openvino.runtime.opset1.tensor_iterator#

class openvino.runtime.opset1.tensor_iterator#

Bases: Node

openvino.impl.op.TensorIterator wraps ov::op::v0::TensorIterator

__init__(self: openvino._pyopenvino.op.tensor_iterator) None#

Methods

__add__(right_node[, auto_broadcast, name])

Return node which applies f(A,B) = A+B to the input nodes element-wise.

__delattr__(name, /)

Implement delattr(self, name).

__dir__()

Default dir() implementation.

__div__(right_node[, auto_broadcast, name])

Return node which applies f(x) = A/B to the input nodes element-wise.

__eq__(right_node[, auto_broadcast, name])

Return node which checks if input nodes are equal element-wise.

__format__(format_spec, /)

Default object formatter.

__ge__(right_node[, auto_broadcast, name])

Return node which checks if left node is greater or equal to the right node element-wise.

__getattr__(self, arg0)

__getattribute__(name, /)

Return getattr(self, name).

__gt__(right_node[, auto_broadcast, name])

Return node which checks if left input node is greater than the right node element-wise.

__hash__()

Return hash(self).

__init__(self)

__init_subclass__

This method is called when a class is subclassed.

__le__(right_node[, auto_broadcast, name])

Return node which checks if left input node is less or equal the right node element-wise.

__lt__(right_node[, auto_broadcast, name])

Return node which checks if left input node is less than the right node element-wise.

__mul__(right_node[, auto_broadcast, name])

Return node which applies f(A,B) = A*B to the input nodes elementwise.

__ne__(right_node[, auto_broadcast, name])

Return node which checks if input nodes are unequal element-wise.

__new__(**kwargs)

__radd__(right)

__rdiv__(right)

__reduce__()

Helper for pickle.

__reduce_ex__(protocol, /)

Helper for pickle.

__repr__(self)

__rmul__(right)

__rsub__(right)

__rtruediv__(right)

__setattr__(name, value, /)

Implement setattr(self, name, value).

__sizeof__()

Size of object in memory, in bytes.

__str__()

Return str(self).

__sub__(right_node[, auto_broadcast, name])

Return node which applies f(x) = A-B to the input nodes element-wise.

__subclasshook__

Abstract classes can override this to customize issubclass().

__truediv__(right_node[, auto_broadcast, name])

Return node which applies f(x) = A/B to the input nodes element-wise.

evaluate(*args, **kwargs)

Overloaded function.

get_attributes(self)

get_body(self)

get_concatenated_slices(self, value, start, ...)

get_element_type(self)

Checks that there is exactly one output and returns it's element type.

get_friendly_name(self)

Gets the friendly name for a node.

get_function(self)

get_input_descriptions(self)

get_input_size(self)

Returns the number of inputs to the node.

get_input_tensor(self, index)

Returns the tensor for the node's input with index i

get_iter_value(self, body_value, iteration)

get_name(self)

Get the unique name of the node

get_num_iterations(self)

get_output_descriptions(self)

get_output_element_type(self, index)

Returns the element type for output i

get_output_partial_shape(self, index)

Returns the partial shape for output i

get_output_shape(self, index)

Returns the shape for output i

get_output_size(self)

Returns the number of outputs from the node.

get_output_tensor(self, index)

Returns the tensor for output i

get_rt_info(self)

Returns PyRTMap which is a dictionary of user defined runtime info.

get_type_info(self)

get_type_name(self)

Returns Type's name from the node.

input(self, input_index)

A handle to the input_index input of this node.

input_value(self, index)

Returns input of the node with index i

input_values(self)

Returns list of node's inputs, in order.

inputs(self)

A list containing a handle for each of this node's inputs, in order.

output(self, output_index)

A handle to the output_index output of this node.

outputs(self)

A list containing a handle for each of this node's outputs, in order.

set_argument(self, arg0, arg1)

set_arguments(*args, **kwargs)

Overloaded function.

