# type: ignore from __future__ import annotations from builtins import list as TensorShape from functools import partial from openvino._pyopenvino import Node from openvino._pyopenvino import PartialShape from openvino._pyopenvino import Type from openvino._pyopenvino.op import Constant from openvino._pyopenvino.op import Parameter from openvino._pyopenvino.op import tensor_iterator from openvino.package_utils import deprecated from openvino.utils.decorators import binary_op from openvino.utils.decorators import nameable_op from openvino.utils.decorators import unary_op from openvino.utils.input_validation import check_valid_attributes from openvino.utils.input_validation import is_non_negative_value from openvino.utils.input_validation import is_positive_value from openvino.utils.node_factory import NodeFactory from openvino.utils.node_factory import _get_node_factory from openvino.utils.types import as_node from openvino.utils.types import as_nodes from openvino.utils.types import get_dtype from openvino.utils.types import get_element_type from openvino.utils.types import get_element_type_str from openvino.utils.types import make_constant_node from typing import get_args import functools import numpy as np import openvino._pyopenvino import openvino._pyopenvino.op import typing """ Factory functions for all openvino ops. """ __all__ = ['Constant', 'Node', 'NodeFactory', 'NodeInput', 'NumericData', 'NumericType', 'Parameter', 'PartialShape', 'ScalarData', 'TensorShape', 'Type', 'absolute', 'acos', 'add', 'as_node', 'as_nodes', 'asin', 'atan', 'avg_pool', 'batch_norm_inference', 'binary_convolution', 'binary_op', 'broadcast', 'ceiling', 'check_valid_attributes', 'clamp', 'concat', 'constant', 'convert', 'convert_like', 'convolution', 'convolution_backprop_data', 'cos', 'cosh', 'ctc_greedy_decoder', 'deformable_convolution', 'deformable_psroi_pooling', 'deprecated', 'depth_to_space', 'detection_output', 'divide', 'elu', 'equal', 'erf', 'exp', 'fake_quantize', 'floor', 'floor_mod', 'gather', 'gather_tree', 'get_args', 'get_dtype', 'get_element_type', 'get_element_type_str', 'greater', 'greater_equal', 'grn', 'group_convolution', 'group_convolution_backprop_data', 'hard_sigmoid', 'interpolate', 'is_non_negative_value', 'is_positive_value', 'less', 'less_equal', 'log', 'logical_and', 'logical_not', 'logical_or', 'logical_xor', 'lrn', 'lstm_cell', 'make_constant_node', 'matmul', 'max_pool', 'maximum', 'minimum', 'mod', 'multiply', 'nameable_op', 'negative', 'non_max_suppression', 'normalize_l2', 'not_equal', 'np', 'one_hot', 'pad', 'parameter', 'partial', 'power', 'prelu', 'prior_box', 'prior_box_clustered', 'proposal', 'psroi_pooling', 'range', 'reduce_logical_and', 'reduce_logical_or', 'reduce_max', 'reduce_mean', 'reduce_min', 'reduce_prod', 'reduce_sum', 'region_yolo', 'relu', 'reshape', 'result', 'reverse_sequence', 'select', 'selu', 'shape_of', 'sigmoid', 'sign', 'sin', 'sinh', 'softmax', 'space_to_depth', 'split', 'sqrt', 'squared_difference', 'squeeze', 'strided_slice', 'subtract', 'tan', 'tanh', 'tensor_iterator', 'tile', 'topk', 'transpose', 'unary_op', 'unsqueeze', 'variadic_split'] def absolute(input_value, *args, **kwargs) -> openvino._pyopenvino.Node: """ Return node which applies f(x) = abs(x) to the input node element-wise. :param node: One of: input node, array or scalar. :param name: Optional new name for output node. :return: New node with Abs operation applied on it. """ def acos(input_value, *args, **kwargs) -> openvino._pyopenvino.Node: """ Apply inverse cosine function on the input node element-wise. :param node: One of: input node, array or scalar. :param name: Optional new name for output node. :return: New node with arccos operation applied on it. """ def add(left, right, *args, **kwargs) -> openvino._pyopenvino.Node: """ Return node which applies f(A,B) = A+B to the input nodes element-wise. :param left_node: The first input node for add operation. :param right_node: The second input node for add operation. :param auto_broadcast: The type of broadcasting specifies rules used for auto-broadcasting of input tensors. Defaults to "NUMPY". :param name: The optional name for output new node. :return: The node performing element-wise addition. """ def asin(input_value, *args, **kwargs) -> openvino._pyopenvino.Node: """ Apply inverse sine function on the input node element-wise. :param node: One of: input node, array or scalar. :param name: Optional new name for output node. :return: New node with arcsin operation applied on it. """ def atan(input_value, *args, **kwargs) -> openvino._pyopenvino.Node: """ Apply inverse tangent function on the input node element-wise. :param node: One of: input node, array or scalar. :param name: Optional new name for output node. :return: New node with arctan operation applied on it. """ def avg_pool(*args, **kwargs) -> openvino._pyopenvino.Node: """ Return average pooling node. :param data_batch: The input node providing data. :param strides: The window movement strides. :param pads_begin: The input data optional padding below filled with zeros. :param pads_end: The input data optional padding below filled with zeros. :param kernel_shape: The pooling window shape. :param exclude_pad: Whether or not to include zero padding in average computations. :param rounding_type: Determines used rounding schema when computing output shape. Acceptable values are: ['floor', 'ceil'] :param auto_pad: Determines how the padding is calculated. Acceptable values: [None, 'same_upper', 'same_lower', 'valid'] :param name: Optional name for the new output node. :return: New node with AvgPool operation applied on its data. """ def batch_norm_inference(*args, **kwargs) -> openvino._pyopenvino.Node: """ Perform layer normalizes a input tensor by mean and variance with appling scale and offset. :param data: The input tensor with data for normalization. :param gamma: The scalar scaling for normalized value. :param beta: The bias added to the scaled normalized value. :param mean: The value for mean normalization. :param variance: The value for variance normalization. :param epsilon: The number to be added to the variance to avoid division by zero when normalizing a value. :param name: The optional name of the output node. :return: The new node which performs BatchNormInference. """ def binary_convolution(*args, **kwargs) -> openvino._pyopenvino.Node: """ Create node performing convolution with binary weights, binary input and integer output. :param data: The node providing data batch tensor. :param filter: The node providing filters tensor. :param strides: The kernel window movement strides. :param pads_begin: The number of pixels to add to the beginning along each axis. :param pads_end: The number of pixels to add to the end along each axis. :param dilations: The distance in width and height between elements (weights) in the filter. :param mode: Defines how input tensor 0/1 values and weights 0/1 are interpreted. :param pad_value: Floating-point value used to fill pad area. :param auto_pad: The type of padding. Range of values: explicit, same_upper, same_lower, valid. :param name: The optional new name for output node. :return: New node performing binary convolution operation. """ def broadcast(*args, **kwargs) -> openvino._pyopenvino.Node: """ Create a node which broadcasts the input node's values along specified axes to a desired shape. :param data: The node with input tensor data. :param target_shape: The node with a new shape we want to broadcast tensor to. :param axes_mapping: The node with a axis positions (0-based) in the result that are being broadcast. :param mode: The type of broadcasting that specifies mapping of input tensor axes to output shape axes. Range of values: NUMPY, EXPLICIT. :param name: Optional new name for output node. :return: New node with broadcast shape. """ def ceiling(input_value, *args, **kwargs) -> openvino._pyopenvino.Node: """ Return node which applies ceiling to the input node element-wise. :param node: The node providing data to ceiling operation. :param name: Optional name for output node. :return: The node performing element-wise ceiling. """ def clamp(*args, **kwargs) -> openvino._pyopenvino.Node: """ Perform clamp element-wise on data from input node. :param data: Input tensor. One of: input node, array or scalar. :param min_value: The lower bound of the range. Scalar value. :param max_value: The upper bound of the range. Scalar value. :param name: Optional output node name. :return: The new node