [1.0, 2.0 ,3.0 ,4.0] When this field is present, the data_type field MUST be DOUBLE or COMPLEX128

   uint64Data

   For uint64 and uint32 values When this field is present, the data_type field MUST be UINT32 or UINT64

   Annotations

   @SerialVersionUID()

10.  final case class TensorShapeProto(dim: Seq[Dimension] = _root_.scala.Seq.empty, unknownFields: UnknownFieldSet = _root_.scalapb.UnknownFieldSet.empty) extends GeneratedMessage with Updatable[TensorShapeProto] with Product with Serializable

    Defines a tensor shape.

    Defines a tensor shape. A dimension can be either an integer value or a symbolic variable. A symbolic variable represents an unknown dimension.

    Annotations

    @SerialVersionUID()

11.  final case class TrainingInfoProto(initialization: Option[GraphProto] = _root_.scala.None, algorithm: Option[GraphProto] = _root_.scala.None, initializationBinding: Seq[StringStringEntryProto] = _root_.scala.Seq.empty, updateBinding: Seq[StringStringEntryProto] = _root_.scala.Seq.empty, unknownFields: UnknownFieldSet = _root_.scalapb.UnknownFieldSet.empty) extends GeneratedMessage with Updatable[TrainingInfoProto] with Product with Serializable

    Training information TrainingInfoProto stores information for training a model.

    Training information TrainingInfoProto stores information for training a model. In particular, this defines two functionalities: an initialization-step and a training-algorithm-step. Initialization resets the model back to its original state as if no training has been performed. Training algorithm improves the model based on input data.

    The semantics of the initialization-step is that the initializers in ModelProto.graph and in TrainingInfoProto.algorithm are first initialized as specified by the initializers in the graph, and then updated by the "initialization_binding" in every instance in ModelProto.training_info.

    The field "algorithm" defines a computation graph which represents a training algorithm's step. After the execution of a TrainingInfoProto.algorithm, the initializers specified by "update_binding" may be immediately updated. If the targeted training algorithm contains consecutive update steps (such as block coordinate descent methods), the user needs to create a TrainingInfoProto for each step.

    initialization

    This field describes a graph to compute the initial tensors upon starting the training process. Initialization graph has no input and can have multiple outputs. Usually, trainable tensors in neural networks are randomly initialized. To achieve that, for each tensor, the user can put a random number operator such as RandomNormal or RandomUniform in TrainingInfoProto.initialization.node and assign its random output to the specific tensor using "initialization_binding". This graph can also set the initializers in "algorithm" in the same TrainingInfoProto; a use case is resetting the number of training iteration to zero. By default, this field is an empty graph and its evaluation does not produce any output. Thus, no initializer would be changed by default.

    algorithm

    This field represents a training algorithm step. Given required inputs, it computes outputs to update initializers in its own or inference graph's initializer lists. In general, this field contains loss node, gradient node, optimizer node, increment of iteration count. An execution of the training algorithm step is performed by executing the graph obtained by combining the inference graph (namely "ModelProto.graph") and the "algorithm" graph. That is, the actual the actual input/initializer/output/node/value_info/sparse_initializer list of the training graph is the concatenation of "ModelProto.graph.input/initializer/output/node/value_info/sparse_initializer" and "algorithm.input/initializer/output/node/value_info/sparse_initializer" in that order. This combined graph must satisfy the normal ONNX conditions. Now, let's provide a visualization of graph combination for clarity. Let the inference graph (i.e., "ModelProto.graph") be tensor_a, tensor_b -> MatMul -> tensor_c -> Sigmoid -> tensor_d and the "algorithm" graph be tensor_d -> Add -> tensor_e The combination process results tensor_a, tensor_b -> MatMul -> tensor_c -> Sigmoid -> tensor_d -> Add -> tensor_e Notice that an input of a node in the "algorithm" graph may reference the output of a node in the inference graph (but not the other way round). Also, inference node cannot reference inputs of "algorithm". With these restrictions, inference graph can always be run independently without training information. By default, this field is an empty graph and its evaluation does not produce any output. Evaluating the default training step never update any initializers.

    initializationBinding

    This field specifies the bindings from the outputs of "initialization" to some initializers in "ModelProto.graph.initializer" and the "algorithm.initializer" in the same TrainingInfoProto. See "update_binding" below for details. By default, this field is empty and no initializer would be changed by the execution of "initialization".

    updateBinding

    Gradient-based training is usually an iterative procedure. In one gradient descent iteration, we apply x = x - r * g where "x" is the optimized tensor, "r" stands for learning rate, and "g" is gradient of "x" with respect to a chosen loss. To avoid adding assignments into the training graph, we split the update equation into y = x - r * g x = y The user needs to save "y = x - r * g" into TrainingInfoProto.algorithm. To tell that "y" should be assigned to "x", the field "update_binding" may contain a key-value pair of strings, "x" (key of StringStringEntryProto) and "y" (value of StringStringEntryProto). For a neural network with multiple trainable (mutable) tensors, there can be multiple key-value pairs in "update_binding". The initializers appears as keys in "update_binding" are considered mutable variables. This implies some behaviors as described below.

    1. We have only unique keys in all "update_binding"s so that two variables may not have the same name. This ensures that one variable is assigned up to once. 2. The keys must appear in names of "ModelProto.graph.initializer" or "TrainingInfoProto.algorithm.initializer". 3. The values must be output names of "algorithm" or "ModelProto.graph.output". 4. Mutable variables are initialized to the value specified by the corresponding initializer, and then potentially updated by "initializer_binding"s and "update_binding"s in "TrainingInfoProto"s. This field usually contains names of trainable tensors (in ModelProto.graph), optimizer states such as momentums in advanced stochastic gradient methods (in TrainingInfoProto.graph), and number of training iterations (in TrainingInfoProto.graph). By default, this field is empty and no initializer would be changed by the execution of "algorithm".

    Annotations

    @SerialVersionUID()

12.  final case class TypeProto(value: Value = onnx.onnx.TypeProto.Value.Empty, denotation: Option[String] = _root_.scala.None, unknownFields: UnknownFieldSet = _root_.scalapb.UnknownFieldSet.empty) extends GeneratedMessage with Updatable[TypeProto] with Product with Serializable

    Types

    Types

    The standard ONNX data types.

    denotation

    An optional denotation can be used to denote the whole type with a standard semantic description as to what is stored inside. Refer to https://github.com/onnx/onnx/blob/master/docs/TypeDenotation.md#type-denotation-definition for pre-defined type denotations.

    Annotations

    @SerialVersionUID()

13.  final case class ValueInfoProto(name: Option[String] = _root_.scala.None, type: Option[TypeProto] = _root_.scala.None, docString: Option[String] = _root_.scala.None, unknownFields: UnknownFieldSet = _root_.scalapb.UnknownFieldSet.empty) extends GeneratedMessage with Updatable[ValueInfoProto] with Product with Serializable

    Defines information on value, including the name, the type, and the shape of the value.

    Defines information on value, including the name, the type, and the shape of the value.

    name

    This field MUST be present in this version of the IR. namespace Value

    type

    This field MUST be present in this version of the IR for inputs and outputs of the top-level graph.

    docString

    A human-readable documentation for this value. Markdown is allowed.

    Annotations

    @SerialVersionUID()

14.  sealed abstract class Version extends GeneratedEnum

    Versioning

    Versioning

    ONNX versioning is specified in docs/IR.md and elaborated on in docs/Versioning.md

    To be compatible with both proto2 and proto3, we will use a version number that is not defined by the default value but an explicit enum number.