npx.npx_var#

Var#

class onnx_array_api.npx.npx_var.Var(*inputs: List[Any], op: Callable | str | Tuple[str, str] | FunctionProto | ModelProto | NodeProto | None = None, dtype: type | None = None, inline: bool = False, n_var_outputs: int | None = 1, input_indices: List[int] | None = None, **kwargs)[source]#

Defines a variable, a result…

Parameters:

inputs – list of inputs
op – apply on operator on the inputs
inline – True to reduce the use of function and inline small functions, this only applies if op is a function
n_var_outputs – number of the operator outputs
input_indices – to select a specific output from the input operator
kwargs – operator attributes

Private attribute:

Parameters:: onnx_input_type – names given to the variables

property T: Var#: Transpose.

astype(dtype) → Var[source]#: Cast

copy() → Var[source]#: Returns a copy of self (use of Identity node).

flatten() → Var[source]#

Flattens a matrix (see numpy.ndarray.flatten()).

Parameters:: axis – only flatten from axis to the end.
Returns:: Var

get(index: int) → Var[source]#

If an operator or a function returns more than one output, this takes only one.

Parameters:: index – index of the output to select
Returns:: Var

property is_function#: Tells if this variable encapsulate a function.

max(axis: TensorType_I__7 | None = None, keepdims: ParTypeint = 0) → Var[source]#: See numpy.max().

mean(axis: OptParTypeTupleType_int | None = None, keepdims: ParTypeint = 0) → Var[source]#: See numpy.mean().

min(axis: TensorType_I__7 | None = None, keepdims: ParTypeint = 0) → Var[source]#: See numpy.min().

prod(axis: TensorType_I__7 | None = None, keepdims: ParTypeint = 0) → Var[source]#: See numpy.prod().

reduce_function(reduce_op, axis: TensorType_I__7 | None = None, keepdims: ParTypeint = 0) → Var[source]#: See numpy.sum() or any other reduce function.

replace_inputs(new_inputs: List[Var], input_indices: List[int] | None = None) → Var[source]#: Replaces inputs by new ones. It creates a copy. It is needed when inlining functions.

reshape(shape: Var) → Var[source]#: Reshape

property self_var#: Returns itself or the variable corresponding to its state after a call to __setitem__.

set_onnx_name(prefix: str)[source]#

Forces this variable to get this name during

Parameters:: prefix – prefix

property shape: Var#: Shape

sum(axis: TensorType_I__7 | None = None, keepdims: ParTypeint = 0) → Var[source]#: See numpy.sum().

to_onnx(target_opsets: Dict[str, int] | None = None, as_function: bool = False, name: str | None = None, domain: str | None = None, attributes: List[str] | None = None, constraints: Dict[Any, TensorType] | None = None, ir_version: int | None = None) → ModelProto | FunctionProto | List[Any][source]#

Converts the recursive graph to ONNX.

Parameters:

target_opsets – dictionary {opset: version}
as_function – conversion to onnx.FunctionProto or onnx.ModelProto
name – function name if as_function is True
domain – function domain if as_function is True
attributes – function attributes if any
constraints – specifies a precise type for the type constraints when a function allows more than one type, this works if there is only one variable to be converted

Returns:

ModelProto, FunctionProto

Cst, Input#

class onnx_array_api.npx.npx_var.Cst(cst: Any)[source]#: Defines a constant.

class onnx_array_api.npx.npx_var.Input(name=None)[source]#

Defines an input, a placeholder.

Parameters:: name – input name or None if undefined