openvino.runtime.opset2

Functions

abs(node[, name])	Return node which applies f(x) = abs(x) to the input node element-wise.
absolute(node[, name])	Return node which applies f(x) = abs(x) to the input node element-wise.
acos(node[, name])	Apply inverse cosine function on the input node element-wise.
add(left_node, right_node[, auto_broadcast, ...])	Return node which applies f(A,B) = A+B to the input nodes element-wise.
asin(node[, name])	Apply inverse sine function on the input node element-wise.
atan(node[, name])	Apply inverse tangent function on the input node element-wise.
avg_pool(data_batch, strides, pads_begin, ...)	Return average pooling node.
batch_norm_inference(data, gamma, beta, ...)	Perform layer normalizes a input tensor by mean and variance with appling scale and offset.
batch_to_space(data, block_shape, ...[, name])	Perform BatchToSpace operation on the input tensor.
binary_convolution(data, filters, strides, ...)	Create node performing convolution with binary weights, binary input and integer output.
broadcast(data, target_shape[, ...])	Create a node which broadcasts the input node's values along specified axes to a desired shape.
ceil(node[, name])	Return node which applies ceiling to the input node element-wise.
ceiling(node[, name])	Return node which applies ceiling to the input node element-wise.
clamp(data, min_value, max_value[, name])	Perform clamp element-wise on data from input node.
concat(nodes, axis[, name])	Concatenate input nodes into single new node along specified axis.
constant(value[, dtype, name])	Create a Constant node from provided value.
convert(data, destination_type[, name])	Return node which casts input node values to specified type.
convert_like(data, like[, name])	Return node which casts data node values to the type of another node.
convolution(data, filters, strides, ...[, ...])	Return node performing batched convolution operation.
convolution_backprop_data(data, filters, strides)	Create node performing a batched-convolution backprop data operation.
cos(node[, name])	Apply cosine function on the input node element-wise.
cosh(node[, name])	Apply hyperbolic cosine function on the input node element-wise.
ctc_greedy_decoder(data, sequence_mask[, ...])	Perform greedy decoding on the logits given in input (best path).
deformable_convolution(data, ...[, ...])	Create node performing deformable convolution.
deformable_psroi_pooling(feature_maps, ...)	Return node performing DeformablePSROIPooling operation.
depth_to_space(node, mode[, block_size, name])	Rearranges input tensor from depth into blocks of spatial data.
detection_output(box_logits, class_preds, ...)	Generate the detection output using information on location and confidence predictions.
divide(left_node, right_node[, ...])	Return node which applies f(x) = A/B to the input nodes element-wise.
elu(data, alpha[, name])	Perform Exponential Linear Unit operation element-wise on data from input node.
equal(left_node, right_node[, ...])	Return node which checks if input nodes are equal element-wise.
erf(node[, name])	Return node which calculates Gauss error function element-wise with given tensor.
exp(node[, name])	Return node which applies exponential function to the input node element-wise.
fake_quantize(data, input_low, input_high, ...)	Perform an element-wise linear quantization on input data.
floor(node[, name])	Return node which applies floor to the input node element-wise.
floor_mod(left_node, right_node[, ...])	Return node performing element-wise FloorMod (division reminder) with two given tensors.
gather(data, indices, axis[, name])	Return Gather node which takes slices from axis of data according to indices.
gather_tree(step_ids, parent_idx, ...[, name])	Perform GatherTree operation.
gelu(node[, name])	Perform Gaussian Error Linear Unit operation element-wise on data from input node.
greater(left_node, right_node[, ...])	Return node which checks if left input node is greater than the right node element-wise.
greater_equal(left_node, right_node[, ...])	Return node which checks if left node is greater or equal to the right node element-wise.
grn(data, bias[, name])	Perform Global Response Normalization with L2 norm (across channels only).
group_convolution(data, filters, strides, ...)	Perform Group Convolution operation on data from input node.
group_convolution_backprop_data(data, ...[, ...])	Perform Group Convolution operation on data from input node.
hard_sigmoid(data, alpha, beta[, name])	Perform Hard Sigmoid operation element-wise on data from input node.
interpolate(image, output_shape, attrs[, name])	Perform interpolation of independent slices in input tensor.
less(left_node, right_node[, ...])	Return node which checks if left input node is less than the right node element-wise.
less_equal(left_node, right_node[, ...])	Return node which checks if left input node is less or equal the right node element-wise.
log(node[, name])	Return node which applies natural logarithm to the input node element-wise.
logical_and(left_node, right_node[, ...])	Return node which perform logical and operation on input nodes element-wise.
logical_not(node[, name])	Return node which applies element-wise logical negation to the input node.
logical_or(left_node, right_node[, ...])	Return node which performs logical OR operation on input nodes element-wise.
logical_xor(left_node, right_node[, ...])	Return node which performs logical XOR operation on input nodes element-wise.
lrn(data, axes[, alpha, beta, bias, size, name])	Return a node which performs element-wise Local Response Normalization (LRN) operation.
lstm_cell(X, initial_hidden_state, ...[, ...])	Return a node which performs LSTMCell operation.
lstm_sequence(X, initial_hidden_state, ...)	Return a node which performs LSTMSequence operation.
matmul(data_a, data_b, transpose_a, transpose_b)	Return the Matrix Multiplication operation.