performing a clamp operation on its input data element-wise. Performs a clipping operation on an input value between a pair of boundary values. For each element in `data`, if the element's value is lower than `min_value`, it will be replaced with `min_value`. If the value is higher than `max_value`, it will be replaced by `max_value`. Intermediate values of `data` are returned without change. Clamp uses the following logic: .. code-block:: python if data < min_value: data=min_value elif data > max_value: data=max_value """ def concat(*args, **kwargs) -> openvino._pyopenvino.Node: """ Concatenate input nodes into single new node along specified axis. :param nodes: The nodes we want concatenate into single new node. :param axis: The axis along which we want to concatenate input nodes. :param name: The optional new name for output node. :return: Return new node that is a concatenation of input nodes. """ def constant(*args, **kwargs) -> openvino._pyopenvino.op.Constant: """ Create a Constant node from provided value. :param value: One of: array of values or scalar to initialize node with. :param dtype: The data type of provided data. :param name: Optional name for output node. :return: The Constant node initialized with provided data. """ def convert(*args, **kwargs) -> openvino._pyopenvino.Node: """ Return node which casts input node values to specified type. :param data: Node which produces the input tensor. :param destination_type: Provides the target type for the conversion. :param name: Optional name for the output node. :return: New node performing the conversion operation. """ def convert_like(left, right, *args, **kwargs) -> openvino._pyopenvino.Node: """ Return node which casts data node values to the type of another node. :param data: Node which produces the input tensor :param like: Node which provides the target type information for the conversion :param name: Optional name for the output node. :return: New node performing the conversion operation. """ def convolution(*args, **kwargs) -> openvino._pyopenvino.Node: """ Return node performing batched convolution operation. :param data: The node providing data batch tensor. :param filter: The node providing filters tensor. :param strides: The kernel window movement strides. :param pads_begin: The number of zero padding elements to add on each axis below 0 coordinate. :param pads_end: The number of zero padding elements to add on each axis above max coordinate :param dilations: The data batch dilation strides. :param auto_pad: The type of padding. Range of values: explicit, same_upper, same_lower, valid. :param name: The optional new name for output node. :return: New node performing batched convolution operation. """ def convolution_backprop_data(*args, **kwargs) -> openvino._pyopenvino.Node: """ Create node performing a batched-convolution backprop data operation. :param data: The node producing data from forward-prop :param filters: The node producing the filters from forward-prop. :param output_shape: The node producing output delta. :param strides: The distance (in pixels) to slide the filter on the feature map over the axes. :param pads_begin: The number of pixels to add to the beginning along each axis. :param pads_end: The number of pixels to add to the end along each axis. :param dilations: The distance in width and height between elements (weights) in the filter. :param name: The node name. :return: The node object representing ConvolutionBackpropData operation. """ def cos(input_value, *args, **kwargs) -> openvino._pyopenvino.Node: """ Apply cosine function on the input node element-wise. :param node: One of: input node, array or scalar. :param name: Optional new name for output node. :return: New node with cos operation applied on it. """ def cosh(input_value, *args, **kwargs) -> openvino._pyopenvino.Node: """ Apply hyperbolic cosine function on the input node element-wise. :param node: One of: input node, array or scalar. :param name: Optional new name for output node. :return: New node with cosh operation applied on it. """ def ctc_greedy_decoder(*args, **kwargs) -> openvino._pyopenvino.Node: """ Perform greedy decoding on the logits given in input (best path). :param data: Logits on which greedy decoding is performed. :param sequence_mask: The tensor with sequence masks for each sequence in the batch. :param merge_repeated: The flag for merging repeated labels during the CTC calculation. :param name: Optional name for output node. :return: The new node performing an CTCGreedyDecoder operation on input tensor. """ def deformable_convolution(*args, **kwargs) -> openvino._pyopenvino.Node: """ Create node performing deformable convolution. :param data: The node providing data batch tensor. :param filter: The node providing filters tensor. :param strides: The distance (in pixels) to slide the filter on the feature map over the axes. :param pads_begin: The number of pixels to add to the beginning along each axis. :param pads_end: The number of pixels to add to the end along each axis. :param dilations: The distance in width and height between elements (weights) in the filter. :param auto_pad: The type of padding. Range of values: explicit, same_upper, same_lower, valid. :param group: The number of groups which both output and input should be split into. :param deformable_group: The number of groups which deformable values and output should be split into along the channel axis. :param name: The optional new name for output node. :return: New node performing deformable convolution operation. """ def deformable_psroi_pooling(*args, **kwargs) -> openvino._pyopenvino.Node: """ Return node performing DeformablePSROIPooling operation. DeformablePSROIPooling computes position-sensitive pooling on regions of interest specified by input. :param feature_maps: 4D tensor with feature maps. :param coords: 2D tensor describing box consisting of tuples: [batch_id, x_1, y_1, x_2, y_2]. :param output_dim: A pooled output channel number. :param spatial_scale: A multiplicative spatial scale factor to translate ROI. :param group_size: The number of groups to encode position-sensitive score. :param mode: Specifies mode for pooling. Range of values: ['bilinear_deformable']. :param spatial_bins_x: Specifies numbers of bins to divide the input feature maps over width. :param spatial_bins_y: Specifies numbers of bins to divide the input feature maps over height. :param trans_std: The value that all transformation (offset) values are multiplied with. :param part_size: The number of parts the output tensor spatial dimensions are divided into. :param offsets: Optional node. 4D input blob with transformation values (offsets). :param name: The optional new name for output node. :return: New node performing DeformablePSROIPooling operation. """ def depth_to_space(*args, **kwargs) -> openvino._pyopenvino.Node: """ Rearranges input tensor from depth into blocks of spatial data. Values from the height and width dimensions are moved to the depth dimension. Input tensor has shape [N,C,H,W], where N is the batch axis, C is the channel or depth, H is the height and W is the width. Output node produces a tensor with shape: [N, C * `block_size` * `block_size`, H / `block_size`, W / `block_size`] :param node: The node with input tensor data. :param mode: Specifies how the input depth dimension is split to block coordinates blocks_first: The input is divided to [block_size, ..., block_size, new_depth] depth_first: The input is divided to [new_depth, block_size, ..., block_size] :param block_size: The size of the spatial block of values describing how the tensor's data is to be rearranged. :param name: Optional output node name. :return: The new node performing an DepthToSpace operation on its input tensor. """ def detection_output(*args, **kwargs) -> openvino._pyopenvino.Node: """ Generate the detection output using information on location and confidence predictions. :param box_logits: The 2D input tensor with box logits. :param class_preds: The 2D input tensor with class predictions. :param proposals: The 3D input tensor with proposals. :param attrs: The dictionary containing key, value pairs for attributes. :param aux_class_preds: The 2D input tensor with additional class predictions information. :param aux_box_preds: The 2D input tensor with additional box predictions information. :param name: Optional name for the output node. :return: Node representing DetectionOutput operation. Available attributes are: * num_classes The number of classes