def ctc_loss(
logits: NodeInput,
logit_length: NodeInput,
labels: NodeInput,
label_length: NodeInput,
blank_index: Optional[NodeInput] = None,
preprocess_collapse_repeated: bool = False,
ctc_merge_repeated: bool = True,
unique: bool = False,
name: Optional[str] = None,
) -> Node:
"""Return a node which performs CTCLoss.
@param logits: 3-D tensor of logits.
@param logit_length: 1-D tensor of lengths for each object from a batch.
@param labels: 2-D tensor of labels for which likelihood is estimated using logits.
@param label_length: 1-D tensor of length for each label sequence.
@param blank_index: Scalar used to mark a blank index.
@param preprocess_collapse_repeated: Flag for preprocessing labels before loss calculation.
@param ctc_merge_repeated: Flag for merging repeated characters in a potential alignment.
@param unique: Flag to find unique elements in a target.
@return The new node which performs CTCLoss
"""
if blank_index is not None:
inputs = as_nodes(logits, logit_length, labels, label_length, blank_index)
else:
inputs = as_nodes(logits, logit_length, labels, label_length)
attributes = {
"preprocess_collapse_repeated": preprocess_collapse_repeated,
"ctc_merge_repeated": ctc_merge_repeated,
"unique": unique,
}
return _get_node_factory_opset4().create("CTCLoss", inputs, attributes)
def ctc_greedy_decoder_seq_len(
data: NodeInput,
sequence_length: NodeInput,
blank_index: Optional[NodeInput] = None,
merge_repeated: bool = True,
classes_index_type: str = "i32",
sequence_length_type: str = "i32",
name: Optional[str] = None,
) -> Node:
"""Return a node which performs CTCGreedyDecoderSeqLen.
@param data: The input 3D tensor. Shape: [batch_size, seq_length, num_classes]
@param sequence_length: Input 1D tensor with sequence length. Shape: [batch_size]
@param blank_index: Scalar or 1D tensor with specifies the class index to use for the blank class.
Optional parameter. Default value is num_classes-1.
@return: The new node which performs CTCGreedyDecoderSeqLen.
"""
if blank_index is not None:
inputs = as_nodes(data, sequence_length, blank_index)
else:
inputs = as_nodes(data, sequence_length)
attributes = {
"merge_repeated": merge_repeated,
"classes_index_type": classes_index_type,
"sequence_length_type": sequence_length_type
}
return _get_node_factory_opset6().create("CTCGreedyDecoderSeqLen", inputs,
attributes)
def deformable_convolution(
data: NodeInput,
offsets: NodeInput,
filters: NodeInput,
strides: List[int],
pads_begin: List[int],
pads_end: List[int],
dilations: List[int],
mask: Optional[NodeInput] = None,
auto_pad: str = "EXPLICIT",
group: int = 1,
deformable_group: int = 1,
bilinear_interpolation_pad: bool = False,
name: Optional[str] = None,
) -> Node:
"""Return a node which performs deformable convolution operation.
@param data: The node providing data batch tensor.
@param offsets: The node providing offset tensor.
@param filters: The node providing filters tensor.
@param strides: The distance (in pixels) to slide the filter on the feature map over the axes.
@param pads_begin: The number of pixels to add to the beginning along each axis.
@param pads_end: The number of pixels to add to the end along each axis.
@param dilations: The distance in width and height between elements (weights) in the filter.
@param mask: The node providing modulation scalar (mask) tensor.
@param auto_pad: The type of padding. Range of values: explicit, same_upper, same_lower, valid.
@param group: The number of groups which both output and input should be split into.
@param deformable_group: The number of groups which deformable values and output should be split
into along the channel axis.
@param bilinear_interpolation_pad: The flag that determines the mode of bilinear interpolation
execution.
@param name: The optional new name for output node.
@return New node performing deformable convolution operation.
