onnx_light.onnx.numpy_helper#
- onnx_light.onnx.numpy_helper.create_random_int(input_shape: tuple[int, ...], dtype: dtype, seed: int = 1) ndarray#
Creates a random integer array for backend test cases.
- 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.
- onnx_light.onnx.numpy_helper.from_array(array: ndarray, /, name: str | None = None) TensorProto#
Converts a numpy array into a TensorProto.
- Parameters:
array – a numpy array.
name – (optional) the name of the tensor.
- Returns:
The converted TensorProto.
- onnx_light.onnx.numpy_helper.from_dict(dict_: dict[Any, Any], name: str | None = None) MapProto#
Converts a Python dictionary into a MapProto.
- Parameters:
dict – Python dictionary.
name – (optional) the name of the map.
- Returns:
The converted MapProto.
- onnx_light.onnx.numpy_helper.from_list(lst: list[Any], name: str | None = None, dtype: int | None = None) SequenceProto#
Converts a Python list into a SequenceProto.
- 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 SequenceProto.
- onnx_light.onnx.numpy_helper.from_optional(opt: Any | None, name: str | None = None, dtype: int | None = None) OptionalProto#
Converts an optional value into an OptionalProto.
- 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 OptionalProto.
- onnx_light.onnx.numpy_helper.saturate_cast(x: ndarray, dtype: dtype) ndarray#
Performs saturate-cast of a numpy array to the target dtype.
Values outside the representable range of the target dtype are clamped to the maximum or minimum representable value of that dtype.
- Parameters:
x – Input array.
dtype – Target numpy dtype.
- Returns:
The clamped and cast array.
- onnx_light.onnx.numpy_helper.to_array(tensor: TensorProto, base_dir: str = '') ndarray#
Converts a TensorProto object to a numpy array.
This function uses ml_dtypes if the dtype is not a native numpy dtype.
- Parameters:
tensor – a TensorProto object.
base_dir – if external tensor exists, base_dir can help to find the path to it.
- Returns:
The converted numpy array.
- onnx_light.onnx.numpy_helper.to_dict(map_proto: MapProto) dict[Any, Any]#
Converts a MapProto to a Python dictionary.
- Parameters:
map_proto – a MapProto object.
- Returns:
The converted dictionary.
- onnx_light.onnx.numpy_helper.to_float8e8m0(x: ndarray, saturate: bool = True, round_mode: str = 'up') ndarray#
Converts a float32 NumPy array to float8e8m0 representation.
If the input is not a float32 array, it will be cast to one first.
- Parameters:
x – Input array to convert.
saturate – Whether to saturate at max/min float8e8m0 value.
round_mode – “nearest”, “up”, or “down”.
- Returns:
Array of ml_dtypes.float8_e8m0fnu values.
- onnx_light.onnx.numpy_helper.to_list(sequence: SequenceProto) list[Any]#
Converts a SequenceProto to a Python list.
- Parameters:
sequence – a SequenceProto object.
- Returns:
The converted list.
- onnx_light.onnx.numpy_helper.to_optional(optional: OptionalProto) Any | None#
Converts an OptionalProto to a Python optional.
- Parameters:
optional – an OptionalProto object.
- Returns:
The converted optional value, or None for UNDEFINED.