onnx.helper

Helper functions to make ONNX graph components

All functions used to create an ONNX graph.

onnx.helper.make_attribute(key: str, value: Any, doc_string: str | None = None, attr_type: int | None = None) → AttributeProto

Makes an AttributeProto based on the value type.

onnx.helper.make_attribute_ref(name: str, attr_type: AttributeProto.AttributeType, doc_string: str | None = None) → AttributeProto

Make an AttributeProto holding a reference to the parent function’s attribute of given name and type.

onnx.helper.make_empty_tensor_value_info(name: str) → ValueInfoProto

onnx.helper.make_function(domain: str, fname: str, inputs: Sequence[str], outputs: Sequence[str], nodes: Sequence[NodeProto], opset_imports: Sequence[OperatorSetIdProto], attributes: Sequence[str] | None = None, attribute_protos: Sequence[AttributeProto] | None = None, doc_string: str | None = None, overload: str | None = None, value_info: Sequence[ValueInfoProto] | None = None) → FunctionProto

onnx.helper.make_graph(nodes: Sequence[NodeProto], name: str, inputs: Sequence[ValueInfoProto], outputs: Sequence[ValueInfoProto], initializer: Sequence[TensorProto] | None = None, doc_string: str | None = None, value_info: Sequence[ValueInfoProto] | None = None, sparse_initializer: Sequence[SparseTensorProto] | None = None) → GraphProto

Construct a GraphProto

Parameters:

• nodes – list of NodeProto

• name (string) – graph name

• inputs – list of ValueInfoProto

• outputs – list of ValueInfoProto

• initializer – list of TensorProto

• doc_string (string) – graph documentation

• value_info – list of ValueInfoProto

• sparse_initializer – list of SparseTensorProto

Returns:

GraphProto

onnx.helper.make_map(name: str, key_type: int, keys: list[Any], values: SequenceProto) → MapProto

Make a Map with specified key-value pair arguments.

Criteria for conversion: - Keys and Values must have the same number of elements - Every key in keys must be of the same type - Every value in values must be of the same type

onnx.helper.make_map_type_proto(key_type: int, value_type: TypeProto) → TypeProto

Makes a map TypeProto.

onnx.helper.make_model(graph: GraphProto, **kwargs: Any) → ModelProto

Construct a ModelProto

Parameters:

• graph (GraphProto) – make_graph returns

• **kwargs – any attribute to add to the returned instance

Returns:

ModelProto

onnx.helper.make_node(op_type: str, inputs: Sequence[str], outputs: Sequence[str], name: str | None = None, doc_string: str | None = None, domain: str | None = None, overload: str | None = None, **kwargs: Any) → NodeProto

Construct a NodeProto.

Parameters:

• op_type (string) – The name of the operator to construct

• inputs (list of string) – list of input names

• outputs (list of string) – list of output names

• name (string, default None) – optional unique identifier for NodeProto

• doc_string (string, default None) – optional documentation string for NodeProto

• domain (string, default None) – optional domain for NodeProto. If it’s None, we will just use default domain (which is empty)

• overload (string, default None) – optional field, used to resolve calls to model-local functions

• **kwargs (dict) – the attributes of the node. The acceptable values are documented in make_attribute().

Returns:

NodeProto

onnx.helper.make_operatorsetid(domain: str, version: int) → OperatorSetIdProto

Construct an OperatorSetIdProto.

Parameters:

• domain (string) – The domain of the operator set id

• version (integer) – Version of operator set id

Returns:

OperatorSetIdProto

onnx.helper.make_opsetid(domain: str, version: int) → OperatorSetIdProto

Construct an OperatorSetIdProto.

Parameters:

• domain (string) – The domain of the operator set id

• version (integer) – Version of operator set id

Returns:

OperatorSetIdProto

onnx.helper.make_model_gen_version(graph: GraphProto, **kwargs: Any) → ModelProto

onnx.helper.make_optional(name: str, elem_type: OptionalProto.DataType, value: google.protobuf.message.Message | None) → OptionalProto

Make an Optional with specified value arguments.

onnx.helper.make_optional_type_proto(inner_type_proto: TypeProto) → TypeProto

Makes an optional TypeProto.

onnx.helper.make_sequence(name: str, elem_type: SequenceProto.DataType, values: Sequence[Any]) → SequenceProto

Make a Sequence with specified value arguments.

onnx.helper.make_sequence_type_proto(inner_type_proto: TypeProto) → TypeProto

Makes a sequence TypeProto.

onnx.helper.make_sparse_tensor(values: TensorProto, indices: TensorProto, dims: Sequence[int]) → SparseTensorProto

Construct a SparseTensorProto

Parameters:

• values (TensorProto) – the values

• indices (TensorProto) – the indices

• dims – the shape

Returns:

SparseTensorProto

onnx.helper.make_sparse_tensor_type_proto(elem_type: int, shape: Sequence[str | int | None] | None, shape_denotation: list[str] | None = None) → TypeProto

Makes a SparseTensor TypeProto based on the data type and shape.

onnx.helper.make_sparse_tensor_value_info(name: str, elem_type: int, shape: Sequence[str | int | None] | None, doc_string: str = '', shape_denotation: list[str] | None = None) → ValueInfoProto

Makes a SparseTensor ValueInfoProto based on the data type and shape.

onnx.helper.make_tensor(name: str, data_type: int, dims: Sequence[int], vals: Any, raw: bool = False) → TensorProto

Make a TensorProto with specified arguments. If raw is False, this function will choose the corresponding proto field to store the values based on data_type. If raw is True, use “raw_data” proto field to store the values, and values should be of type bytes in this case.

Parameters:

• name (string) – tensor name

• data_type (int) – a value such as onnx.TensorProto.FLOAT

• dims (List[int]) – shape

• vals – values

• raw (bool) – if True, vals contains the serialized content of the tensor, otherwise, vals should be a list of values of the type defined by data_type

Returns:

TensorProto

onnx.helper.make_tensor_sequence_value_info(name: str, elem_type: int, shape: Sequence[str | int | None] | None, doc_string: str = '', elem_shape_denotation: list[str] | None = None) → ValueInfoProto

Makes a Sequence[Tensors] ValueInfoProto based on the data type and shape.

onnx.helper.make_tensor_type_proto(elem_type: int, shape: Sequence[str | int | None] | None, shape_denotation: list[str] | None = None) → TypeProto

Makes a Tensor TypeProto based on the data type and shape.

onnx.helper.make_training_info(algorithm: GraphProto, algorithm_bindings: list[tuple[str, str]], initialization: GraphProto | None, initialization_bindings: list[tuple[str, str]] | None) → TrainingInfoProto

onnx.helper.make_tensor_value_info(name: str, elem_type: int, shape: Sequence[str | int | None] | None, doc_string: str = '', shape_denotation: list[str] | None = None) → ValueInfoProto

Makes a ValueInfoProto based on the data type and shape.

onnx.helper.make_value_info(name: str, type_proto: TypeProto, doc_string: str = '') → ValueInfoProto

Makes a ValueInfoProto with the given type_proto.

Type Mappings

onnx.helper.get_all_tensor_dtypes() → KeysView[int]

Get all tensor types from TensorProto.

Returns:

all tensor types from TensorProto

onnx.helper.np_dtype_to_tensor_dtype(np_dtype: dtype) → int

Convert a numpy’s dtype to corresponding tensor type. It can be used while converting numpy arrays to tensors.

Parameters:

np_dtype – numpy’s data_type

Returns:

TensorsProto’s data_type

onnx.helper.tensor_dtype_to_field(tensor_dtype: int) → str

Convert a TensorProto’s data_type to corresponding field name for storage. It can be used while making tensors.

Parameters:

tensor_dtype – TensorProto’s data_type

Returns:

field name

onnx.helper.tensor_dtype_to_np_dtype(tensor_dtype: int) → dtype

Convert a TensorProto’s data_type to corresponding numpy dtype. It can be used while making tensor.

Parameters:

tensor_dtype – TensorProto’s data_type

Returns:

numpy’s data_type

onnx.helper.tensor_dtype_to_storage_tensor_dtype(tensor_dtype: int) → int

Convert a TensorProto’s data_type to corresponding data_type for storage.

Parameters:

tensor_dtype – TensorProto’s data_type

Returns:

data_type for storage

onnx.helper.tensor_dtype_to_string(tensor_dtype: int) → str

Get the name of given TensorProto’s data_type.

Parameters:

tensor_dtype – TensorProto’s data_type

Returns:

the name of data_type

Tools

onnx.helper.find_min_ir_version_for(opsetidlist: Sequence[OperatorSetIdProto], ignore_unknown: bool = False) → int

Given list of opset ids, determine minimum IR version required.

Parameters:

• opsetidlist – A sequence of OperatorSetIdProto.

• ignore_unknown – If True, ignore unknown domain and return default minimum version for that domain.

Returns:

The minimum IR version required (integer)

onnx.helper.create_op_set_id_version_map(table: list[tuple[str, int, int, int] | tuple[str, int, int, int, int]]) → dict[tuple[str, int], int]

Create a map from (opset-domain, opset-version) to ir-version from above table.

Other functions

get_attribute_value(attr)
get_node_attr_value(node, attr_name)
set_metadata_props(proto, dict_value)
set_model_props(model, dict_value)
printable_attribute(attr[, subgraphs])
printable_dim(dim)
printable_graph(graph[, prefix])	Display a GraphProto as a string.
printable_node(node[, prefix, subgraphs])
printable_tensor_proto(t)
printable_type(t)
printable_value_info(v)