to be predicted. Range of values: positive integer number Default value: None Required: yes * background_label_id The background label id. Range of values: integer value Default value: 0 Required: no * top_k Maximum number of results to be kept per batch after NMS step. Range of values: integer value Default value: -1 Required: no * variance_encoded_in_target The flag that denotes if variance is encoded in target. Range of values: {False, True} Default value: False Required: no * keep_top_k Maximum number of bounding boxes per batch to be kept after NMS step. Range of values: integer values Default value: None Required: yes * code_type The type of coding method for bounding boxes. Range of values: {'caffe.PriorBoxParameter.CENTER_SIZE', 'caffe.PriorBoxParameter.CORNER'} Default value: 'caffe.PriorBoxParameter.CORNER' Required: no * share_location The flag that denotes if bounding boxes are shared among different classes. Range of values: {True, False} Default value: True Required: no * nms_threshold The threshold to be used in the NMS stage. Range of values: floating point value Default value: None Required: yes * confidence_threshold Specifies the minimum confidence threshold for detection boxes to be considered. Range of values: floating point value Default value: 0 Required: no * clip_after_nms The flag that denotes whether to perform clip bounding boxes after non-maximum suppression or not. Range of values: {True, False} Default value: False Required: no * clip_before_nms The flag that denotes whether to perform clip bounding boxes before non-maximum suppression or not. Range of values: {True, False} Default value: False Required: no * decrease_label_id The flag that denotes how to perform NMS. Range of values: False - perform NMS like in Caffe*. True - perform NMS like in MxNet*. Default value: False Required: no * normalized The flag that denotes whether input tensors with boxes are normalized. Range of values: {True, False} Default value: False Required: no * input_height The input image height. Range of values: positive integer number Default value: 1 Required: no * input_width The input image width. Range of values: positive integer number Default value: 1 Required: no * objectness_score The threshold to sort out confidence predictions. Range of values: non-negative float number Default value: 0 Required: no Example of attribute dictionary: .. code-block:: python # just required ones attrs = { 'num_classes': 85, 'keep_top_k': [1, 2, 3], 'nms_threshold': 0.645, } attrs = { 'num_classes': 85, 'keep_top_k': [1, 2, 3], 'nms_threshold': 0.645, 'normalized': True, 'clip_before_nms': True, 'input_height': [32], 'input_width': [32], } Optional attributes which are absent from dictionary will be set with corresponding default. """ def divide(left, right, *args, **kwargs) -> openvino._pyopenvino.Node: """ Return node which applies f(x) = A/B to the input nodes element-wise. :param left_node: The node providing dividend data. :param right_node: The node providing divisor data. :param auto_broadcast: Specifies rules used for auto-broadcasting of input tensors. :param name: Optional name for output node. :return: The node performing element-wise division. """ def elu(*args, **kwargs) -> openvino._pyopenvino.Node: """ Perform Exponential Linear Unit operation element-wise on data from input node. Computes exponential linear: alpha * (exp(data) - 1) if < 0, data otherwise. For more information refer to: [Fast and Accurate Deep Network Learning by Exponential Linear Units](http://arxiv.org/abs/1511.07289) :param data: Input tensor. One of: input node, array or scalar. :param alpha: Scalar multiplier for negative values. :param name: Optional output node name. :return: The new node performing an ELU operation on its input data element-wise. """ def equal(left, right, *args, **kwargs) -> openvino._pyopenvino.Node: """ Return node which checks if input nodes are equal element-wise. :param left_node: The first input node for equal operation. :param right_node: The second input node for equal operation. :param auto_broadcast: The type of broadcasting specifies rules used for auto-broadcasting of input tensors. :param name: The optional name for output new node. :return: The node performing element-wise equality check. """ def erf(input_value, *args, **kwargs) -> openvino._pyopenvino.Node: """ Return node which calculates Gauss error function element-wise with given tensor. :param node: The node providing data for operation. :param name: The optional name for new output node. :return: The new node performing element-wise Erf operation. """ def exp(input_value, *args, **kwargs) -> openvino._pyopenvino.Node: """ Return node which applies exponential function to the input node element-wise. :param node: The node providing data for operation. :param name: The optional name for new output node. :return: The new node performing natural exponential operation. """ def fake_quantize(*args, **kwargs) -> openvino._pyopenvino.Node: """ Perform an element-wise linear quantization on input data. :param data: The node with data tensor. :param input_low: The node with the minimum for input values. :param input_high: The node with the maximum for input values. :param output_low: The node with the minimum quantized value. :param output_high: The node with the maximum quantized value. :param levels: The number of quantization levels. Integer value. :param auto_broadcast: The type of broadcasting specifies rules used for auto-broadcasting of input tensors. :return: New node with quantized value. Input floating point values are quantized into a discrete set of floating point values. .. code-block:: python if x <= input_low: output = output_low if x > input_high: output = output_high else: output = fake_quantize(output) Fake quantize uses the following logic: \\f[ output = \\dfrac{round( \\dfrac{data - input\\_low}{(input\\_high - input\\_low)\\cdot (levels-1)})} {(levels-1)\\cdot (output\\_high - output\\_low)} + output\\_low \\f] """ def floor(input_value, *args, **kwargs) -> openvino._pyopenvino.Node: """ Return node which applies floor to the input node element-wise. :param node: The input node providing data. :param name: The optional name for new output node. :return: The node performing element-wise floor operation. """ def floor_mod(left, right, *args, **kwargs) -> openvino._pyopenvino.Node: """ Return node performing element-wise FloorMod (division reminder) with two given tensors. :param left_node: The first input node for FloorMod operation. :param right_node: The second input node for FloorMod operation. :param auto_broadcast: Specifies rules used for auto-broadcasting of input tensors. :param name: Optional name for output node. :return: The node performing element-wise FloorMod operation. """ def gather(*args, **kwargs) -> openvino._pyopenvino.Node: """ Return Gather node which takes slices from axis of data according to indices. :param data: The tensor from which slices are gathered. :param indices: Tensor with indexes to gather. :param axis: The dimension index to gather data from. :param name: Optional name for output node. :return: The new node performing a Gather operation on the data input tensor. """ def gather_tree(*args, **kwargs) -> openvino._pyopenvino.Node: """ Perform GatherTree operation. :param step_ids: The tensor with indices from per each step. :param parent_idx: The tensor with with parent beam indices. :param max_seq_len: The tensor with maximum lengths for each sequence in the batch. :param end_token: The scalar tensor with value of the end marker in a sequence. :param name: Optional name for output node. :return: The new node performing a GatherTree operation. The GatherTree node generates the complete beams from the indices per each step and the parent beam indices. GatherTree uses the following logic: .. code-block:: python for batch in range(BATCH_SIZE): for beam in range(BEAM_WIDTH): max_sequence_in_beam = min(MAX_TIME, max_seq_len[batch]) parent = parent_idx[max_sequence_in_beam - 1, batch, beam] for level in reversed(range(max_sequence_in_beam - 1)): final_idx[level, batch, beam] = step_idx[level, batch, parent] parent = parent_idx[level, batch, parent] """ def greater(left, right, *args, **kwargs) -> openvino._pyopenvino.Node: """ Return node which checks if left input node is greater than the right node element-wise. :param left_node: The first input node providing data. :param right_node: The second input node providing data. :param auto_broadcast: The type of broadcasting specifies rules used for auto-broadcasting of input tensors. :param name: The optional new name for output node. :return: The node performing element-wise check whether left_node is greater than right_node. """ def greater_equal(left, right, *args, **kwargs) -> openvino._pyopenvino.Node: """ Return node which checks if left node is greater or equal to the right node element-wise. :param left_node: The first input node providing data. :param right_node: The second input node providing data. :param auto_broadcast: The type of broadcasting specifies rules used for auto-broadcasting of input tensors. :param name: The optional new name for output node. :return: The node performing element-wise check whether left_node is greater than or equal right_node. """ def grn(data: openvino._pyopenvino.Node, bias: float, name: typing.Optional[str] = None) -> openvino._pyopenvino.Node: """ Perform Global Response Normalization with L2 norm (across channels only). Computes GRN operation on channels for input tensor: \\f[ output_i = \\dfrac{input_i}{\\sqrt{\\sum_{i}^{C} input_i}} \\f] :param data: The node with data tensor. :param bias: The bias added to the variance. Scalar value. :param name: Optional output node name. :return: The new node performing a GRN operation on tensor's channels. """ def group_convolution(*args, **kwargs) -> openvino._pyopenvino.Node: """ Perform Group Convolution operation on data from input node. :param data: The node producing input data. :param filters: The node producing filters data. :param strides: The distance (in pixels) to slide the filter on the feature map over the axes. :param pads_begin: The number of pixels to add at the beginning along each axis. :param pads_end: The number of pixels to add at the end along each axis. :param dilations: The distance in width and height between elements (weights) in the filter. :param auto_pad: Describes how to perform padding. Possible values: EXPLICIT: Pad dimensions are explicity specified SAME_LOWER: Pad dimensions computed to match input shape Ceil(num_dims/2) at the beginning and Floor(num_dims/2) at the end SAME_UPPER: Pad dimensions computed to match input shape Floor(num_dims/2) at the beginning and Ceil(num_dims/2) at the end VALID: No padding :param name: Optional output node name. :return: The new node performing a Group Convolution operation on tensor from input node. """ def group_convolution_backprop_data(*args, **kwargs) -> openvino._pyopenvino.Node: """ Perform Group Convolution operation on data from input node. :param data: The node producing input data. :param filters: The node producing filter data. :param strides: The distance (in pixels) to slide the filter on the feature map over the axes. :param output_shape: The node that specifies spatial shape of the output. :param pads_begin: The number of pixels to add at the beginning along each axis. :param pads_end: The number of pixels to add at the end along each axis. :param dilations: The distance in width and height between elements (weights) in the filter. :param auto_pad: Describes how to perform padding. Possible values: EXPLICIT: Pad dimensions are explicity specified SAME_LOWER: Pad dimensions computed to match input shape Ceil(num_dims/2) at the beginning and Floor(num_dims/2) at the end SAME_UPPER: Pad dimensions computed to match input shape Floor(num_dims/2) at the beginning and Ceil(num_dims/2) at the end VALID: No padding :param output_padding: The additional amount of paddings added per each spatial axis in the output tensor. :param name: Optional output node name. :return: The new node performing a Group Convolution operation on tensor from input node. """ def hard_sigmoid(*args, **kwargs) -> openvino._pyopenvino.Node: """ Perform Hard Sigmoid operation element-wise on data from input node. :param data: The node with data tensor. :param alpha: A node producing the alpha parameter. :param beta: A node producing the beta parameter :param name: Optional output node name. :return: The new node performing a Hard Sigmoid element-wise on input tensor. Hard Sigmoid uses the following logic: .. code-block:: python y = max(0, min(1, alpha * data + beta)) """ def interpolate(*args, **kwargs) -> openvino._pyopenvino.Node: """ Perform interpolation of independent slices in input tensor. :param image: The node providing input tensor with data for interpolation. :param output_shape: 1D tensor describing output shape for spatial axes. :param attrs: The dictionary containing key, value pairs for attributes. :param name: Optional name for the output node. :return: Node representing interpolation operation. Available attributes are: * axes Specify spatial dimension indices where interpolation is applied. Type: list of non-negative integer numbers. Required: yes. * mode Specifies type of interpolation. Range of values: one of {nearest, linear, cubic, area} Type: string Required: yes * align_corners A flag that specifies whether to align corners or not. True means the alignment is applied, False means the alignment isn't applied. Range of values: True or False. Default: True. Required: no * antialias A flag that specifies whether to perform anti-aliasing. Range of values: False - do not perform anti-aliasing True - perform anti-aliasing Default value: False Required: no * pads_begin Specify the number of pixels to add to the beginning of the image being interpolated. A scalar that specifies padding for each spatial dimension. Range of values: list of non-negative integer numbers. Default value: 0 Required: no * pads_end Specify the number of pixels to add to the beginning of the image being interpolated. A scalar that specifies padding for each spatial dimension. Range of values: list of non-negative integer numbers. Default value: 0 Required: no Example of attribute dictionary: .. code-block:: python # just required ones attrs = { 'axes': [2, 3], 'mode': 'cubic', } attrs = { 'axes': [2, 3], 'mode': 'cubic', 'antialias': True, 'pads_begin': [2, 2, 2], } Optional attributes which are absent from dictionary will be set with corresponding default. """ def less(left, right, *args, **kwargs) -> openvino._pyopenvino.Node: """ Return node which checks if left input node is less than the right node element-wise. :param left_node: The first input node providing data. :param right_node: The second input node providing data. :param auto_broadcast: The type of broadcasting specifies rules used for auto-broadcasting of input tensors. :param name: The optional new name for output node. :return: The node performing element-wise check whether left_node is less than the right_node. """ def less_equal(left, right, *args, **kwargs) -> openvino._pyopenvino.Node: """ Return node which checks if left input node is less or equal the right node element-wise. :param left_node: The first input node providing data. :param right_node: The second input node providing data. :param auto_broadcast: The type of broadcasting specifies rules used for auto-broadcasting of input tensors. :param name: The optional new name for output node. :return: The node performing element-wise check whether left_node is less than or equal the right_node. """ def log(input_value, *args, **kwargs) -> openvino._pyopenvino.Node: """ Return node which applies natural logarithm to the input node element-wise. :param node: The input node providing data for operation. :param name: The optional new name for output node. :return: The new node performing log operation element-wise. """ def logical_and(left, right, *args, **kwargs) -> openvino._pyopenvino.Node: """ Return node which perform logical and operation on input nodes element-wise. :param left_node: The first input node providing data. :param right_node: The second input node providing data. :param auto_broadcast: The type of broadcasting that specifies mapping of input tensor axes to output shape axes. Range of values: numpy, explicit. :param name: The optional new name for output node. :return: The node performing logical and operation on input nodes corresponding elements. """ def logical_not(input_value, *args, **kwargs) -> openvino._pyopenvino.Node: """ Return node which applies element-wise logical negation to the input node. :param node: The input node providing data. :param name: The optional new name for output node. :return: The node performing element-wise logical NOT operation with given tensor. """ def logical_or(left, right, *args, **kwargs) -> openvino._pyopenvino.Node: """ Return node which performs logical