365 lines
18 KiB
Python
365 lines
18 KiB
Python
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# 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 Shape
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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.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 assert_list_of_ints
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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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import functools
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import numpy as np
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import openvino._pyopenvino
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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', 'ScalarData', 'Shape', 'TensorShape', 'as_node', 'as_nodes', 'assert_list_of_ints', 'assign', 'binary_op', 'broadcast', 'bucketize', 'check_valid_attributes', 'cum_sum', 'embedding_bag_offsets_sum', 'embedding_bag_packed_sum', 'embedding_segments_sum', 'extract_image_patches', 'get_dtype', 'get_element_type', 'get_element_type_str', 'gru_cell', 'is_non_negative_value', 'is_positive_value', 'make_constant_node', 'nameable_op', 'non_max_suppression', 'non_zero', 'np', 'partial', 'read_value', 'rnn_cell', 'roi_align', 'scatter_elements_update', 'scatter_update', 'shape_of', 'shuffle_channels', 'topk', 'unary_op']
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def assign(*args, **kwargs) -> openvino._pyopenvino.Node:
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"""
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Return a node which produces the Assign operation.
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:param new_value: Node producing a value to be assigned to a variable.
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:param variable_id: Id of a variable to be updated.
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:param name: Optional name for output node.
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:return: Assign node
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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 broadcast_spec: The type of broadcasting that specifies mapping of input tensor axes
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to output shape axes. Range of values: NUMPY, EXPLICIT, BIDIRECTIONAL.
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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 bucketize(*args, **kwargs) -> openvino._pyopenvino.Node:
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"""
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Return a node which produces the Bucketize operation.
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:param data: Input data to bucketize
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:param buckets: 1-D of sorted unique boundaries for buckets
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:param output_type: Output tensor type, "i64" or "i32", defaults to i64
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:param with_right_bound: indicates whether bucket includes the right or left
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edge of interval. default true = includes right edge
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:param name: Optional name for output node.
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:return: Bucketize node
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"""
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def cum_sum(*args, **kwargs) -> openvino._pyopenvino.Node:
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"""
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Construct a cumulative summation operation.
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:param arg: The tensor to be summed.
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:param axis: zero dimension tensor specifying axis position along which sum will be performed.
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:param exclusive: if set to true, the top element is not included
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:param reverse: if set to true, will perform the sums in reverse direction
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:return: New node performing the operation
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"""
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def embedding_bag_offsets_sum(*args, **kwargs) -> openvino._pyopenvino.Node:
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"""
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Return a node which performs sums of bags of embeddings without the intermediate embeddings.
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:param emb_table: Tensor containing the embedding lookup table.
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:param indices: Tensor with indices.
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:param offsets: Tensor containing the starting index positions of each bag in indices.
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:param per_sample_weights: Tensor with weights for each sample.
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:param default_index: Scalar containing default index in embedding table to fill empty bags.
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:param name: Optional name for output node.
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:return: The new node which performs EmbeddingBagOffsetsSum
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"""
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def embedding_bag_packed_sum(*args, **kwargs) -> openvino._pyopenvino.Node:
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"""
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Return an EmbeddingBagPackedSum node.
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EmbeddingSegmentsSum constructs an output tensor by replacing every index in a given
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input tensor with a row (from the weights matrix) at that index
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:param emb_table: Tensor containing the embedding lookup table.
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:param indices: Tensor with indices.
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:param per_sample_weights: Weights to be multiplied with embedding table.
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:param name: Optional name for output node.
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:return: EmbeddingBagPackedSum node
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"""
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def embedding_segments_sum(*args, **kwargs) -> openvino._pyopenvino.Node:
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"""
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Return an EmbeddingSegmentsSum node.
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EmbeddingSegmentsSum constructs an output tensor by replacing every index in a given
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input tensor with a row (from the weights matrix) at that index
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:param emb_table: Tensor containing the embedding lookup table.
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:param indices: Tensor with indices.
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:param segment_ids: Tensor with indices into the output Tensor
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:param num_segments: Tensor with number of segments.
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:param default_index: Scalar containing default index in embedding table to fill empty bags.
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:param per_sample_weights: Weights to be multiplied with embedding table.
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:param name: Optional name for output node.
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:return: EmbeddingSegmentsSum node
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"""
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def extract_image_patches(*args, **kwargs) -> openvino._pyopenvino.Node:
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"""
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Return a node which produces the ExtractImagePatches operation.
