Primitives

Collaboration diagram for Primitives:

Classes
struct	cldnn_activation_desc
	Activation using rectified linear unit or parameterized rectified linear unit. More...
struct	cldnn_batch_norm_desc
	Batch normalization primitive. More...
struct	cldnn_concatenation_desc
struct	cldnn_convolution_desc
	Performs forward spatial convolution with weight sharing. Also supports built-in Relu cldnn_activation_desc available by setting it in arguments. More...
struct	cldnn_crop_desc
	Performs crop operation on input. More...
struct	cldnn_custom_gpu_primitive_desc
	This primitive executes a custom kernel provided by the application. More...
struct	cldnn_data_desc
	Provides input data to topology. More...
struct	cldnn_deconvolution_desc
	Performs transposed convolution. Also supports built-in Relu cldnn_activation_desc available by setting it in arguments. More...
struct	cldnn_detection_output_desc
	Generates a list of detections based on location and confidence predictions by doing non maximum suppression. More...
struct	cldnn_eltwise_desc
	Performs elementwise operations (sum, subtract, max or product) on two input primitives Also supports built-in Relu cldnn_activation_desc available by setting it in arguments. . More...
struct	cldnn_fully_connected_desc
	Performs forward fully connected layer (inner product). Also supports built-in Relu cldnn_activation_desc available by setting it in arguments. More...
struct	cldnn_input_layout_desc
	Provides input layout for a data to be passed later to network. More...
struct	cldnn_lrn_desc
	Local response normalization. More...
struct	cldnn_normalize_desc
	Normalizes the input using an L2 norm and multiplies the output with scale value. The scale can be equal for all channels or one scale per channel. More...
struct	cldnn_permute_desc
	Permutes data in the memory, with respect to provided order. More...
struct	cldnn_pooling_desc
	Performs "pooling" operation which is a form of non-linear down-sampling. More...
struct	cldnn_prior_box_desc
	Generates a set of default bounding boxes with different sizes and aspect ratios. More...
struct	cldnn_proposal_desc
struct	cldnn_region_yolo_desc
	region softmax specific for yolo2 topology More...
struct	cldnn_reorder_desc
	Changes how data is ordered in memory. Value type is not changed & all information is preserved. More...
struct	cldnn_reorg_yolo_desc
	yolo2 topology specific data reorganization primitive More...
struct	cldnn_reshape_desc
	Changes information about inputs's layout effectively creating new memory which share underlaying buffer but is interpreted in a different way (different shape). More...
struct	cldnn_roi_pooling_desc
struct	cldnn_scale_desc
	Performs elementwise product of input and scale_input. More...
struct	cldnn_softmax_desc
	Normalizes results so they sum to 1. The scope of normalization is defined by a member dimension. More...
struct	cldnn_split_desc
	Performs split operation on input. More...
struct	cldnn_upsampling_desc
	Performs nearest neighbor/bilinear upsampling Also supports built-in Relu activation available by setting it in arguments. More...

Macros
#define	CLDNN_ROI_VECTOR_SIZE   5

Enumerations
enum	cldnn_concatenation_axis { cldnn_concatenation_along_b = 0, cldnn_concatenation_along_f = CLDNN_TENSOR_BATCH_DIM_MAX, cldnn_concatenation_along_x = CLDNN_TENSOR_BATCH_DIM_MAX + CLDNN_TENSOR_FEATURE_DIM_MAX, cldnn_concatenation_along_y = cldnn_concatenation_along_x + 1 }
enum	cldnn_prior_box_code_type { cldnn_code_type_corner, cldnn_code_type_center_size, cldnn_code_type_corner_size }
	Select method for coding the prior-boxes in Detection Output layer ( cldnn_detection_output_desc ).
enum	cldnn_eltwise_mode { cldnn_eltwise_sum, cldnn_eltwise_sub, cldnn_eltwise_max, cldnn_eltwise_prod }
	Select mode for eltwise layer ( cldnn_eltwise_desc ​). More...
enum	cldnn_lrn_norm_region { cldnn_lrn_norm_region_across_channel, cldnn_lrn_norm_region_within_channel }
enum	cldnn_pooling_mode { cldnn_pooling_max, cldnn_pooling_average, cldnn_pooling_average_no_padding }
	Select method for Pooling layer ( cldnn_pooling_desc ). More...
enum	cldnn_softmax_dimension { cldnn_softmax_normalize_f, cldnn_softmax_normalize_x, cldnn_softmax_normalize_y, cldnn_softmax_normalize_fyx }
	Enum type to specify softmax's normalization scope (see cldnn_softmax_desc::dimension).
enum	cldnn_upsampling_sample_type { cldnn_upsampling_nearest, cldnn_upsampling_bilinear }
	Sample mode for upsampling layer ( cldnn_upsampling_desc ​). More...