OR operation on input nodes element-wise. :param left_node: The first input node providing data. :param right_node: The second input node providing data. :param auto_broadcast: The type of broadcasting that specifies mapping of input tensor axes to output shape axes. Range of values: numpy, explicit. :param name: The optional new name for output node. :return: The node performing logical or operation on input nodes corresponding elements. """ def logical_xor(left, right, *args, **kwargs) -> openvino._pyopenvino.Node: """ Return node which performs logical XOR operation on input nodes element-wise. :param left_node: The first input node providing data. :param right_node: The second input node providing data. :param auto_broadcast: The type of broadcasting that specifies mapping of input tensor axes to output shape axes. Range of values: numpy, explicit. :param name: The optional new name for output node. :return: The node performing logical or operation on input nodes corresponding elements. """ def lrn(*args, **kwargs) -> openvino._pyopenvino.Node: """ Return a node which performs element-wise Local Response Normalization (LRN) operation. :param data: Input data. :param alpha: A scale factor (usually positive). :param beta: An exponent. :param bias: An offset (usually positive) to avoid dividing by 0. :param size: Width of the 1-D normalization window. :param name: An optional name of the output node. :return: The new node which performs LRN. """ def lstm_cell(*args, **kwargs) -> openvino._pyopenvino.Node: """ Return a node which performs LSTMCell operation. :param X: The input tensor with shape: [batch_size, input_size]. :param initial_hidden_state: The hidden state tensor with shape: [batch_size, hidden_size]. :param initial_cell_state: The cell state tensor with shape: [batch_size, hidden_size]. :param W: The weight tensor with shape: [4*hidden_size, input_size]. :param R: The recurrence weight tensor with shape: [4*hidden_size, hidden_size]. :param B: The bias tensor for gates with shape: [4*hidden_size]. :param hidden_size: Specifies hidden state size. :param activations: The list of three activation functions for gates. :param activations_alpha: The list of alpha parameters for activation functions. :param activations_beta: The list of beta parameters for activation functions. :param clip: Specifies bound values [-C, C] for tensor clipping performed before activations. :param name: An optional name of the output node. :return: The new node represents LSTMCell. Node outputs count: 2. """ def matmul(*args, **kwargs) -> openvino._pyopenvino.Node: """ Return the Matrix Multiplication operation. :param data_a: left-hand side matrix :param data_b: right-hand side matrix :param transpose_a: should the first matrix be transposed before operation :param transpose_b: should the second matrix be transposed :return: MatMul operation node """ def max_pool(*args, **kwargs) -> openvino._pyopenvino.Node: """ Perform max pooling operation with given parameters on provided data. :param data: The node providing input data. :param strides: The distance (in pixels) to slide the filter on the feature map over the axes. :param pads_begin: The number of pixels to add at the beginning along each axis. :param pads_end: The number of pixels to add at the end along each axis. :param kernel_shape: The pooling operation kernel shape. :param rounding_type: Determines used rounding schema when computing output shape. Acceptable values are: ['floor', 'ceil'] :param auto_pad: Determines how the padding is calculated. Acceptable values: [None, 'same_upper', 'same_lower', 'valid'] :param name: The optional name for the created output node. :return: The new node performing max pooling operation. """ def maximum(left, right, *args, **kwargs) -> openvino._pyopenvino.Node: """ Return node which applies the maximum operation to input nodes elementwise. :param left_node: The first input node for maximum operation. :param right_node: The second input node for maximum operation. :param auto_broadcast: The type of broadcasting specifies rules used for auto-broadcasting of input tensors. Defaults to "NUMPY". :param name: The optional name for output new node. :return: The node performing element-wise maximum operation. """ def minimum(left, right, *args, **kwargs) -> openvino._pyopenvino.Node: """ Return node which applies the minimum operation to input nodes elementwise. :param left_node: The first input node for minimum operation. :param right_node: The second input node for minimum operation. :param auto_broadcast: The type of broadcasting specifies rules used for auto-broadcasting of input tensors. Defaults to "NUMPY". :param name: The optional name for output new node. :return: The node performing element-wise minimum operation. """ def mod(left, right, *args, **kwargs) -> openvino._pyopenvino.Node: """ Return node performing element-wise division reminder with two given tensors. :param left_node: The first input node for mod operation. :param right_node: The second input node for mod operation. :param auto_broadcast: Specifies rules used for auto-broadcasting of input tensors. :param name: Optional name for output node. :return: The node performing element-wise Mod operation. """ def multiply(left, right, *args, **kwargs) -> openvino._pyopenvino.Node: """ Return node which applies f(A,B) = A*B to the input nodes elementwise. :param left_node: The first input node for multiply operation. :param right_node: The second input node for multiply operation. :param auto_broadcast: The type of broadcasting specifies rules used for auto-broadcasting of input tensors. Defaults to "NUMPY". :param name: The optional name for output new node. :return: The node performing element-wise multiplication. """ def negative(input_value, *args, **kwargs) -> openvino._pyopenvino.Node: """ Return node which applies f(x) = -x to the input node elementwise. :param node: Input node for negative operation. :param name: The optional name for output new node. :return: The node performing element-wise multiplicaion by -1. """ def non_max_suppression(*args, **kwargs) -> openvino._pyopenvino.Node: """ Return a node which performs NonMaxSuppression. :param boxes: Tensor with box coordinates. :param scores: Tensor with box scores. :param max_output_boxes_per_class: Tensor Specifying maximum number of boxes to be selected per class. :param iou_threshold: Tensor specifying intersection over union threshold :param score_threshold: Tensor specifying minimum score to consider box for the processing. :param box_encoding: Format of boxes data encoding. Range of values: corner or cente. :param sort_result_descending: Flag that specifies whenever it is necessary to sort selected boxes across batches or not. :return: The new node which performs NonMaxSuppression """ def normalize_l2(*args, **kwargs) -> openvino._pyopenvino.Node: """ Construct an NormalizeL2 operation. :param data: Node producing the input tensor :param axes: Node indicating axes along which L2 reduction is calculated :param eps: The epsilon added to L2 norm :param eps_mode: how eps is combined with L2 value (`add` or `max`) :return: New node which performs the L2 normalization. """ def not_equal(left, right, *args, **kwargs) -> openvino._pyopenvino.Node: """ Return node which checks if input nodes are unequal element-wise. :param left_node: The first input node for not-equal operation. :param right_node: The second input node for not-equal operation. :param auto_broadcast: The type of broadcasting specifies rules used for auto-broadcasting of input tensors. :param name: The optional name for output new node. :return: The node performing element-wise inequality check. """ def one_hot(*args, **kwargs) -> openvino._pyopenvino.Node: """ Create node performing one-hot encoding on input data. :param indices: Input tensor of rank N with indices of any supported integer data type. :param depth: Scalar of any supported integer type that specifies number of classes and the size of one-hot dimension. :param on_value: Scalar of any type that is the value that the locations in output tensor represented by indices in input take. :param off_value: Scalar of any type that is the value that the locations not represented by indices in input take. :param name: The optional name for new output node. :return: New node performing one-hot operation. """ def pad(*args, **kwargs) -> openvino._pyopenvino.Node: """ Return a generic padding operation. :param arg: The node producing input tensor to