Protos¶

This structures are defined with protobuf in files onnx/*.proto. It is recommended to use function in module onnx.helper to create them instead of directly instantiated them. Every structure can be printed with function print and is rendered as a json string.

AttributeProto¶

This class is used to define an attribute of an operator defined itself by a NodeProto. It is 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.

class onnx.AttributeProto¶

FunctionProto¶

This defines a function. It is not a model but can be used to define custom operators used in a model.

class onnx.FunctionProto¶

GraphProto¶

This defines a graph or a set of nodes called from a loop or a test for example. 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.

class onnx.GraphProto¶

MapProto¶

This defines a map or a dictionary. It specifies an associative table, defined by keys and values. MapProto is formed with a repeated field of keys (of type INT8, INT16, INT32, INT64, UINT8, UINT16, UINT32, UINT64, or STRING) and values (of type TENSOR, SPARSE_TENSOR, SEQUENCE, or MAP). Key types and value types have to remain the same throughout the instantiation of the MapProto.

class onnx.MapProto¶

ModelProto¶

This defines a model. That is the type every converting library returns after converting a machine learned model. 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.

class onnx.ModelProto¶

NodeProto¶

This defines an operator. A model is a combination of mathematical functions, each of them represented as an onnx operator, stored in a NodeProto. 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.

class onnx.NodeProto¶

OperatorProto¶

This class is rarely used by users. An OperatorProto represents the immutable specification of the signature and semantics of an operator. Operators are declared as part of an OperatorSet, which also defines the domain name for the set. Operators are uniquely identified by a three part identifier (domain, op_type, since_version) where

domain is the domain of an operator set that contains this operator specification.
op_type is the name of the operator as referenced by a NodeProto.op_type
since_version is the version of the operator set that this operator was initially declared in.

class onnx.OperatorProto¶

OperatorSetIdProto¶

This is the type of attribute opset_import of class ModelProto. This attribute specifies the versions of operators used in the model. Every operator or node belongs to a domain. All operators for the same domain share the same version.

class onnx.OperatorSetIdProto¶

OperatorSetProto¶

An OperatorSetProto represents an immutable set of immutable operator specifications. The domain of the set (OperatorSetProto.domain) is a reverse-DNS name that disambiguates operator sets defined by independent entities. The version of the set (opset_version) is a monotonically increasing integer that indicates changes to the membership of the operator set. Operator sets are uniquely identified by a two part identifier (domain, opset_version) Like ModelProto, OperatorSetProto is intended as a top-level file/wire format, and thus has the standard format headers in addition to the operator set information.

class onnx.OperatorSetProto¶

OptionalProto¶

Some input or output of a model are optional. This class must be used in this case. An instance of class OptionalProto may contain or not an instance of type TensorProto, SparseTensorProto, SequenceProto, MapProto and OptionalProto.

class onnx.OptionalProto¶

SequenceProto¶

This defines a dense, ordered, collection of elements that are of homogeneous types. Sequences can be made out of tensors, maps, or sequences. If a sequence is made out of tensors, the tensors must have the same element type (i.e. int32). In some cases, the tensors in a sequence can have different shapes. Whether the tensors can have different shapes or not depends on the type/shape associated with the corresponding ValueInfo. For example, Sequence<Tensor<float, [M,N]> means that all tensors have same shape. However, Sequence<Tensor<float, [omitted,omitted]> means they can have different shapes (all of rank 2), where omitted means the corresponding dimension has no symbolic/constant value. Finally, Sequence<Tensor<float, omitted>> means that the different tensors can have different ranks, when the shape itself is omitted from the tensor-type. For a more complete description, refer to Static tensor shapes.

class onnx.SequenceProto¶

SparseTensorProto¶

This defines a sparse tensor. 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.

class onnx.SparseTensorProto¶

StringStringEntryProto¶

This is equivalent to a pair of strings. This is used to store metadata in ModelProto.

class onnx.StringStringEntryProto¶

TensorProto¶

This defines a tensor. A tensor is fully described with a shape (see ShapeProto), the element type (see TypeProto), and the elements themselves. All available types are listed in onnx.mapping.

class onnx.TensorProto¶

class Segment¶

TensorShapeProto¶

This defines the shape of a tensor or a sparse tensor. It is a list of dimensions. A dimension can be either an integer value or a symbolic variable. A symbolic variable represents an unknown dimension.

class onnx.TensorShapeProto¶

class Dimension¶

TrainingInfoProto¶

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.

class onnx.TrainingInfoProto¶

TypeProto¶

This defines a type of a tensor which consists in an element type and a shape (ShapeProto).

class onnx.TypeProto¶

class Map¶

class Opaque¶

class Optional¶

class Sequence¶

class SparseTensor¶

class Tensor¶

ValueInfoProto¶

This defines a input or output type of a GraphProto. It contains a name, a type (TypeProto), and a documentation string.

class onnx.ValueInfoProto¶