Class List

Here are the classes, structs, unions and interfaces with brief descriptions:

[detail level 123]

▶Ncldnn
▶Ndetail
Cbuild_option_traits	Helper template to convert build_option_type value to particular build_option class
▶Ndetails	Helper structs used in tensor constructor with dim_vec_kinds
Cdim_vec_kind_init	Template class used in tensor constructor using dim_vec_kinds
Cdim_vec_limits	Template class with max_dimensionalities and dimension offset for dimension kinds
Cdim_vec_limits< dim_vec_kind::batch >
Cdim_vec_limits< dim_vec_kind::feature >
Cdim_vec_limits< dim_vec_kind::spatial >
Cmemory_c_to_cpp_converter
▶Ninstrumentation
Cprofiling_info	Represents list of profiling_interval
Cprofiling_interval	Represents prifiling interval as its name and value
Cprofiling_period	Abstract class to represent profiling period
Cprofiling_period_basic	Basic profiling_period implementation which stores data as an simple period value
Ctimer	Helper class to calculate time periods
▶Nmeta
Call
Call< Val, Values... >
Calways_false
Calways_false_ty_val
Cis_any_of
Cis_any_of< T, U, Rest... >
Cval_tuple
Cactivation	Activation using rectified linear unit or parameterized rectified linear unit
Cbatch_norm	Batch normalization primitive
Cbuild_option	Represents user-provided program build option
Cbuild_option_bool	build_option specialization for boolean options
Cbuild_option_directory	build_option specialization for selecting a directory
Cbuild_option_outputs	build_option specialization for program outputs list
Cbuild_option_tuning_config	build_option specialization for tuning config
Cbuild_options	Represents program build options list
Cconcatenation
Cconvolution	Performs forward spatial convolution with weight sharing. Also supports built-in Relu cldnn_activation_desc available by setting it in arguments
Ccrop	Performs crop operation on input
Ccustom_gpu_primitive	This primitive executes a custom kernel provided by the application
Cdata	Provides input data to topology
Cdata_type_to_type	Converts data_types to C++ type
Cdata_type_traits	Helper class to identify key properties for data_types
Cdeconvolution	Performs transposed convolution. Also supports built-in Relu activation available by setting it in arguments
Cdetection_output	Generates a list of detections based on location and confidence predictions by doing non maximum suppression
Celtwise	Performs elementwise operations (sum, subtract, max or product) on two input primitives Also supports built-in Relu activation available by setting it in arguments.
Cengine	Represents clDNN engine object
Cengine_configuration	Configuration parameters for created engine
Cerror	ClDNN specific exception type
Cevent	Represents an clDNN Event object
Cformat	Represents memory formats (orders). In CNN most of data is described as 4 dimensional blocks. In Intel(R) clDNN library we describe memory with 4 letters
Cformat_traits	Format information helper class
Cfully_connected	Performs forward fully connected layer (inner product). Also supports built-in Relu cldnn_activation_desc available by setting it in arguments.
Chalf_impl
Cinput_layout	Provides input layout for a data to be passed later to network
Clayout	Describes memory layout
Clrn	Local response normalization
Cmemory	Represents buffer with particular layout
Cnetwork	Executable network allocated from program
Cnetwork_output	Represents network output returned by network::get_output()
Cnormalize	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
Cpadding	Represents data padding information
Cpermute	Permutes data in the memory, with respect to provided order
Cpointer	Helper class to get an access memory data
Cpooling	Performs "pooling" operation which is a form of non-linear down-sampling
▶Cprimitive	Base class of network primitive description
Cfixed_size_vector_ref	Initialize fields common for all primitives
Cprimitive_id_arr
Cprimitive_base	Base class for all primitives implementations
Cprior_box	Generates a set of default bounding boxes with different sizes and aspect ratios
Cprogram	Compiled program build from topology by engine
Cproposal
Cregion_yolo	Normalizes results so they sum to 1