"""
if mask is None:
inputs = as_nodes(data, offsets, filters)
else:
inputs = as_nodes(data, offsets, filters, mask)
return _get_node_factory_opset8().create(
"DeformableConvolution",
inputs,
{
"strides": strides,
"pads_begin": pads_begin,
"pads_end": pads_end,
"dilations": dilations,
"auto_pad": auto_pad,
"group": group,
"deformable_group": deformable_group,
"bilinear_interpolation_pad": bilinear_interpolation_pad
},
)
def non_max_suppression(
boxes: NodeInput,
scores: NodeInput,
max_output_boxes_per_class: Optional[NodeInput] = None,
iou_threshold: Optional[NodeInput] = None,
score_threshold: Optional[NodeInput] = None,
soft_nms_sigma: Optional[NodeInput] = None,
box_encoding: str = "corner",
sort_result_descending: bool = True,
output_type: str = "i64",
name: Optional[str] = None,
) -> 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 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
"""
if max_output_boxes_per_class is None:
max_output_boxes_per_class = make_constant_node(0, np.int64)
if iou_threshold is None:
iou_threshold = make_constant_node(0, np.float32)
if score_threshold is None:
score_threshold = make_constant_node(0, np.float32)
if soft_nms_sigma is None:
inputs = as_nodes(
boxes, scores, max_output_boxes_per_class, iou_threshold, score_threshold
)
else:
inputs = as_nodes(
boxes, scores, max_output_boxes_per_class, iou_threshold, score_threshold, soft_nms_sigma
)
attributes = {
"box_encoding": box_encoding,
"sort_result_descending": sort_result_descending,
"output_type": output_type,
}
return _get_node_factory_opset5().create("NonMaxSuppression", inputs, attributes)
def roi_align(
data: NodeInput,
rois: NodeInput,
batch_indices: NodeInput,
pooled_h: int,
pooled_w: int,
sampling_ratio: int,
spatial_scale: float,
mode: str,
name: Optional[str] = None,
) -> Node:
"""Return a node which performs ROIAlign.
@param data: Input data.
@param rois: RoIs (Regions of Interest) to pool over.
@param batch_indices: Tensor with each element denoting the index of
the corresponding image in the batch.
@param pooled_h: Height of the ROI output feature map.
@param pooled_w: Width of the ROI output feature map.
@param sampling_ratio: Number of bins over height and width to use to calculate
each output feature map element.
@param spatial_scale: Multiplicative spatial scale factor to translate ROI coordinates.
@param mode: Method to perform pooling to produce output feature map elements.
@return The new node which performs ROIAlign
"""
inputs = as_nodes(data, rois, batch_indices)
attributes = {
"pooled_h": pooled_h,
"pooled_w": pooled_w,
"sampling_ratio": sampling_ratio,
"spatial_scale": spatial_scale,
"mode": mode,
}
return _get_node_factory_opset3().create("ROIAlign", inputs, attributes)
def scatter_elements_update(
data: NodeInput,
indices: NodeInput,
updates: NodeInput,
axis: NodeInput,
name: Optional[str] = None,
) -> Node:
"""Return a node which produces a ScatterElementsUpdate operation.
@param data: The input tensor to be updated.
@param indices: The tensor with indexes which will be updated.
@param updates: The tensor with update values.
@param axis: The axis for scatter.
@return ScatterElementsUpdate node
ScatterElementsUpdate creates a copy of the first input tensor with updated elements
specified with second and third input tensors.
For each entry in `updates`, the target index in `data` is obtained by combining
the corresponding entry in `indices` with the index of the entry itself: the
index-value for dimension equal to `axis` is obtained from the value of the
corresponding entry in `indices` and the index-value for dimension not equal
to `axis` is obtained from the index of the entry itself.
