Group Execution model utilities#
- group ov_dev_exec_model
Contains
ExecutionNodeand its properties.Variables
-
static const char ORIGINAL_NAMES[] = "originalLayersNames"#
Used to get a string of layer names separated by a comma from the original IR, which were fused/merged to the current executable primitive.
-
static const char IMPL_TYPE[] = "primitiveType"#
Used to get a type of the executable primitive.
-
static const char OUTPUT_PRECISIONS[] = "outputPrecisions"#
Used to get output precisions of the executable primitive.
-
static const char PERF_COUNTER[] = "execTimeMcs"#
Used to get a value of execution time of the executable primitive.
-
static const char OUTPUT_LAYOUTS[] = "outputLayouts"#
Used to get output layouts of primitive.
-
static const char EXECUTION_ORDER[] = "execOrder"#
Used to get an execution order of primitive.
-
static const char LAYER_TYPE[] = "layerType"#
Used to get a type of primitive.
-
static const char RUNTIME_PRECISION[] = "runtimePrecision"#
Used to get runtime precision of the executable primitive.
-
class ExecutionNode : public ov::op::Op
- #include <exec_model_info.hpp>
The Execution node which is used to represent node in execution graph.
It contains the following type of information in node runtime information:
ExecGraphInfoSerialization::ORIGINAL_NAMES
ExecGraphInfoSerialization::IMPL_TYPE
ExecGraphInfoSerialization::OUTPUT_PRECISIONS
ExecGraphInfoSerialization::PERF_COUNTER
ExecGraphInfoSerialization::OUTPUT_LAYOUTS
ExecGraphInfoSerialization::EXECUTION_ORDER
ExecGraphInfoSerialization::LAYER_TYPE
ExecGraphInfoSerialization::RUNTIME_PRECISION
Public Functions
-
ExecutionNode()
A default constructor with no node inputs and 0 output ports.
-
ExecutionNode(const ov::OutputVector &arguments, size_t output_size = 1)
Constructs a new execution node with a given parameters.
- Parameters:
arguments – [in] Inputs nodes
output_size – [in] A number of output ports
-
std::shared_ptr<ov::Node> clone_with_new_inputs(const ov::OutputVector &inputs) const override
Creates a new execution node with the same state, but different input nodes.
- Parameters:
inputs – [in] The input nodes
- Returns:
A newly created execution node
-
virtual bool visit_attributes(ov::AttributeVisitor&) override
Visits attributes of the node.
- Parameters:
visitor – [in] An attribute visitor
- Returns:
Returns
trueif an operation has completed successfully
-
namespace ov#
transformation aligns elementwise constant inputs ranks with its output rank
A namespace with const values for Execution Graph parameters names.
Executable Model Info is represented in ov::Model format with general ExecutionNode nodes inside including connections between the nodes. Each node describes an executable hardware-specific primitive and stores its parameters within ExecutionNode::get_rt_info map. There is a list of general keys for the parameters map.
OpenVINO C++ API.
Resolves transpose_b key from MatMul operation if corresponding input is constant or FakeQuantize by inserting Transpose.
Functions
- LP_TRANSFORMATIONS_API void mark_as_bias (const std::shared_ptr< Node > &node)
- LP_TRANSFORMATIONS_API bool marked_as_bias (const std::shared_ptr< const Node > &node)
-
void postpone_fp16_compression(RTMap &rt_info)#
-
bool is_fp16_compression_postponed(const RTMap &rt_info)#
-
void do_not_postpone_fp16_compression(RTMap &rt_info)#
getFusedNames return string with operation names separated by coma in alphabetical order
- Parameters:
node – [in] The node will be used to get FusedNames attribute
getFusedNamesVector return vector of fused names sorted in alphabetical order
- Parameters:
node – [in] The node will be used to get FusedNames attribute
- Returns:
vector of strings
getPrimitivesPriority return string with primitive priorities value
- Parameters:
node – [in] The node will be used to get PrimitivesPriority attribute
-
std::shared_ptr<ov::MappedMemory> load_mmap_object(const std::string &path)#
Returns mapped memory for a file from provided path. Instead of reading files, we can map the memory via mmap for Linux in order to avoid time-consuming reading and reduce memory consumption.
- Parameters:
path – Path to a file which memory will be mmaped.
- Returns:
MappedMemory shared ptr object which keep mmaped memory and control the lifetime.
- OPENVINO_API std::ostream & operator<< (std::ostream &s, const AxisSet &axis_set)
- OPENVINO_API std::ostream & operator<< (std::ostream &s, const AxisVector &axis_vector)
- OPENVINO_API std::ostream & operator<< (std::ostream &s, const Coordinate &coordinate)
- OPENVINO_API std::ostream & operator<< (std::ostream &s, const CoordinateDiff &coordinate_diff)
- OPENVINO_API std::ostream & operator<< (std::ostream &str, const Dimension &dimension)
Insert a human-readable representation of a dimension into an output stream.