Functions
cldnn_primitive_type_id	cldnn_activation_type_id (cldnn_status *status)
cldnn_primitive_type_id	cldnn_batch_norm_type_id (cldnn_status *status)
cldnn_primitive_type_id	cldnn_concatenation_type_id (cldnn_status *status)
cldnn_primitive_type_id	cldnn_convolution_type_id (cldnn_status *status)
cldnn_primitive_type_id	cldnn_crop_type_id (cldnn_status *status)
cldnn_primitive_type_id	cldnn_custom_gpu_primitive_type_id (cldnn_status *status)
cldnn_primitive_type_id	cldnn_data_type_id (cldnn_status *status)
cldnn_primitive_type_id	cldnn_deconvolution_type_id (cldnn_status *status)
cldnn_primitive_type_id	cldnn_detection_output_type_id (cldnn_status *status)
cldnn_primitive_type_id	cldnn_eltwise_type_id (cldnn_status *status)
cldnn_primitive_type_id	cldnn_fully_connected_type_id (cldnn_status *status)
cldnn_primitive_type_id	cldnn_input_layout_type_id (cldnn_status *status)
cldnn_primitive_type_id	cldnn_lrn_type_id (cldnn_status *status)
cldnn_primitive_type_id	cldnn_normalize_type_id (cldnn_status *status)
cldnn_primitive_type_id	cldnn_permute_type_id (cldnn_status *status)
cldnn_primitive_type_id	cldnn_pooling_type_id (cldnn_status *status)
cldnn_primitive_type_id	cldnn_prior_box_type_id (cldnn_status *status)
cldnn_primitive_type_id	cldnn_proposal_type_id (cldnn_status *status)
cldnn_primitive_type_id	cldnn_region_yolo_type_id (cldnn_status *status)
cldnn_primitive_type_id	cldnn_reorder_type_id (cldnn_status *status)
cldnn_primitive_type_id	cldnn_reorg_yolo_type_id (cldnn_status *status)
cldnn_primitive_type_id	cldnn_reshape_type_id (cldnn_status *status)
cldnn_primitive_type_id	cldnn_roi_pooling_type_id (cldnn_status *status)
cldnn_primitive_type_id	cldnn_scale_type_id (cldnn_status *status)
cldnn_primitive_type_id	cldnn_softmax_type_id (cldnn_status *status)
cldnn_primitive_type_id	cldnn_split_type_id (cldnn_status *status)
cldnn_primitive_type_id	cldnn_upsampling_type_id (cldnn_status *status)

Detailed Description

---

Class Documentation

cldnn_activation_desc

struct cldnn_activation_desc

Activation using rectified linear unit or parameterized rectified linear unit.

Can get one negative slope or negative slope per channel.

Algorithm:

out(i,x,y) = max(0, in(i,x,y)) + slope(i) * min(0, in(i,x,y))

Where:

• out(i,x,y) : value at x, y from i-th feature map after activation.
• in(i,x,y) : value at x, y from i-th feature map before activation.
• slope(i) : the slope value of the i-th feature map (can be shared across channels or one slope per channel).

Definition at line 41 of file activation.h.

Collaboration diagram for cldnn_activation_desc:

Class Members
cldnn_activation_func	activation_func	activation function.
cldnn_activation_additional_params	additional_params	Activation additional params. activation_relu_negative_slope - additional_params.a is a negative slope activation_brelu - additional_params.a is a upper bound activation_linear - additional_params.a/b uses as a*val + b.
cldnn_primitive_id	additional_params_input	Activation additional params stored on a memory object activation_relu_negative_slope - negative slope per feature map activation_brelu - upper bound per feature map activation_linear - a,b per feature map.
cldnn_primitive_id	id	Primitive id unique within a topology.
cldnn_primitive_id_arr	input	Input primitives ids.
cldnn_padding	output_padding	Output padding information.
cldnn_primitive_type_id	type	Primitive type identificator.

cldnn_batch_norm_desc

struct cldnn_batch_norm_desc

Batch normalization primitive.

Performs batch normalization as described in "Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift" by Ioffe, Szegedy
See: http://arxiv.org/abs/1502.03167

Algorithm:
global stats can be computed as:
out[i] = in[i] - mean[b] / sqrt(variance[b] + epsilon)

Definition at line 42 of file batch_norm.h.