set_attribute(self, arg0, arg1)

set_body(self, body)

set_friendly_name(self, name)

Sets a friendly name for a node.

set_function(self, func)

set_input_descriptions(self, inputs)

set_invariant_input(self, body_parameter, value)

set_merged_input(self, body_parameter, ...)

set_output_descriptions(self, outputs)

set_sliced_input(self, parameter, value, ...)

validate(self)

Attributes

friendly_name

name

rt_info

shape

type_info

__add__(right_node: Node | int | float | ndarray, auto_broadcast: str = 'NUMPY', name: str | None = None) Node#

Return node which applies f(A,B) = A+B to the input nodes element-wise.

Parameters:
  • left_node – The first input node for add operation.

  • right_node – The second input node for add operation.

  • auto_broadcast – The type of broadcasting specifies rules used for auto-broadcasting of input tensors. Defaults to “NUMPY”.

  • name – The optional name for output new node.

Returns:

The node performing element-wise addition.

__class__#

alias of pybind11_type

__delattr__(name, /)#

Implement delattr(self, name).

__dir__()#

Default dir() implementation.

__div__(right_node: Node | int | float | ndarray, auto_broadcast: str = 'NUMPY', name: str | None = None) Node#

Return node which applies f(x) = A/B to the input nodes element-wise.

Parameters:
  • left_node – The node providing dividend data.

  • right_node – The node providing divisor data.

  • auto_broadcast – Specifies rules used for auto-broadcasting of input tensors.

  • name – Optional name for output node.

Returns:

The node performing element-wise division.

__eq__(right_node: Node | int | float | ndarray, auto_broadcast: str = 'NUMPY', name: str | None = None) Node#

Return node which checks if input nodes are equal element-wise.

Parameters:
  • left_node – The first input node for equal operation.

  • right_node – The second input node for equal operation.

  • auto_broadcast – The type of broadcasting specifies rules used for auto-broadcasting of input tensors.

  • name – The optional name for output new node.

Returns:

The node performing element-wise equality check.

__format__(format_spec, /)#

Default object formatter.

__ge__(right_node: Node | int | float | ndarray, auto_broadcast: str = 'NUMPY', name: str | None = None) Node#

Return node which checks if left node is greater or equal to the right node element-wise.

Parameters:
  • left_node – The first input node providing data.

  • right_node – The second input node providing data.

  • auto_broadcast – The type of broadcasting specifies rules used for auto-broadcasting of input tensors.

  • name – The optional new name for output node.

Returns:

The node performing element-wise check whether left_node is greater than or equal right_node.

__getattr__(self: openvino._pyopenvino.Node, arg0: str) cpp_function#
__getattribute__(name, /)#

Return getattr(self, name).

__gt__(right_node: Node | int | float | ndarray, auto_broadcast: str = 'NUMPY', name: str | None = None) Node#

Return node which checks if left input node is greater than the right node element-wise.

Parameters:
  • left_node – The first input node providing data.

  • right_node – The second input node providing data.

  • auto_broadcast – The type of broadcasting specifies rules used for auto-broadcasting of input tensors.

  • name – The optional new name for output node.

Returns:

The node performing element-wise check whether left_node is greater than right_node.

__hash__()#

Return hash(self).

__init__(self: openvino._pyopenvino.op.tensor_iterator) None#
__init_subclass__()#

This method is called when a class is subclassed.

The default implementation does nothing. It may be overridden to extend subclasses.

__le__(right_node: Node | int | float | ndarray, auto_broadcast: str = 'NUMPY', name: str | None = None) Node#

Return node which checks if left input node is less or equal the right node element-wise.

Parameters:
  • left_node – The first input node providing data.

  • right_node – The second input node providing data.

  • auto_broadcast – The type of broadcasting specifies rules used for auto-broadcasting of input tensors.

  • name – The optional new name for output node.

Returns:

The node performing element-wise check whether left_node is less than or equal the right_node.

