Global Namespace¶

Overview¶

// namespaces

namespace ExecGraphInfoSerialization;
namespace FileUtils;
namespace InferenceEngine;
    namespace InferenceEngine::CPUConfigParams;
    namespace InferenceEngine::GNAConfigParams;
    namespace InferenceEngine::GPUConfigParams;
    namespace InferenceEngine::GPUContextParams;
    namespace InferenceEngine::HeteroConfigParams;
    namespace InferenceEngine::Metrics;
    namespace InferenceEngine::MultiDeviceConfigParams;
    namespace InferenceEngine::PluginConfigInternalParams;
    namespace InferenceEngine::PluginConfigParams;
    namespace InferenceEngine::PrecisionUtils;
        namespace InferenceEngine::PrecisionUtils::details;
    namespace InferenceEngine::gpu;
    namespace InferenceEngine::itt;
        namespace InferenceEngine::itt::domains;
namespace ngraph;
    namespace ngraph::builder;
        namespace ngraph::builder::opset1;
    namespace ngraph::coordinates;
    namespace ngraph::descriptor;
        namespace ngraph::descriptor::layout;
    namespace ngraph::element;
    namespace ngraph::file_util;
    namespace ngraph::op;
        namespace ngraph::op::util;
            namespace ngraph::op::util::detail;
            namespace ngraph::op::util::error;
        namespace ngraph::op::v0;
        namespace ngraph::op::v1;
        namespace ngraph::op::v10;
        namespace ngraph::op::v11;
        namespace ngraph::op::v3;
        namespace ngraph::op::v4;
        namespace ngraph::op::v5;
        namespace ngraph::op::v6;
        namespace ngraph::op::v7;
        namespace ngraph::op::v8;
        namespace ngraph::op::v9;
    namespace ngraph::opset1;
    namespace ngraph::opset10;
    namespace ngraph::opset11;
    namespace ngraph::opset2;
    namespace ngraph::opset3;
    namespace ngraph::opset4;
    namespace ngraph::opset5;
    namespace ngraph::opset6;
    namespace ngraph::opset7;
    namespace ngraph::opset8;
    namespace ngraph::opset9;
    namespace ngraph::pass;
        namespace ngraph::pass::low_precision;
            namespace ngraph::pass::low_precision::itt;
                namespace ngraph::pass::low_precision::itt::domains;
            namespace ngraph::pass::low_precision::precision_set;
    namespace ngraph::pattern;
        namespace ngraph::pattern::op;
    namespace ngraph::reduction;
    namespace ngraph::runtime;
        namespace ngraph::runtime::opt_kernel;
        namespace ngraph::runtime::reference;
            namespace ngraph::runtime::reference::adaptive_pool;
            namespace ngraph::runtime::reference::detail;
            namespace ngraph::runtime::reference::details;
            namespace ngraph::runtime::reference::fake_quantize_details;
            namespace ngraph::runtime::reference::fft_common;
            namespace ngraph::runtime::reference::internal;
            namespace ngraph::runtime::reference::interpolate_pil;
            namespace ngraph::runtime::reference::kernel;
            namespace ngraph::runtime::reference::nms_common;
namespace openvino;
    namespace openvino::cc;
        namespace openvino::cc::internal;
    namespace openvino::itt;
namespace opset9;
namespace ov;
    namespace ov::batch_util;
    namespace ov::cmp;
    namespace ov::descriptor;
    namespace ov::detail;
    namespace ov::device;
        namespace ov::device::capability;
    namespace ov::element;
    namespace ov::exec_model_info;
    namespace ov::frontend;
        namespace ov::frontend::type;
    namespace ov::helpers;
    namespace ov::hint;
    namespace ov::intel_auto;
    namespace ov::intel_cpu;
    namespace ov::intel_gna;
    namespace ov::intel_gpu;
        namespace ov::intel_gpu::capability;
        namespace ov::intel_gpu::hint;
        namespace ov::intel_gpu::memory_type;
        namespace ov::intel_gpu::ocl;
    namespace ov::intel_myriad;
        namespace ov::intel_myriad::hddl;
    namespace ov::internal;
    namespace ov::layout;
    namespace ov::log;
    namespace ov::op;
        namespace ov::op::ShapeInferLSTM;
        namespace ov::op::ShapeInferRange;
        namespace ov::op::convolution;
            namespace ov::op::convolution::validate;
        namespace ov::op::deformable_conv;
            namespace ov::op::deformable_conv::validate;
        namespace ov::op::eye;
        namespace ov::op::gather_nd;
        namespace ov::op::internal;
        namespace ov::op::pooling;
        namespace ov::op::psroi_pooling;
            namespace ov::op::psroi_pooling::validate;
        namespace ov::op::rnn;
        namespace ov::op::roi_pooling;
            namespace ov::op::roi_pooling::validate;
        namespace ov::op::slice;
        namespace ov::op::util;
            namespace ov::op::util::detail;
            namespace ov::op::util::embedding;
            namespace ov::op::util::error;
            namespace ov::op::util::rfft_common_validation;
        namespace ov::op::v0;
        namespace ov::op::v1;
            namespace ov::op::v1::pad;
            namespace ov::op::v1::utils;
                namespace ov::op::v1::utils::one_hot;
        namespace ov::op::v10;
        namespace ov::op::v11;
        namespace ov::op::v3;
        namespace ov::op::v4;
            namespace ov::op::v4::ctc_loss;
        namespace ov::op::v5;
        namespace ov::op::v6;
        namespace ov::op::v7;
        namespace ov::op::v8;
        namespace ov::op::v9;
    namespace ov::opset1;
    namespace ov::opset10;
    namespace ov::opset11;
    namespace ov::opset2;
    namespace ov::opset3;
    namespace ov::opset4;
    namespace ov::opset5;
    namespace ov::opset6;
    namespace ov::opset7;
    namespace ov::opset8;
    namespace ov::opset9;
    namespace ov::pass;
        namespace ov::pass::pattern;
            namespace ov::pass::pattern::op;
        namespace ov::pass::transpose_sinking;
            namespace ov::pass::transpose_sinking::utils;
                namespace ov::pass::transpose_sinking::utils::sink_backward;
                namespace ov::pass::transpose_sinking::utils::sink_forward;
    namespace ov::preprocess;
    namespace ov::runtime;
    namespace ov::streams;
    namespace ov::threading;
    namespace ov::util;
        namespace ov::util::dim;
namespace pass;
    namespace pass::low_precision;
        namespace pass::low_precision::BaseMatcherPass public ngraph;
            namespace pass::low_precision::BaseMatcherPass public ngraph::pass;
namespace pugixml;
    namespace pugixml::utils;
namespace std;
namespace util;

// typedefs

typedef struct ov_compiled_model ov_compiled_model_t;
typedef struct ov_core ov_core_t;
typedef struct ov_version ov_version_t;
typedef struct ov_dimension ov_dimension_t;
typedef struct ov_infer_request ov_infer_request_t;
typedef struct ov_layout ov_layout_t;
typedef struct ov_model ov_model_t;
typedef struct ov_output_const_port ov_output_const_port_t;
typedef struct ov_output_port ov_output_port_t;
typedef struct ov_partial_shape ov_partial_shape_t;
typedef struct ov_preprocess_prepostprocessor ov_preprocess_prepostprocessor_t;
typedef struct ov_preprocess_input_info ov_preprocess_input_info_t;
typedef struct ov_preprocess_input_tensor_info ov_preprocess_input_tensor_info_t;
typedef struct ov_preprocess_output_info ov_preprocess_output_info_t;
typedef struct ov_preprocess_output_tensor_info ov_preprocess_output_tensor_info_t;
typedef struct ov_preprocess_input_model_info ov_preprocess_input_model_info_t;
typedef struct ov_preprocess_preprocess_steps ov_preprocess_preprocess_steps_t;
typedef ov_dimension_t ov_rank_t;
typedef struct ov_remote_context ov_remote_context_t;
typedef struct ov_tensor ov_tensor_t;
typedef std::unordered_map<std::shared_ptr<ov::opset1::Parameter>, std::unordered_set<ov::label_t>> P2Btype;
typedef std::unordered_map<ov::element::Type_t, ov::element::Type, EnumClassHash> precisions_map;
typedef std::unordered_map<ov::NodeTypeInfo, std::function<bool(const std::shared_ptr<ov::Node>&, const precisions_map&)>> type_to_fuse_map;
typedef std::unordered_map<ov::Node::type_info_t, std::function<void(const ov::Node&, std::ostream&ss)>> visualize_tree_ops_map_t;

