onnx.defs#

Opset Version#

onnx.defs.onnx_opset_version() → int[source]#

Return current opset for domain ai.onnx.

Operators and Functions Schemas#

onnx.defs.has()#

has_schema(*args, **kwargs) Overloaded function.

  1. has_schema(op_type: str, domain: str = ‘’) -> bool

  2. has_schema(op_type: str, max_inclusive_version: int, domain: str = ‘’) -> bool

onnx.defs.get_schema(*args, **kwargs)#

Overloaded function.

  1. get_schema(op_type: str, max_inclusive_version: int, domain: str = ‘’) -> onnx.onnx_cpp2py_export.defs.OpSchema

Return the schema of the operator op_type and for a specific version.

  1. get_schema(op_type: str, domain: str = ‘’) -> onnx.onnx_cpp2py_export.defs.OpSchema

Return the schema of the operator op_type and for a specific version.

onnx.defs.get_all_schemas() → List[onnx.onnx_cpp2py_export.defs.OpSchema]#

Return the schema of all existing operators for the latest version.

onnx.defs.get_all_schemas_with_history() → List[onnx.onnx_cpp2py_export.defs.OpSchema]#

Return the schema of all existing operators and all versions.

onnx.defs.get_function_ops() → list[OpSchema][source]#

Return operators defined as functions.

onnx.defs.register_schema(schema: OpSchema) → None[source]#

Register a user provided OpSchema.

The function extends available operator set versions for the provided domain if necessary.

Parameters:

schema – The OpSchema to register.

onnx.defs.deregister_schema(op_type: str, version: int, domain: str) → None#

Deregister the specified OpSchema.

class OpSchema#

class onnx.defs.OpSchema#

Schema of an operator.

class AttrType#

Members:

FLOAT

INT

STRING

TENSOR

GRAPH

FLOATS

INTS

STRINGS

TENSORS

GRAPHS

SPARSE_TENSOR

SPARSE_TENSORS

TYPE_PROTO

TYPE_PROTOS

FLOAT = <AttrType.FLOAT: 1>#
FLOATS = <AttrType.FLOATS: 6>#
GRAPH = <AttrType.GRAPH: 5>#
GRAPHS = <AttrType.GRAPHS: 10>#
INT = <AttrType.INT: 2>#
INTS = <AttrType.INTS: 7>#
SPARSE_TENSOR = <AttrType.SPARSE_TENSOR: 11>#
SPARSE_TENSORS = <AttrType.SPARSE_TENSORS: 12>#
STRING = <AttrType.STRING: 3>#
STRINGS = <AttrType.STRINGS: 8>#
TENSOR = <AttrType.TENSOR: 4>#
TENSORS = <AttrType.TENSORS: 9>#
TYPE_PROTO = <AttrType.TYPE_PROTO: 13>#
TYPE_PROTOS = <AttrType.TYPE_PROTOS: 14>#
property name#
property value#
class Attribute#
property default_value#
property description#
property name#
property required#
property type#
class DifferentiationCategory#

Members:

Unknown

Differentiable

NonDifferentiable

Differentiable = <DifferentiationCategory.Differentiable: 1>#
NonDifferentiable = <DifferentiationCategory.NonDifferentiable: 2>#
Unknown = <DifferentiationCategory.Unknown: 0>#
property name#
property value#
class FormalParameter#
property description#
property differentiation_category#
property is_homogeneous#
property min_arity#
property name#
property option#
property type_str#
property types#
class FormalParameterOption#

Members:

Single

Optional

Variadic

Optional = <FormalParameterOption.Optional: 1>#
Single = <FormalParameterOption.Single: 0>#
Variadic = <FormalParameterOption.Variadic: 2>#
property name#
property value#
class SupportType#

Members:

COMMON

EXPERIMENTAL

COMMON = <SupportType.COMMON: 0>#
EXPERIMENTAL = <SupportType.EXPERIMENTAL: 1>#
property name#
property value#
class TypeConstraintParam#
property allowed_type_strs#
property description#
property type_param_str#
property all_function_opset_versions#
property attributes#
property context_dependent_function_opset_versions#
property deprecated#
property doc#
property domain#
property file#
property function_body#
property function_opset_versions#
get_context_dependent_function(self: onnx.onnx_cpp2py_export.defs.OpSchema, arg0: bytes, arg1: List[bytes]) → bytes#
get_context_dependent_function_with_opset_version(self: onnx.onnx_cpp2py_export.defs.OpSchema, arg0: int, arg1: bytes, arg2: List[bytes]) → bytes#
get_function_with_opset_version(self: onnx.onnx_cpp2py_export.defs.OpSchema, arg0: int) → bytes#
property has_context_dependent_function#
property has_data_propagation_function#
property has_function#
property has_type_and_shape_inference_function#
property inputs#
static is_infinite(arg0: int) → bool#
property line#
property max_input#
property max_output#
property min_input#
property min_output#
property name#
property outputs#
property since_version#
property support_level#
property type_constraints#

Exceptions#

class onnx.defs.SchemaError#

Constants#

Domains officially supported in onnx package.

from onnx.defs import (
    ONNX_DOMAIN,
    ONNX_ML_DOMAIN,
    AI_ONNX_PREVIEW_TRAINING_DOMAIN,
)
print(f"ONNX_DOMAIN={ONNX_DOMAIN!r}")
print(f"ONNX_ML_DOMAIN={ONNX_ML_DOMAIN!r}")
print(f"AI_ONNX_PREVIEW_TRAINING_DOMAIN={AI_ONNX_PREVIEW_TRAINING_DOMAIN!r}")

ONNX_DOMAIN=''
ONNX_ML_DOMAIN='ai.onnx.ml'
AI_ONNX_PREVIEW_TRAINING_DOMAIN='ai.onnx.preview.training'