ngraph.opset7¶

Module Attributes

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. acosh(node[, name]) Apply hyperbolic inverse cosine function on the input node element-wise. add(left_node, right_node[, auto_broadcast, …]) Return node which applies f(x) = A+B to the input nodes element-wise. asin(node[, name]) Apply inverse sine function on the input node element-wise. asinh(node[, name]) Apply hyperbolic inverse sinus function on the input node element-wise. assign(new_value, variable_id[, name]) Return a node which produces the Assign operation. atan(node[, name]) Apply inverse tangent function on the input node element-wise. atanh(node[, name]) Apply hyperbolic 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. bucketize(data, buckets[, output_type, …]) Return a node which produces the Bucketize operation. 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). ctc_greedy_decoder_seq_len(data, sequence_length) Return a node which performs CTCGreedyDecoderSeqLen. ctc_loss(logits, logit_length, labels, …) Return a node which performs CTCLoss. cum_sum(arg, axis[, exclusive, reverse, name]) Construct a cumulative summation operation. cumsum(arg, axis[, exclusive, reverse, name]) Construct a cumulative summation operation. 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. dft(data, axes[, signal_size]) Return a node which performs DFT operation. divide(left_node, right_node[, …]) Return node which applies f(x) = A/B to the input nodes element-wise. einsum(inputs, equation) Return a node which performs Einsum operation. elu(data, alpha[, name]) Perform Exponential Linear Unit operation element-wise on data from input node. embedding_bag_offsets_sum(emb_table, …[, …]) Return a node which performs sums of bags of embeddings without the intermediate embeddings. embedding_bag_packed_sum(emb_table, indices) Return an EmbeddingBagPackedSum node. embedding_segments_sum(emb_table, indices, …) Return an EmbeddingSegmentsSum 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. extract_image_patches(image, sizes, strides, …) Return a node which produces the ExtractImagePatches operation. 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[, batch_dims]) Return a node which performs Gather. gather_elements(data, indices[, axis, name]) Return a node which performs GatherElements. gather_nd(data, indices[, batch_dims, name]) Return a node which performs GatherND. gather_tree(step_ids, parent_idx, …[, name]) Perform GatherTree operation. gelu(data, approximation_mode[, name]) Return a node which performs Gelu activation function. 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. gru_cell(X, initial_hidden_state, W, R, B, …) Perform GRUCell operation on the tensor from input node. gru_sequence(X, initial_hidden_state, …[, …]) Return a node which performs GRUSequence operation. hard_sigmoid(data, alpha, beta[, name]) Perform Hard Sigmoid operation element-wise on data from input node. hsigmoid(data[, name]) Return a node which performs HSigmoid. hswish(data[, name]) Return a node which performs HSwish (hard version of Swish). idft(data, axes[, signal_size]) Return a node which performs IDFT operation. 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. log_softmax(data, axis[, name]) Apply LogSoftmax operation on each element of input tensor. 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. loop(trip_count, execution_condition, …[, …]) Perform recurrent execution of the network described in the body, iterating through the data. 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. mish(data[, name]) Return a node which performs Mish. 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(x) = A*B to the input nodes elementwise. mvn(data, axes, normalize_variance, eps, …) Return a node which performs MeanVarianceNormalization (MVN). 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. non_zero(data[, output_type, name]) Return the indices of the elements that are non-zero. 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 ngraph 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. read_value(init_value, variable_id[, name]) Return a node which produces the Assign operation. reduce_l1(node, reduction_axes[, keep_dims, …]) L1-reduction operation on input tensor, eliminating the specified reduction axes. reduce_l2(node, reduction_axes[, keep_dims, …]) L2-reduction operation on input tensor, eliminating the specified reduction axes. 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 (Function). reverse_sequence(input, seq_lengths, …[, name]) Return a node which produces a ReverseSequence operation. rnn_cell(X, initial_hidden_state, W, R, B, …) Perform RNNCell operation on tensor from input node. rnn_sequence(X, initial_hidden_state, …[, …]) Return a node which performs RNNSequence operation. roi_align(data, rois, batch_indices, …[, name]) Return a node which performs ROIAlign. roi_pooling(input, coords, output_size, …) Return a node which produces an ROIPooling operation. roll(data, shift, axes) Return a node which performs Roll operation. round(data[, mode, name]) Apply Round operation on each element of input tensor. scatter_elements_update(data, indices, …) Return a node which produces a ScatterElementsUpdate operation. scatter_update(data, indices, updates, axis) Return a node which produces a ScatterUpdate 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[, output_type, name]) Return a node which produces a tensor containing the shape of its input data. shuffle_channels(data, axis, group[, name]) Perform permutation on data in the channel dimension of the input tensor. 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. softplus(data[, name]) Apply SoftPlus 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. swish(data[, beta, name]) Return a node which performing Swish activation function Swish(x, beta=1.0) = x * sigmoid(x * beta)). 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. tensor_iterator(inputs, graph_body, …[, name]) Perform recurrent execution of the network described in the body, iterating through the data. tile(data, repeats[, name]) Return a node which dynamically repeats(replicates) the input data tensor. topk(data, k, axis, mode, sort[, …]) 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

Exceptions

Modules

 ngraph.opset7.ops Factory functions for all ngraph ops.