1808 lines
85 KiB
Python
1808 lines
85 KiB
Python
# type: ignore
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from __future__ import annotations
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from builtins import list as TensorShape
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from functools import partial
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from openvino._pyopenvino import Node
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from openvino._pyopenvino import PartialShape
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from openvino._pyopenvino import Type
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from openvino._pyopenvino.op import Constant
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from openvino._pyopenvino.op import Parameter
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from openvino._pyopenvino.op import tensor_iterator
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from openvino.package_utils import deprecated
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from openvino.utils.decorators import binary_op
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from openvino.utils.decorators import nameable_op
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from openvino.utils.decorators import unary_op
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from openvino.utils.input_validation import check_valid_attributes
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from openvino.utils.input_validation import is_non_negative_value
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from openvino.utils.input_validation import is_positive_value
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from openvino.utils.node_factory import NodeFactory
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from openvino.utils.node_factory import _get_node_factory
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from openvino.utils.types import as_node
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from openvino.utils.types import as_nodes
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from openvino.utils.types import get_dtype
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from openvino.utils.types import get_element_type
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from openvino.utils.types import get_element_type_str
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from openvino.utils.types import make_constant_node
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from typing import get_args
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import functools
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import numpy as np
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import openvino._pyopenvino
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import openvino._pyopenvino.op
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import typing
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"""
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Factory functions for all openvino ops.
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"""
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__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']
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def absolute(input_value, *args, **kwargs) -> openvino._pyopenvino.Node:
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"""
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Return node which applies f(x) = abs(x) to the input node element-wise.
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:param node: One of: input node, array or scalar.
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:param name: Optional new name for output node.
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:return: New node with Abs operation applied on it.
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"""
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def acos(input_value, *args, **kwargs) -> openvino._pyopenvino.Node:
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"""
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Apply inverse cosine function on the input node element-wise.
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:param node: One of: input node, array or scalar.
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:param name: Optional new name for output node.
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:return: New node with arccos operation applied on it.
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"""
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def add(left, right, *args, **kwargs) -> openvino._pyopenvino.Node:
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"""
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Return node which applies f(A,B) = A+B to the input nodes element-wise.
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:param left_node: The first input node for add operation.
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:param right_node: The second input node for add operation.
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:param auto_broadcast: The type of broadcasting specifies rules used for
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auto-broadcasting of input tensors. Defaults to "NUMPY".
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:param name: The optional name for output new node.
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:return: The node performing element-wise addition.
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"""
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def asin(input_value, *args, **kwargs) -> openvino._pyopenvino.Node:
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"""
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Apply inverse sine function on the input node element-wise.
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:param node: One of: input node, array or scalar.
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:param name: Optional new name for output node.
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:return: New node with arcsin operation applied on it.
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"""
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def atan(input_value, *args, **kwargs) -> openvino._pyopenvino.Node:
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"""
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Apply inverse tangent function on the input node element-wise.
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:param node: One of: input node, array or scalar.
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:param name: Optional new name for output node.
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:return: New node with arctan operation applied on it.
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"""
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def avg_pool(*args, **kwargs) -> openvino._pyopenvino.Node:
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"""
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Return average pooling node.
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:param data_batch: The input node providing data.
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:param strides: The window movement strides.
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:param pads_begin: The input data optional padding below filled with zeros.
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:param pads_end: The input data optional padding below filled with zeros.
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:param kernel_shape: The pooling window shape.
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:param exclude_pad: Whether or not to include zero padding in average computations.
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:param rounding_type: Determines used rounding schema when computing output shape. Acceptable
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values are: ['floor', 'ceil']
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:param auto_pad: Determines how the padding is calculated. Acceptable values:
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[None, 'same_upper', 'same_lower', 'valid']
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:param name: Optional name for the new output node.
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:return: New node with AvgPool operation applied on its data.
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"""
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def batch_norm_inference(*args, **kwargs) -> openvino._pyopenvino.Node:
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"""
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Perform layer normalizes a input tensor by mean and variance with appling scale and offset.
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:param data: The input tensor with data for normalization.