// enums

enum AttributeSource;
enum ov_color_format_e;
enum ov_element_type_e;
enum ov_preprocess_resize_algorithm_e;
enum ov_status_e;

// structs

struct EnumClassHash;
struct ov_ProfilingInfo_t;
struct ov_available_devices_t;
struct ov_callback_t;
struct ov_compiled_model_t;
struct ov_core_t;
struct ov_core_version;
struct ov_core_version_list;
struct ov_core_version_list_t;
struct ov_core_version_t;
struct ov_dimension;
struct ov_infer_request_t;
struct ov_layout_t;
struct ov_model_t;
struct ov_output_const_port_t;
struct ov_output_port_t;
struct ov_partial_shape;
struct ov_preprocess_input_info_t;
struct ov_preprocess_input_model_info_t;
struct ov_preprocess_input_tensor_info_t;
struct ov_preprocess_output_info_t;
struct ov_preprocess_output_tensor_info_t;
struct ov_preprocess_prepostprocessor_t;
struct ov_preprocess_preprocess_steps_t;
struct ov_profiling_info_list_t;
struct ov_profiling_info_t;
struct ov_rank_t;
struct ov_shape_t;
struct ov_tensor_t;
struct ov_version;
struct parse_result;

// classes

class AttributeParameters;
template <InferenceEngine::Precision::ePrecision precision>
class BlobFactory;
class ConvertReduceBase;
class CvtReduceBase;
class MemorySolver;
template <class T>
class SharedAttribute;
class ngraph;

// global variables

 ov_property_key_supported_properties;
 ov_property_key_available_devices;
 ov_property_key_optimal_number_of_infer_requests;
 ov_property_key_range_for_async_infer_requests;
 ov_property_key_range_for_streams;
 ov_property_key_device_full_name;
 ov_property_key_device_capabilities;
 ov_property_key_model_name;
 ov_property_key_optimal_batch_size;
 ov_property_key_max_batch_size;
 ov_property_key_cache_dir;
 ov_property_key_num_streams;
 ov_property_key_affinity;
 ov_property_key_inference_num_threads;
 ov_property_key_hint_use_hyper_threading;
 ov_property_key_hint_performance_mode;
 ov_property_key_hint_scheduling_core_type;
 ov_property_key_hint_inference_precision;
 ov_property_key_hint_num_requests;
 ov_property_key_log_level;
 ov_property_key_hint_model_priority;
 ov_property_key_enable_profiling;
 ov_property_key_device_priorities;
 ov_property_key_hint_execution_mode;
std::vector<int64_t> strides;
std::vector<int64_t> dilation;
std::vector<int64_t> pads_begin;
std::vector<int64_t> pads_end;
std::vector<int64_t> output_padding;
NGRAPH_EXTERN_C NGRAPH_API const char \* NGRAPH_VERSION_NUMBER;

// global functions

OPENVINO_C_VAR(const char \*);
const char \* ov_get_error_info(ov_status_e status);
void ov_free(const char \* content);

ov_compiled_model_inputs_size(
    const ov_compiled_model_t \* compiled_model,
    size_t \* size
    );

ov_compiled_model_input(
    const ov_compiled_model_t \* compiled_model,
    ov_output_const_port_t \*\* input_port
    );

ov_compiled_model_input_by_index(
    const ov_compiled_model_t \* compiled_model,
    const size_t index,
    ov_output_const_port_t \*\* input_port
    );

ov_compiled_model_input_by_name(
    const ov_compiled_model_t \* compiled_model,
    const char \* name,
    ov_output_const_port_t \*\* input_port
    );

ov_compiled_model_outputs_size(
    const ov_compiled_model_t \* compiled_model,
    size_t \* size
    );

ov_compiled_model_output(
    const ov_compiled_model_t \* compiled_model,
    ov_output_const_port_t \*\* output_port
    );

ov_compiled_model_output_by_index(
    const ov_compiled_model_t \* compiled_model,
    const size_t index,
    ov_output_const_port_t \*\* output_port
    );

ov_compiled_model_output_by_name(
    const ov_compiled_model_t \* compiled_model,
    const char \* name,
    ov_output_const_port_t \*\* output_port
    );

ov_compiled_model_get_runtime_model(
    const ov_compiled_model_t \* compiled_model,
    ov_model_t \*\* model
    );

ov_compiled_model_create_infer_request(
    const ov_compiled_model_t \* compiled_model,
    ov_infer_request_t \*\* infer_request
    );

ov_compiled_model_set_property(const ov_compiled_model_t \* compiled_model, ...);

ov_compiled_model_get_property(
    const ov_compiled_model_t \* compiled_model,
    const char \* property_key,
    char \*\* property_value
    );

ov_compiled_model_export_model(
    const ov_compiled_model_t \* compiled_model,
    const char \* export_model_path
    );

ov_compiled_model_free(ov_compiled_model_t \* compiled_model);

ov_compiled_model_get_context(
    const ov_compiled_model_t \* compiled_model,
    ov_remote_context_t \*\* context
    );

ov_get_openvino_version(ov_version_t \* version);
ov_version_free(ov_version_t \* version);
ov_core_create(ov_core_t \*\* core);
ov_core_create_with_config(const char \* xml_config_file, ov_core_t \*\* core);
ov_core_free(ov_core_t \* core);

ov_core_read_model(
    const ov_core_t \* core,
    const char \* model_path,
    const char \* bin_path,
    ov_model_t \*\* model
    );

ov_core_read_model_from_memory(
    const ov_core_t \* core,
    const char \* model_str,
    const ov_tensor_t \* weights,
    ov_model_t \*\* model
    );

ov_core_compile_model(
    const ov_core_t \* core,
    const ov_model_t \* model,
    const char \* device_name,
    const size_t property_args_size,
    ov_compiled_model_t \*\* compiled_model,
    ...
    );

ov_core_compile_model_from_file(
    const ov_core_t \* core,
    const char \* model_path,
    const char \* device_name,
    const size_t property_args_size,
    ov_compiled_model_t \*\* compiled_model,
    ...
    );

ov_core_set_property(const ov_core_t \* core, const char \* device_name, ...);

ov_core_get_property(
    const ov_core_t \* core,
    const char \* device_name,
    const char \* property_key,
    char \*\* property_value
    );

ov_core_get_available_devices(
    const ov_core_t \* core,
    ov_available_devices_t \* devices
    );

ov_available_devices_free(ov_available_devices_t \* devices);

ov_core_import_model(
    const ov_core_t \* core,
    const char \* content,
    const size_t content_size,
    const char \* device_name,
    ov_compiled_model_t \*\* compiled_model
    );

OPENVINO_C_API(void);

ov_core_create_context(
    const ov_core_t \* core,
    const char \* device_name,
    const size_t context_args_size,
    ov_remote_context_t \*\* context,
    ...
    );

ov_core_compile_model_with_context(
    const ov_core_t \* core,
    const ov_model_t \* model,
    const ov_remote_context_t \* context,
    const size_t property_args_size,
    ov_compiled_model_t \*\* compiled_model,
    ...
    );

ov_core_get_default_context(
    const ov_core_t \* core,
    const char \* device_name,
    ov_remote_context_t \*\* context
    );

ov_dimension_is_dynamic(const ov_dimension_t dim);

ov_infer_request_set_tensor(
    ov_infer_request_t \* infer_request,
    const char \* tensor_name,
    const ov_tensor_t \* tensor
    );

ov_infer_request_set_tensor_by_port(
    ov_infer_request_t \* infer_request,
    const ov_output_port_t \* port,
    const ov_tensor_t \* tensor
    );

ov_infer_request_set_tensor_by_const_port(
    ov_infer_request_t \* infer_request,
    const ov_output_const_port_t \* port,
    const ov_tensor_t \* tensor
    );

ov_infer_request_set_input_tensor_by_index(
    ov_infer_request_t \* infer_request,
    const size_t idx,
    const ov_tensor_t \* tensor
    );