"""
return _get_node_factory_opset3().create(
"ScatterElementsUpdate", as_nodes(data, indices, updates, axis))
def mvn(
data: Node,
axes: Node,
normalize_variance: bool,
eps: float,
eps_mode: str,
name: Optional[str] = None,
) -> Node:
"""Return a node which performs MeanVarianceNormalization (MVN).
@param data: The node with data tensor.
@param axes: The node with axes to reduce on.
@param normalize_variance: Denotes whether to perform variance normalization.
@param eps: The number added to the variance to avoid division by zero
when normalizing the value. Scalar value.
@param eps_mode: how eps is applied (`inside_sqrt` or `outside_sqrt`)
@param name: Optional output node name.
@return The new node performing a MVN operation on input tensor.
"""
inputs = as_nodes(data, axes)
attributes = {
"normalize_variance": normalize_variance,
"eps": eps,
"eps_mode": eps_mode
}
return _get_node_factory_opset6().create("MVN", inputs, attributes)
def topk(
data: NodeInput,
k: NodeInput,
axis: int,
mode: str,
sort: str,
index_element_type: str = "i32",
name: Optional[str] = None,
) -> 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)
"""
return _get_node_factory_opset3().create(
"TopK",
as_nodes(data, k),
{
"axis": axis,
"mode": mode,
"sort": sort,
"index_element_type": index_element_type
},
)
def random_uniform(output_shape: NodeInput,
min_val: NodeInput,
max_val: NodeInput,
output_type: str,
global_seed: int = 0,
op_seed: int = 0) -> Node:
"""Return a node which generates sequence of random values from uniform distribution.
@param output_shape: Tensor with shape of the output tensor.
@param min_val: Tensor with the lower bound on the range of random values to generate.
@param max_val: Tensor with the upper bound on the range of random values to generate.
@param output_type: Specifies the output tensor type, possible values:
'i64', 'i32', 'f64', 'f32', 'f16', 'bf16'.
@param global_seed: Specifies global seed value. Required to be a positive integer or 0.
@param op_seed: Specifies operational seed value. Required to be a positive integer or 0.
@return The new node which performs generation of random values from uniform distribution.
"""
inputs = as_nodes(output_shape, min_val, max_val)
if global_seed < 0:
raise RuntimeError(
"global_seed should be positive or 0. Got: {}".format(global_seed))
if op_seed < 0:
raise RuntimeError(
"op_seed should be positive or 0. Got: {}".format(op_seed))
attributes = {
"output_type": output_type,
"global_seed": global_seed,
"op_seed": op_seed,
}
return _get_node_factory_opset8().create("RandomUniform", inputs,
attributes)
def hswish(data: NodeInput, name: Optional[str] = None,) -> Node:
"""Return a node which performs HSwish (hard version of Swish).
@param data: Tensor with input data floating point type.
@return The new node which performs HSwish
"""
return _get_node_factory_opset4().create("HSwish", as_nodes(data), {})
def softplus(data: NodeInput, name: Optional[str] = None) -> Node:
"""Apply SoftPlus operation on each element of input tensor.
@param data: The tensor providing input data.
@return The new node with SoftPlus operation applied on each element.
"""
return _get_node_factory_opset4().create("SoftPlus", as_nodes(data), {})
def hsigmoid(data: NodeInput, name: Optional[str] = None,) -> Node:
"""Return a node which performs HSigmoid.
@param data: Tensor with input data floating point type.
@return: The new node which performs HSigmoid
"""
return _get_node_factory_opset5().create("HSigmoid", as_nodes(data), {})
def matrix_nms(
boxes: NodeInput,
scores: NodeInput,
sort_result_type: str = "none",
sort_result_across_batch: bool = False,
output_type: str = "i64",
score_threshold: float = 0.0,
nms_top_k: int = -1,
keep_top_k: int = -1,
background_class: int = -1,
decay_function: str = "linear",
gaussian_sigma: float = 2.0,
post_threshold: float = 0.0,
normalized: bool = True
) -> Node:
"""Return a node which performs MatrixNms.
