onnx.helper

tools

onnx.helper.find_min_ir_version_for(opsetidlist: List[onnx.onnx_ml_pb2.OperatorSetIdProto]) → int

onnx.helper.split_complex_to_pairs(ca: Sequence[numpy.complex64]) → Sequence[int]

make function

onnx.helper.make_node(op_type: str, inputs: Sequence[str], outputs: Sequence[str], name: Optional[str] = None, doc_string: Optional[str] = None, domain: Optional[str] = None, **kwargs: Any) → onnx.onnx_ml_pb2.NodeProto

Construct a NodeProto.

Arguments:

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)

**kwargs (dict): the attributes of the node. The acceptable values

are documented in make_attribute().

onnx.helper.make_graph(nodes: Sequence[onnx.onnx_ml_pb2.NodeProto], name: str, inputs: Sequence[onnx.onnx_ml_pb2.ValueInfoProto], outputs: Sequence[onnx.onnx_ml_pb2.ValueInfoProto], initializer: Optional[Sequence[onnx.onnx_ml_pb2.TensorProto]] = None, doc_string: Optional[str] = None, value_info: Sequence[onnx.onnx_ml_pb2.ValueInfoProto] = [], sparse_initializer: Optional[Sequence[onnx.onnx_ml_pb2.SparseTensorProto]] = None) → onnx.onnx_ml_pb2.GraphProto

onnx.helper.make_model(graph: onnx.onnx_ml_pb2.GraphProto, **kwargs: Any) → onnx.onnx_ml_pb2.ModelProto

onnx.helper.make_operatorsetid(domain: str, version: int) → onnx.onnx_ml_pb2.OperatorSetIdProto

Construct an OperatorSetIdProto.

Arguments:

domain (string): The domain of the operator set id version (integer): Version of operator set id

onnx.helper.make_opsetid(domain: str, version: int) → onnx.onnx_ml_pb2.OperatorSetIdProto

onnx.helper.make_tensor(name, data_type, dims, vals, raw=False)

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.

onnx.helper.make_sparse_tensor(values: onnx.onnx_ml_pb2.TensorProto, indices: onnx.onnx_ml_pb2.TensorProto, dims: Sequence[int]) → onnx.onnx_ml_pb2.SparseTensorProto

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

Make a Sequence with specified value arguments.

onnx.helper.make_map(name: str, key_type: int, keys: List[Any], values: onnx.onnx_data_pb2.SequenceProto) → onnx.onnx_data_pb2.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_optional(name: Text, elem_type: OptionalProto.DataType, value: Optional[Any]) → OptionalProto

Make an Optional with specified value arguments.

onnx.helper.make_attribute(key: str, value: Any, doc_string: Optional[str] = None) → onnx.onnx_ml_pb2.AttributeProto

Makes an AttributeProto based on the value type.

onnx.helper.make_empty_tensor_value_info(name: str) → onnx.onnx_ml_pb2.ValueInfoProto

onnx.helper.make_tensor_value_info(name: str, elem_type: int, shape: Optional[Sequence[Optional[Union[str, int]]]], doc_string: str = '', shape_denotation: Optional[List[str]] = None) → onnx.onnx_ml_pb2.ValueInfoProto

Makes a ValueInfoProto based on the data type and shape.

onnx.helper.make_tensor_type_proto(elem_type: int, shape: Optional[Sequence[Optional[Union[str, int]]]], shape_denotation: Optional[List[str]] = None) → onnx.onnx_ml_pb2.TypeProto

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

onnx.helper.make_sparse_tensor_type_proto(elem_type: int, shape: Optional[Sequence[Optional[Union[str, int]]]], shape_denotation: Optional[List[str]] = None) → onnx.onnx_ml_pb2.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: Optional[Sequence[Optional[Union[str, int]]]], doc_string: str = '', shape_denotation: Optional[List[str]] = None) → onnx.onnx_ml_pb2.ValueInfoProto

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

onnx.helper.make_sequence_type_proto(inner_type_proto: onnx.onnx_ml_pb2.TypeProto) → onnx.onnx_ml_pb2.TypeProto

Makes a sequence TypeProto.

onnx.helper.make_optional_type_proto(inner_type_proto: onnx.onnx_ml_pb2.TypeProto) → onnx.onnx_ml_pb2.TypeProto

Makes an optional TypeProto.

onnx.helper.make_value_info(name: str, type_proto: onnx.onnx_ml_pb2.TypeProto, doc_string: str = '') → onnx.onnx_ml_pb2.ValueInfoProto

Makes a ValueInfoProto with the given type_proto.

onnx.helper.make_tensor_sequence_value_info(name: str, elem_type: int, shape: Optional[Sequence[Optional[Union[str, int]]]], doc_string: str = '', elem_shape_denotation: Optional[List[str]] = None) → onnx.onnx_ml_pb2.ValueInfoProto

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

onnx.helper.make_sequence_value_info(name: str, elem_type: int, shape: Optional[Sequence[Optional[Union[str, int]]]], doc_string: str = '', elem_shape_denotation: Optional[List[str]] = None) → onnx.onnx_ml_pb2.ValueInfoProto

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

onnx.helper.make_training_info(algorithm: onnx.onnx_ml_pb2.GraphProto, algorithm_bindings: List[Tuple[str, str]], initialization: Optional[onnx.onnx_ml_pb2.GraphProto], initialization_bindings: Optional[List[Tuple[str, str]]]) → onnx.onnx_ml_pb2.TrainingInfoProto

getter

onnx.helper.get_attribute_value(attr: onnx.onnx_ml_pb2.AttributeProto) → Any

print

onnx.helper.printable_attribute(attr: onnx.onnx_ml_pb2.AttributeProto, subgraphs: bool = False) → Union[str, Tuple[str, List[onnx.onnx_ml_pb2.GraphProto]]]

onnx.helper.printable_dim(dim: onnx.onnx_ml_pb2.Dimension) → str

onnx.helper.printable_type(t: onnx.onnx_ml_pb2.TypeProto) → str

onnx.helper.printable_graph(graph: onnx.onnx_ml_pb2.GraphProto, prefix: str = '') → str

onnx.helper.printable_node(node: onnx.onnx_ml_pb2.NodeProto, prefix: str = '', subgraphs: bool = False) → Union[str, Tuple[str, List[onnx.onnx_ml_pb2.GraphProto]]]

onnx.helper.printable_tensor_proto(t: onnx.onnx_ml_pb2.TensorProto) → str

onnx.helper.printable_value_info(v: onnx.onnx_ml_pb2.ValueInfoProto) → str

previous

Serialization

next

onnx.numpy_helper