Class Hierarchy

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This inheritance list is sorted roughly, but not completely, alphabetically:

[detail level 123]

▶Ccldnn::build_option	Represents user-provided program build option
Ccldnn::build_option_bool< OptType >	build_option specialization for boolean options
Ccldnn::build_option_directory< OptType >	build_option specialization for selecting a directory
Ccldnn::build_option_outputs	build_option specialization for program outputs list
Ccldnn::build_option_tuning_config	build_option specialization for tuning config
Ccldnn::detail::build_option_traits< OptType >	Helper template to convert build_option_type value to particular build_option class
Ccldnn::build_options	Represents program build options list
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
Ccldnn::data_type_to_type< Data_Type >	Converts data_types to C++ type
Ccldnn::data_type_traits	Helper class to identify key properties for data_types
Ccldnn::details::dim_vec_kind_init< Kind >	Template class used in tensor constructor using dim_vec_kinds
Ccldnn::details::dim_vec_limits< Kind >	Template class with max_dimensionalities and dimension offset for dimension kinds
Ccldnn::details::dim_vec_limits< dim_vec_kind::batch >
Ccldnn::details::dim_vec_limits< dim_vec_kind::feature >
Ccldnn::details::dim_vec_limits< dim_vec_kind::spatial >
Ccldnn::engine	Represents clDNN engine object
Ccldnn::engine_configuration	Configuration parameters for created engine
Ccldnn::event	Represents an clDNN Event object
▶Cfalse_type
Ccldnn::meta::always_false< T >
Ccldnn::meta::always_false_ty_val< Ty, Val >
Ccldnn::meta::is_any_of< T, U >
Ccldnn::primitive::fixed_size_vector_ref	Initialize fields common for all primitives
Ccldnn::format	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
Ccldnn::format_traits	Format information helper class
Ccldnn::half_impl
▶Cintegral_constant
Ccldnn::meta::all< Val, Values... >
Ccldnn::layout	Describes memory layout
Ccldnn::memory	Represents buffer with particular layout
Ccldnn::details::memory_c_to_cpp_converter
Ccldnn::network	Executable network allocated from program
Ccldnn::network_output	Represents network output returned by network::get_output()
Ccldnn::padding	Represents data padding information
Ccldnn::pointer< T >	Helper class to get an access memory data
▶Ccldnn::primitive	Base class of network primitive description
Ccldnn::primitive_base< PType, DTO >	Base class for all primitives implementations
▶Ccldnn::primitive_base< activation, cldnn_activation_desc >
Ccldnn::activation	Activation using rectified linear unit or parameterized rectified linear unit
▶Ccldnn::primitive_base< batch_norm, cldnn_batch_norm_desc >
Ccldnn::batch_norm	Batch normalization primitive
▶Ccldnn::primitive_base< concatenation, cldnn_concatenation_desc >
Ccldnn::concatenation
▶Ccldnn::primitive_base< convolution, cldnn_convolution_desc >
Ccldnn::convolution	Performs forward spatial convolution with weight sharing. Also supports built-in Relu cldnn_activation_desc available by setting it in arguments
▶Ccldnn::primitive_base< crop, cldnn_crop_desc >
Ccldnn::crop	Performs crop operation on input
▶Ccldnn::primitive_base< custom_gpu_primitive, cldnn_custom_gpu_primitive_desc >
Ccldnn::custom_gpu_primitive	This primitive executes a custom kernel provided by the application
▶Ccldnn::primitive_base< data, cldnn_data_desc >
Ccldnn::data	Provides input data to topology
▶Ccldnn::primitive_base< deconvolution, cldnn_deconvolution_desc >
Ccldnn::deconvolution	Performs transposed convolution. Also supports built-in Relu activation available by setting it in arguments
▶Ccldnn::primitive_base< detection_output, cldnn_detection_output_desc >