__lt__(right_node: Node | int | float | ndarray, auto_broadcast: str = 'NUMPY', name: str | None = None) Node#

Return node which checks if left input node is less than the right node element-wise.

Parameters:
  • left_node – The first input node providing data.

  • right_node – The second input node providing data.

  • auto_broadcast – The type of broadcasting specifies rules used for auto-broadcasting of input tensors.

  • name – The optional new name for output node.

Returns:

The node performing element-wise check whether left_node is less than the right_node.

__mul__(right_node: Node | int | float | ndarray, auto_broadcast: str = 'NUMPY', name: str | None = None) Node#

Return node which applies f(A,B) = A*B to the input nodes elementwise.

Parameters:
  • left_node – The first input node for multiply operation.

  • right_node – The second input node for multiply operation.

  • auto_broadcast – The type of broadcasting specifies rules used for auto-broadcasting of input tensors. Defaults to “NUMPY”.

  • name – The optional name for output new node.

Returns:

The node performing element-wise multiplication.

__ne__(right_node: Node | int | float | ndarray, auto_broadcast: str = 'NUMPY', name: str | None = None) Node#

Return node which checks if input nodes are unequal element-wise.

Parameters:
  • left_node – The first input node for not-equal operation.

  • right_node – The second input node for not-equal operation.

  • auto_broadcast – The type of broadcasting specifies rules used for auto-broadcasting of input tensors.

  • name – The optional name for output new node.

Returns:

The node performing element-wise inequality check.

__new__(**kwargs)#
__radd__(right)#
__rdiv__(right)#
__reduce__()#

Helper for pickle.

__reduce_ex__(protocol, /)#

Helper for pickle.

__repr__(self: openvino._pyopenvino.op.tensor_iterator) str#
__rmul__(right)#
__rsub__(right)#
__rtruediv__(right)#
__setattr__(name, value, /)#

Implement setattr(self, name, value).

__sizeof__()#

Size of object in memory, in bytes.

__str__()#

Return str(self).

__sub__(right_node: Node | int | float | ndarray, auto_broadcast: str = 'NUMPY', name: str | None = None) Node#

Return node which applies f(x) = A-B to the input nodes element-wise.

Parameters:
  • left_node – The node providing data for left hand side of operator.

  • right_node – The node providing data for right hand side of operator.

  • auto_broadcast – The type of broadcasting that specifies mapping of input tensor axes to output shape axes. Range of values: numpy, explicit.

  • name – The optional name for output node.

Returns:

The new output node performing subtraction operation on both tensors element-wise.

__subclasshook__()#

Abstract classes can override this to customize issubclass().

This is invoked early on by abc.ABCMeta.__subclasscheck__(). It should return True, False or NotImplemented. If it returns NotImplemented, the normal algorithm is used. Otherwise, it overrides the normal algorithm (and the outcome is cached).

__truediv__(right_node: Node | int | float | ndarray, auto_broadcast: str = 'NUMPY', name: str | None = None) Node#

Return node which applies f(x) = A/B to the input nodes element-wise.

Parameters:
  • left_node – The node providing dividend data.

  • right_node – The node providing divisor data.

  • auto_broadcast – Specifies rules used for auto-broadcasting of input tensors.

  • name – Optional name for output node.

Returns:

The node performing element-wise division.

evaluate(*args, **kwargs)#

Overloaded function.