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:param gamma: The scalar scaling for normalized value.
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:param beta: The bias added to the scaled normalized value.
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:param mean: The value for mean normalization.
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:param variance: The value for variance normalization.
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:param epsilon: The number to be added to the variance to avoid division
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by zero when normalizing a value.
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:param name: The optional name of the output node.
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:return: The new node which performs BatchNormInference.
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"""
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def binary_convolution(*args, **kwargs) -> openvino._pyopenvino.Node:
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"""
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Create node performing convolution with binary weights, binary input and integer output.
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:param data: The node providing data batch tensor.
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:param filter: The node providing filters tensor.
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:param strides: The kernel window movement strides.
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:param pads_begin: The number of pixels to add to the beginning along each axis.
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:param pads_end: The number of pixels to add to the end along each axis.
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:param dilations: The distance in width and height between elements (weights) in the filter.
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:param mode: Defines how input tensor 0/1 values and weights 0/1 are interpreted.
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:param pad_value: Floating-point value used to fill pad area.
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:param auto_pad: The type of padding. Range of values: explicit, same_upper, same_lower, valid.
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:param name: The optional new name for output node.
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:return: New node performing binary convolution operation.
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"""
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def broadcast(*args, **kwargs) -> openvino._pyopenvino.Node:
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"""
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Create a node which broadcasts the input node's values along specified axes to a desired shape.
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:param data: The node with input tensor data.
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:param target_shape: The node with a new shape we want to broadcast tensor to.
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:param axes_mapping: The node with a axis positions (0-based) in the result
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that are being broadcast.
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:param mode: The type of broadcasting that specifies mapping of input tensor axes
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to output shape axes. Range of values: NUMPY, EXPLICIT.
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:param name: Optional new name for output node.
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:return: New node with broadcast shape.
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"""
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def ceiling(input_value, *args, **kwargs) -> openvino._pyopenvino.Node:
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"""
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Return node which applies ceiling to the input node element-wise.
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:param node: The node providing data to ceiling operation.
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:param name: Optional name for output node.
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:return: The node performing element-wise ceiling.
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"""
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def clamp(*args, **kwargs) -> openvino._pyopenvino.Node:
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"""
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Perform clamp element-wise on data from input node.
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:param data: Input tensor. One of: input node, array or scalar.
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:param min_value: The lower bound of the <min_value;max_value> range. Scalar value.
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:param max_value: The upper bound of the <min_value;max_value> range. Scalar value.
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:param name: Optional output node name.
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:return: The new node performing a clamp operation on its input data element-wise.
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Performs a clipping operation on an input value between a pair of boundary values.
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For each element in `data`, if the element's value is lower than `min_value`,
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it will be replaced with `min_value`. If the value is higher than `max_value`,
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it will be replaced by `max_value`.
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Intermediate values of `data` are returned without change.
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Clamp uses the following logic:
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.. code-block:: python
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if data < min_value:
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data=min_value
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elif data > max_value:
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data=max_value
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"""
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def concat(*args, **kwargs) -> openvino._pyopenvino.Node:
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"""
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Concatenate input nodes into single new node along specified axis.
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:param nodes: The nodes we want concatenate into single new node.
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:param axis: The axis along which we want to concatenate input nodes.
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:param name: The optional new name for output node.
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:return: Return new node that is a concatenation of input nodes.
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"""
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def constant(*args, **kwargs) -> openvino._pyopenvino.op.Constant:
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"""
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Create a Constant node from provided value.
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:param value: One of: array of values or scalar to initialize node with.
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:param dtype: The data type of provided data.
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:param name: Optional name for output node.
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:return: The Constant node initialized with provided data.
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"""
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def convert(*args, **kwargs) -> openvino._pyopenvino.Node:
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"""
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Return node which casts input node values to specified type.
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:param data: Node which produces the input tensor.
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:param destination_type: Provides the target type for the conversion.
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:param name: Optional name for the output node.
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:return: New node performing the conversion operation.
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"""
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def convert_like(left, right, *args, **kwargs) -> openvino._pyopenvino.Node:
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"""
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Return node which casts data node values to the type of another node.
