Functions
Node	batch_norm_inference (NodeInput data, NodeInput gamma, NodeInput beta, NodeInput mean, NodeInput variance, float epsilon, Optional[str] name=None)
	Perform layer normalizes a input tensor by mean and variance with appling scale and offset. More...

Node	gather_nd (NodeInput data, NodeInput indices, Optional[int] batch_dims=0, Optional[str] name=None)
	Return a node which performs GatherND. More...

Node	log_softmax (NodeInput data, int axis, Optional[str] name=None)
	Apply LogSoftmax operation on each element of input tensor. More...

Node	non_max_suppression (NodeInput boxes, NodeInput scores, Optional[NodeInput] max_output_boxes_per_class=None, Optional[NodeInput] iou_threshold=None, Optional[NodeInput] score_threshold=None, Optional[NodeInput] soft_nms_sigma=None, str box_encoding="corner", bool sort_result_descending=True, str output_type="i64", Optional[str] name=None)
	Return a node which performs NonMaxSuppression. More...

Node	round (NodeInput data, str mode="half_to_even", Optional[str] name=None)
	Apply Round operation on each element of input tensor. More...

Node	lstm_sequence (NodeInput X, NodeInput initial_hidden_state, NodeInput initial_cell_state, NodeInput sequence_lengths, NodeInput W, NodeInput R, NodeInput B, int hidden_size, str direction, List[str] activations=None, List[float] activations_alpha=None, List[float] activations_beta=None, float clip=0.0, Optional[str] name=None)
	Return a node which performs LSTMSequence operation. More...

Node	hsigmoid (NodeInput data, Optional[str] name=None)
	Return a node which performs HSigmoid. More...

Node	gru_sequence (NodeInput X, NodeInput initial_hidden_state, NodeInput sequence_lengths, NodeInput W, NodeInput R, NodeInput B, int hidden_size, str direction, List[str] activations=None, List[float] activations_alpha=None, List[float] activations_beta=None, float clip=0.0, bool linear_before_reset=False, Optional[str] name=None)
	Return a node which performs GRUSequence operation. More...

Node	rnn_sequence (NodeInput X, NodeInput initial_hidden_state, NodeInput sequence_lengths, NodeInput W, NodeInput R, NodeInput B, int hidden_size, str direction, List[str] activations=None, List[float] activations_alpha=None, List[float] activations_beta=None, float clip=0.0, Optional[str] name=None)
	Return a node which performs RNNSequence operation. More...

Node	loop (NodeInput trip_count, NodeInput execution_condition, Optional[str] name=None)
	Return a node which performs Loop. More...

Function Documentation

◆ batch_norm_inference()

Node ngraph.opset5.ops.batch_norm_inference	(	NodeInput	data,
		NodeInput	gamma,
		NodeInput	beta,
		NodeInput	mean,
		NodeInput	variance,
		float	epsilon,
		Optional[str]	name = `None`
	)

Perform layer normalizes a input tensor by mean and variance with appling scale and offset.

@param data: The input tensor with data for normalization.
@param gamma: The scalar scaling for normalized value.
@param beta: The bias added to the scaled normalized value.
@param mean: The value for mean normalization.
@param variance: The value for variance normalization.
@param epsilon: The  number to be added to the variance to avoid division
                by zero when normalizing a value.
@param name: The optional name of the output node.
@return: The new node which performs BatchNormInference.

◆ gather_nd()

Node ngraph.opset5.ops.gather_nd	(	NodeInput	data,
		NodeInput	indices,
		Optional[int]	batch_dims = `0`,
		Optional[str]	name = `None`
	)

Return a node which performs GatherND.

@param data:       N-D tensor with data for gathering
@param indices:    K-D tensor of tuples with indices by which data is gathered
@param batch_dims: Scalar value of batch dimensions
@return: The new node which performs GatherND

◆ gru_sequence()

Node ngraph.opset5.ops.gru_sequence	(	NodeInput	X,
		NodeInput	initial_hidden_state,
		NodeInput	sequence_lengths,
		NodeInput	W,
		NodeInput	R,
		NodeInput	B,
		int	hidden_size,
		str	direction,
		List[str]	activations = `None`,
		List[float]	activations_alpha = `None`,
		List[float]	activations_beta = `None`,
		float	clip = `0.0`,
		bool	linear_before_reset = `False`,
		Optional[str]	name = `None`
	)

Return a node which performs GRUSequence operation.

@param X: The input tensor. Shape: [batch_size, seq_length, input_size].
@param initial_hidden_state: The hidden state tensor.
Shape: [batch_size, num_directions, hidden_size].
@param sequence_lengths: Specifies real sequence lengths for each batch element.
Shape: [batch_size]. Integer type.
@param W: Tensor with weights for matrix multiplication operation with input portion of data.
Shape: [num_directions, 3*hidden_size, input_size].
@param R: The tensor with weights for matrix multiplication operation with hidden state.
Shape: [num_directions, 3*hidden_size, hidden_size].
@param B: The sum of biases (weight and recurrence).
For linear_before_reset set True the shape is [num_directions, 4*hidden_size].
Otherwise the shape is [num_directions, 3*hidden_size].
@param hidden_size: Specifies hidden state size.
@param direction: Specifies if the RNN is forward, reverse, or bidirectional.
@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 linear_before_reset: Flag denotes if the layer behaves according to the modification
of GRU described in the formula in the ONNX documentation.
@param name: An optional name of the output node.

@return: The new node represents GRUSequence. Node outputs count: 2.