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:param image: 4-D Input data to extract image patches.
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:param sizes: Patch size in the format of [size_rows, size_cols].
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:param strides: Patch movement stride in the format of [stride_rows, stride_cols]
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:param rates: Element seleciton rate for creating a patch.
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:param auto_pad: Padding type.
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:param name: Optional name for output node.
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:return: ExtractImagePatches node
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"""
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def gru_cell(*args, **kwargs) -> openvino._pyopenvino.Node:
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"""
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Perform GRUCell operation on the tensor from input node.
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GRUCell represents a single GRU Cell that computes the output
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using the formula described in the paper: https://arxiv.org/abs/1406.1078
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Note this class represents only single *cell* and not whole *layer*.
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:param X: The input tensor with shape: [batch_size, input_size].
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:param initial_hidden_state: The hidden state tensor at current time step with shape:
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[batch_size, hidden_size].
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:param W: The weights for matrix multiplication, gate order: zrh.
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Shape: [3*hidden_size, input_size].
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:param R: The recurrence weights for matrix multiplication.
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Shape: [3*hidden_size, hidden_size].
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:param B: The sum of biases (weight and recurrence).
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For linear_before_reset set True the shape is [4*hidden_size].
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Otherwise the shape is [3*hidden_size].
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:param hidden_size: The number of hidden units for recurrent cell.
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Specifies hidden state size.
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:param activations: The vector of activation functions used inside recurrent cell.
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:param activation_alpha: The vector of alpha parameters for activation functions in
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order respective to activation list.
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:param activation_beta: The vector of beta parameters for activation functions in order
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respective to activation list.
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:param clip: The value defining clipping range [-clip, clip] on input of
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activation functions.
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:param linear_before_reset: Flag denotes if the layer behaves according to the modification
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of GRUCell described in the formula in the ONNX documentation.
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:param name: Optional output node name.
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:return: The new node performing a GRUCell operation on tensor from input node.
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"""
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def non_max_suppression(*args, **kwargs) -> openvino._pyopenvino.Node:
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"""
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Return a node which performs NonMaxSuppression.
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:param boxes: Tensor with box coordinates.
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:param scores: Tensor with box scores.
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:param max_output_boxes_per_class: Tensor Specifying maximum number of boxes
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to be selected per class.
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:param iou_threshold: Tensor specifying intersection over union threshold
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:param score_threshold: Tensor specifying minimum score to consider box for the processing.
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:param box_encoding: Format of boxes data encoding.
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:param sort_result_descending: Flag that specifies whenever it is necessary to sort selected
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boxes across batches or not.
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:param output_type: Output element type.
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:return: The new node which performs NonMaxSuppression
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"""
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def non_zero(*args, **kwargs) -> openvino._pyopenvino.Node:
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"""
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Return the indices of the elements that are non-zero.
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:param data: Input data.
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:param output_type: Output tensor type.
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:return: The new node which performs NonZero
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"""
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def read_value(*args, **kwargs) -> openvino._pyopenvino.Node:
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"""
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Return a node which produces the Assign operation.
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:param init_value: Node producing a value to be returned instead of an unassigned variable.
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:param variable_id: Id of a variable to be read.
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:param name: Optional name for output node.
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:return: ReadValue node
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"""
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def rnn_cell(*args, **kwargs) -> openvino._pyopenvino.Node:
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"""
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Perform RNNCell operation on tensor from input node.
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It follows notation and equations defined as in ONNX standard:
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https://github.com/onnx/onnx/blob/master/docs/Operators.md#RNN
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Note this class represents only single *cell* and not whole RNN *layer*.
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:param X: The input tensor with shape: [batch_size, input_size].
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:param initial_hidden_state: The hidden state tensor at current time step with shape:
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[batch_size, hidden_size].
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:param W: The weight tensor with shape: [hidden_size, input_size].
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:param R: The recurrence weight tensor with shape: [hidden_size,
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hidden_size].
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:param B: The sum of biases (weight and recurrence) with shape: [hidden_size].
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:param hidden_size: The number of hidden units for recurrent cell.
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Specifies hidden state size.
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:param activations: The vector of activation functions used inside recurrent cell.
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:param activation_alpha: The vector of alpha parameters for activation functions in
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order respective to activation list.
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:param activation_beta: The vector of beta parameters for activation functions in order
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respective to activation list.
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:param clip: The value defining clipping range [-clip, clip] on input of
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activation functions.
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:param name: Optional output node name.