ov_infer_request_set_input_tensor(
    ov_infer_request_t \* infer_request,
    const ov_tensor_t \* tensor
    );

ov_infer_request_set_output_tensor_by_index(
    ov_infer_request_t \* infer_request,
    const size_t idx,
    const ov_tensor_t \* tensor
    );

ov_infer_request_set_output_tensor(
    ov_infer_request_t \* infer_request,
    const ov_tensor_t \* tensor
    );

ov_infer_request_get_tensor(
    const ov_infer_request_t \* infer_request,
    const char \* tensor_name,
    ov_tensor_t \*\* tensor
    );

ov_infer_request_get_tensor_by_const_port(
    const ov_infer_request_t \* infer_request,
    const ov_output_const_port_t \* port,
    ov_tensor_t \*\* tensor
    );

ov_infer_request_get_tensor_by_port(
    const ov_infer_request_t \* infer_request,
    const ov_output_port_t \* port,
    ov_tensor_t \*\* tensor
    );

ov_infer_request_get_input_tensor_by_index(
    const ov_infer_request_t \* infer_request,
    const size_t idx,
    ov_tensor_t \*\* tensor
    );

ov_infer_request_get_input_tensor(
    const ov_infer_request_t \* infer_request,
    ov_tensor_t \*\* tensor
    );

ov_infer_request_get_output_tensor_by_index(
    const ov_infer_request_t \* infer_request,
    const size_t idx,
    ov_tensor_t \*\* tensor
    );

ov_infer_request_get_output_tensor(
    const ov_infer_request_t \* infer_request,
    ov_tensor_t \*\* tensor
    );

ov_infer_request_infer(ov_infer_request_t \* infer_request);
ov_infer_request_cancel(ov_infer_request_t \* infer_request);
ov_infer_request_start_async(ov_infer_request_t \* infer_request);
ov_infer_request_wait(ov_infer_request_t \* infer_request);

ov_infer_request_wait_for(
    ov_infer_request_t \* infer_request,
    const int64_t timeout
    );

ov_infer_request_set_callback(
    ov_infer_request_t \* infer_request,
    const ov_callback_t \* callback
    );

ov_infer_request_free(ov_infer_request_t \* infer_request);

ov_infer_request_get_profiling_info(
    const ov_infer_request_t \* infer_request,
    ov_profiling_info_list_t \* profiling_infos
    );

ov_profiling_info_list_free(ov_profiling_info_list_t \* profiling_infos);
ov_layout_create(const char \* layout_desc, ov_layout_t \*\* layout);
ov_layout_free(ov_layout_t \* layout);
ov_layout_to_string(const ov_layout_t \* layout);
ov_model_free(ov_model_t \* model);

ov_model_const_input(
    const ov_model_t \* model,
    ov_output_const_port_t \*\* input_port
    );

ov_model_const_input_by_name(
    const ov_model_t \* model,
    const char \* tensor_name,
    ov_output_const_port_t \*\* input_port
    );

ov_model_const_input_by_index(
    const ov_model_t \* model,
    const size_t index,
    ov_output_const_port_t \*\* input_port
    );

ov_model_input(const ov_model_t \* model, ov_output_port_t \*\* input_port);

ov_model_input_by_name(
    const ov_model_t \* model,
    const char \* tensor_name,
    ov_output_port_t \*\* input_port
    );

ov_model_input_by_index(
    const ov_model_t \* model,
    const size_t index,
    ov_output_port_t \*\* input_port
    );

ov_model_const_output(
    const ov_model_t \* model,
    ov_output_const_port_t \*\* output_port
    );

ov_model_const_output_by_index(
    const ov_model_t \* model,
    const size_t index,
    ov_output_const_port_t \*\* output_port
    );

ov_model_const_output_by_name(
    const ov_model_t \* model,
    const char \* tensor_name,
    ov_output_const_port_t \*\* output_port
    );

ov_model_output(const ov_model_t \* model, ov_output_port_t \*\* output_port);

ov_model_output_by_index(
    const ov_model_t \* model,
    const size_t index,
    ov_output_port_t \*\* output_port
    );

ov_model_output_by_name(
    const ov_model_t \* model,
    const char \* tensor_name,
    ov_output_port_t \*\* output_port
    );

ov_model_inputs_size(const ov_model_t \* model, size_t \* input_size);
ov_model_outputs_size(const ov_model_t \* model, size_t \* output_size);
ov_model_is_dynamic(const ov_model_t \* model);

ov_model_reshape(
    const ov_model_t \* model,
    const char \*\* tensor_names,
    const ov_partial_shape_t \* partial_shapes,
    size_t size
    );

ov_model_reshape_input_by_name(
    const ov_model_t \* model,
    const char \* tensor_name,
    const ov_partial_shape_t partial_shape
    );

ov_model_reshape_single_input(
    const ov_model_t \* model,
    const ov_partial_shape_t partial_shape
    );

ov_model_reshape_by_port_indexes(
    const ov_model_t \* model,
    const size_t \* port_indexes,
    const ov_partial_shape_t \* partial_shape,
    size_t size
    );

ov_model_reshape_by_ports(
    const ov_model_t \* model,
    const ov_output_port_t \*\* output_ports,
    const ov_partial_shape_t \* partial_shapes,
    size_t size
    );

ov_model_get_friendly_name(const ov_model_t \* model, char \*\* friendly_name);

ov_const_port_get_shape(
    const ov_output_const_port_t \* port,
    ov_shape_t \* tensor_shape
    );

ov_port_get_shape(const ov_output_port_t \* port, ov_shape_t \* tensor_shape);

ov_port_get_any_name(
    const ov_output_const_port_t \* port,
    char \*\* tensor_name
    );

ov_port_get_partial_shape(
    const ov_output_const_port_t \* port,
    ov_partial_shape_t \* partial_shape
    );

ov_port_get_element_type(
    const ov_output_const_port_t \* port,
    ov_element_type_e \* tensor_type
    );

ov_output_port_free(ov_output_port_t \* port);
ov_output_const_port_free(ov_output_const_port_t \* port);

ov_partial_shape_create(
    const int64_t rank,
    const ov_dimension_t \* dims,
    ov_partial_shape_t \* partial_shape_obj
    );

ov_partial_shape_create_dynamic(
    const ov_rank_t rank,
    const ov_dimension_t \* dims,
    ov_partial_shape_t \* partial_shape_obj
    );

ov_partial_shape_create_static(
    const int64_t rank,
    const int64_t \* dims,
    ov_partial_shape_t \* partial_shape_obj
    );

ov_partial_shape_free(ov_partial_shape_t \* partial_shape);

ov_partial_shape_to_shape(
    const ov_partial_shape_t partial_shape,
    ov_shape_t \* shape
    );

ov_shape_to_partial_shape(
    const ov_shape_t shape,
    ov_partial_shape_t \* partial_shape
    );

ov_partial_shape_is_dynamic(const ov_partial_shape_t partial_shape);
ov_partial_shape_to_string(const ov_partial_shape_t partial_shape);

ov_preprocess_prepostprocessor_create(
    const ov_model_t \* model,
    ov_preprocess_prepostprocessor_t \*\* preprocess
    );

ov_preprocess_prepostprocessor_free(ov_preprocess_prepostprocessor_t \* preprocess);

ov_preprocess_prepostprocessor_get_input_info(
    const ov_preprocess_prepostprocessor_t \* preprocess,
    ov_preprocess_input_info_t \*\* preprocess_input_info
    );

ov_preprocess_prepostprocessor_get_input_info_by_name(
    const ov_preprocess_prepostprocessor_t \* preprocess,
    const char \* tensor_name,
    ov_preprocess_input_info_t \*\* preprocess_input_info
    );

ov_preprocess_prepostprocessor_get_input_info_by_index(
    const ov_preprocess_prepostprocessor_t \* preprocess,
    const size_t tensor_index,
    ov_preprocess_input_info_t \*\* preprocess_input_info
    );

ov_preprocess_input_info_free(ov_preprocess_input_info_t \* preprocess_input_info);

ov_preprocess_input_info_get_tensor_info(
    const ov_preprocess_input_info_t \* preprocess_input_info,
    ov_preprocess_input_tensor_info_t \*\* preprocess_input_tensor_info
    );

