onnx.numpy_helper

bfloat16_to_float32(data[, dims])	Converts ndarray of bf16 (as uint32) to f32 (as uint32).
float8e4m3_to_float32(data[, dims, fn, uz])	Converts ndarray of float8, e4m3 (as uint32) to f32 (as uint32).
float8e5m2_to_float32(data[, dims, fn, uz])	Converts ndarray of float8, e5m2 (as uint32) to f32 (as uint32).
from_array(arr[, name])	Converts a numpy array to a tensor def.
from_dict(dict_[, name])	Converts a Python dictionary into a map def.
from_list(lst[, name, dtype])	Converts a list into a sequence def.
from_optional(opt[, name, dtype])	Converts an optional value into a Optional def.
to_array(tensor[, base_dir])	Converts a tensor def object to a numpy array.
to_dict(map_proto)	Converts a map def to a Python dictionary.
to_list(sequence)	Converts a sequence def to a Python list.
to_optional(optional)	Converts an optional def to a Python optional.

array

onnx.numpy_helper.from_array(arr: ndarray, name: str | None = None) → TensorProto

Converts a numpy array to a tensor def.

Parameters:

• arr – a numpy array.

• name – (optional) the name of the tensor.

Returns:

the converted tensor def.

Return type:

TensorProto

onnx.numpy_helper.to_array(tensor: TensorProto, base_dir: str = '') → ndarray

Converts a tensor def object to a numpy array.

Parameters:

• tensor – a TensorProto object.

• base_dir – if external tensor exists, base_dir can help to find the path to it

Returns:

the converted array.

Return type:

arr

sequence

onnx.numpy_helper.to_list(sequence: SequenceProto) → list[Any]

Converts a sequence def to a Python list.

Parameters:

sequence – a SequenceProto object.

Returns:

the converted list.

Return type:

list

onnx.numpy_helper.from_list(lst: list[Any], name: str | None = None, dtype: int | None = None) → SequenceProto

Converts a list into a sequence def.

Parameters:

• lst – a Python list

• name – (optional) the name of the sequence.

• dtype – (optional) type of element in the input list, used for specifying sequence values when converting an empty list.

Returns:

the converted sequence def.

Return type:

SequenceProto

dictionary

onnx.numpy_helper.to_dict(map_proto: MapProto) → dict[Any, Any]

Converts a map def to a Python dictionary.

Parameters:

map_proto – a MapProto object.

Returns:

The converted dictionary.

onnx.numpy_helper.from_dict(dict_: dict[Any, Any], name: str | None = None) → MapProto

Converts a Python dictionary into a map def.

Parameters:

• dict – Python dictionary

• name – (optional) the name of the map.

Returns:

the converted map def.

Return type:

MapProto

optional

onnx.numpy_helper.to_optional(optional: OptionalProto) → Any | None

Converts an optional def to a Python optional.

Parameters:

optional – an OptionalProto object.

Returns:

the converted optional.

Return type:

opt

onnx.numpy_helper.from_optional(opt: Any | None, name: str | None = None, dtype: int | None = None) → OptionalProto

Converts an optional value into a Optional def.

Parameters:

• opt – a Python optional

• name – (optional) the name of the optional.

• dtype – (optional) type of element in the input, used for specifying optional values when converting empty none. dtype must be a valid OptionalProto.DataType value

Returns:

the converted optional def.

Return type:

optional

tools

onnx.numpy_helper.convert_endian(tensor: TensorProto) → None

Call to convert endianness of raw data in tensor.

Parameters:

tensor – TensorProto to be converted.

onnx.numpy_helper.combine_pairs_to_complex(fa: Sequence[int]) → list[complex]

onnx.numpy_helper.create_random_int(input_shape: tuple[int], dtype: dtype, seed: int = 1) → ndarray

Create random integer array for backend/test/case/node.

Parameters:

• input_shape – The shape for the returned integer array.

• dtype – The NumPy data type for the returned integer array.

• seed – The seed for np.random.

Returns:

Random integer array.

Return type:

np.ndarray

cast

onnx.numpy_helper.bfloat16_to_float32(data: int16 | int32 | ndarray, dims: int | Sequence[int] | None = None) → ndarray

Converts ndarray of bf16 (as uint32) to f32 (as uint32).

Parameters:

• data – A numpy array, empty dimensions are allowed if dims is None.

• dims – If specified, the function reshapes the results.

Returns:

A numpy array of float32 with the same dimension if dims is None, or reshaped to dims if specified

onnx.numpy_helper.float8e4m3_to_float32(data: int16 | int32 | ndarray, dims: int | Sequence[int] | None = None, fn: bool = True, uz: bool = False) → ndarray

Converts ndarray of float8, e4m3 (as uint32) to f32 (as uint32).

See Float stored in 8 bits for technical details.

Parameters:

• data – A numpy array, empty dimensions are allowed if dims is None.

• dims – If specified, the function reshapes the results.

• fn – No infinite values.

• uz – No negative zero.

Returns:

A numpy array of float32 with the same dimension if dims is None, or reshaped to dims if specified.

onnx.numpy_helper.float8e5m2_to_float32(data: int16 | int32 | ndarray, dims: int | Sequence[int] | None = None, fn: bool = False, uz: bool = False) → ndarray

Converts ndarray of float8, e5m2 (as uint32) to f32 (as uint32).

See Float stored in 8 bits for technical details.

Parameters:

• data – A numpy array, empty dimensions are allowed if dims is None.

• dims – If specified, the function reshapes the results.

• fn – No infinite values.

• uz – No negative zero.

Returns:

A numpy array of float32 with the same dimension if dims is None, or reshaped to dims if specified