Specifications

Type Mappings

NP_TYPE_TO_TENSOR_TYPE

<<<

import pprint
from onnx.mapping import NP_TYPE_TO_TENSOR_TYPE

pprint.pprint(NP_TYPE_TO_TENSOR_TYPE)

>>>

    {dtype('bool'): 9,
     dtype('uint8'): 2,
     dtype('int8'): 3,
     dtype('uint16'): 4,
     dtype('int16'): 5,
     dtype('float32'): 1,
     dtype('int32'): 6,
     dtype('int64'): 7,
     dtype('float16'): 10,
     dtype('uint32'): 12,
     dtype('uint64'): 13,
     dtype('float64'): 11,
     dtype('complex64'): 14,
     dtype('complex128'): 15,
     dtype('O'): 8}

OP_SET_ID_VERSION_MAP

<<<

import pprint
from onnx.helper import OP_SET_ID_VERSION_MAP

pprint.pprint(OP_SET_ID_VERSION_MAP)

>>>

    {('ai.onnx', 1): 3,
     ('ai.onnx', 5): 3,
     ('ai.onnx', 6): 3,
     ('ai.onnx', 7): 3,
     ('ai.onnx', 8): 3,
     ('ai.onnx', 9): 4,
     ('ai.onnx', 10): 5,
     ('ai.onnx', 11): 6,
     ('ai.onnx', 12): 7,
     ('ai.onnx', 13): 7,
     ('ai.onnx', 14): 7,
     ('ai.onnx', 15): 8,
     ('ai.onnx', 16): 8,
     ('ai.onnx', 17): 8,
     ('ai.onnx', 18): 8,
     ('ai.onnx.ml', 1): 3,
     ('ai.onnx.ml', 2): 6,
     ('ai.onnx.ml', 3): 8,
     ('ai.onnx.preview.training', 1): 7,
     ('ai.onnx.training', 1): 7}

OPTIONAL_ELEMENT_TYPE_TO_FIELD

<<<

import pprint
from onnx.mapping import OPTIONAL_ELEMENT_TYPE_TO_FIELD

pprint.pprint(OPTIONAL_ELEMENT_TYPE_TO_FIELD)

>>>

    {1: 'tensor_value',
     2: 'sparse_tensor_value',
     3: 'sequence_value',
     4: 'map_value',
     5: 'optional_value'}

STORAGE_ELEMENT_TYPE_TO_FIELD

<<<

import pprint
from onnx.mapping import STORAGE_ELEMENT_TYPE_TO_FIELD

pprint.pprint(STORAGE_ELEMENT_TYPE_TO_FIELD)

>>>

    {1: 'tensor_values',
     2: 'sparse_tensor_values',
     3: 'sequence_values',
     4: 'map_values',
     5: 'optional_value'}

STORAGE_TENSOR_TYPE_TO_FIELD

<<<

import pprint
from onnx.mapping import STORAGE_TENSOR_TYPE_TO_FIELD

pprint.pprint(STORAGE_TENSOR_TYPE_TO_FIELD)

>>>

    {1: 'float_data',
     4: 'int32_data',
     6: 'int32_data',
     7: 'int64_data',
     8: 'string_data',
     9: 'int32_data',
     11: 'double_data',
     12: 'uint64_data',
     13: 'uint64_data',
     14: 'float_data',
     15: 'double_data'}

TENSOR_TYPE_TO_NP_TYPE

<<<

import pprint
from onnx.mapping import TENSOR_TYPE_TO_NP_TYPE

pprint.pprint(TENSOR_TYPE_TO_NP_TYPE)

>>>

    {1: dtype('float32'),
     2: dtype('uint8'),
     3: dtype('int8'),
     4: dtype('uint16'),
     5: dtype('int16'),
     6: dtype('int32'),
     7: dtype('int64'),
     8: dtype('O'),
     9: dtype('bool'),
     10: dtype('float16'),
     11: dtype('float64'),
     12: dtype('uint32'),
     13: dtype('uint64'),
     14: dtype('complex64'),
     15: dtype('complex128'),
     16: dtype('float32')}

TENSOR_TYPE_TO_STORAGE_TENSOR_TYPE

<<<

import pprint
from onnx.mapping import TENSOR_TYPE_TO_STORAGE_TENSOR_TYPE

pprint.pprint(TENSOR_TYPE_TO_STORAGE_TENSOR_TYPE)

>>>

    {1: 1,
     2: 6,
     3: 6,
     4: 6,
     5: 6,
     6: 6,
     7: 7,
     8: 8,
     9: 6,
     10: 4,
     11: 11,
     12: 12,
     13: 13,
     14: 1,
     15: 11,
     16: 4}

Opset Version

onnx.defs.onnx_opset_version() → int

Return current opset for domain ai.onnx.

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

Return the schema of all existing operators and all versions.

Operators and Functions Schemas

onnx.defs.get_function_ops() → List[OpSchema]

Return operators defined as functions.

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.

2. 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.