@param boxes: Tensor with box coordinates.
@param scores: Tensor with box scores.
@param sort_result_type: Specifies order of output elements, possible values:
'class': sort selected boxes by class id (ascending)
'score': sort selected boxes by score (descending)
'none': do not guarantee the order.
@param sort_result_across_batch: Specifies whenever it is necessary to sort selected boxes
across batches or not
@param output_type: Specifies the output tensor type, possible values:
'i64', 'i32'
@param score_threshold: Specifies minimum score to consider box for the processing
@param nms_top_k: Specifies maximum number of boxes to be selected per class, -1 meaning
to keep all boxes
@param keep_top_k: Specifies maximum number of boxes to be selected per batch element, -1
meaning to keep all boxes
@param background_class: Specifies the background class id, -1 meaning to keep all classes
@param decay_function: Specifies decay function used to decay scores, possible values:
'gaussian', 'linear'
@param gaussian_sigma: Specifies gaussian_sigma parameter for gaussian decay_function
@param post_threshold: Specifies threshold to filter out boxes with low confidence score
after decaying
@param normalized: Specifies whether boxes are normalized or not
@return: The new node which performs MatrixNms
"""
inputs = as_nodes(boxes, scores)
attributes = {
"sort_result_type": sort_result_type,
"sort_result_across_batch": sort_result_across_batch,
"output_type": output_type,
"score_threshold": score_threshold,
"nms_top_k": nms_top_k,
"keep_top_k": keep_top_k,
"background_class": background_class,
"decay_function": decay_function,
"gaussian_sigma": gaussian_sigma,
"post_threshold": post_threshold,
"normalized": normalized
}
return _get_node_factory_opset8().create("MatrixNms", inputs, attributes)
def idft(
data: NodeInput,
axes: NodeInput,
signal_size: Optional[NodeInput] = None,
) -> Node:
"""Return a node which performs IDFT operation.
@param data: Tensor with transformed data.
@param axes: Tensor with axes to transform.
@param signal_size: Tensor specifying signal size with respect to axes from the input 'axes'.
@return: The new node which performs IDFT operation on the input data tensor.
"""
if signal_size is None:
inputs = as_nodes(data, axes)
else:
inputs = as_nodes(data, axes, signal_size)
return _get_node_factory_opset7().create("IDFT", inputs)
def adaptive_avg_pool(data: NodeInput, output_shape: NodeInput) -> Node:
"""Return a node which performs AdaptiveAvgPool operation.
@param data: The list of input nodes
@param output_shape: the shape of spatial dimentions after operation
@return: The new node performing AdaptiveAvgPool operation on the data
"""
inputs = as_nodes(data, output_shape)
return _get_node_factory_opset8().create("AdaptiveAvgPool", inputs)
def rnn_sequence(
X: NodeInput,
initial_hidden_state: NodeInput,
sequence_lengths: NodeInput,
W: NodeInput,
R: NodeInput,
B: NodeInput,
hidden_size: int,
direction: str,
activations: List[str] = None,
activations_alpha: List[float] = None,
activations_beta: List[float] = None,
clip: float = 0.0,
name: Optional[str] = None,
) -> Node:
"""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.
"""
if activations is None:
activations = ["tanh"]
if activations_alpha is None:
activations_alpha = []
if activations_beta is None:
activations_beta = []
inputs = as_nodes(X, initial_hidden_state, sequence_lengths, W, R, B)
attributes = {
"hidden_size": hidden_size,
"direction": direction.lower(),
"activations": activations,
"activations_alpha": activations_alpha,
"activations_beta": activations_beta,
"clip": clip,
}
return _get_node_factory_opset5().create("RNNSequence", inputs, attributes)
def rnn_cell(
X: NodeInput,
initial_hidden_state: NodeInput,
W: NodeInput,
R: NodeInput,
B: NodeInput,
hidden_size: int,
activations: List[str],
activations_alpha: List[float],
activations_beta: List[float],
clip: float = 0.0,
name: Optional[str] = None,
) -> Node:
"""Perform RNNCell operation on tensor from input node.