Inserts the string
?ifdimensionis dynamic; else insertsdimension.get_length().- Parameters:
str – The output stream targeted for insertion.
dimension – The dimension to be inserted into
str.
- Returns:
A reference to
strafter insertion.
-
template<typename Type, typename Value>
std::enable_if<std::is_convertible<Value, std::string>::value, Type>::type as_enum(const Value &value)# Returns the enum value matching the string.
-
template<typename Value>
const std::string &as_string(Value value)# Returns the string matching the enum value.
-
static inline std::ostream &write_all_to_stream(std::ostream &str)#
-
template<typename T, typename ...TS>
std::ostream &write_all_to_stream(std::ostream &str, T &&arg, TS&&... args)#
-
template<class T, typename std::enable_if<!std::is_same<typename std::decay<T>::type, std::string>::value>::type* = nullptr>
std::string stringify(T &&arg)#
-
template<class T, typename std::enable_if<std::is_same<typename std::decay<T>::type, std::string>::value>::type* = nullptr>
T &stringify(T &&arg)#
- OPENVINO_API void traverse_nodes (const std::shared_ptr< const Model > &p, const std::function< void(const std::shared_ptr< Node > &)> &f)
- OPENVINO_API void traverse_nodes (const Model *p, const std::function< void(const std::shared_ptr< Node > &)> &f)
- OPENVINO_API void traverse_nodes (const NodeVector &subgraph_results, const std::function< void(const std::shared_ptr< Node > &)> &f, const NodeVector &subgraph_params={})
Visit each node in a sub-graph of the entire graph.
Traverses a sub-graph starting from subgraph_results moving up towards parameter nodes. Traversal stops if it hits a node in subgraph_params.
Most useful for finding parameters of a graph directly from the result nodes and not from function parameters or extracting a subgraph relevant to the computation of certain outputs
- OPENVINO_API void replace_node (const std::shared_ptr< Node > &target, const std::shared_ptr< Node > &replacement, const std::vector< int64_t > &output_order)
Replace the node
targetwith the nodereplacement, i.e., redirect all users and control dependencies oftargettoreplacement.This is primarily used in graph-rewriting passes. For example, we might “fuse” two Concat operations as follows:
(Step 0: Original graph)
A B | | v v N0[Concat, concatenation_axis=3] C | | v v N1[Concat, concatenation_axis=3] | | v v some_user another_user
(Step 1: Construct replacement)
shared_ptr<Node> new_N1 = make_shared<op::Concat>({A,B,C},3);
A————————————-—. | | | B————-—)—. | | | | v v | | N0[Concat, concatenation_axis=3] C–—)—)—. | | | | | v v v v v N1[Concat, concatenation_axis=3] new_N1[Concat, concatenation_axis=3] | | v v some_user another_user
(Step 2: Replace N1 with new_N1)
replace_node(N1, new_N1);
A————————————-—. | | | B————-—)—. | | | | v v | | N0[Concat, concatenation_axis=3] C–—)—)—. | | | | | v v v v v N1[Concat, concatenation_axis=3] new_N1[Concat, concatenation_axis=3] | | v v some_user another_user
(Step 3: N0 and N1 are now dead, nodes will be freed)
[happens automatically, once all shared_ptrs to N1 are released]
A————————————-—. | B————-—)—. | | | | C–—)—)—. | | | v v v new_N1[Concat, concatenation_axis=3] | | v v some_user another_user
NOTE 1: replace_node is not type-safe (the graph is not revalidated). For example, the following is allowed, even if node
some_userrequires an input of shape 2x2:(Before) A(shape=2x2) B(shape=3x3) | v some_user(requires 2x2 input)
(After — graph is now invalid)
NOTE 2: it is possible to insert a cycle into the graph with replace_node, resulting in an invalid graph. Care must be taken to avoid this. One common example is when you are attempting to insert a new nodereplace_node(A, B); A(shape=2x2) B(shape=3x3) | v some_user(requires 2x2 input)
M“after”a nodeN`. For example, you might expect this to work:shared_ptr<Node> M = make_shared<SomeUnaryOp>(N); replace_node(M, N);
The problem is that at replacement time, N itself is a user of M. So we end up introducing a cycle as follows:
other users…N | v
other users…||| vvv N------------>M | v
To avoid the cycle, a valid way to perform the above desired insertion would be,||| vvv .----. | | | | N `----->M | v other users...auto new_N = N->clone_with_new_inputs(N->input_values()); shared_ptr<Node> M = make_shared<SomeUnaryOp>(new_N); replace_node(N, M);
- OPENVINO_API void replace_node (const std::shared_ptr< Node > &target, const OutputVector &replacement_values)
Replace target.outputs[i] with replacement_values[i] and transfer control dependents and.