Collaboration diagram for cldnn_batch_norm_desc:

Class Members
float	epsilon	Epsilon.
cldnn_primitive_id	id	Primitive id unique within a topology.
cldnn_primitive_id_arr	input	Input primitives ids.
cldnn_primitive_id	mean	Primitive id containing mean data.
cldnn_padding	output_padding	Output padding information.
cldnn_primitive_type_id	type	Primitive type identificator.
cldnn_primitive_id	variance	Primitive id containing variance.

cldnn_concatenation_desc

struct cldnn_concatenation_desc

Concatenation is used to concatenate multiple sources into one destination along specified dimension. Note that all other dimensions (except the one along which concatenation take place) must have the same value in each source and each source should have the same format.

Alogrithm:

int outputIdx = 0
for(i : input)
{
    for(f : i.features)
    {
        output[outputIdx] = f
        outputIdx += 1
    }
}

Where:

• input : data structure holding all source inputs for this primitive
• output : data structure holding output data for this primitive
• i.features : number of features in currently processed input
• outputIdx : index of destination feature

Definition at line 61 of file concatenation.h.

Collaboration diagram for cldnn_concatenation_desc:

Class Members
cldnn_concatenation_axis	axis	Dimension along which concatenation should take place.
cldnn_primitive_id	id	Primitive id unique within a topology.
cldnn_primitive_id_arr	input	Input primitives ids.
cldnn_padding	output_padding	Output padding information.
cldnn_primitive_type_id	type	Primitive type identificator.

cldnn_convolution_desc

struct cldnn_convolution_desc

Performs forward spatial convolution with weight sharing. Also supports built-in Relu cldnn_activation_desc available by setting it in arguments.

Parameters are defined in context of "direct" convolution, but actual algorithm is not implied.

Definition at line 36 of file convolution.h.

Collaboration diagram for cldnn_convolution_desc:

Class Members
float	activation_negative_slope	Relu activation slope.
cldnn_primitive_id_arr	bias	Array of primitive ids containing bias data. Size of array should be equivalent to split.
cldnn_tensor	dilation	Defines gaps in the input - dilation rate k=1 is normal convolution, k=2 means skipping one pixel per input, k=4 means skipping 3 pixels. As an example in one dimension, a filter w of size 3 would compute over input x the following: w[0]*x[0] + w[1]*x[1] + w[2]*x[2] for dilation of 1. For dilation 2 the filter would instead compute w[0]*x[0] + w[1]*x[2] + w[2]*x[4].
cldnn_primitive_id	id	Primitive id unique within a topology.
cldnn_primitive_id_arr	input	Input primitives ids.
cldnn_tensor	input_offset	Defines a shift, relative to (0,0) position of the input buffer, where (0,0) point of the convolution window should start calculations.
cldnn_padding	output_padding	Output padding information.
cldnn_tensor	output_size	User-defined output data size of the primitive (w/o padding).
uint32_t	split	On how many cards split the computation to.
cldnn_tensor	stride	Defines shift in input buffer between adjacent calculations of output values.
cldnn_primitive_type_id	type	Primitive type identificator.
cldnn_primitive_id_arr	weights	Array of primitive ids containing weights data. Size of array should be equivalent to split.
uint32_t	with_activation	Enable Relu activation.
uint32_t	with_output_size	Indicates that the primitive has user-defined output size (non-zero value).

cldnn_crop_desc

struct cldnn_crop_desc

Performs crop operation on input.

Crops the input to the shape of reference_input accross all dimensions taking into account specified input offsets.

Examples
Crop without offset example:

Crop with offset example:

Requirements
- Input, reference and offset layout (order) has to be the same
- Input size cannot be greater than reference size in any dimension
- All sizes have to have positive numbers
- Reference size plus offset cannot exceed input size
Breaking any of this conditions will cause exeption throw.

Definition at line 49 of file crop.h.

Collaboration diagram for cldnn_crop_desc:

Class Members
cldnn_primitive_id	id	Primitive id unique within a topology.
cldnn_primitive_id_arr	input	Input primitives ids.
cldnn_tensor	offsets	Input offsets.
cldnn_padding	output_padding	Output padding information.
cldnn_tensor	reference_input	Reference input tensor with the required dimensions.
cldnn_primitive_type_id	type	Primitive type identificator.

cldnn_custom_gpu_primitive_desc

struct cldnn_custom_gpu_primitive_desc

This primitive executes a custom kernel provided by the application.

The application is required to provide all relevant details for executing the custom kernel such as: sources, entry point, work sizes and parameter bindings.

Definition at line 36 of file custom_gpu_primitive.h.