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:return: The new node performing a RNNCell operation on tensor from input node.
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"""
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def roi_align(*args, **kwargs) -> openvino._pyopenvino.Node:
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"""
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Return a node which performs ROIAlign.
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:param data: Input data.
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:param rois: RoIs (Regions of Interest) to pool over.
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:param batch_indices: Tensor with each element denoting the index of
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the corresponding image in the batch.
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:param pooled_h: Height of the ROI output feature map.
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:param pooled_w: Width of the ROI output feature map.
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:param sampling_ratio: Number of bins over height and width to use to calculate
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each output feature map element.
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:param spatial_scale: Multiplicative spatial scale factor to translate ROI coordinates.
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:param mode: Method to perform pooling to produce output feature map elements.
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:return: The new node which performs ROIAlign
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"""
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def scatter_elements_update(*args, **kwargs) -> openvino._pyopenvino.Node:
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"""
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Return a node which produces a ScatterElementsUpdate operation.
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:param data: The input tensor to be updated.
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:param indices: The tensor with indexes which will be updated.
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:param updates: The tensor with update values.
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:param axis: The axis for scatter.
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:return: ScatterElementsUpdate node
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ScatterElementsUpdate creates a copy of the first input tensor with updated elements
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specified with second and third input tensors.
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For each entry in `updates`, the target index in `data` is obtained by combining
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the corresponding entry in `indices` with the index of the entry itself: the
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index-value for dimension equal to `axis` is obtained from the value of the
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corresponding entry in `indices` and the index-value for dimension not equal
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to `axis` is obtained from the index of the entry itself.
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"""
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def scatter_update(*args, **kwargs) -> openvino._pyopenvino.Node:
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"""
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Return a node which produces a ScatterUpdate operation.
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ScatterUpdate sets new values to slices from data addressed by indices.
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:param data: The input tensor to be updated.
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:param indices: The tensor with indexes which will be updated.
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:param updates: The tensor with update values.
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:param axis: The axis at which elements will be updated.
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:return: ScatterUpdate node
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"""
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def shape_of(*args, **kwargs) -> openvino._pyopenvino.Node:
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"""
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Return a node which produces a tensor containing the shape of its input data.
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:param data: The tensor containing the input data.
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:param output_type: Output element type.
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:return: ShapeOf node
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"""
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def shuffle_channels(*args, **kwargs) -> openvino._pyopenvino.Node:
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"""
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Perform permutation on data in the channel dimension of the input tensor.
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:param data: The node with input tensor.
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:param axis: Channel dimension index in the data tensor.
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A negative value means that the index should be calculated
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from the back of the input data shape.
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:param group: The channel dimension specified by the axis parameter
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should be split into this number of groups.
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:param name: Optional output node name.
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:return: The new node performing a permutation on data in the channel dimension
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of the input tensor.
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The operation is the equivalent with the following transformation of the input tensor
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`data` of shape [N, C, H, W]:
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`data_reshaped` = reshape(`data`, [N, group, C / group, H * W])
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`data_transposed` = transpose(`data_reshaped`, [0, 2, 1, 3])
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`output` = reshape(`data_transposed`, [N, C, H, W])
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For example:
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.. code-block:: python
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Inputs: tensor of shape [1, 6, 2, 2]
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data = [[[[ 0., 1.], [ 2., 3.]],
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[[ 4., 5.], [ 6., 7.]],
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[[ 8., 9.], [10., 11.]],
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[[12., 13.], [14., 15.]],
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[[16., 17.], [18., 19.]],
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[[20., 21.], [22., 23.]]]]
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axis = 1
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groups = 3
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Output: tensor of shape [1, 6, 2, 2]
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output = [[[[ 0., 1.], [ 2., 3.]],
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[[ 8., 9.], [10., 11.]],
|
||
|
|
[[16., 17.], [18., 19.]],
|
||
|
|
[[ 4., 5.], [ 6., 7.]],
|
||
|
|
[[12., 13.], [14., 15.]],
|
||
|
|
[[20., 21.], [22., 23.]]]]
|
||
|
|
|
||
|
|
"""
|
||
|
|
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')
|
||
|
|
:param index_element_type: Type of output tensor with indices.
|
||
|
|
:return: The new node which performs TopK (both indices and values)
|
||
|
|
|
||
|
|
"""
|
||
|
|
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_opset3: functools.partial # value = functools.partial(<function _get_node_factory at memory_address>, 'opset3')
|