ov_preprocess_input_tensor_info_free(ov_preprocess_input_tensor_info_t \* preprocess_input_tensor_info);

ov_preprocess_input_info_get_preprocess_steps(
    const ov_preprocess_input_info_t \* preprocess_input_info,
    ov_preprocess_preprocess_steps_t \*\* preprocess_input_steps
    );

ov_preprocess_preprocess_steps_free(ov_preprocess_preprocess_steps_t \* preprocess_input_process_steps);

ov_preprocess_preprocess_steps_resize(
    ov_preprocess_preprocess_steps_t \* preprocess_input_process_steps,
    const ov_preprocess_resize_algorithm_e resize_algorithm
    );

ov_preprocess_preprocess_steps_scale(
    ov_preprocess_preprocess_steps_t \* preprocess_input_process_steps,
    float value
    );

ov_preprocess_preprocess_steps_mean(
    ov_preprocess_preprocess_steps_t \* preprocess_input_process_steps,
    float value
    );

ov_preprocess_preprocess_steps_crop(
    ov_preprocess_preprocess_steps_t \* preprocess_input_process_steps,
    int32_t \* begin,
    int32_t begin_size,
    int32_t \* end,
    int32_t end_size
    );

ov_preprocess_preprocess_steps_convert_layout(
    ov_preprocess_preprocess_steps_t \* preprocess_input_process_steps,
    ov_layout_t \* layout
    );

ov_preprocess_preprocess_steps_reverse_channels(ov_preprocess_preprocess_steps_t \* preprocess_input_process_steps);

ov_preprocess_input_tensor_info_set_element_type(
    ov_preprocess_input_tensor_info_t \* preprocess_input_tensor_info,
    const ov_element_type_e element_type
    );

ov_preprocess_input_tensor_info_set_color_format(
    ov_preprocess_input_tensor_info_t \* preprocess_input_tensor_info,
    const ov_color_format_e colorFormat
    );

ov_preprocess_input_tensor_info_set_color_format_with_subname(
    ov_preprocess_input_tensor_info_t \* preprocess_input_tensor_info,
    const ov_color_format_e colorFormat,
    const size_t sub_names_size,
    ...
    );

ov_preprocess_input_tensor_info_set_spatial_static_shape(
    ov_preprocess_input_tensor_info_t \* preprocess_input_tensor_info,
    const size_t input_height,
    const size_t input_width
    );

ov_preprocess_input_tensor_info_set_memory_type(
    ov_preprocess_input_tensor_info_t \* preprocess_input_tensor_info,
    const char \* mem_type
    );

ov_preprocess_preprocess_steps_convert_element_type(
    ov_preprocess_preprocess_steps_t \* preprocess_input_process_steps,
    const ov_element_type_e element_type
    );

ov_preprocess_preprocess_steps_convert_color(
    ov_preprocess_preprocess_steps_t \* preprocess_input_process_steps,
    const ov_color_format_e colorFormat
    );

ov_preprocess_input_tensor_info_set_from(
    ov_preprocess_input_tensor_info_t \* preprocess_input_tensor_info,
    const ov_tensor_t \* tensor
    );

ov_preprocess_input_tensor_info_set_layout(
    ov_preprocess_input_tensor_info_t \* preprocess_input_tensor_info,
    ov_layout_t \* layout
    );

ov_preprocess_prepostprocessor_get_output_info(
    const ov_preprocess_prepostprocessor_t \* preprocess,
    ov_preprocess_output_info_t \*\* preprocess_output_info
    );

ov_preprocess_prepostprocessor_get_output_info_by_index(
    const ov_preprocess_prepostprocessor_t \* preprocess,
    const size_t tensor_index,
    ov_preprocess_output_info_t \*\* preprocess_output_info
    );

ov_preprocess_prepostprocessor_get_output_info_by_name(
    const ov_preprocess_prepostprocessor_t \* preprocess,
    const char \* tensor_name,
    ov_preprocess_output_info_t \*\* preprocess_output_info
    );

ov_preprocess_output_info_free(ov_preprocess_output_info_t \* preprocess_output_info);

ov_preprocess_output_info_get_tensor_info(
    const ov_preprocess_output_info_t \* preprocess_output_info,
    ov_preprocess_output_tensor_info_t \*\* preprocess_output_tensor_info
    );

ov_preprocess_output_tensor_info_free(ov_preprocess_output_tensor_info_t \* preprocess_output_tensor_info);

ov_preprocess_output_set_element_type(
    ov_preprocess_output_tensor_info_t \* preprocess_output_tensor_info,
    const ov_element_type_e element_type
    );

ov_preprocess_input_info_get_model_info(
    const ov_preprocess_input_info_t \* preprocess_input_info,
    ov_preprocess_input_model_info_t \*\* preprocess_input_model_info
    );

ov_preprocess_input_model_info_free(ov_preprocess_input_model_info_t \* preprocess_input_model_info);

ov_preprocess_input_model_info_set_layout(
    ov_preprocess_input_model_info_t \* preprocess_input_model_info,
    ov_layout_t \* layout
    );

ov_preprocess_prepostprocessor_build(
    const ov_preprocess_prepostprocessor_t \* preprocess,
    ov_model_t \*\* model
    );

ov_rank_is_dynamic(const ov_rank_t rank);

ov_remote_context_create_tensor(
    const ov_remote_context_t \* context,
    const ov_element_type_e type,
    const ov_shape_t shape,
    const size_t object_args_size,
    ov_tensor_t \*\* remote_tensor,
    ...
    );

ov_remote_context_get_params(
    const ov_remote_context_t \* context,
    size_t \* size,
    char \*\* params
    );

ov_remote_context_create_host_tensor(
    const ov_remote_context_t \* context,
    const ov_element_type_e type,
    const ov_shape_t shape,
    ov_tensor_t \*\* tensor
    );

OPENVINO_C_API(void);

ov_remote_tensor_get_params(
    ov_tensor_t \* tensor,
    size_t \* size,
    char \*\* params
    );

ov_shape_create(const int64_t rank, const int64_t \* dims, ov_shape_t \* shape);
ov_shape_free(ov_shape_t \* shape);

ov_tensor_create_from_host_ptr(
    const ov_element_type_e type,
    const ov_shape_t shape,
    void \* host_ptr,
    ov_tensor_t \*\* tensor
    );

ov_tensor_create(
    const ov_element_type_e type,
    const ov_shape_t shape,
    ov_tensor_t \*\* tensor
    );

ov_tensor_set_shape(ov_tensor_t \* tensor, const ov_shape_t shape);
ov_tensor_get_shape(const ov_tensor_t \* tensor, ov_shape_t \* shape);

ov_tensor_get_element_type(
    const ov_tensor_t \* tensor,
    ov_element_type_e \* type
    );

ov_tensor_get_size(const ov_tensor_t \* tensor, size_t \* elements_size);
ov_tensor_get_byte_size(const ov_tensor_t \* tensor, size_t \* byte_size);
ov_tensor_data(const ov_tensor_t \* tensor, void \*\* data);
ov_tensor_free(ov_tensor_t \* tensor);
OV_CC_DOMAINS(ov_pass);

template <class T>
void shape_infer(
    const ov::op::v0::FakeQuantize \* op,
    const std::vector<T>& input_shapes,
    std::vector<T>& output_shapes
    );

template <class OpType, class T>
void copy_shape_infer(
    const OpType \* op,
    const std::vector<T>& input_shapes,
    std::vector<T>& output_shapes
    );

template <class OpType, class T>
void first_input_passthrough_infer(
    const OpType \* op,
    const std::vector<T>& input_shapes,
    std::vector<T>& output_shapes
    );

template <class OpType, class T>
void eltwise_shape_infer(
    const OpType \* op,
    const std::vector<T>& input_shapes,
    std::vector<T>& output_shapes
    );

template <class TShape, class TData>
bool get_data_as(
    const ov::Node \* op,
    size_t idx,
    std::vector<TData>& data_out,
    const std::map<size_t, ov::HostTensorPtr>& constant_data = {}
    );

template <class TShape>
bool get_data_as_int64(
    size_t idx,
    const ov::Node \* op,
    std::vector<int64_t>& axes_value,
    const std::map<size_t, ov::HostTensorPtr>& constant_data = {}
    );