Collaboration diagram for cldnn_custom_gpu_primitive_desc:

Class Members
cldnn_kernel_build_options	build_options	The kernel's build options.
cldnn_work_group_sizes	gws	The global working sizes.
int	gws_num	The number of global work sizes.
cldnn_primitive_id	id	Primitive id unique within a topology.
cldnn_primitive_id_arr	input	Input primitives ids.
cldnn_kernel_arguments	kernel_arguments	Argument bindings for the entry point function.
int	kernel_arguments_num	The number of arguments used by the kernel.
cldnn_kernel_entry_point	kernel_entry_point	The name of the entry point function in the kernel.
cldnn_primitive_id_arr	kernels_code	Source code for the kernel.
cldnn_work_group_sizes	lws	The local working sizes.
int	lws_num	The number of local work sizes.
cldnn_layout	output_layout	The output layout declared by the primitive.
cldnn_padding	output_padding	Output padding information.
cldnn_primitive_type_id	type	Primitive type identificator.

cldnn_data_desc

struct cldnn_data_desc

Provides input data to topology.

This primitive allows to pass data which is known at topology creation (constants). For example, weights and biases for scoring networks.

Note

Passing data at topology may improve network performance if data optimization is enabled.

Definition at line 37 of file data.h.

Collaboration diagram for cldnn_data_desc:

Class Members
cldnn_primitive_id	id	Primitive id unique within a topology.
cldnn_primitive_id_arr	input	Input primitives ids.
cldnn_memory	mem	Memory object which contains data. Note If memory is attached by cldnn_attach_memory(), attached buffer should be valid on cldnn_build_network() call.
cldnn_padding	output_padding	Output padding information.
cldnn_primitive_type_id	type	Primitive type identificator.

cldnn_deconvolution_desc

struct cldnn_deconvolution_desc

Performs transposed convolution. Also supports built-in Relu cldnn_activation_desc available by setting it in arguments.

Deconvolution is similar to convolution layer with the weights flipped on the axis and stride and input padding parameters used in opposite sense as in convolution.

Definition at line 36 of file deconvolution.h.

Collaboration diagram for cldnn_deconvolution_desc:

Class Members
float	activation_negative_slope	Relu activation slope.
cldnn_primitive_id_arr	bias	Array of primitive ids containing bias data. Size of array should be equivalent to split or should be empty (if not using bias).
cldnn_primitive_id	id	Primitive id unique within a topology.
cldnn_primitive_id_arr	input	Input primitives ids.
cldnn_tensor	input_offset	Defines a shift, relative to (0,0) position of the input buffer, where (0,0) point of the deconvolution window should start calculations.
cldnn_padding	output_padding	Output padding information.
cldnn_tensor	output_size	User-defined output data size of the primitive (w/o padding).
uint32_t	split	On how many cards split the computation to.
cldnn_tensor	stride	Defines the spatial dimensions of stride of adjacent elements in input buffer.
cldnn_primitive_type_id	type	Primitive type identificator.
cldnn_primitive_id_arr	weights	Array of primitive ids containing weights data. Size of array should be equivalent to split.
uint32_t	with_activation	Enables Relu activation.
uint32_t	with_output_size	Indicates that the primitive has user-defined output size (non-zero value).

cldnn_detection_output_desc

struct cldnn_detection_output_desc

Generates a list of detections based on location and confidence predictions by doing non maximum suppression.

Each row is a 7 dimension vector, which stores: [image_id, label, confidence, xmin, ymin, xmax, ymax]. If number of detections per image is lower than keep_top_k, will write dummy results at the end with image_id=-1.

Definition at line 44 of file detection_output.h.

Collaboration diagram for cldnn_detection_output_desc:

Class Members
int	background_label_id	Background label id (-1 if there is no background class).
int32_t	code_type	Type of coding method for bounding box. See cldnn_prior_box_code_type.
float	confidence_threshold	Only keep detections with confidences larger than this threshold.
float	eta	Used for adaptive NMS.
cldnn_primitive_id	id	Primitive id unique within a topology.
cldnn_primitive_id_arr	input	Input primitives ids.
uint32_t	keep_top_k	Number of total bounding boxes to be kept per image after NMS step.
float	nms_threshold	Threshold for NMS step.
uint32_t	num_classes	Number of classes to be predicted.
cldnn_padding	output_padding	Output padding information.
uint32_t	share_location	If not 0, bounding box are shared among different classes.
int	top_k	Maximum number of results to be kept in NMS.
cldnn_primitive_type_id	type	Primitive type identificator.
uint32_t	variance_encoded_in_target	If not 0, variance is encoded in target; otherwise we need to adjust the predicted offset accordingly.

cldnn_eltwise_desc

struct cldnn_eltwise_desc

Performs elementwise operations (sum, subtract, max or product) on two input primitives Also supports built-in Relu cldnn_activation_desc available by setting it in arguments. .

• both inputs have to have equal sizes in all dimensions
• format of both inputs has to be the same

Definition at line 51 of file eltwise.h.