Internal module

Schema submodule

class onnx.onnx_cpp2py_export.defs.OpSchema

Schema of an operator.

class AttrType(self: onnx.onnx_cpp2py_export.defs.OpSchema.AttrType, value: int)

Members:

FLOAT

INT

STRING

TENSOR

GRAPH

FLOATS

INTS

STRINGS

TENSORS

GRAPHS

SPARSE_TENSOR

SPARSE_TENSORS

TYPE_PROTO

TYPE_PROTOS

__eq__(self: object, other: object) → bool

__getstate__(self: object) → int

__hash__(self: object) → int

__index__(self: onnx.onnx_cpp2py_export.defs.OpSchema.AttrType) → int

__init__(self: onnx.onnx_cpp2py_export.defs.OpSchema.AttrType, value: int) → None

__int__(self: onnx.onnx_cpp2py_export.defs.OpSchema.AttrType) → int

__members__ = {'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>}

__ne__(self: object, other: object) → bool

__repr__(self: object) → str

__setstate__(self: onnx.onnx_cpp2py_export.defs.OpSchema.AttrType, state: int) → None

__str__()

name(self: handle) -> str

property name

class DifferentiationCategory(self: onnx.onnx_cpp2py_export.defs.OpSchema.DifferentiationCategory, value: int)

Members:

Unknown

Differentiable

NonDifferentiable

__eq__(self: object, other: object) → bool

__getstate__(self: object) → int

__hash__(self: object) → int

__index__(self: onnx.onnx_cpp2py_export.defs.OpSchema.DifferentiationCategory) → int

__init__(self: onnx.onnx_cpp2py_export.defs.OpSchema.DifferentiationCategory, value: int) → None

__int__(self: onnx.onnx_cpp2py_export.defs.OpSchema.DifferentiationCategory) → int

__members__ = {'Differentiable': <DifferentiationCategory.Differentiable: 1>, 'NonDifferentiable': <DifferentiationCategory.NonDifferentiable: 2>, 'Unknown': <DifferentiationCategory.Unknown: 0>}

__ne__(self: object, other: object) → bool

__repr__(self: object) → str

__setstate__(self: onnx.onnx_cpp2py_export.defs.OpSchema.DifferentiationCategory, state: int) → None

__str__()

name(self: handle) -> str

property name

class FormalParameterOption(self: onnx.onnx_cpp2py_export.defs.OpSchema.FormalParameterOption, value: int)

Members:

Single

Optional

Variadic

__eq__(self: object, other: object) → bool

__getstate__(self: object) → int

__hash__(self: object) → int

__index__(self: onnx.onnx_cpp2py_export.defs.OpSchema.FormalParameterOption) → int

__init__(self: onnx.onnx_cpp2py_export.defs.OpSchema.FormalParameterOption, value: int) → None

__int__(self: onnx.onnx_cpp2py_export.defs.OpSchema.FormalParameterOption) → int

__members__ = {'Optional': <FormalParameterOption.Optional: 1>, 'Single': <FormalParameterOption.Single: 0>, 'Variadic': <FormalParameterOption.Variadic: 2>}

__ne__(self: object, other: object) → bool

__repr__(self: object) → str

__setstate__(self: onnx.onnx_cpp2py_export.defs.OpSchema.FormalParameterOption, state: int) → None

__str__()

name(self: handle) -> str

property name

class SupportType(self: onnx.onnx_cpp2py_export.defs.OpSchema.SupportType, value: int)

Members:

COMMON

EXPERIMENTAL

__eq__(self: object, other: object) → bool

__getstate__(self: object) → int

__hash__(self: object) → int

__index__(self: onnx.onnx_cpp2py_export.defs.OpSchema.SupportType) → int

__init__(self: onnx.onnx_cpp2py_export.defs.OpSchema.SupportType, value: int) → None

__int__(self: onnx.onnx_cpp2py_export.defs.OpSchema.SupportType) → int

__members__ = {'COMMON': <SupportType.COMMON: 0>, 'EXPERIMENTAL': <SupportType.EXPERIMENTAL: 1>}

__ne__(self: object, other: object) → bool

__repr__(self: object) → str

__setstate__(self: onnx.onnx_cpp2py_export.defs.OpSchema.SupportType, state: int) → None

__str__()

name(self: handle) -> str

property name

__init__(*args, **kwargs)

property _function_body

_infer_node_outputs(self: onnx.onnx_cpp2py_export.defs.OpSchema, nodeBytes: bytes, valueTypesByNameBytes: Dict[str, bytes], inputDataByNameBytes: Dict[str, bytes] = {}, inputSparseDataByNameBytes: Dict[str, bytes] = {}, opsetImports: Dict[str, int] = {}, irVersion: int = 8) → Dict[str, bytes]

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

static is_infinite(arg0: int) → bool

exception onnx.onnx_cpp2py_export.defs.SchemaError

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

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

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

Return the schema of all existing operators and all versions.

onnx.onnx_cpp2py_export.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.

2. 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.onnx_cpp2py_export.defs.has_schema(op_type: str, domain: str = '') → bool

onnx.onnx_cpp2py_export.defs.schema_version_map() → Dict[str, Tuple[int, int]]