Creorder	Changes how data is ordered in memory. Value type is not changed & all information is preserved
Creorg_yolo	Normalizes results so they sum to 1
Creshape	Changes information about inputs's layout effectively creating new memory which share underlaying buffer but is interpreted in a different way (different shape)
Croi_pooling
Cscale	Performs elementwise product of input and scale_input
Csoftmax	Normalizes results so they sum to 1
Csplit	Performs split operation on input
Ctensor	N-dimensional vector. Mostly used to represent memory size
Ctopology	Network topology to be defined by user
Ctuning_config_options	Tuning configuration
Ctype_to_data_type	Converts C++ type to data_types
Cupsampling	Performs nearest neighbor/bilinear upsampling Also supports built-in Relu activation available by setting it in arguments
Ccldnn_activation_additional_params	Activation additional params
Ccldnn_activation_desc	Activation using rectified linear unit or parameterized rectified linear unit
Ccldnn_arg	Custom primitive kernel argument type
Ccldnn_batch_norm_desc	Batch normalization primitive
Ccldnn_build_option	Represents network build option
Ccldnn_concatenation_desc
Ccldnn_convolution_desc	Performs forward spatial convolution with weight sharing. Also supports built-in Relu cldnn_activation_desc available by setting it in arguments
Ccldnn_crop_desc	Performs crop operation on input
Ccldnn_custom_gpu_primitive_desc	This primitive executes a custom kernel provided by the application
Ccldnn_data_desc	Provides input data to topology
Ccldnn_deconvolution_desc	Performs transposed convolution. Also supports built-in Relu cldnn_activation_desc available by setting it in arguments
Ccldnn_detection_output_desc	Generates a list of detections based on location and confidence predictions by doing non maximum suppression
Ccldnn_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.
Ccldnn_engine_configuration	Configuration parameters for created engine
Ccldnn_engine_info	Information about the engine returned by cldnn_get_engine_info()
Ccldnn_float_arr	Represents reference to an array of floats
Ccldnn_fully_connected_desc	Performs forward fully connected layer (inner product). Also supports built-in Relu cldnn_activation_desc available by setting it in arguments
Ccldnn_input_layout_desc	Provides input layout for a data to be passed later to network
Ccldnn_layout	Memory layout description
Ccldnn_lrn_desc	Local response normalization
Ccldnn_network_output	Output information for executed cldnn_network
Ccldnn_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
Ccldnn_padding	Padding information
Ccldnn_permute_desc	Permutes data in the memory, with respect to provided order
Ccldnn_pooling_desc	Performs "pooling" operation which is a form of non-linear down-sampling
Ccldnn_primitive_desc	Basic primitive descriptor structure
Ccldnn_primitive_id_arr	Represents reference to an array of primitive ids
Ccldnn_prior_box_desc	Generates a set of default bounding boxes with different sizes and aspect ratios
Ccldnn_profiling_interval	Profiling information for an executed network primitive
Ccldnn_proposal_desc
Ccldnn_region_yolo_desc	Region softmax specific for yolo2 topology
Ccldnn_reorder_desc	Changes how data is ordered in memory. Value type is not changed & all information is preserved
Ccldnn_reorg_yolo_desc	Yolo2 topology specific data reorganization primitive
Ccldnn_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)
Ccldnn_roi_pooling_desc
Ccldnn_scale_desc	Performs elementwise product of input and scale_input
Ccldnn_softmax_desc	Normalizes results so they sum to 1. The scope of normalization is defined by a member dimension
Ccldnn_split_desc	Performs split operation on input
Ccldnn_tensor	N-dimensional vector. Mostly used to represent memory size
Ccldnn_tensor_arr	Represents reference to an array of tensor
Ccldnn_tuning_config	Tuning config
Ccldnn_uint16_t_arr	Represents reference to an array of uint16_t
Ccldnn_upsampling_desc	Performs nearest neighbor/bilinear upsampling Also supports built-in Relu activation available by setting it in arguments
Ccldnn_version