Collaboration diagram for cldnn_eltwise_desc:

Class Members
float	activation_negative_slope	Relu activation slope.
cldnn_primitive_id	id	Primitive id unique within a topology.
cldnn_primitive_id_arr	input	Input primitives ids.
int32_t	mode	Eltwise mode. See cldnn_eltwise_mode.
cldnn_padding	output_padding	Output padding information.
cldnn_primitive_type_id	type	Primitive type identificator.
uint32_t	with_activation	Enables Relu activation.

cldnn_fully_connected_desc

struct cldnn_fully_connected_desc

Performs forward fully connected layer (inner product). Also supports built-in Relu cldnn_activation_desc available by setting it in arguments.

Definition at line 35 of file fully_connected.h.

Collaboration diagram for cldnn_fully_connected_desc:

Class Members
float	activation_negative_slope	Relu activation slope.
cldnn_primitive_id	bias	Primitive id containing bias data.
cldnn_primitive_id	id	Primitive id unique within a topology.
cldnn_primitive_id_arr	input	Input primitives ids.
cldnn_padding	output_padding	Output padding information.
cldnn_primitive_type_id	type	Primitive type identificator.
cldnn_primitive_id	weights	Primitive id containing weights data.
uint32_t	with_activation	Enable Relu activation.

cldnn_input_layout_desc

struct cldnn_input_layout_desc

Provides input layout for a data to be passed later to network.

This primitive allows to define the layout for input data which will be passed to network before execution. For example, network input images.

Note

User should call network::set_input_data() for every input_layout primitive before network execution.

See also

network::set_input_data(), cldnn::data

Definition at line 39 of file input_layout.h.

Collaboration diagram for cldnn_input_layout_desc:

Class Members
cldnn_primitive_id	id	Primitive id unique within a topology.
cldnn_primitive_id_arr	input	Input primitives ids.
cldnn_layout	layout	Defines layout for the data will be passed to network.
cldnn_padding	output_padding	Output padding information.
cldnn_primitive_type_id	type	Primitive type identificator.

cldnn_lrn_desc

struct cldnn_lrn_desc

Local response normalization.

LRN layer as described in chapter 3.3 of "ImageNet Classification with Deep Convolutional Neural Networks" by Khrizevsky, Sutskever, Hinton.
See: http://www.cs.toronto.edu/~fritz/absps/imagenet.pdf

Alogrithm:

b(i,x,y) = a(i,x,y) / (k+alpha*sum(min(N-1, i+n/2); j=max(0,i-n/2); a(j,x,y)^2))

Where:

• b(i,x,y) : value at x, y from i-th feature map after normalization
• b(i,x,y) : value at x, y from i-th feature map before normalization
• N : number of feature maps
• n : size of normalization
• k, alpha, beta : hyper parameters (equal to 2, 10e-4, 0.75 in paper).

Definition at line 50 of file lrn.h.

Collaboration diagram for cldnn_lrn_desc:

Class Members
float	alpha	Hyper parameter "alpha".
float	beta	Hyper parameter "beta".
cldnn_primitive_id	id	Primitive id unique within a topology.
cldnn_primitive_id_arr	input	Input primitives ids.
float	k	Hyper parameter "k".
cldnn_lrn_norm_region	norm_region	Normalize across or within channel.
cldnn_padding	output_padding	Output padding information.
uint32_t	size	Size of normalization.
cldnn_primitive_type_id	type	Primitive type identificator.

cldnn_normalize_desc

struct cldnn_normalize_desc

Normalizes the input using an L2 norm and multiplies the output with scale value. The scale can be equal for all channels or one scale per channel.

The L2 norm is computed as:
Across spatial mode (across_spatial=true)-
norm(i,x,y) = sqrt( Σ( in(f,w,h)^2 ) + epsilon ) where f in range (0,num_of_features), w in range (0,input_width), h in range (0,input_height).
The summation is performed over all the pixels in the batch.
Within spatial mode (across_spatial=false)-
norm(i,x,y) = sqrt( Σ( in(f,x,y)^2 ) + epsilon ) where f in range (0,num_of_features).
The summation is performed over this (x,y) position on all the features.

Algorithm:

out(i,x,y) = ( in(i,x,y) / norm(i,x,y) ) * scale(i)

Where:

• out(i,x,y) : value at x, y from i-th feature map after normalization.
• in(i,x,y) : value at x, y from i-th feature map before normalization.
• norm(i,x,y) : L2 norm as described above.
• scale(i) : the scale value of the i-th feature map.

Definition at line 49 of file normalize.h.