- OPENVINO_API void replace_node (const std::shared_ptr< Node > &target, const std::shared_ptr< Node > &replacement)
- OPENVINO_API void replace_nodes (const std::shared_ptr< Model > &f, const std::unordered_map< std::shared_ptr< op::v0::Parameter >, std::shared_ptr< op::v0::Parameter > > ¶meter_replacement_map, const std::unordered_map< std::shared_ptr< Node >, std::shared_ptr< Node > > &body_replacement_map)
Replace multiple nodes in a function.
Limitations:
No check is made that the replaced nodes in
parameter_replacement_mapare actually among the bound parameters off. (If a parameter appears in the map that is not bound byf, it will be silently ignored.)If a parameter node appears as a key in both
parameter_replacement_mapand inbody_replacement_map, behavior is unspecified.
- Parameters:
f – Model where replacement is taking place.
parameter_replacement_map – A mapping from parameter shared pointers to parameter shared pointers. For each pair (k,v) in the map, parameter k is replaced by parameter v, except if k==v or k is not a parameter bound by f, in which case the pair (k,v) is ignored.
body_replacement_map – A mapping from node shared pointers to node shared pointers. For each pair (k,v) in the map, node k is replaced by node v, except if k==v, the pair (k,v) is ignored. Note that if k is a parameter, its users will be redirected to v, but k will not be replaced in the function’s parameter list.
Topological sort of nodes needed to compute root_nodes.
- OPENVINO_API bool compare_constants (const std::shared_ptr< Node > &n1, const std::shared_ptr< Node > &n2)
- OPENVINO_API bool replace_output_update_name (Output< Node > node, const Output< Node > &node_input)
- OPENVINO_API bool replace_node_update_name (const std::shared_ptr< Node > &target, const std::shared_ptr< Node > &replacement)
- OPENVINO_API void serialize (const std::shared_ptr< const ov::Model > &m, const std::string &xml_path, const std::string &bin_path="", ov::pass::Serialize::Version version=ov::pass::Serialize::Version::UNSPECIFIED)
Serialize given model into IR. The generated .xml and .bin files will be saved into provided paths. This method serializes model “as-is” that means no weights compression and other possible transformations are applied. It is recommended to use ov::save_model function instead of ov::serialize, because it is aligned with default model conversion flow.
- OPENVINO_API void save_model (const std::shared_ptr< const ov::Model > &model, const std::string &output_model, bool compress_to_fp16=true)
Save given model into IR. Floating point weights are compressed to FP16 by default. This method saves a model to IR applying all necessary transformations that usually applied in model conversion flow provided by mo tool. Paricularly, floatting point weights are compressed to FP16.
- Parameters:
model – Model which will be converted to IR representation.
output_model – Path to the output model file, must have extension .xml
compress_to_fp16 – Whether to compress floatting point weights to FP16 (true by default)
- OPENVINO_API std::ostream & operator<< (std::ostream &str, const Interval &interval)
- OPENVINO_API std::ostream & operator<< (std::ostream &, const Model &)
- OPENVINO_API ov::Dimension get_batch (const std::shared_ptr< const ov::Model > &f)
Helper method to get associated batch size for a Model.
Checks layout of each parameter in a Model and extracts value for N (B) dimension. All values are then merged and returned
- Throws:
ov::AssertFailure – with details in case of error. Possible errors are:
There is no parameter with layout set. Model shall have at least one parameter with layout with ‘N’ dimension. Recommended fix is to use
Parameter::set_layoutAPI, e.g.model->get_parameters()[some_index]->set_layout("NCHW");Several parameters have conflicting N dimension, e.g. param1 NCHW{1,3,224,224} and param2 NCHW{2,3,224,224}. This is ambiguous, most probably first dimension is incorrectly marked as ‘batch’ (N) in some layout. User shall fix it before using of ‘get_batch’ (in example above correct layout for param2 from ‘NCHW’ to ‘CHWN’)
- Parameters:
f – Model where to look for a batch_size value
- Returns:
Dimension representing current batch size. Can represent a number or be a dynamic
- OPENVINO_API void set_batch (const std::shared_ptr< ov::Model > &model, ov::Dimension batch_size)
Helper method to set batch size to a Model.