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:param data: Node which produces the input tensor
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:param like: Node which provides the target type information for the conversion
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:param name: Optional name for the output node.
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:return: New node performing the conversion operation.
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"""
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def convolution(*args, **kwargs) -> openvino._pyopenvino.Node:
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"""
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Return node performing batched convolution operation.
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:param data: The node providing data batch tensor.
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:param filter: The node providing filters tensor.
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:param strides: The kernel window movement strides.
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:param pads_begin: The number of zero padding elements to add on each axis below 0 coordinate.
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:param pads_end: The number of zero padding elements to add on each axis above max coordinate
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:param dilations: The data batch dilation strides.
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:param auto_pad: The type of padding. Range of values: explicit, same_upper, same_lower, valid.
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:param name: The optional new name for output node.
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:return: New node performing batched convolution operation.
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"""
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def convolution_backprop_data(*args, **kwargs) -> openvino._pyopenvino.Node:
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"""
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Create node performing a batched-convolution backprop data operation.
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:param data: The node producing data from forward-prop
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:param filters: The node producing the filters from forward-prop.
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:param output_shape: The node producing output delta.
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:param strides: The distance (in pixels) to slide the filter on the feature map
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over the axes.
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:param pads_begin: The number of pixels to add to the beginning along each axis.
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:param pads_end: The number of pixels to add to the end along each axis.
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:param dilations: The distance in width and height between elements (weights)
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in the filter.
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:param name: The node name.
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:return: The node object representing ConvolutionBackpropData operation.
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"""
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def cos(input_value, *args, **kwargs) -> openvino._pyopenvino.Node:
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"""
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Apply cosine function on the input node element-wise.
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:param node: One of: input node, array or scalar.
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:param name: Optional new name for output node.
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:return: New node with cos operation applied on it.
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"""
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def cosh(input_value, *args, **kwargs) -> openvino._pyopenvino.Node:
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"""
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Apply hyperbolic cosine function on the input node element-wise.
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:param node: One of: input node, array or scalar.
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:param name: Optional new name for output node.
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:return: New node with cosh operation applied on it.
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"""
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def ctc_greedy_decoder(*args, **kwargs) -> openvino._pyopenvino.Node:
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"""
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Perform greedy decoding on the logits given in input (best path).
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:param data: Logits on which greedy decoding is performed.
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:param sequence_mask: The tensor with sequence masks for each sequence in the batch.
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:param merge_repeated: The flag for merging repeated labels during the CTC calculation.
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:param name: Optional name for output node.
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:return: The new node performing an CTCGreedyDecoder operation on input tensor.
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"""
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def deformable_convolution(*args, **kwargs) -> openvino._pyopenvino.Node:
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"""
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Create node performing deformable convolution.
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:param data: The node providing data batch tensor.
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:param filter: The node providing filters tensor.
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:param strides: The distance (in pixels) to slide the filter on the feature map over the axes.
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:param pads_begin: The number of pixels to add to the beginning along each axis.
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:param pads_end: The number of pixels to add to the end along each axis.
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:param dilations: The distance in width and height between elements (weights) in the filter.
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:param auto_pad: The type of padding. Range of values: explicit, same_upper, same_lower, valid.
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:param group: The number of groups which both output and input should be split into.
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:param deformable_group: The number of groups which deformable values and output should be split
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into along the channel axis.
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:param name: The optional new name for output node.
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:return: New node performing deformable convolution operation.
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"""
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def deformable_psroi_pooling(*args, **kwargs) -> openvino._pyopenvino.Node:
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"""
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Return node performing DeformablePSROIPooling operation.
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DeformablePSROIPooling computes position-sensitive pooling
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on regions of interest specified by input.
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:param feature_maps: 4D tensor with feature maps.
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:param coords: 2D tensor describing box consisting of tuples: [batch_id, x_1, y_1, x_2, y_2].
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:param output_dim: A pooled output channel number.
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:param spatial_scale: A multiplicative spatial scale factor to translate ROI.
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:param group_size: The number of groups to encode position-sensitive score.