Collaboration diagram for cldnn_normalize_desc:

Class Members
uint32_t	across_spatial	Determines if the normalization is done across or within spatial (see documentation above).
float	epsilon	Epsilon for not dividing by zero while normalizing.
cldnn_primitive_id	id	Primitive id unique within a topology.
cldnn_primitive_id_arr	input	Input primitives ids.
cldnn_padding	output_padding	Output padding information.
cldnn_primitive_id	scale_input	Scale input primitive id with values needed for scaling after the normalization. Scale x dimension should be 1 (if all channels have the same scale) or equal to input feature size (one scale per channel). All other dimensions should be 1.
cldnn_primitive_type_id	type	Primitive type identificator.

cldnn_permute_desc

struct cldnn_permute_desc

Permutes data in the memory, with respect to provided order.

Permute order is set as vector with positions meaning corresponding to tensor. Vector values represent dimensions to be permuted in bfyx format. For example:
input_dimensions = tensor{ 5, 3, 6, 3 }
permute_order = { 2, 3, 1, 0 }
output_dimensions = { 6, 3, 3, 5 }

When permute_order is { 0, 1, 2, 3 } then input_dimensions = output_dimensions

Definition at line 41 of file permute.h.

Collaboration diagram for cldnn_permute_desc:

Class Members
cldnn_primitive_id	id	Primitive id unique within a topology.
cldnn_primitive_id_arr	input	Input primitives ids.
cldnn_padding	output_padding	Output padding information.
cldnn_uint16_t_arr	permute_order	Array of permuted output order in bfyx format.
cldnn_primitive_type_id	type	Primitive type identificator.

cldnn_pooling_desc

struct cldnn_pooling_desc

Performs "pooling" operation which is a form of non-linear down-sampling.

Pools the input image by taking the max, average, etc. within regions.

Definition at line 46 of file pooling.h.

Collaboration diagram for cldnn_pooling_desc:

Class Members
cldnn_primitive_id	id	Primitive id unique within a topology.
cldnn_primitive_id_arr	input	Input primitives ids.
cldnn_tensor	input_offset	Defines a shift, relative to (0,0) position of the input buffer, where (0,0) point of the pooling window should start calculations.
int32_t	mode	Pooling method. See cldnn_pooling_mode.
cldnn_padding	output_padding	Output padding information.
cldnn_tensor	output_size	User-defined output data size of the primitive (w/o padding).
cldnn_tensor	size	Pooling kernel size.
cldnn_tensor	stride	Defines shift in input buffer between adjacent calculations of output values.
cldnn_primitive_type_id	type	Primitive type identificator.
uint32_t	with_output_size	Indicates that the primitive has user-defined output size (non-zero value).

cldnn_prior_box_desc

struct cldnn_prior_box_desc

Generates a set of default bounding boxes with different sizes and aspect ratios.

The prior-boxes are shared across all the images in a batch (since they have the same width and height). First feature stores the mean of each prior coordinate. Second feature stores the variance of each prior coordinate.

Definition at line 37 of file prior_box.h.

Collaboration diagram for cldnn_prior_box_desc:

Class Members
cldnn_float_arr	aspect_ratios	Various of aspect ratios. Duplicate ratios will be ignored.
uint32_t	clip	If not 0, will clip the prior so that it is within [0, 1].
uint32_t	flip	If not 0, will flip each aspect ratio. For example, if there is aspect ratio "r", aspect ratio "1.0/r" we will generated as well.
cldnn_primitive_id	id	Primitive id unique within a topology.
cldnn_tensor	img_size	Image width and height.
cldnn_primitive_id_arr	input	Input primitives ids.
cldnn_float_arr	max_sizes	Maximum box sizes in pixels.
cldnn_float_arr	min_sizes	Minimum box sizes in pixels.
float	offset	Offset to the top left corner of each cell.
cldnn_padding	output_padding	Output padding information.
float	step_height	Step height.
float	step_width	Step width.
cldnn_primitive_type_id	type	Primitive type identificator.
cldnn_float_arr	variance	Variance for adjusting the prior boxes.

cldnn_proposal_desc

struct cldnn_proposal_desc

Definition at line 35 of file proposal.h.

Collaboration diagram for cldnn_proposal_desc:

Class Members
int	feature_stride
cldnn_primitive_id	id	Primitive id unique within a topology.
cldnn_primitive_id_arr	input	Input primitives ids.
float	iou_threshold
int	max_proposals
int	min_bbox_size
cldnn_padding	output_padding	Output padding information.
int	post_nms_topn
int	pre_nms_topn
cldnn_float_arr	ratios
cldnn_float_arr	scales
cldnn_primitive_type_id	type	Primitive type identificator.

cldnn_region_yolo_desc

struct cldnn_region_yolo_desc

region softmax specific for yolo2 topology

Algorithm:

Where:

Definition at line 39 of file region_yolo.h.