It follows notation and equations defined as in ONNX standard:
https://github.com/onnx/onnx/blob/master/docs/Operators.md#RNN
Note this class represents only single *cell* and not whole RNN *layer*.
@param X: The input tensor with shape: [batch_size, input_size].
@param initial_hidden_state: The hidden state tensor at current time step with shape:
[batch_size, hidden_size].
@param W: The weight tensor with shape: [hidden_size, input_size].
@param R: The recurrence weight tensor with shape: [hidden_size,
hidden_size].
@param B: The sum of biases (weight and recurrence) with shape: [hidden_size].
@param hidden_size: The number of hidden units for recurrent cell.
Specifies hidden state size.
@param activations: The vector of activation functions used inside recurrent cell.
@param activation_alpha: The vector of alpha parameters for activation functions in
order respective to activation list.
@param activation_beta: The vector of beta parameters for activation functions in order
respective to activation list.
@param clip: The value defining clipping range [-clip, clip] on input of
activation functions.
@param name: Optional output node name.
@return The new node performing a RNNCell operation on tensor from input node.
"""
if activations is None:
activations = ["tanh"]
if activations_alpha is None:
activations_alpha = []
if activations_beta is None:
activations_beta = []
input_nodes = as_nodes(X, initial_hidden_state, W, R, B)
attributes = {
"hidden_size": hidden_size,
"activations": activations,
"activations_alpha": activations_alpha,
"activations_beta": activations_beta,
"clip": clip,
}
return _get_node_factory_opset3().create("RNNCell", input_nodes,
attributes)
def loop(
trip_count: NodeInput,
execution_condition: NodeInput,
inputs: List[Node],
graph_body: GraphBody,
slice_input_desc: List[TensorIteratorSliceInputDesc],
merged_input_desc: List[TensorIteratorMergedInputDesc],
invariant_input_desc: List[TensorIteratorInvariantInputDesc],
body_output_desc: List[TensorIteratorBodyOutputDesc],
concat_output_desc: List[TensorIteratorConcatOutputDesc],
body_condition_output_idx: int,
current_iteration_input_idx: int = -1,
name: Optional[str] = None,
) -> Node:
"""Perform recurrent execution of the network described in the body, iterating through the data.
@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.
@param inputs: The provided to TensorIterator operator.
@param graph_body: The graph representing the body we execute.
@param slice_input_desc: The descriptors describing sliced inputs, that is nodes
representing tensors we iterate through, processing single
data slice in one iteration.
@param merged_input_desc: The descriptors describing merged inputs, that is nodes
representing variables with initial value at first iteration,
which may be changing through iterations.
@param invariant_input_desc: The descriptors describing invariant inputs, that is nodes
representing variable with persistent value through all
iterations.
@param body_output_desc: The descriptors describing body outputs from specified
iteration.
@param concat_output_desc: The descriptors describing specified output values through
all the iterations concatenated into one node.
@param body_condition_output_idx: Determines the purpose of the corresponding result in
the graph_body. This result will determine the dynamic
exit condition. If the value of this result is False,
then iterations stop.
@param current_iteration_input_idx: Determines the purpose of the corresponding parameter
in the graph_body. This parameter will be used as
an iteration counter. Optional.
@return: The new node which performs Loop.
