npx.npx_numpy_tensors#

class onnx_array_api.npx.npx_numpy_tensors.EagerNumpyTensor(tensor: ndarray)[source]#: Defines a value for a specific backend.

class onnx_array_api.npx.npx_numpy_tensors.JitNumpyTensor(tensor: ndarray)[source]#: Defines a value for a specific backend.

class onnx_array_api.npx.npx_numpy_tensors.NumpyTensor(tensor: ndarray)[source]#

Default backend based on onnx.reference.ReferenceEvaluator().

Parameters:

input_names – input names
onx – onnx model

class Evaluator(tensor_class: type, input_names: List[str], onx: ModelProto)[source]#

Wraps class onnx.reference.ReferenceEvaluator to have a signature closer to python function.

run(*inputs: List[NumpyTensor]) → List[NumpyTensor][source]#

Executes the function.

Parameters:: inputs – function inputs
Returns:: outputs

const_cast(to: Any | None = None) → EagerTensor[source]#: Casts a constant without any ONNX conversion.

classmethod create_function(input_names: List[str], onx: ModelProto) → Callable[source]#

Creates a python function calling the onnx backend used by this class.

Parameters:: onx – onnx model
Returns:: python function

property dims#: Returns the dimensions of the tensor. First dimension is the batch dimension if the tensor has more than one dimension.

property dtype: Any#: Returns the element type of this tensor.

classmethod get_ir_version(ir_version)[source]#: Updates the IR version. This method should be overloaded. By default, it returns ir_version.

classmethod get_opsets(opsets)[source]#: Updates the opsets for a given backend. This method should be overloaded. By default, it returns opsets.

property key: Any#: Unique key for a tensor of the same type.

property ndim#: Returns the number of dimensions (rank).

numpy()[source]#: Returns the array converted into a numpy array.

property shape: Tuple[int, ...]#: Returns the shape of the tensor.

property tensor_type: TensorType#: Returns the tensor type of this tensor.

property tensor_type_dims: TensorType#: Returns the tensor type of this tensor. This property is used to define a key used to cache a jitted function. Same keys keys means same ONNX graph. Different keys usually means same ONNX graph but different input shapes.

property value: ndarray#: Returns the value of this tensor as a numpy array.