Collaboration diagram for cldnn_region_yolo_desc:

Class Members
uint32_t	classes	paramter classes
uint32_t	coords	paramter coords
cldnn_primitive_id	id	Primitive id unique within a topology.
cldnn_primitive_id_arr	input	Input primitives ids.
uint32_t	num	paramter num
cldnn_padding	output_padding	Output padding information.
cldnn_primitive_type_id	type	Primitive type identificator.

cldnn_reorder_desc

struct cldnn_reorder_desc

Changes how data is ordered in memory. Value type is not changed & all information is preserved.

Corresponding values are bitwise equal before/after reorder. Also merged with subtraction layer, which can subtract values while doing reordering. NOTE THAT THIS WILL SUBTRACT THE SAME VALUES FROM EACH BATCH.

Definition at line 37 of file reorder.h.

Collaboration diagram for cldnn_reorder_desc:

Class Members
cldnn_primitive_id	id	Primitive id unique within a topology.
cldnn_primitive_id_arr	input	Input primitives ids.
cldnn_primitive_id	mean_subtract	Primitive id to get mean subtract values. Ignored if subtract_per_featrue is set.
cldnn_data_type	output_data_type	Requested memory data type.
cldnn_format_type	output_format	Requested memory format.
cldnn_padding	output_padding	Output padding information.
cldnn_float_arr	subtract_per_feature	Array of mean subtract values.
cldnn_primitive_type_id	type	Primitive type identificator.

cldnn_reorg_yolo_desc

struct cldnn_reorg_yolo_desc

yolo2 topology specific data reorganization primitive

Algorithm:

Where:

Definition at line 39 of file reorg_yolo.h.

Collaboration diagram for cldnn_reorg_yolo_desc:

Class Members
cldnn_primitive_id	id	Primitive id unique within a topology.
cldnn_primitive_id_arr	input	Input primitives ids.
cldnn_padding	output_padding	Output padding information.
uint32_t	stride	paramter stride
cldnn_primitive_type_id	type	Primitive type identificator.

cldnn_reshape_desc

struct cldnn_reshape_desc

Changes information about inputs's layout effectively creating new memory which share underlaying buffer but is interpreted in a different way (different shape).

Note

reshape primitive is supposed only to reinterpret shape of the memory therefore it's not possible to change neither data type nor format of the input buffer and total number of elements in input and output (excluding paddings) must match.

Definition at line 37 of file reshape.h.

Collaboration diagram for cldnn_reshape_desc:

Class Members
cldnn_primitive_id	id	Primitive id unique within a topology.
cldnn_primitive_id_arr	input	Input primitives ids.
cldnn_padding	output_padding	Output padding information.
cldnn_tensor	output_shape	Requested memory shape.
cldnn_primitive_type_id	type	Primitive type identificator.

cldnn_roi_pooling_desc

struct cldnn_roi_pooling_desc

Definition at line 34 of file roi_pooling.h.

Collaboration diagram for cldnn_roi_pooling_desc:

Class Members
int	group_sz	Group size as defined by PSRoIPooling when > 0, else if 0 means regular RoIPooling.
cldnn_primitive_id	id	Primitive id unique within a topology.
cldnn_primitive_id_arr	input	Input primitives ids.
int32_t	mode	Pooling method. See cldnn_pooling_mode.
cldnn_padding	output_padding	Output padding information.
int	pooled_height	Output height.
int	pooled_width	Output width.
float	spatial_scale	Ratio of the coordinates used in RoIs to the width (and height) of the input data.
cldnn_primitive_type_id	type	Primitive type identificator.

cldnn_scale_desc

struct cldnn_scale_desc

Performs elementwise product of input and scale_input.

Scale input dimension should be equal to input dimension or be 1 if it is not there.
Input size : 2x3x4x5(BFYX)
Possible scale inputs sizes :
2x3x4x5 - works the same as(axis == 0 == -4) in caffe
1x3x4x5 - works the same as(axis == 1 == -3) in caffe
1x1x4x5 - works the same as(axis == 2 == -2) in caffe
1x1x1x5 - works the same as(axis == 3 == -1) in caffe
1x1x1x1 - works the same as empty shape(scalar) in caffe
When scale_input is the same as input, the behavior is the same as cldnn_eltwise_desc with product operation.
Performs scale over feature when the scale feature size is equal to input feature size.
Performs scale over feature in batch when the scale feature and scale batch sizes are equal to input feature and input batch sizes.
Optionally it can also add provided biases by setting bias_term.

Definition at line 46 of file scale.h.

Collaboration diagram for cldnn_scale_desc:

Class Members
cldnn_primitive_id	bias	Primitive id containing bias data.
cldnn_primitive_id	id	Primitive id unique within a topology.
cldnn_primitive_id_arr	input	Input primitives ids.
cldnn_padding	output_padding	Output padding information.
cldnn_primitive_type_id	type	Primitive type identificator.

cldnn_softmax_desc

struct cldnn_softmax_desc

Normalizes results so they sum to 1. The scope of normalization is defined by a member dimension.