"""
attributes = {
"body": graph_body.serialize(),
"input_descriptions": {"slice_input_desc": [desc.serialize() for desc in slice_input_desc],
"merged_input_desc": [desc.serialize() for desc in merged_input_desc],
"invariant_input_desc": [desc.serialize() for desc in invariant_input_desc]},
"output_descriptions": {"body_output_desc": [desc.serialize() for desc in body_output_desc],
"concat_output_desc": [desc.serialize() for desc in concat_output_desc]},
"special_body_ports": {"body_condition_output_idx": body_condition_output_idx,
"current_iteration_input_idx": current_iteration_input_idx}
}
return _get_node_factory_opset5().create("Loop", as_nodes(trip_count, execution_condition, *inputs),
attributes)
def slice(data: NodeInput,
start: NodeInput,
stop: NodeInput,
step: NodeInput,
axes: NodeInput = None) -> Node:
"""Return a node which generates Slice operation.
@param data: The node providing input data.
@param start: The node providing start indices (inclusively).
@param stop: The node providing stop indices (exclusively).
@param step: The node providing step values.
@param axes: The optional node providing axes to slice, default [0, 1, ..., len(start)-1].
"""
if axes is None:
inputs = as_nodes(data, start, stop, step)
else:
inputs = as_nodes(data, start, stop, step, axes)
return _get_node_factory_opset8().create("Slice", inputs)
def einsum(inputs: List[Node], equation: str) -> Node:
"""Return a node which performs Einsum operation.
@param inputs: The list of input nodes
@param equation: Einsum equation
@return: The new node performing Einsum operation on the inputs
"""
attributes = {"equation": equation}
return _get_node_factory_opset7().create("Einsum", as_nodes(*inputs),
attributes)
def round(data: NodeInput, mode: str = "half_to_even", name: Optional[str] = None) -> Node:
"""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.
"""
return _get_node_factory_opset5().create("Round", as_nodes(data), {"mode": mode.upper()})
def multiclass_nms(
boxes: NodeInput,
scores: NodeInput,
sort_result_type: str = "none",
sort_result_across_batch: bool = False,
output_type: str = "i64",
iou_threshold: float = 0.0,
score_threshold: float = 0.0,
nms_top_k: int = -1,
keep_top_k: int = -1,
background_class: int = -1,
nms_eta: float = 1.0,
normalized: bool = True
) -> Node:
"""Return a node which performs MulticlassNms.
@param boxes: Tensor with box coordinates.
@param scores: Tensor with box scores.
@param sort_result_type: Specifies order of output elements, possible values:
'class': sort selected boxes by class id (ascending)
'score': sort selected boxes by score (descending)
'none': do not guarantee the order.
@param sort_result_across_batch: Specifies whenever it is necessary to sort selected boxes
across batches or not
@param output_type: Specifies the output tensor type, possible values:
'i64', 'i32'
@param iou_threshold: Specifies intersection over union threshold
@param score_threshold: Specifies minimum score to consider box for the processing
@param nms_top_k: Specifies maximum number of boxes to be selected per class, -1 meaning
to keep all boxes
@param keep_top_k: Specifies maximum number of boxes to be selected per batch element, -1
meaning to keep all boxes
@param background_class: Specifies the background class id, -1 meaning to keep all classes
@param nms_eta: Specifies eta parameter for adpative NMS, in close range [0, 1.0]
@param normalized: Specifies whether boxes are normalized or not
@return: The new node which performs MuticlassNms
"""
inputs = as_nodes(boxes, scores)
attributes = {
"sort_result_type": sort_result_type,
"sort_result_across_batch": sort_result_across_batch,
"output_type": output_type,
"iou_threshold": iou_threshold,
"score_threshold": score_threshold,
"nms_top_k": nms_top_k,
"keep_top_k": keep_top_k,
"background_class": background_class,
"nms_eta": nms_eta,
"normalized": normalized
}
return _get_node_factory_opset8().create("MulticlassNms", inputs, attributes)
def swish(
data: NodeInput,
beta: Optional[NodeInput] = None,
name: Optional[str] = None,
) -> Node:
"""Return a node which performing Swish activation function Swish(x, beta=1.0) = x * sigmoid(x * beta)).
@param data: Tensor with input data floating point type.