template <class TShape>
bool get_data_as_float(
    size_t idx,
    const ov::Node \* op,
    std::vector<float>& axes_value,
    const std::map<size_t, ov::HostTensorPtr>& constant_data = {}
    );

template <class TShape>
bool get_data_as_shape(
    size_t idx,
    const ov::Node \* op,
    TShape& shape,
    const std::map<size_t, ov::HostTensorPtr>& constant_data = {}
    );

bool get_data_as_shape< ov::PartialShape >(
    size_t idx,
    const ov::Node \* op,
    ov::PartialShape& shape,
    const std::map<size_t, std::shared_ptr<ngraph::runtime::HostTensor>>& constant_data
    );

template <class TDim>
void check_divided_result(
    const ov::Node \* op,
    const TDim& quotient,
    const TDim& dividend,
    const typename TDim::value_type& divisor
    );

void check_divided_result< ov::Dimension >(
    const ov::Node \* op,
    const ov::Dimension& quotient,
    const ov::Dimension& dividend,
    const typename ov::Dimension::value_type& divisor
    );

template <typename T>
std::vector<T> read_vector(std::shared_ptr<ngraph::runtime::Tensor> tv);

template <class T, ngraph::element::Type_t ET>
std::vector<T> array_2_vector(
    typename ngraph::element_type_traits<ET>::value_type \* data,
    size_t size
    );

template <typename T>
std::vector<T> host_tensor_2_vector(ngraph::HostTensorPtr tensor);

std::vector<float> NGRAPH_API read_float_vector(std::shared_ptr<ngraph::runtime::Tensor> tv);
std::vector<int64_t> NGRAPH_API read_index_vector(std::shared_ptr<ngraph::runtime::Tensor> tv);

NGRAPH_API std::ostream& operator << (
    std::ostream& os,
    const ngraph::NodeVector& nv
    );

NGRAPH_API const char \* get_ngraph_version_string();

template <class T>
void dynamic_inference(
    const T& input_shape,
    T& output_shape,
    bool keep_dims
    );

void dynamic_inference< ov::PartialShape >(
    const ov::PartialShape& input_shape,
    ov::PartialShape& output_shape,
    bool keep_dims
    );

template <class T>
void reduce_shape_infer(
    const ov::op::util::ReductionBase \* op,
    bool keep_dims,
    const T& input_shape,
    T& output_shape,
    const std::map<size_t, std::shared_ptr<ngraph::runtime::HostTensor>>& constant_data = {}
    );

template <class T>
void shape_infer(
    const ov::op::util::ArithmeticReductionKeepDims \* op,
    const std::vector<T>& input_shapes,
    std::vector<T>& output_shapes,
    const std::map<size_t, std::shared_ptr<ngraph::runtime::HostTensor>>& constant_data = {}
    );

template <class T>
void shape_infer(
    const ov::op::util::LogicalReductionKeepDims \* op,
    const std::vector<T>& input_shapes,
    std::vector<T>& output_shapes,
    const std::map<size_t, std::shared_ptr<ngraph::runtime::HostTensor>>& constant_data = {}
    );

template <class T>
void shape_infer(
    const ov::opset1::Reshape \* op,
    const std::vector<T>& input_shapes,
    std::vector<T>& output_shapes,
    const std::map<size_t, std::shared_ptr<ngraph::runtime::HostTensor>>& constant_data = {}
    );

template <class T>
void dynamic_shape(T& output_shape);

void dynamic_shape< ov::PartialShape >(ov::PartialShape& output_shape);

template <class T>
void shape_of_shape_infer(
    const T& input_shape,
    T& output_shape
    );

template <class T>
void shape_infer(
    const ov::opset1::ShapeOf \* op,
    const std::vector<T>& input_shapes,
    std::vector<T>& output_shapes
    );

template <class T>
void shape_infer(
    const ov::opset3::ShapeOf \* op,
    const std::vector<T>& input_shapes,
    std::vector<T>& output_shapes
    );

InferenceEngine::Blob::Ptr make_blob_with_precision(const InferenceEngine::TensorDesc& desc);

InferenceEngine::Blob::Ptr make_blob_with_precision(
    const InferenceEngine::TensorDesc& desc,
    void \* ptr
    );

InferenceEngine::Blob::Ptr make_blob_with_precision(
    const InferenceEngine::TensorDesc& desc,
    const std::shared_ptr<InferenceEngine::IAllocator>& alloc
    );

InferenceEngine::Blob::Ptr make_plain_blob(
    InferenceEngine::Precision prec,
    const InferenceEngine::SizeVector dims
    );

template <class... Args>
InferenceEngine::Blob::Ptr make_blob_with_precision(
    InferenceEngine::Precision precision,
    Args&&... args
    );

template <typename T>
void CopyVectorToBlob(
    const InferenceEngine::Blob::Ptr outputBlob,
    const std::vector<T>& inputVector
    );

int ie_memcpy(void \* dest, size_t destsz, void const \* src, size_t count);
parse_result ParseXml(const char \* file_path);