be padded. :param pads_begin: number of padding elements to be added before position 0 on each axis of arg. :param pads_end: number of padding elements to be added after the last element. :param pad_mode: "constant", "edge", "reflect" or "symmetric" :param arg_pad_value: value used for padding if pad_mode is "constant" :return: Pad operation node. """ def parameter(*args, **kwargs) -> openvino._pyopenvino.op.Parameter: """ Return an openvino Parameter object. :param shape: The shape of the output tensor. :param dtype: The type of elements of the output tensor. Defaults to np.float32. :param name: The optional name for output new node. :return: The node that specifies input to the model. """ def power(left, right, *args, **kwargs) -> openvino._pyopenvino.Node: """ Return node which perform element-wise exponentiation operation. :param left_node: The node providing the base of operation. :param right_node: The node providing the exponent of operation. :param name: The optional name for the new output node. :param auto_broadcast: The type of broadcasting specifies rules used for auto-broadcasting of input tensors. :return: The new node performing element-wise exponentiation operation on input nodes. """ def prelu(*args, **kwargs) -> openvino._pyopenvino.Node: """ Perform Parametrized Relu operation element-wise on data from input node. :param data: The node with data tensor. :param slope: The node with the multipliers for negative values. :param name: Optional output node name. :return: The new node performing a PRelu operation on tensor's channels. PRelu uses the following logic: .. code-block:: python if data < 0: data = data * slope elif data >= 0: data = data """ def prior_box(*args, **kwargs) -> openvino._pyopenvino.Node: """ Generate prior boxes of specified sizes and aspect ratios across all dimensions. :param layer_shape: Shape of layer for which prior boxes are computed. :param image_shape: Shape of image to which prior boxes are scaled. :param attrs: The dictionary containing key, value pairs for attributes. :param name: Optional name for the output node. :return: Node representing prior box operation. Available attributes are: * min_size The minimum box size (in pixels). Range of values: positive floating point numbers Default value: [] Required: no * max_size The maximum box size (in pixels). Range of values: positive floating point numbers Default value: [] Required: no * aspect_ratio Aspect ratios of prior boxes. Range of values: set of positive floating point numbers Default value: [] Required: no * flip The flag that denotes that each aspect_ratio is duplicated and flipped. Range of values: {True, False} Default value: False Required: no * clip The flag that denotes if each value in the output tensor should be clipped to [0,1] interval. Range of values: {True, False} Default value: False Required: no * step The distance between box centers. Range of values: floating point non-negative number Default value: 0 Required: no * offset This is a shift of box respectively to top left corner. Range of values: floating point non-negative number Default value: None Required: yes * variance The variance denotes a variance of adjusting bounding boxes. The attribute could contain 0, 1 or 4 elements. Range of values: floating point positive numbers Default value: [] Required: no * scale_all_sizes The flag that denotes type of inference. Range of values: False - max_size is ignored True - max_size is used Default value: True Required: no * fixed_ratio This is an aspect ratio of a box. Range of values: a list of positive floating-point numbers Default value: None Required: no * fixed_size This is an initial box size (in pixels). Range of values: a list of positive floating-point numbers Default value: None Required: no * density This is the square root of the number of boxes of each type. Range of values: a list of positive floating-point numbers Default value: None Required: no Example of attribute dictionary: .. code-block:: python # just required ones attrs = { 'offset': 85, } attrs = { 'offset': 85, 'flip': True, 'clip': True, 'fixed_size': [32, 64, 128] } Optional attributes which are absent from dictionary will be set with corresponding default. """ def prior_box_clustered(*args, **kwargs) -> openvino._pyopenvino.Node: """ Generate prior boxes of specified sizes normalized to the input image size. :param output_size: 1D tensor with two integer elements [height, width]. Specifies the spatial size of generated grid with boxes. :param image_size: 1D tensor with two integer elements [image_height, image_width] that specifies shape of the image for which boxes are generated. :param attrs: The dictionary containing key, value pairs for attributes. :param name: Optional name for the output node. :return: Node representing PriorBoxClustered operation. Available attributes are: * widths Specifies desired boxes widths in pixels. Range of values: floating point positive numbers. Default value: 1.0 Required: no * heights Specifies desired boxes heights in pixels. Range of values: floating point positive numbers. Default value: 1.0 Required: no * clip The flag that denotes if each value in the output tensor should be clipped within [0,1]. Range of values: {True, False} Default value: True Required: no * step_widths The distance between box centers. Range of values: floating point positive number Default value: 0.0 Required: no * step_heights The distance between box centers. Range of values: floating point positive number Default value: 0.0 Required: no * offset The shift of box respectively to the top left corner. Range of values: floating point positive number Default value: None Required: yes * variance Denotes a variance of adjusting bounding boxes. Range of values: floating point positive numbers Default value: [] Required: no Example of attribute dictionary: .. code-block:: python # just required ones attrs = { 'offset': 85, } attrs = { 'offset': 85, 'clip': False, 'step_widths': [1.5, 2.0, 2.5] } Optional attributes which are absent from dictionary will be set with corresponding default. """ def proposal(*args, **kwargs) -> openvino._pyopenvino.Node: """ Filter bounding boxes and outputs only those with the highest prediction confidence. :param class_probs: 4D input floating point tensor with class prediction scores. :param bbox_deltas: 4D input floating point tensor with box logits. :param image_shape: The 1D input tensor with 3 or 4 elements describing image shape. :param attrs: The dictionary containing key, value pairs for attributes. :param name: Optional name for the output node. :return: Node representing Proposal operation. * base_size The size of the anchor to which scale and ratio attributes are applied. Range of values: a positive unsigned integer number Default value: None Required: yes * pre_nms_topn The number of bounding boxes before the NMS operation. Range of values: a positive unsigned integer number Default value: None Required: yes * post_nms_topn The number of bounding boxes after the NMS operation. Range of values: a positive unsigned integer number Default value: None Required: yes * nms_thresh The minimum value of the proposal to be taken into consideration. Range of values: a positive floating-point number Default value: None Required: yes * feat_stride The step size to slide over boxes (in pixels). Range of values: a positive unsigned integer Default value: None Required: yes * min_size The minimum size of box to be taken into consideration. Range of values: a positive unsigned integer number Default value: None Required: yes * ratio The ratios for anchor generation. Range of values: a list of floating-point numbers Default value: None Required: yes * scale The scales for anchor generation. Range of values: a list of floating-point numbers Default value: None Required: yes * clip_before_nms The flag that specifies whether to perform clip bounding boxes before non-maximum suppression or not. Range of values: True or False Default value: True Required: no * clip_after_nms The flag that specifies whether to perform clip bounding boxes after non-maximum suppression or not. Range of values: True or False Default value: False Required: no * normalize The flag that specifies whether to perform normalization of output boxes to [0,1] interval or not. Range of values: True or False Default value: False Required: no * box_size_scale Specifies the scale factor applied to logits of box sizes before decoding. Range of values: a positive floating-point number Default value: 1.0 Required: no * box_coordinate_scale Specifies the scale factor applied to logits of box coordinates before decoding. Range of values: a positive floating-point number Default value: 1.0 Required: no * framework Specifies how the box coordinates are calculated. Range of values: "" (empty string) - calculate box coordinates like in Caffe* tensorflow - calculate box coordinates like in the TensorFlow* Object Detection API models Default value: "" (empty string) Required: no Example of attribute dictionary: .. code-block:: python # just required ones attrs = { 'base_size': 85, 'pre_nms_topn': 10, 'post_nms_topn': 20, 'nms_thresh': 0.34, 'feat_stride': 16, 'min_size': 32, 'ratio': [0.1, 1.5, 2.0, 2.5], 'scale': [2, 3, 3, 4], } Optional attributes which are absent from dictionary will be set with corresponding default. """ def psroi_pooling(*args, **kwargs) -> openvino._pyopenvino.Node: """ Return a node which produces a PSROIPooling operation. :param input: Input feature map `{N, C, ...