  1. evaluate(self: openvino._pyopenvino.Node, output_values: List[ov::Tensor], input_values: List[ov::Tensor], evaluationContext: openvino._pyopenvino.RTMap) -> bool

    Evaluate the node on inputs, putting results in outputs

    param output_tensors:

    Tensors for the outputs to compute. One for each result.

    type output_tensors:

    List[openvino.runtime.Tensor]

    param input_tensors:

    Tensors for the inputs. One for each inputs.

    type input_tensors:

    List[openvino.runtime.Tensor]

    param evaluation_context:

    Storage of additional settings and attributes that can be used

    when evaluating the function. This additional information can be shared across nodes. :type evaluation_context: openvino.runtime.RTMap :rtype: bool

  2. evaluate(self: openvino._pyopenvino.Node, output_values: List[ov::Tensor], input_values: List[ov::Tensor]) -> bool

    Evaluate the function on inputs, putting results in outputs

    param output_tensors:

    Tensors for the outputs to compute. One for each result.

    type output_tensors:

    List[openvino.runtime.Tensor]

    param input_tensors:

    Tensors for the inputs. One for each inputs.

    type input_tensors:

    List[openvino.runtime.Tensor]

    rtype:

    bool

property friendly_name#
get_attributes(self: openvino._pyopenvino.Node) dict#
get_body(self: openvino._pyopenvino.op.tensor_iterator) object#
get_concatenated_slices(self: openvino._pyopenvino.op.tensor_iterator, value: ov::Output<ov::Node>, start: int, stride: int, part_size: int, end: int, axis: int) ov::Output<ov::Node>#
get_element_type(self: openvino._pyopenvino.Node) openvino._pyopenvino.Type#

Checks that there is exactly one output and returns it’s element type.

Returns:

Type of the output.

Return type:

openvino.runtime.Type

get_friendly_name(self: openvino._pyopenvino.Node) str#

Gets the friendly name for a node. If no friendly name has been set via set_friendly_name then the node’s unique name is returned.

Returns:

Friendly name of the node.

Return type:

str

get_function(self: openvino._pyopenvino.op.tensor_iterator) object#
get_input_descriptions(self: openvino._pyopenvino.op.tensor_iterator) list#
get_input_size(self: openvino._pyopenvino.Node) int#

Returns the number of inputs to the node.

Returns:

Number of inputs.

Return type:

int

get_input_tensor(self: openvino._pyopenvino.Node, index: int) ov::descriptor::Tensor#

Returns the tensor for the node’s input with index i

Parameters:

index (int) – Index of Input.

Returns:

Tensor of the input i

Return type:

openvino._pyopenvino.DescriptorTensor

get_iter_value(self: openvino._pyopenvino.op.tensor_iterator, body_value: ov::Output<ov::Node>, iteration: int = -1) ov::Output<ov::Node>#
get_name(self: openvino._pyopenvino.Node) str#

Get the unique name of the node

Returns:

Unique name of the node.

Return type:

str

get_num_iterations(self: openvino._pyopenvino.op.tensor_iterator) int#
get_output_descriptions(self: openvino._pyopenvino.op.tensor_iterator) list#
get_output_element_type(self: openvino._pyopenvino.Node, index: int) openvino._pyopenvino.Type#

Returns the element type for output i

Parameters:

index (int) – Index of the output.

Returns:

Type of the output i

Return type:

openvino.runtime.Type

get_output_partial_shape(self: openvino._pyopenvino.Node, index: int) openvino._pyopenvino.PartialShape#

Returns the partial shape for output i

Parameters:

index (int) – Index of the output.

Returns:

PartialShape of the output i

Return type:

openvino.runtime.PartialShape

get_output_shape(self: openvino._pyopenvino.Node, index: int) openvino._pyopenvino.Shape#

Returns the shape for output i

Parameters:

index – Index of the output.

Returns:

Shape of the output i

Return type:

openvino.runtime.Shape

get_output_size(self: openvino._pyopenvino.Node) int#

Returns the number of outputs from the node.

Returns:

Number of outputs.

Return type:

int

get_output_tensor(self: openvino._pyopenvino.Node, index: int) ov::descriptor::Tensor#

Returns the tensor for output i

Parameters:

index (int) – Index of the output.

Returns:

Tensor of the output i

Return type:

openvino._pyopenvino.DescriptorTensor

get_rt_info(self: openvino._pyopenvino.Node) openvino._pyopenvino.RTMap#

Returns PyRTMap which is a dictionary of user defined runtime info.