// macros

#define ADD_MATCHER(obj, region, ...)
#define ADD_MATCHER_FOR_THIS(region, ...)
#define AUTO_CONFIG_KEY(name)
#define CALL_OVERLOAD(name, exc_class, ctx, ...)
#define CL_HPP_MINIMUM_OPENCL_VERSION
#define CL_HPP_TARGET_OPENCL_VERSION
#define CONFIG_KEY(name)
#define CONFIG_KEY_INTERNAL(name)
#define CONFIG_VALUE(name)
#define CONFIG_VALUE_INTERNAL(name)
#define COPY_TENSOR(a)
#define COUNT_ARGS_MAXN(...)
#define CPU_CONFIG_KEY(name)
#define DECLARE_GPU_PARAM_KEY(name, ...)
#define DECLARE_GPU_PARAM_VALUE(name)
#define EXEC_NETWORK_METRIC_KEY(name)
#define EXPAND_ARGS(args)
#define FOREACH_CHILD(c, p, tag)
#define FRONTEND_API
#define FRONTEND_CAT(x, y)
#define FRONTEND_CAT_(x, y)
#define FRONTEND_C_API
#define FRONTEND_EXPAND(X)
#define FRONT_END_CHECK_IMPLEMENTED(COND, NAME)
#define FRONT_END_GENERAL_CHECK(...)
#define FRONT_END_INITIALIZATION_CHECK(...)
#define FRONT_END_NOT_IMPLEMENTED(NAME)
#define FRONT_END_OP_CONVERSION_CHECK(...)
#define FRONT_END_THROW(MSG)
#define GEN_VAR_COMMON(...)
#define GEN_VAR_PADDLE(in_names, out_names, ...)
#define GLUE(x, y)
#define GNA_CONFIG_KEY(name)
#define GNA_CONFIG_VALUE(name)
#define GPU_CONFIG_KEY(name)
#define GPU_METRIC_KEY(name)
#define GPU_PARAM_KEY(name)
#define GPU_PARAM_VALUE(name)
#define HETERO_CONFIG_KEY(name)
#define IE_ASSERT(EXPRESSION)
#define IE_CREATE_EXTENSION
#define IE_CREATE_PLUGIN
#define IE_DEFINE_EXTENSION_CREATE_FUNCTION(ExtensionType)
#define IE_DEFINE_EXTENSION_CREATE_FUNCTION_DECLARATION(_IE_CREATE_EXTENSION_FUNC)
#define IE_DEFINE_PLUGIN_CREATE_FUNCTION(PluginType, version, ...)
#define IE_DEFINE_PLUGIN_CREATE_FUNCTION_DECLARATION(_IE_CREATE_PLUGIN_FUNC)
#define IE_EXCEPTION_SWITCH(STATUS, TYPE_ALIAS, ...)
#define IE_SET_METRIC_RETURN(name, ...)
#define IE_THROW(...)
#define IE_VERSION_MAJOR
#define IE_VERSION_MINOR
#define IE_VERSION_PATCH
#define ITT_FUNCTION_NAME
#define LP_TRANSFORMATIONS_API
#define MAKE_MAP_COMMON(FRAMEWORK, ...)
#define MAKE_MAP_onnx(...)
#define MAKE_MAP_paddle(...)
#define MAKE_MAP_tensorflow(...)
#define MAKE_MAP_tensorflow_lite(...)
#define MATCHER_SCOPE(region)
#define METRIC_KEY(name)
#define METRIC_VALUE(name)
#define MULTI_CONFIG_KEY(name)
#define NGRAPH_API
#define NGRAPH_API_C
#define NGRAPH_CHECK(...)
#define NGRAPH_CHECK_HELPER(exc_class, ctx, ...)
#define NGRAPH_CHECK_HELPER1(exc_class, ctx, check)
#define NGRAPH_CHECK_HELPER2(exc_class, ctx, check, ...)
#define NGRAPH_DEBUG
#define NGRAPH_DEPRECATED(msg)
#define NGRAPH_ENUM_DEPRECATED(msg)
#define NGRAPH_ERR
#define NGRAPH_EXTERN_C
#define NGRAPH_HELPER_DLL_EXPORT
#define NGRAPH_HELPER_DLL_IMPORT
#define NGRAPH_INFO
#define NGRAPH_OP(a, b)
#define NGRAPH_OP(a, b)
#define NGRAPH_OP(a, b)
#define NGRAPH_OP(a, b)
#define NGRAPH_OP(a, b)
#define NGRAPH_OP(a, b)
#define NGRAPH_OP(a, b)
#define NGRAPH_OP(a, b)
#define NGRAPH_OP(a, b)
#define NGRAPH_OP(a, b)
#define NGRAPH_OP(a, b)
#define NGRAPH_RTTI_DECLARATION
#define NGRAPH_RTTI_DEFINITION(...)
#define NGRAPH_SUPPRESS_DEPRECATED_END
#define NGRAPH_SUPPRESS_DEPRECATED_START
#define NGRAPH_UNREACHABLE(...)
#define NGRAPH_WARN
#define NODE_VALIDATION_CHECK(node, ...)
#define OPENVINO_API
#define OPENVINO_API_C(...)
#define OPENVINO_ASSERT(...)
#define OPENVINO_ASSERT_HELPER(exc_class, ctx, ...)
#define OPENVINO_ASSERT_HELPER1(exc_class, ctx, check)
#define OPENVINO_ASSERT_HELPER2(exc_class, ctx, check, ...)
#define OPENVINO_CREATE_EXTENSIONS(extensions)
#define OPENVINO_C_API(...)
#define OPENVINO_C_API_CALLBACK
#define OPENVINO_C_VAR(...)
#define OPENVINO_DEBUG
#define OPENVINO_ERR
#define OPENVINO_EXTENSION_API
#define OPENVINO_EXTENSION_C_API
#define OPENVINO_FRAMEWORK_MAP(FRAMEWORK, ...)
#define OPENVINO_INFO
#define OPENVINO_NOT_IMPLEMENTED
#define OPENVINO_OP(...)
#define OPENVINO_RTTI(...)
#define OPENVINO_THROW(...)
#define OPENVINO_VERSION_MAJOR
#define OPENVINO_VERSION_MINOR
#define OPENVINO_VERSION_PATCH
#define OPENVINO_WARN
#define OVERLOAD_MACRO(name, count)
#define OVERLOAD_MACRO1(name, count)
#define OVERLOAD_MACRO2(name, count)
#define OV_CASE(Case, Type)
#define OV_CASE2(Case1, Case2, Type1, Type2)
#define OV_CC_CAT
#define OV_CC_EXPAND
#define OV_CC_TOSTRING
#define OV_COLLECT_ATTACHED_EXTENSIONS(FRAMEWORK)
#define OV_CREATE_PLUGIN
#define OV_DEFINE_PLUGIN_CREATE_FUNCTION(PluginType, version, ...)
#define OV_FRONTEND_API_VERSION
#define OV_ITT_DOMAIN(...)
#define OV_ITT_GROUP(group)
#define OV_ITT_SCOPE(group, ...)
#define OV_ITT_SCOPED_TASK(...)
#define OV_ITT_SCOPE_CHAIN(group, ...)
#define OV_ITT_SCOPE_NEXT(group, ...)
#define OV_ITT_SCOPE_SKIP(group, chainId)
#define OV_ITT_TASK_CHAIN(...)
#define OV_ITT_TASK_NEXT(...)
#define OV_ITT_TASK_SKIP(chainId)
#define OV_PP_ARG_N(_0, _1, _2, _3, _4, N, ...)
#define OV_PP_ARG_PLACEHOLDER_1
#define OV_PP_CAT(x, y)
#define OV_PP_CAT3(x, y, z)
#define OV_PP_CAT3_(x, y, z)
#define OV_PP_CAT4(x, y, z, w)
#define OV_PP_CAT4_(x, y, z, w)
#define OV_PP_CAT_(x, y)
#define OV_PP_EXPAND(X)
#define OV_PP_IS_ENABLED(x)
#define OV_PP_IS_ENABLED1(val)
#define OV_PP_IS_ENABLED2(arg1_or_junk)
#define OV_PP_NARG(...)
#define OV_PP_NARG_(...)
#define OV_PP_NO_ARGS(NAME)
#define OV_PP_OVERLOAD(NAME, ...)
#define OV_PP_RSEQ_N()
#define OV_PP_SECOND_ARG(...)
#define OV_PP_SECOND_ARG_(...)
#define OV_PP_SECOND_ARG_GET(ignored, val, ...)
#define OV_PP_TOSTRING(...)
#define OV_PP_TOSTRING_(...)
#define OV_SWITCH(Module, fn, ctx, val, ...)
#define OV_THREAD_OMP
#define OV_THREAD_SEQ
#define OV_THREAD_TBB
#define OV_THREAD_TBB_AUTO
#define PROJECT_ROOT_DIR
#define PROJECT_ROOT_DIR
#define REGISTER_DISABLED_PASS(obj, region, ...)
#define REGISTER_PASS(obj, region, ...)

#define RETURN_ARG_COUNT( \
    _1_, \
    _2_, \
    _3_, \
    _4_, \
    _5_, \
    _6, \
    _7, \
    _8, \
    _9, \
    _10, \
    _11, \
    _12, \
    _13, \
    _14, \
    _15, \
    _16, \
    _17, \
    _18, \
    _19, \
    _20, \
    _21, \
    _22, \
    _23, \
    _24, \
    _25, \
    count, \
    ... \
    )

#define TBB_PREVIEW_WAITING_FOR_WORKERS
#define THROW_IE_LPT_EXCEPTION(node)
#define THROW_TRANSFORMATION_EXCEPTION
#define TRANSFORMATIONS_API
#define TYPE_CASE(a)
#define USE_FACTORY(precision)
#define _NGRAPH_RTTI_DEFINITION_COMMON(CLASS)
#define _NGRAPH_RTTI_DEFINITION_NO_PARENT(CLASS, TYPE_NAME)
#define _NGRAPH_RTTI_DEFINITION_WITH_PARENT(CLASS, TYPE_NAME, PARENT_CLASS)
#define _OPENVINO_OP_REG(a, b)
#define _OPENVINO_OP_REG
#define _OPENVINO_OP_REG
#define _OPENVINO_OP_REG
#define _OPENVINO_OP_REG
#define _OPENVINO_OP_REG(a, b)
#define _OPENVINO_OP_REG(a, b)
#define _OPENVINO_OP_REG(a, b)
#define _OPENVINO_OP_REG
#define _OPENVINO_OP_REG
#define _OPENVINO_OP_REG(a, b)
#define _OPENVINO_OP_REG
#define _OPENVINO_OP_REG
#define _OPENVINO_OP_REG(a, b)
#define _OPENVINO_OP_REG(a, b)
#define _OPENVINO_OP_REG(a, b)
#define _OPENVINO_OP_REG
#define _OPENVINO_OP_REG(a, b)
#define _OPENVINO_OP_REG
#define _OPENVINO_OP_REG(a, b)
#define _OPENVINO_OP_REG(a, b)
#define _OPENVINO_OP_REG
#define _OPENVINO_RTTI_DEFINITION_SELECTOR(_1, _2, _3, NAME, ...)
#define _OPENVINO_RTTI_EXPAND(X)
#define _OPENVINO_RTTI_OP_WITH_TYPE(TYPE_NAME)
#define _OPENVINO_RTTI_OP_WITH_TYPE_VERSION(TYPE_NAME, VERSION_NAME)
#define _OPENVINO_RTTI_WITH_TYPE(TYPE_NAME)
#define _OPENVINO_RTTI_WITH_TYPE_VERSION(TYPE_NAME, VERSION_NAME)

#define _OPENVINO_RTTI_WITH_TYPE_VERSIONS_PARENT( \
    TYPE_NAME, \
    VERSION_NAME, \
    PARENT_CLASS \
    )

#define _OPENVINO_RTTI_WITH_TYPE_VERSION_PARENT( \
    TYPE_NAME, \
    VERSION_NAME, \
    PARENT_CLASS \
    )

#define createNodeIfRegistered(Module, key, ...)
#define cu32(x)
#define registerNodeIfRequired(Module, Name, key, Impl)
#define strncasecmp

Detailed Documentation¶

Global Functions¶

OPENVINO_C_VAR(const char \*)

gpu plugin properties key for remote context/tensor

Read-write property<uint32_t string>: video decoder surface plane in a shared memory blob parameter map.