max_pool(data, strides, pads_begin, ...[, ...])	Perform max pooling operation with given parameters on provided data.
maximum(left_node, right_node[, ...])	Return node which applies the maximum operation to input nodes elementwise.
minimum(left_node, right_node[, ...])	Return node which applies the minimum operation to input nodes elementwise.
mod(left_node, right_node[, auto_broadcast, ...])	Return node performing element-wise division reminder with two given tensors.
multiply(left_node, right_node[, ...])	Return node which applies f(A,B) = A*B to the input nodes elementwise.
mvn(data[, across_channels, ...])	Perform Mean Variance Normalization operation on data from input node.
negative(node[, name])	Return node which applies f(x) = -x to the input node elementwise.
non_max_suppression(boxes, scores[, ...])	Return a node which performs NonMaxSuppression.
normalize_l2(data, axes, eps, eps_mode[, name])	Construct an NormalizeL2 operation.
not_equal(left_node, right_node[, ...])	Return node which checks if input nodes are unequal element-wise.
one_hot(indices, depth, on_value, off_value, ...)	Create node performing one-hot encoding on input data.
pad(arg, pads_begin, pads_end, pad_mode[, ...])	Return a generic padding operation.
parameter(shape[, dtype, name])	Return an openvino Parameter object.
power(left_node, right_node[, ...])	Return node which perform element-wise exponentiation operation.
prelu(data, slope[, name])	Perform Parametrized Relu operation element-wise on data from input node.
prior_box(layer_shape, image_shape, attrs[, ...])	Generate prior boxes of specified sizes and aspect ratios across all dimensions.
prior_box_clustered(output_size, image_size, ...)	Generate prior boxes of specified sizes normalized to the input image size.
proposal(class_probs, bbox_deltas, ...[, name])	Filter bounding boxes and outputs only those with the highest prediction confidence.
psroi_pooling(input, coords, output_dim, ...)	Return a node which produces a PSROIPooling operation.
range(start, stop, step[, name])	Return a node which produces the Range operation.
reduce_logical_and(node, reduction_axes[, ...])	Logical AND reduction operation on input tensor, eliminating the specified reduction axes.
reduce_logical_or(node, reduction_axes[, ...])	Logical OR reduction operation on input tensor, eliminating the specified reduction axes.
reduce_max(node, reduction_axes[, ...])	Max-reduction operation on input tensor, eliminating the specified reduction axes.
reduce_mean(node, reduction_axes[, ...])	Mean-reduction operation on input tensor, eliminating the specified reduction axes.
reduce_min(node, reduction_axes[, ...])	Min-reduction operation on input tensor, eliminating the specified reduction axes.
reduce_prod(node, reduction_axes[, ...])	Product-reduction operation on input tensor, eliminating the specified reduction axes.
reduce_sum(node, reduction_axes[, ...])	Perform element-wise sums of the input tensor, eliminating the specified reduction axes.
region_yolo(input, coords, classes, num, ...)	Return a node which produces the RegionYolo operation.
relu(node[, name])	Perform rectified linear unit operation on input node element-wise.
reorg_yolo(input, stride[, name])	Return a node which produces the ReorgYolo operation.
reshape(node, output_shape, special_zero[, name])	Return reshaped node according to provided parameters.
result(data[, name])	Return a node which represents an output of a graph (Model).
reverse_sequence(input, seq_lengths, ...[, name])	Return a node which produces a ReverseSequence operation.
roi_pooling(input, coords[, output_roi, ...])	Return a node which produces an ROIPooling operation.
select(cond, then_node, else_node[, ...])	Perform an element-wise selection operation on input tensors.
selu(data, alpha, lambda_value[, name])	Perform a Scaled Exponential Linear Unit (SELU) operation on input node element-wise.
shape_of(data[, name])	Return a node which produces a tensor containing the shape of its input data.
sigmoid(data[, name])	Return a node which applies the sigmoid function element-wise.
sign(node[, name])	Perform element-wise sign operation.
sin(node[, name])	Apply sine function on the input node element-wise.
sinh(node[, name])	Apply hyperbolic sine function on the input node element-wise.
softmax(data, axis[, name])	Apply softmax operation on each element of input tensor.
space_to_batch(data, block_shape, ...[, name])	Perform SpaceToBatch operation on the input tensor.
space_to_depth(data, mode[, block_size, name])	Perform SpaceToDepth operation on the input tensor.
split(data, axis, num_splits[, name])	Return a node which splits the input tensor into same-length slices.
sqrt(node[, name])	Return node which applies square root to the input node element-wise.
squared_difference(x1, x2[, auto_broadcast, ...])	Perform an element-wise squared difference between two tensors.
squeeze(data, axes[, name])	Perform squeeze operation on input tensor.
strided_slice(data, begin, end, strides, ...)	Return a node which dynamically repeats(replicates) the input data tensor.
subtract(left_node, right_node[, ...])	Return node which applies f(x) = A-B to the input nodes element-wise.
tan(node[, name])	Apply tangent function on the input node element-wise.
tanh(node[, name])	Return node which applies hyperbolic tangent to the input node element-wise.
tile(data, repeats[, name])	Return a node which dynamically repeats(replicates) the input data tensor.
topk(data, k, axis, mode, sort[, name])	Return a node which performs TopK.
transpose(data, input_order[, name])	Return a node which transposes the data in the input tensor.
unsqueeze(data, axes[, name])	Perform unsqueeze operation on input tensor.
variadic_split(data, axis, split_lengths[, name])	Return a node which splits the input tensor into variadic length slices.

Classes

tensor_iterator

openvino.impl.op.TensorIterator wraps ov::op::v0::TensorIterator