Algorithm:

b = e^a/sum(N-1; j=0; e^j)

Where:

• N : number of values to normalize
• b : value after normalization
• a : value before normalization

Definition at line 50 of file softmax.h.

Collaboration diagram for cldnn_softmax_desc:

Class Members
cldnn_softmax_dimension	dimension	Defines a scope of a single softmax normalization. Being given a 4-dimensional input, which consists of b,f,y,x dimensions, softmax normalizes data which are divided into multiple independent sets. Specific behavior is determined by this parameter, as follows: when set to cldnn_softmax_normalize_x each input row is normalized independently, when set to cldnn_softmax_normalize_y each input column is normalized independently, when set to cldnn_softmax_normalize_f each in-depth vector of input is normalized independently, when set to cldnn_softmax_normalize_fyx each 3d image within input is normalized independently,
cldnn_primitive_id	id	Primitive id unique within a topology.
cldnn_primitive_id_arr	input	Input primitives ids.
cldnn_padding	output_padding	Output padding information.
cldnn_primitive_type_id	type	Primitive type identificator.

cldnn_split_desc

struct cldnn_split_desc

Performs split operation on input.

splits the input data into n parts, for each user provides name and offsets.
User cannot use split primitive directly.
It is needed to refer to the output ids with the name "<split_prim_id>:<split_output_id>".

Assumptions
- offsets1 < offsets2 < offsets3 < ...
- size[n] = offsets[n+1] - offsets[n];
- last element: size[n] = split_input.size - offsets[n];
- no buffer overlapping, as the output size is calculated using offset and input size
- split primitive id cannot be used by any other primitive (user needs to use output_ids only)
Breaking any of this conditions will cause exeption throw.

Example:
Splitting output to 2 parts by the features:
input_size = { 2, 4, 3, 5 };
split_id = "split";
output_ids_offsets[0] = { "out0", { 0,0,0,0 } };
output_ids_offsets[1] = { "out1", { 0,2,0,0 } };
After split there would be 2 primitives: "split:out0" and "split:out1" which contain 2 feature maps (lower and upper)

Definition at line 54 of file split.h.

Collaboration diagram for cldnn_split_desc:

Class Members
cldnn_primitive_id	id	Primitive id unique within a topology.
cldnn_primitive_id_arr	input	Input primitives ids.
cldnn_primitive_id_arr	output_ids	List of output_ids.
cldnn_tensor_arr	output_offsets	Array of tensors with offsets.
cldnn_padding	output_padding	Output padding information.
cldnn_primitive_type_id	type	Primitive type identificator.

cldnn_upsampling_desc

struct cldnn_upsampling_desc

Performs nearest neighbor/bilinear upsampling Also supports built-in Relu activation available by setting it in arguments.

Definition at line 44 of file upsampling.h.

Collaboration diagram for cldnn_upsampling_desc:

Class Members
float	activation_negative_slope	Relu activation slope.
cldnn_primitive_id	id	Primitive id unique within a topology.
cldnn_primitive_id_arr	input	Input primitives ids.
uint32_t	num_filter	Parameters num_filter Input filter. Only used by bilinear sample_type.
cldnn_padding	output_padding	Output padding information.
int32_t	sample_type	Parameters sample_type Upsampling method (nearest neighbor/bilinear).
uint32_t	scale	Parameters scale Upsampling scale.
cldnn_primitive_type_id	type	Primitive type identificator.
uint32_t	with_activation	Enables Relu activation.

Enumeration Type Documentation

cldnn_eltwise_mode

enum cldnn_eltwise_mode

Select mode for eltwise layer ( cldnn_eltwise_desc ​).

Enumerator
cldnn_eltwise_sum	Eltwise sum.
cldnn_eltwise_sub	Eltwise subtract.
cldnn_eltwise_max	Eltwise max.
cldnn_eltwise_prod	Eltwise product (Hamarad).

Definition at line 34 of file eltwise.h.

cldnn_pooling_mode

enum cldnn_pooling_mode

Select method for Pooling layer ( cldnn_pooling_desc ).

Enumerator
cldnn_pooling_max	Maximum-pooling method.
cldnn_pooling_average	Average-pooling method.
cldnn_pooling_average_no_padding	Average-pooling method without values which are outside of the input.

Definition at line 34 of file pooling.h.

cldnn_upsampling_sample_type

enum cldnn_upsampling_sample_type

Sample mode for upsampling layer ( cldnn_upsampling_desc ​).

Enumerator
cldnn_upsampling_nearest	upsampling nearest neighbor.
cldnn_upsampling_bilinear	upsampling bilinear.

Definition at line 34 of file upsampling.h.