Read-write property<uint32_t string>: video decoder surface handle in a shared memory blob parameter map.

Read-write property<void *>: OpenCL memory handle in a shared memory blob parameter map.

Read-write property<void *>: OpenCL queue handle in a shared context.

Read-write property<int string>: In case of multi-tile system, this key identifies tile within given context.

Read-write property<int string>: ID of device in OpenCL context if multiple devices are present in the context.

< Read-write property: shared device context type, can be either pure OpenCL (OCL) or shared video decoder (VA_SHARED) context. Value is string, it can be one of below strings: “OCL” - Pure OpenCL context “VA_SHARED” - Context shared with a video decoding device Read-write property<void *>: identifies OpenCL context handle in a shared context or shared memory blob parameter map.

Read-write property<void *>: video acceleration device/display handle in a shared context or shared memory blob parameter map.

Read-write property: type of internal shared memory in a shared memory blob parameter map. Value is string, it can be one of below strings: “OCL_BUFFER” - Shared OpenCL buffer blob “OCL_IMAGE2D” - Shared OpenCL 2D image blob “USM_USER_BUFFER” - Shared USM pointer allocated by user “USM_HOST_BUFFER” - Shared USM pointer type with host allocation type allocated by plugin “USM_DEVICE_BUFFER” - Shared USM pointer type with device allocation type allocated by plugin “VA_SURFACE” - Shared video decoder surface or D3D 2D texture blob “DX_BUFFER” - Shared D3D buffer blob

ov_model_is_dynamic(const ov_model_t \* model)

Returns true if any of the ops defined in the model is dynamic shape..

Parameters:

model

A pointer to the ov_model_t.

Returns:

true if model contains dynamic shapes

ov_preprocess_input_tensor_info_set_memory_type(
    ov_preprocess_input_tensor_info_t \* preprocess_input_tensor_info,
    const char \* mem_type
    )

Set ov_preprocess_input_tensor_info_t memory type.

Parameters:

preprocess_input_tensor_info	A pointer to the ov_preprocess_input_tensor_info_t.
mem_type	Memory type. Refer to `ov_remote_context.h` to get memory type string info.

Returns:

Status code of the operation: OK(0) for success.

template <class TShape>
bool get_data_as_shape(
    size_t idx,
    const ov::Node \* op,
    TShape& shape,
    const std::map<size_t, ov::HostTensorPtr>& constant_data = {}
    )

Get the operator’s constant data as shape of type T.

The constant data are get as size_t (Dimension value type for static shape). If pointed input is signed the output shape dimension can be wrongly interpreted.

Parameters:

TShape	Shape type.
idx	Operator’s input index.
op	Pointer to operator.
shape	Output shape made from constant data.
constant_data	Map with constant tensors. Optional default empty.

Returns:

true If constant data acquired as shape otherwise throws NodeValidation exception.

bool get_data_as_shape< ov::PartialShape >(
    size_t idx,
    const ov::Node \* op,
    ov::PartialShape& shape,
    const std::map<size_t, std::shared_ptr<ngraph::runtime::HostTensor>>& constant_data
    )

Get the operator’s constant data as ov::PartialShape.

If data not get as constant then try evaluate this input as partial shape from input’s bounds and labels.

The constant data are get as int64_t. If pointed input is unsigned then output shape dimension can be wrongly interpreted.

Parameters:

idx	Operator’s input index.
op	Pointer to operator.
shape	Output shape made from constant data.
constant_data	Map with constant tensors. Optional default empty.

Returns:

true If constant data acquired as shape otherwise throws NodeValidation exception.

template <class TDim>
void check_divided_result(
    const ov::Node \* op,
    const TDim& quotient,
    const TDim& dividend,
    const typename TDim::value_type& divisor
    )

Check for valid quotient of dimension division.

If quotient is not valid (quotient * divisor != dividend) throw NodeValidationFailure exception.

Parameters:

TDim	Type of dimension.
op	Pointer to operator.
quotient	Dimension result after division.
dividend	Original dimension.
divisor	Dimension divide value.

InferenceEngine::Blob::Ptr make_blob_with_precision(const InferenceEngine::TensorDesc& desc)

Creates Blob::Ptr with precision.

Parameters:

desc	The TensorDesc object

Returns:

A Blob::Ptr pointer

InferenceEngine::Blob::Ptr make_blob_with_precision(
    const InferenceEngine::TensorDesc& desc,
    void \* ptr
    )

Makes a blob with precision.

Parameters:

desc	The TensorDesc object
ptr	The pointer to a raw memory

Returns:

A Blob::Ptr pointer

InferenceEngine::Blob::Ptr make_blob_with_precision(
    const InferenceEngine::TensorDesc& desc,
    const std::shared_ptr<InferenceEngine::IAllocator>& alloc
    )

Makes a blob with precision.

Parameters:

desc	The description
alloc	The IAllocator object

Returns:

A Blob::Ptr pointer

InferenceEngine::Blob::Ptr make_plain_blob(
    InferenceEngine::Precision prec,
    const InferenceEngine::SizeVector dims
    )

Creates a plain Blob::Ptr.

Parameters:

prec	The Precision value
dims	The dims

Returns:

A Blob::Ptr pointer

template <class... Args>
InferenceEngine::Blob::Ptr make_blob_with_precision(
    InferenceEngine::Precision precision,
    Args&&... args
    )

Creates Blob::Ptr with precision.

Parameters:

precision	The precision
args	The arguments
Args	Variadic template arguments

Returns:

A Blob::Ptr pointer

template <typename T>
void CopyVectorToBlob(
    const InferenceEngine::Blob::Ptr outputBlob,
    const std::vector<T>& inputVector
    )

Copy data from std::vector to Blob.

Parameters:

T	type of data in std::vector
outputBlob	An output blob to copy to
inputVector	An input std::vector to copy from

int ie_memcpy(void \* dest, size_t destsz, void const \* src, size_t count)

Copies bytes between buffers with security enhancements Copies count bytes from src to dest. If the source and destination overlap, the behavior is undefined.

Parameters:

dest	A Pointer to the object to copy to
destsz	A max number of bytes to modify in the destination (typically the size of the destination object)
src	A pointer to the object to copy from
count	A number of bytes to copy

Returns:

zero on success and non-zero value on error.

parse_result ParseXml(const char \* file_path)

Parses a file and returns parse_result.

Parameters:

file_path

The file path

Returns:

The parse_result.

Macros¶

#define AUTO_CONFIG_KEY(name)

A macro which provides an AUTO-mangled name for configuration key with name name

#define CONFIG_KEY(name)

shortcut for defining configuration keys

#define CONFIG_KEY_INTERNAL(name)

Shortcut for defining internal configuration keys.

#define CONFIG_VALUE(name)

shortcut for defining configuration values

#define CONFIG_VALUE_INTERNAL(name)

Shortcut for defining internal configuration values.

#define CPU_CONFIG_KEY(name)

shortcut for defining configuration keys

#define DECLARE_GPU_PARAM_KEY(name, ...)

Shortcut for defining object parameter keys.

#define DECLARE_GPU_PARAM_VALUE(name)

Shortcut for defining possible values for object parameter keys.