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:param mode: Specifies mode for pooling. Range of values: ['bilinear_deformable'].
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:param spatial_bins_x: Specifies numbers of bins to divide the input feature maps over width.
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:param spatial_bins_y: Specifies numbers of bins to divide the input feature maps over height.
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:param trans_std: The value that all transformation (offset) values are multiplied with.
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:param part_size: The number of parts the output tensor spatial dimensions are divided into.
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:param offsets: Optional node. 4D input blob with transformation values (offsets).
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:param name: The optional new name for output node.
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:return: New node performing DeformablePSROIPooling operation.
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"""
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def depth_to_space(*args, **kwargs) -> openvino._pyopenvino.Node:
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"""
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Rearranges input tensor from depth into blocks of spatial data.
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Values from the height and width dimensions are moved to the depth dimension.
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Input tensor has shape [N,C,H,W], where N is the batch axis, C is the channel or depth,
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H is the height and W is the width.
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Output node produces a tensor with shape:
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[N, C * `block_size` * `block_size`, H / `block_size`, W / `block_size`]
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:param node: The node with input tensor data.
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:param mode: Specifies how the input depth dimension is split to block coordinates
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blocks_first: The input is divided to [block_size, ..., block_size, new_depth]
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depth_first: The input is divided to [new_depth, block_size, ..., block_size]
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:param block_size: The size of the spatial block of values describing
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how the tensor's data is to be rearranged.
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:param name: Optional output node name.
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:return: The new node performing an DepthToSpace operation on its input tensor.
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"""
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def detection_output(*args, **kwargs) -> openvino._pyopenvino.Node:
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"""
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Generate the detection output using information on location and confidence predictions.
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:param box_logits: The 2D input tensor with box logits.
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:param class_preds: The 2D input tensor with class predictions.
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:param proposals: The 3D input tensor with proposals.
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:param attrs: The dictionary containing key, value pairs for attributes.
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:param aux_class_preds: The 2D input tensor with additional class predictions information.
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:param aux_box_preds: The 2D input tensor with additional box predictions information.
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:param name: Optional name for the output node.
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:return: Node representing DetectionOutput operation.
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Available attributes are:
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* num_classes The number of classes to be predicted.
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Range of values: positive integer number
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Default value: None
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Required: yes
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* background_label_id The background label id.
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Range of values: integer value
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Default value: 0
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Required: no
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* top_k Maximum number of results to be kept per batch after NMS step.
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Range of values: integer value
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Default value: -1
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Required: no
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* variance_encoded_in_target The flag that denotes if variance is encoded in target.
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Range of values: {False, True}
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Default value: False
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Required: no
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* keep_top_k Maximum number of bounding boxes per batch to be kept after NMS step.
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Range of values: integer values
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Default value: None
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Required: yes
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* code_type The type of coding method for bounding boxes.
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Range of values: {'caffe.PriorBoxParameter.CENTER_SIZE',
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'caffe.PriorBoxParameter.CORNER'}
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Default value: 'caffe.PriorBoxParameter.CORNER'
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Required: no
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* share_location The flag that denotes if bounding boxes are shared among different
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classes.
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Range of values: {True, False}
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Default value: True
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Required: no
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* nms_threshold The threshold to be used in the NMS stage.
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Range of values: floating point value
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Default value: None
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Required: yes
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* confidence_threshold Specifies the minimum confidence threshold for detection boxes to be
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considered.
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Range of values: floating point value
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Default value: 0
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Required: no
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* clip_after_nms The flag that denotes whether to perform clip bounding boxes after
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non-maximum suppression or not.
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Range of values: {True, False}
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Default value: False
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Required: no
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* clip_before_nms The flag that denotes whether to perform clip bounding boxes before
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non-maximum suppression or not.
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Range of values: {True, False}
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Default value: False
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Required: no
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* decrease_label_id The flag that denotes how to perform NMS.
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Range of values: False - perform NMS like in Caffe*.
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True - perform NMS like in MxNet*.
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Default value: False
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Required: no
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|
|
* 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(<function _get_node_factory at memory_address>, 'opset1')
|