◆ hsigmoid()

Node ngraph.opset5.ops.hsigmoid	(	NodeInput	data,
		Optional[str]	name = `None`
	)

Return a node which performs HSigmoid.

@param data: Tensor with input data floating point type.
@return: The new node which performs HSigmoid

◆ log_softmax()

Node ngraph.opset5.ops.log_softmax	(	NodeInput	data,
		int	axis,
		Optional[str]	name = `None`
	)

Apply LogSoftmax operation on each element of input tensor.

@param data: The tensor providing input data.
@param axis: An axis along which LogSoftmax should be calculated
@return: The new node with LogSoftmax operation applied on each element.

◆ loop()

Node ngraph.opset5.ops.loop	(	NodeInput	trip_count,
		NodeInput	execution_condition,
		Optional[str]	name = `None`
	)

Return a node which performs Loop.

@param trip_count: A scalar or 1D tensor with 1 element specifying
    maximum number of iterations.
@param execution_condition: A scalar or 1D tensor with 1 element
    specifying whether to execute the first iteration or not.
@return: The new node which performs Loop.

◆ lstm_sequence()

Node ngraph.opset5.ops.lstm_sequence	(	NodeInput	X,
		NodeInput	initial_hidden_state,
		NodeInput	initial_cell_state,
		NodeInput	sequence_lengths,
		NodeInput	W,
		NodeInput	R,
		NodeInput	B,
		int	hidden_size,
		str	direction,
		List[str]	activations = `None`,
		List[float]	activations_alpha = `None`,
		List[float]	activations_beta = `None`,
		float	clip = `0.0`,
		Optional[str]	name = `None`
	)

Return a node which performs LSTMSequence operation.

@param X: The input tensor. Shape: [batch_size, seq_length, input_size].
@param initial_hidden_state:    The hidden state tensor.
                                Shape: [batch_size, num_directions, hidden_size].
@param initial_cell_state:      The cell state tensor.
                                Shape: [batch_size, num_directions, hidden_size].
@param sequence_lengths:        Specifies real sequence lengths for each batch element.
                                Shape: [batch_size]. Integer type.
@param W: Tensor with weights for matrix multiplication operation with input portion of data.
          Expected format: fico
          Shape: [num_directions, 4*hidden_size, input_size].
@param R: The tensor with weights for matrix multiplication operation with hidden state.
          Expected format: fico
          Shape: [num_directions, 4*hidden_size, hidden_size].
@param B: The sum of biases (weight and recurrence). Expected format: fico
          Shape: [num_directions, 4*hidden_size].
@param hidden_size: Specifies hidden state size.
@param direction: Specifies if the RNN is forward, reverse, or bidirectional.
@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 LSTMSequence. Node outputs count: 3.

◆ non_max_suppression()

Node ngraph.opset5.ops.non_max_suppression	(	NodeInput	boxes,
		NodeInput	scores,
		Optional[NodeInput]	max_output_boxes_per_class = `None`,
		Optional[NodeInput]	iou_threshold = `None`,
		Optional[NodeInput]	score_threshold = `None`,
		Optional[NodeInput]	soft_nms_sigma = `None`,
		str	box_encoding = `"corner"`,
		bool	sort_result_descending = `True`,
		str	output_type = `"i64"`,
		Optional[str]	name = `None`
	)

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 soft_nms_sigma: Tensor specifying the sigma parameter for Soft-NMS.
@param box_encoding: Format of boxes data encoding.
@param sort_result_descending: Flag that specifies whenever it is necessary to sort selected
                               boxes across batches or not.
@param output_type: Output element type.
@return: The new node which performs NonMaxSuppression

◆ rnn_sequence()

Node ngraph.opset5.ops.rnn_sequence	(	NodeInput	X,
		NodeInput	initial_hidden_state,
		NodeInput	sequence_lengths,
		NodeInput	W,
		NodeInput	R,
		NodeInput	B,
		int	hidden_size,
		str	direction,
		List[str]	activations = `None`,
		List[float]	activations_alpha = `None`,
		List[float]	activations_beta = `None`,
		float	clip = `0.0`,
		Optional[str]	name = `None`
	)

Return a node which performs RNNSequence operation.

@param X: The input tensor. Shape: [batch_size, seq_length, input_size].
@param initial_hidden_state:    The hidden state tensor.
                                Shape: [batch_size, num_directions, hidden_size].
@param sequence_lengths:        Specifies real sequence lengths for each batch element.
                                Shape: [batch_size]. Integer type.
@param W: Tensor with weights for matrix multiplication operation with input portion of data.
          Shape: [num_directions, hidden_size, input_size].
@param R: The tensor with weights for matrix multiplication operation with hidden state.
          Shape: [num_directions, hidden_size, hidden_size].
@param B: The sum of biases (weight and recurrence).
          Shape: [num_directions, hidden_size].
@param hidden_size: Specifies hidden state size.
@param direction: Specifies if the RNN is forward, reverse, or bidirectional.
@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 RNNSequence. Node outputs count: 2.

◆ round()

Node ngraph.opset5.ops.round	(	NodeInput	data,
		str	mode = `"half_to_even"`,
		Optional[str]	name = `None`
	)

Apply Round operation on each element of input tensor.

@param data: The tensor providing input data.
@param mode: Rule to round halfway cases. If set to 'half_to_even' then halfs round to the nearest even
    integer or rounding in such a way that the result heads away from zero if `mode` attribute is
    'half_away_from_zero`.
@param name: An optional name of the output node.
@return: The new node with Round operation applied on each element.

Functions