Ccldnn::detection_output	Generates a list of detections based on location and confidence predictions by doing non maximum suppression
▶Ccldnn::primitive_base< eltwise, cldnn_eltwise_desc >
Ccldnn::eltwise	Performs elementwise operations (sum, subtract, max or product) on two input primitives Also supports built-in Relu activation available by setting it in arguments.
▶Ccldnn::primitive_base< fully_connected, cldnn_fully_connected_desc >
Ccldnn::fully_connected	Performs forward fully connected layer (inner product). Also supports built-in Relu cldnn_activation_desc available by setting it in arguments.
▶Ccldnn::primitive_base< input_layout, cldnn_input_layout_desc >
Ccldnn::input_layout	Provides input layout for a data to be passed later to network
▶Ccldnn::primitive_base< lrn, cldnn_lrn_desc >
Ccldnn::lrn	Local response normalization
▶Ccldnn::primitive_base< normalize, cldnn_normalize_desc >
Ccldnn::normalize	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::primitive_base< permute, cldnn_permute_desc >
Ccldnn::permute	Permutes data in the memory, with respect to provided order
▶Ccldnn::primitive_base< pooling, cldnn_pooling_desc >
Ccldnn::pooling	Performs "pooling" operation which is a form of non-linear down-sampling
▶Ccldnn::primitive_base< prior_box, cldnn_prior_box_desc >
Ccldnn::prior_box	Generates a set of default bounding boxes with different sizes and aspect ratios
▶Ccldnn::primitive_base< proposal, cldnn_proposal_desc >
Ccldnn::proposal
▶Ccldnn::primitive_base< region_yolo, cldnn_region_yolo_desc >
Ccldnn::region_yolo	Normalizes results so they sum to 1
▶Ccldnn::primitive_base< reorder, cldnn_reorder_desc >
Ccldnn::reorder	Changes how data is ordered in memory. Value type is not changed & all information is preserved
▶Ccldnn::primitive_base< reorg_yolo, cldnn_reorg_yolo_desc >
Ccldnn::reorg_yolo	Normalizes results so they sum to 1
▶Ccldnn::primitive_base< reshape, cldnn_reshape_desc >
Ccldnn::reshape	Changes information about inputs's layout effectively creating new memory which share underlaying buffer but is interpreted in a different way (different shape)
▶Ccldnn::primitive_base< roi_pooling, cldnn_roi_pooling_desc >
Ccldnn::roi_pooling
▶Ccldnn::primitive_base< scale, cldnn_scale_desc >
Ccldnn::scale	Performs elementwise product of input and scale_input
▶Ccldnn::primitive_base< softmax, cldnn_softmax_desc >
Ccldnn::softmax	Normalizes results so they sum to 1
▶Ccldnn::primitive_base< split, cldnn_split_desc >
Ccldnn::split	Performs split operation on input
▶Ccldnn::primitive_base< upsampling, cldnn_upsampling_desc >
Ccldnn::upsampling	Performs nearest neighbor/bilinear upsampling Also supports built-in Relu activation available by setting it in arguments
Ccldnn::primitive::primitive_id_arr
Ccldnn::instrumentation::profiling_info	Represents list of profiling_interval
Ccldnn::instrumentation::profiling_interval	Represents prifiling interval as its name and value
▶Ccldnn::instrumentation::profiling_period	Abstract class to represent profiling period
Ccldnn::instrumentation::profiling_period_basic	Basic profiling_period implementation which stores data as an simple period value
Ccldnn::program	Compiled program build from topology by engine
▶Cruntime_error
Ccldnn::error	ClDNN specific exception type
Ccldnn::tensor	N-dimensional vector. Mostly used to represent memory size
Ccldnn::instrumentation::timer< ClockTy >	Helper class to calculate time periods
Ccldnn::topology	Network topology to be defined by user
▶Ctrue_type
Ccldnn::meta::all< Values >
Ccldnn::tuning_config_options	Tuning configuration
▶Ctype
Ccldnn::meta::is_any_of< T, U, Rest... >
Ccldnn::type_to_data_type< T >	Converts C++ type to data_types
Ccldnn::meta::val_tuple< Ty, Vals >