Returns:

A dictionary of user defined data.

Return type:

openvino.runtime.RTMap

get_type_info(self: openvino._pyopenvino.Node) ov::DiscreteTypeInfo#
get_type_name(self: openvino._pyopenvino.Node) str#

Returns Type’s name from the node.

Returns:

String representing Type’s name.

Return type:

str

input(self: openvino._pyopenvino.Node, input_index: int) ov::Input<ov::Node>#

A handle to the input_index input of this node.

Parameters:

input_index (int) – Index of Input.

Returns:

Input of this node.

Return type:

openvino.runtime.Input

input_value(self: openvino._pyopenvino.Node, index: int) ov::Output<ov::Node>#

Returns input of the node with index i

Parameters:

index (int) – Index of Input.

Returns:

Input of this node.

Return type:

openvino.runtime.Input

input_values(self: openvino._pyopenvino.Node) List[ov::Output<ov::Node>]#

Returns list of node’s inputs, in order.

Returns:

List of node’s inputs

Return type:

List[openvino.runtime.Input]

inputs(self: openvino._pyopenvino.Node) List[ov::Input<ov::Node>]#

A list containing a handle for each of this node’s inputs, in order.

Returns:

List of node’s inputs.

Return type:

List[openvino.runtime.Input]

property name#
output(self: openvino._pyopenvino.Node, output_index: int) ov::Output<ov::Node>#

A handle to the output_index output of this node.

Parameters:

output_index (int) – Index of Output.

Returns:

Output of this node.

Return type:

openvino.runtime.Output

outputs(self: openvino._pyopenvino.Node) List[ov::Output<ov::Node>]#

A list containing a handle for each of this node’s outputs, in order.

Returns:

List of node’s outputs.

Return type:

List[openvino.runtime.Output]

property rt_info#
set_argument(self: openvino._pyopenvino.Node, arg0: int, arg1: ov::Output<ov::Node>) None#
set_arguments(*args, **kwargs)#

Overloaded function.

  1. set_arguments(self: openvino._pyopenvino.Node, arg0: List[openvino._pyopenvino.Node]) -> None

  2. set_arguments(self: openvino._pyopenvino.Node, arg0: List[ov::Output<ov::Node>]) -> None

  3. set_arguments(self: openvino._pyopenvino.Node, arg0: List[ov::Output<ov::Node>]) -> None

set_attribute(self: openvino._pyopenvino.Node, arg0: str, arg1: object) None#
set_body(self: openvino._pyopenvino.op.tensor_iterator, body: ov::Model) None#
set_friendly_name(self: openvino._pyopenvino.Node, name: str) None#

Sets a friendly name for a node. This does not overwrite the unique name of the node and is retrieved via get_friendly_name(). Used mainly for debugging. The friendly name may be set exactly once.

Parameters:

name (str) – Friendly name to set.

set_function(self: openvino._pyopenvino.op.tensor_iterator, func: ov::Model) None#
set_input_descriptions(self: openvino._pyopenvino.op.tensor_iterator, inputs: list) None#
set_invariant_input(self: openvino._pyopenvino.op.tensor_iterator, body_parameter: openvino._pyopenvino.op.Parameter, value: ov::Output<ov::Node>) None#
set_merged_input(self: openvino._pyopenvino.op.tensor_iterator, body_parameter: openvino._pyopenvino.op.Parameter, initial_value: ov::Output<ov::Node>, successive_value: ov::Output<ov::Node>) None#
set_output_descriptions(self: openvino._pyopenvino.op.tensor_iterator, outputs: list) None#
set_sliced_input(self: openvino._pyopenvino.op.tensor_iterator, parameter: openvino._pyopenvino.op.Parameter, value: ov::Output<ov::Node>, start: int, stride: int, part_size: int, end: int, axis: int) None#
property shape#
property type_info#
validate(self: openvino._pyopenvino.Node) None#