#define EXEC_NETWORK_METRIC_KEY(name)

shortcut for defining common Inference Engine ExecutableNetwork metrics

#define FOREACH_CHILD(c, p, tag)

Defines convinient for-each based cycle to iterate over node children.

Parameters:

c	Child node name
p	Parent node name
tag	The tag represented as a string value

#define FRONT_END_CHECK_IMPLEMENTED(COND, NAME)

Assert macro.

Parameters:

COND	Condition. If ‘false’, throws ‘NotImplementedFailure’
NAME	Name of the function that is not implemented
ov::frontend::NotImplementedFailure

#define FRONT_END_GENERAL_CHECK(...)

Macro to check whether a boolean condition holds.

Parameters:

cond	Condition to check
…	Additional error message info to be added to the error message via the `<<` stream-insertion operator. Note that the expressions here will be evaluated lazily, i.e., only if the `cond` evalutes to `false`.
ov::frontend::GeneralFailure	if `cond` is false.

#define FRONT_END_INITIALIZATION_CHECK(...)

Macro to check whether a boolean condition holds.

Parameters:

cond	Condition to check
…	Additional error message info to be added to the error message via the `<<` stream-insertion operator. Note that the expressions here will be evaluated lazily, i.e., only if the `cond` evalutes to `false`.
ov::frontend::InitializationFailure	if `cond` is false.

#define FRONT_END_NOT_IMPLEMENTED(NAME)

Assert macro.

Parameters:

NAME	Name of the function that is not implemented
ov::frontend::NotImplementedFailure

#define FRONT_END_OP_CONVERSION_CHECK(...)

Macro to check whether a boolean condition holds.

Parameters:

cond	Condition to check
…	Additional error message info to be added to the error message via the `<<` stream-insertion operator. Note that the expressions here will be evaluated lazily, i.e., only if the `cond` evalutes to `false`.
ov::frontend::OpConversionFailure	if `cond` is false.

#define FRONT_END_THROW(MSG)

Assert macro.

Parameters:

MSG	Error message
ov::frontend::GeneralFailure

#define GNA_CONFIG_KEY(name)

Shortcut for defining configuration keys.

#define GNA_CONFIG_VALUE(name)

Shortcut for defining configuration values.

#define GPU_CONFIG_KEY(name)

shortcut for defining configuration keys

#define GPU_METRIC_KEY(name)

shortcut for defining GPU plugin metrics

#define GPU_PARAM_KEY(name)

Shortcut for defining configuration keys.

#define GPU_PARAM_VALUE(name)

Shortcut for defining configuration values.

#define HETERO_CONFIG_KEY(name)

Shortcut for defining HETERO configuration keys.

#define IE_ASSERT(EXPRESSION)

Uses assert() function if NDEBUG is not defined, InferenceEngine exception otherwise.

#define IE_CREATE_EXTENSION

Defines a name of a function creating extension instance.

#define IE_CREATE_PLUGIN

Defines a name of a function creating plugin instance.

#define IE_DEFINE_EXTENSION_CREATE_FUNCTION(ExtensionType)

Generates extension creation function.

#define IE_DEFINE_PLUGIN_CREATE_FUNCTION(PluginType, version, ...)

Defines the exported IE_CREATE_PLUGIN function which is used to create a plugin instance.

#define IE_EXCEPTION_SWITCH(STATUS, TYPE_ALIAS, ...)

Generate Switch statement over error codes adn maps them to coresponding exceptions type.

#define IE_SET_METRIC_RETURN(name, ...)

Return metric value with specified name and arguments .... Example:

IE_SET_METRIC_RETURN(SUPPORTED_CONFIG_KEYS, configKeys);

Parameters:

name	The metric name
…	A metric value

Returns:

A metric value wrapped with Parameter and returned to a calling function

#define IE_THROW(...)

A macro used to throw specified exception with a description.

#define IE_VERSION_MAJOR

Defines Inference Engine major version.

#define IE_VERSION_MINOR

Defines Inference Engine minor version.

#define IE_VERSION_PATCH

Defines Inference Engine patch version.

#define METRIC_KEY(name)

shortcut for defining common Inference Engine metrics

#define METRIC_VALUE(name)

shortcut for defining metric values

#define MULTI_CONFIG_KEY(name)

A macro which provides a MULTI-mangled name for configuration key with name name

#define NGRAPH_RTTI_DECLARATION

Helper macro that puts necessary declarations of RTTI block inside a class definition. Should be used in the scope of class that requires type identification besides one provided by C++ RTTI. Recommended to be used for all classes that are inherited from class ov::Node to enable pattern matching for them. Accepts necessary type identification details like type of the operation, version and optional parent class.

Applying this macro within a class definition provides declaration of type_info static constant for backward compatibility with old RTTI definition for Node, static function get_type_info_static which returns a reference to an object that is equal to type_info but not necessary to the same object, and get_type_info virtual function that overrides Node::get_type_info and returns a reference to the same object that get_type_info_static gives.

Use this macro as a public part of the class definition:

class MyOp : public Node
{
    public:
        // Don't use Node as a parent for type_info, it doesn't have any value and
        prohibited
        NGRAPH_RTTI_DECLARATION;

        ...
};

class MyInheritedOp : public MyOp
{
    public:
        NGRAPH_RTTI_DECLARATION;

        ...
};

To complete type identification for a class, use NGRAPH_RTTI_DEFINITION.

#define OPENVINO_ASSERT(...)

Macro to check whether a boolean condition holds.

Parameters:

cond	Condition to check
…	Additional error message info to be added to the error message via the `<<` stream-insertion operator. Note that the expressions here will be evaluated lazily, i.e., only if the `cond` evalutes to `false`.
ov::AssertFailure	if `cond` is false.

#define OPENVINO_CREATE_EXTENSIONS(extensions)

Macro generates the entry point for the library.

Parameters:

vector

of extensions

#define OPENVINO_RTTI(...)

Use this macro as a public part of the class definition:

class MyClass
{
    public:
        OPENVINO_RTTI("MyClass", "my_version");

        ...
};

class MyClass2: public MyClass
{
    public:
        OPENVINO_RTTI("MyClass2", "my_version2", MyClass);

        ...
};

OPENVINO_RTTI(name) OPENVINO_RTTI(name, version_id) OPENVINO_RTTI(name, version_id, parent) OPENVINO_RTTI(name, version_id, parent, old_version)

Parameters:

TYPE_NAME	a string literal of type const char* that names your class in type identification namespace; It is your choice how to name it, but it should be unique among all OPENVINO_RTTI_DECLARATION-enabled classes that can be used in conjunction with each other in one transformation flow.
VERSION_NAME	is an name of operation version to distinguish different versions of operations that shares the same TYPE_NAME
PARENT_CLASS	is an optional direct or indirect parent class for this class; define it only in case if there is a need to capture any operation from some group of operations that all derived from some common base class. Don’t use Node as a parent, it is a base class for all operations and doesn’t provide ability to define some perfect subset of operations. PARENT_CLASS should define RTTI with OPENVINO_RTTI_{DECLARATION/DEFINITION} macros.
_VERSION_INDEX	is an unsigned integer index to distinguish different versions of operations that shares the same TYPE_NAME (for backward compatibility)

#define OPENVINO_THROW(...)

Macro to signal a code path that is unreachable in a successful execution. It’s implemented with OPENVINO_ASSERT macro.

Parameters:

…	Additional error message that should describe why that execution path is unreachable.
ov::AssertFailure	if the macro is executed.

#define OPENVINO_VERSION_MAJOR

Defines OpenVINO major version.

#define OPENVINO_VERSION_MINOR

Defines OpenVINO minor version.

#define OPENVINO_VERSION_PATCH

Defines OpenVINO patch version.

#define TYPE_CASE(a)

Used in evaluator switch statement so that the case type and evaluate call are guaranteed to have the types match.

Use this in an evaluate_*() function like this switch (arg0->get_element_type()) {TYPE_CASE(i8) (arg0, arg1, out, broadcast_spec); break; TYPE_CASE(i16) (arg0, arg1, out, broadcast_spec); break; … }

Each TYPE_CASE statement expands like this: case element::Type_t::a: rc = evaluate<element::Type_t::a>(arg0, arg1, out, broadcast_spec)

Don’t forget to put a break after each statement or it will fall through and generate a runtime error.