}`. :param coords: Coordinates of bounding boxes. :param output_dim: Output channel number. :param group_size: Number of groups to encode position-sensitive scores. :param spatial_scale: Ratio of input feature map over input image size. :param spatial_bins_x: Numbers of bins to divide the input feature maps over. :param spatial_bins_y: Numbers of bins to divide the input feature maps over. :param mode: Mode of pooling - "avg" or "bilinear". :return: PSROIPooling node """ def range(*args, **kwargs) -> openvino._pyopenvino.Node: """ Return a node which produces the Range operation. :param start: The start value of the generated range. :param stop: The stop value of the generated range. :param step: The step value for the generated range. :param name: Optional name for output node. :return: Range node """ def reduce_logical_and(*args, **kwargs) -> openvino._pyopenvino.Node: """ Logical AND reduction operation on input tensor, eliminating the specified reduction axes. :param node: The tensor we want to reduce. :param reduction_axes: The axes to eliminate through AND operation. :param keep_dims: If set to True it holds axes that are used for reduction. :param name: Optional name for output node. :return: The new node performing reduction operation. """ def reduce_logical_or(*args, **kwargs) -> openvino._pyopenvino.Node: """ Logical OR reduction operation on input tensor, eliminating the specified reduction axes. :param node: The tensor we want to reduce. :param reduction_axes: The axes to eliminate through OR operation. :param keep_dims: If set to True it holds axes that are used for reduction. :param name: Optional name for output node. :return: The new node performing reduction operation. """ def reduce_max(*args, **kwargs) -> openvino._pyopenvino.Node: """ Max-reduction operation on input tensor, eliminating the specified reduction axes. :param node: The tensor we want to max-reduce. :param reduction_axes: The axes to eliminate through max operation. :param keep_dims: If set to True it holds axes that are used for reduction. :param name: Optional name for output node. """ def reduce_mean(*args, **kwargs) -> openvino._pyopenvino.Node: """ Mean-reduction operation on input tensor, eliminating the specified reduction axes. :param node: The tensor we want to mean-reduce. :param reduction_axes: The axes to eliminate through mean operation. :param keep_dims: If set to True it holds axes that are used for reduction. :param name: Optional name for output node. :return: The new node performing mean-reduction operation. """ def reduce_min(*args, **kwargs) -> openvino._pyopenvino.Node: """ Min-reduction operation on input tensor, eliminating the specified reduction axes. :param node: The tensor we want to min-reduce. :param reduction_axes: The axes to eliminate through min operation. :param keep_dims: If set to True it holds axes that are used for reduction :param name: Optional name for output node. """ def reduce_prod(*args, **kwargs) -> openvino._pyopenvino.Node: """ Product-reduction operation on input tensor, eliminating the specified reduction axes. :param node: The tensor we want to product-reduce. :param reduction_axes: The axes to eliminate through product operation. :param keep_dims: If set to True it holds axes that are used for reduction :param name: Optional name for output node. :return: The new node performing product-reduction operation. """ def reduce_sum(*args, **kwargs) -> openvino._pyopenvino.Node: """ Perform element-wise sums of the input tensor, eliminating the specified reduction axes. :param node: The node providing data for operation. :param reduction_axes: The axes to eliminate through summation. :param keep_dims: If set to True it holds axes that are used for reduction :param name: The optional new name for output node. :return: The new node performing summation along `reduction_axes` element-wise. """ def region_yolo(*args, **kwargs) -> openvino._pyopenvino.Node: """ Return a node which produces the RegionYolo operation. :param input: Input data :param coords: Number of coordinates for each region :param classes: Number of classes for each region :param num: Number of regions :param do_softmax: Compute softmax :param mask: Mask :param axis: Axis to begin softmax on :param end_axis: Axis to end softmax on :param anchors: A flattened list of pairs `[width, height]` that describes prior box sizes :param name: Optional name for output node. :return: RegionYolo node """ def relu(input_value, *args, **kwargs) -> openvino._pyopenvino.Node: """ Perform rectified linear unit operation on input node element-wise. :param node: One of: input node, array or scalar. :param name: The optional output node name. :return: The new node performing relu operation on its input element-wise. """ def reshape(*args, **kwargs) -> openvino._pyopenvino.Node: """ Return reshaped node according to provided parameters. :param node: The tensor we want to reshape. :param output_shape: The node with a new shape for input tensor. :param special_zero: The boolean variable that controls how zero values in shape are interpreted. If special_zero is false, then 0 is interpreted as-is which means that output shape will contain a zero dimension at the specified location. Input and output tensors are empty in this case. If special_zero is true, then all zeros in shape implies the copying of corresponding dimensions from data.shape into the output shape. Range of values: False or True :return: The node reshaping an input tensor. """ def result(input_value, *args, **kwargs) -> openvino._pyopenvino.Node: """ Return a node which represents an output of a graph (Model). :param data: The tensor containing the input data :return: Result node """ def reverse_sequence(*args, **kwargs) -> openvino._pyopenvino.Node: """ Return a node which produces a ReverseSequence operation. :param input: tensor with input data to reverse :param seq_lengths: 1D tensor of integers with sequence lengths in the input tensor. :param batch_axis: index of the batch dimension. :param seq_axis: index of the sequence dimension. :return: ReverseSequence node """ def select(*args, **kwargs) -> openvino._pyopenvino.Node: """ Perform an element-wise selection operation on input tensors. :param cond: Tensor with selection mask of type `boolean`. :param then_node: Tensor providing data to be selected if respective `cond` item value is `True`. :param else_node: Tensor providing data to be selected if respective `cond` item value is `False`. :param auto_broadcast: Mode specifies rules used for auto-broadcasting of input tensors. :param name: The optional new name for output node. :return: The new node with values selected according to provided arguments. """ def selu(*args, **kwargs) -> openvino._pyopenvino.Node: """ Perform a Scaled Exponential Linear Unit (SELU) operation on input node element-wise. :param data: input node, array or scalar. :param alpha: Alpha coefficient of SELU operation :param lambda_value: Lambda coefficient of SELU operation :param name: The optional output node name. :return: The new node performing relu operation on its input element-wise. """ def shape_of(*args, **kwargs) -> openvino._pyopenvino.Node: """ Return a node which produces a tensor containing the shape of its input data. :param data: The tensor containing the input data. :return: ShapeOf node """ def sigmoid(input_value, *args, **kwargs) -> openvino._pyopenvino.Node: """ Return a node which applies the sigmoid function element-wise. :param