@return The new node which performs Swish
"""
if beta is None:
beta = make_constant_node(1.0, np.float32)
return _get_node_factory_opset4().create("Swish", as_nodes(data, beta), {})
def reduce_l2(
node: NodeInput, reduction_axes: NodeInput, keep_dims: bool = False, name: Optional[str] = None
) -> Node:
"""L2-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.
"""
return _get_node_factory_opset4().create(
"ReduceL2", as_nodes(node, reduction_axes), {"keep_dims": keep_dims}
)
def roll(
data: NodeInput,
shift: NodeInput,
axes: NodeInput,
) -> Node:
"""Return a node which performs Roll operation.
@param data: The node with data tensor.
@param shift: The node with the tensor with numbers of places by which elements are shifted.
@param axes: The node with the tensor with axes along which elements are shifted.
@return The new node performing a Roll operation on the input tensor.
"""
inputs = as_nodes(data, shift, axes)
return _get_node_factory_opset7().create("Roll", inputs)
def lstm_cell(
X: NodeInput,
initial_hidden_state: NodeInput,
initial_cell_state: NodeInput,
W: NodeInput,
R: NodeInput,
B: NodeInput,
hidden_size: int,
activations: List[str] = None,
activations_alpha: List[float] = None,
activations_beta: List[float] = None,
clip: float = 0.0,
name: Optional[str] = None,
) -> 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.
"""
if activations is None:
activations = ["sigmoid", "tanh", "tanh"]
if activations_alpha is None:
activations_alpha = []
if activations_beta is None:
activations_beta = []
node_inputs = as_nodes(X, initial_hidden_state, initial_cell_state, W, R, B)
attributes = {
"hidden_size": hidden_size,
"activations": activations,
"activations_alpha": activations_alpha,
"activations_beta": activations_beta,
"clip": clip,
}
return _get_node_factory_opset4().create("LSTMCell", node_inputs, attributes)
def scatter_update(data: Node,
indices: NodeInput,
updates: NodeInput,
axis: NodeInput,
name: Optional[str] = None) -> Node:
"""Return a node which produces a ScatterUpdate operation.
ScatterUpdate sets new values to slices from data addressed by indices.
@param data: The input tensor to be updated.
@param indices: The tensor with indexes which will be updated.
@param updates: The tensor with update values.
@param axis: The axis at which elements will be updated.
@return ScatterUpdate node
"""
return _get_node_factory_opset3().create(
"ScatterUpdate", as_nodes(data, indices, updates, axis))
def gelu(
data: Node,
approximation_mode: str,
name: Optional[str] = None,
) -> Node:
"""Return a node which performs Gelu activation function.
@param data: The node with data tensor.
@param approximation_mode: defines which approximation to use ('tanh' or 'erf')
@param name: Optional output node name.
@return The new node performing a Gelu activation with the input tensor.
"""
inputs = as_nodes(data)
attributes = {"approximation_mode": approximation_mode}
return _get_node_factory_opset7().create("Gelu", inputs, attributes)
def gather(
data: NodeInput,
indices: NodeInput,
axis: NodeInput,
batch_dims: Optional[int] = 0,
) -> Node:
"""Return a node which performs Gather.
@param data: N-D tensor with data for gathering
@param indices: N-D tensor with indices by which data is gathered
@param axis: axis along which elements are gathered
@param batch_dims: number of batch dimensions
@return: The new node which performs Gather
"""
inputs = as_nodes(data, indices, axis)
attributes = {"batch_dims": batch_dims}
return _get_node_factory_opset7().create("Gather", inputs, attributes)
def gather_nd(
data: NodeInput,
indices: NodeInput,
batch_dims: Optional[int] = 0,
name: Optional[str] = None,
) -> Node:
"""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
"""
inputs = as_nodes(data, indices)
attributes = {"batch_dims": batch_dims}
return _get_node_factory_opset8().create("GatherND", inputs, attributes)