data: The tensor containing the input data :return: Sigmoid node """ def sign(input_value, *args, **kwargs) -> openvino._pyopenvino.Node: """ Perform element-wise sign operation. :param node: One of: input node, array or scalar. :param name: The optional new name for output node. :return: The node with mapped elements of the input tensor to -1 (if it is negative), 0 (if it is zero), or 1 (if it is positive). """ def sin(input_value, *args, **kwargs) -> openvino._pyopenvino.Node: """ Apply sine function on the input node element-wise. :param node: One of: input node, array or scalar. :param name: Optional new name for output node. :return: New node with sin operation applied on it. """ def sinh(input_value, *args, **kwargs) -> openvino._pyopenvino.Node: """ Apply hyperbolic sine function on the input node element-wise. :param node: One of: input node, array or scalar. :param name: Optional new name for output node. :return: New node with sin operation applied on it. """ def softmax(*args, **kwargs) -> openvino._pyopenvino.Node: """ Apply softmax operation on each element of input tensor. :param data: The tensor providing input data. :param axis: An axis along which Softmax should be calculated :return: The new node with softmax operation applied on each element. """ def space_to_depth(*args, **kwargs) -> openvino._pyopenvino.Node: """ Perform SpaceToDepth operation on the input tensor. SpaceToDepth rearranges blocks of spatial data into depth. The operator :return: a copy of the input tensor where values from the height and width dimensions are moved to the depth dimension. :param data: The node with data tensor. :param mode: Specifies how the output depth dimension is gathered from block coordinates. blocks_first: The output depth is gathered from [block_size, ..., block_size, C] depth_first: The output depth is gathered from [C, block_size, ..., block_size] :param block_size: The size of the block of values to be moved. Scalar value. :param name: Optional output node name. :return: The new node performing a SpaceToDepth operation on input tensor. """ def split(*args, **kwargs) -> openvino._pyopenvino.Node: """ Return a node which splits the input tensor into same-length slices. :param data: The input tensor to be split :param axis: Axis along which the input data will be split :param num_splits: Number of the output tensors that should be produced :return: Split node """ def sqrt(input_value, *args, **kwargs) -> openvino._pyopenvino.Node: """ Return node which applies square root to the input node element-wise. :param node: One of: input node, array or scalar. :param name: Optional new name for output node. :return: The new node with sqrt operation applied element-wise. """ def squared_difference(left, right, *args, **kwargs) -> openvino._pyopenvino.Node: """ Perform an element-wise squared difference between two tensors. \\f[ y[i] = (x_1[i] - x_2[i])^2 \\f] :param x1: The node with first input tensor. :param x2: The node with second input tensor. :param auto_broadcast: The type of broadcasting that specifies mapping of input tensor axes to output shape axes. Range of values: numpy, explicit. :param name: Optional new name for output node. :return: The new node performing a squared difference between two tensors. """ def squeeze(*args, **kwargs) -> openvino._pyopenvino.Node: """ Perform squeeze operation on input tensor. :param data: The node with data tensor. :param axes: list of non-negative integers, indicate the dimensions to squeeze. One of: input node or array. :param name: Optional new name for output node. :return: The new node performing a squeeze operation on input tensor. Remove single-dimensional entries from the shape of a tensor. Takes a parameter `axes` with a list of axes to squeeze. If `axes` is not provided, all the single dimensions will be removed from the shape. If an `axis` is selected with shape entry not equal to one, an error is raised. For example: Inputs: tensor with shape [1, 2, 1, 3, 1, 1], axes=[2, 4] Result: tensor with shape [1, 2, 3, 1] """ def strided_slice(*args, **kwargs) -> openvino._pyopenvino.Node: """ Return a node which dynamically repeats(replicates) the input data tensor. :param data: The tensor to be sliced :param begin: 1D tensor with begin indexes for input blob slicing :param end: 1D tensor with end indexes for input blob slicing :param strides: The slicing strides :param begin_mask: A mask applied to the 'begin' input indicating which elements shoud be ignored :param end_mask: A mask applied to the 'end' input indicating which elements shoud be ignored :param new_axis_mask: A mask indicating dimensions where '1' should be inserted :param shrink_axis_mask: A mask indicating which dimensions should be deleted :param ellipsis_mask: Indicates positions where missing dimensions should be inserted :return: StridedSlice node """ def subtract(left, right, *args, **kwargs) -> openvino._pyopenvino.Node: """ Return node which applies f(x) = A-B to the input nodes element-wise. :param left_node: The node providing data for left hand side of operator. :param right_node: The node providing data for right hand side of operator. :param auto_broadcast: The type of broadcasting that specifies mapping of input tensor axes to output shape axes. Range of values: numpy, explicit. :param name: The optional name for output node. :return: The new output node performing subtraction operation on both tensors element-wise. """ def tan(input_value, *args, **kwargs) -> openvino._pyopenvino.Node: """ Apply tangent function on the input node element-wise. :param node: One of: input node, array or scalar. :param name: Optional new name for output node. :return: New node with tan operation applied on it. """ def tanh(input_value, *args, **kwargs) -> openvino._pyopenvino.Node: """ Return node which applies hyperbolic tangent to the input node element-wise. :param node: One of: input node, array or scalar. :param name: Optional new name for output node. :return: New node with tanh operation applied on it. """ def tile(*args, **kwargs) -> openvino._pyopenvino.Node: """ Return a node which dynamically repeats(replicates) the input data tensor. :param data: The input tensor to be tiled :param repeats: Per-dimension replication factors :return: Tile node """ def topk(*args, **kwargs) -> openvino._pyopenvino.Node: """ Return a node which performs TopK. :param data: Input data. :param k: K. :param axis: TopK Axis. :param mode: Compute TopK largest ('max') or smallest ('min') :param sort: Order of output elements (sort by: 'none', 'index' or 'value') :return: The new node which performs TopK (both indices and values) """ def transpose(*args, **kwargs) -> openvino._pyopenvino.Node: """ Return a node which transposes the data in the input tensor. :param data: The input tensor to be transposed :param input_order: Permutation of axes to be applied to the input tensor :return: Transpose node """ def unsqueeze(data: typing.Union[openvino._pyopenvino.Node, int, float, numpy.ndarray], axes: typing.Union[openvino._pyopenvino.Node, int, float, numpy.ndarray], name: typing.Optional[str] = None) -> openvino._pyopenvino.Node: """ Perform unsqueeze operation on input tensor. Insert single-dimensional entries to the shape of a tensor. Takes one required argument axes, a list of dimensions that will be inserted. Dimension indices in axes are as seen in the output tensor. For example: Inputs: tensor with shape [3, 4, 5], axes=[0, 4] Result: tensor with shape [1, 3, 4, 5, 1] :param data: The node with data tensor. :param axes: list of non-negative integers, indicate the dimensions to be inserted. One of: input node or array. :return: The new node performing an unsqueeze operation on input tensor. """ def variadic_split(*args, **kwargs) -> openvino._pyopenvino.Node: """ Return a node which splits the input tensor into variadic length slices. :param data: The input tensor to be split :param axis: Axis along which the input data will be split :param split_lengths: Sizes of the output tensors along the split axis :return: VariadicSplit node """ NodeInput: typing._UnionGenericAlias # value = typing.Union[openvino._pyopenvino.Node, int, float, numpy.ndarray] NumericData: typing._UnionGenericAlias # value = typing.Union[int, float, numpy.ndarray] NumericType: typing._UnionGenericAlias # value = typing.Union[type, numpy.dtype] ScalarData: typing._UnionGenericAlias # value = typing.Union[int, float] _get_node_factory_opset1: functools.partial # value = functools.partial(, 'opset1')