Checks layout of each parameter in a Model and sets value for N (B) dimension. Then performs validation and type propagation
- Throws:
ov::AssertFailure – with details in case of error. Possible errors are:
There is no parameter with N dimension in layout. Model shall have at least one parameter with layout with ‘N’ dimension. Recommended fix is to use
Parameter::set_layoutAPI, e.g.model->get_parameters()[some_index]->set_layout("NCHW");Several parameters have conflicting N dimension, e.g. param1 NCHW{1,3,224,224} and param2 NCHW{3,224,224,1}. This is ambiguous (1 != 3), most probably some dimension is incorrectly marked as ‘batch’ (N) in some layout. User shall fix it before using of ‘set_batch’ (in example above correct layout for param2 from ‘NCHW’ to ‘CHWN’)
Validation fails after setting batch_size. Model becomes in inconsistent state after new batch size value is applied. Possible reason could be that layout was not set for some parameters, or batch size can’t be applied to model at all
- Parameters:
model – model where to set batch_size value
batch_size – Batch size value. For dynamic batch size, Dimension::dynamic() can be passed.
- OPENVINO_API std::string node_validation_failure_loc_string (const Node *node)
- OPENVINO_API std::ostream & operator<< (std::ostream &, const Node &)
- OPENVINO_API std::ostream & operator<< (std::ostream &, const Node *)
- void OPENVINO_API check_new_args_count (const Node *const node, const OutputVector &new_args)
Check new arguments size if match node inputs count.
This check is required in cloning ov::Node.
- Parameters:
node – Pointer to node.
new_args – Vector with new outputs to check.
- OPENVINO_API std::ostream & operator<< (std::ostream &out, const Input< Node > &input)
- OPENVINO_API std::ostream & operator<< (std::ostream &out, const Input< const Node > &input)
- OPENVINO_API std::ostream & operator<< (std::ostream &out, const Output< Node > &output)
- OPENVINO_API std::ostream & operator<< (std::ostream &out, const Output< const Node > &output)
- OPENVINO_API OutputVector as_output_vector (const NodeVector &args)
- OPENVINO_API NodeVector as_node_vector (const OutputVector &values)
- OPENVINO_API ResultVector as_result_vector (const OutputVector &values)
Returns a ResultVector referencing values.
- OPENVINO_API PartialShape operator+ (const PartialShape &s1, const PartialShape &s2)
Elementwise addition of two PartialShape objects.
If
s1ors2has dynamic rank, returns PartialShape::dynamic().If
s1 ands2` both have static rank, and their ranks are unequal, throws std::invalid_argument.If
s1ands2both have static rank, and their ranks are equal, returns a new shape whoseith dimension iss1[i] + s2[i].
- Parameters:
s1 – Left operand for addition.
s2 – Right operand for addition.
- Throws:
std::invalid_argument – If
s1ands2have inconsistent ranks.- Returns:
The result of elementwise adding
s1tos2(see description).
- OPENVINO_API std::ostream & operator<< (std::ostream &str, const PartialShape &shape)
Inserts a human-readable representation of a PartialShape into an output stream.
The output to the stream is in “informal” notation. In other words:
If
shapehas dynamic rank, inserts the string?.If
shapehas static rank, inserts the string{, then inserts each dimension ofshapeinto the output stream separated by commas, then inserts}.
PartialShape s1{PartialShape::dynamic())}; PartialShape s2{}; PartialShape s3{1,Dimension::dynamic(),2,3}; PartialShape s4{2,3,4}; std::cout << s1 << std::endl << s2 << std::endl << s3 << std::endl << s4 << std::endl;
? {} {1,?,2,3} {2,3,4}- Parameters:
str – The output stream targeted for insertion.
shape – The shape to be inserted into
str.
- Returns:
A reference to
strafter insertion.
- OPENVINO_API void copy_runtime_info (const std::shared_ptr< ov::Node > &from, const std::shared_ptr< ov::Node > &to)
- OPENVINO_API void copy_runtime_info (const std::shared_ptr< ov::Node > &from, ov::NodeVector to)
- OPENVINO_API void copy_runtime_info (const ov::NodeVector &from, const std::shared_ptr< ov::Node > &to)
- OPENVINO_API void copy_runtime_info (const ov::NodeVector &from, ov::NodeVector to)
- OPENVINO_API void copy_output_runtime_info (const ov::OutputVector &from, ov::OutputVector to)
- OPENVINO_API std::ostream & operator<< (std::ostream &os, const RuntimeAttribute &attrubute)
-
template<typename ForwardIt>
size_t shape_size(ForwardIt start_dim, const ForwardIt end_dim)# Number of elements in a subset of dimensions of a shape. Returns a product of dimensions in a range [start_dim;end_dim)
-
template<typename SHAPE_TYPE>
size_t shape_size(const SHAPE_TYPE &shape)# Number of elements in spanned by a shape.
-
template<typename SHAPE_TYPE>
std::vector<size_t> row_major_strides(const SHAPE_TYPE &shape)# Row-major strides for a shape.
-
template<typename SHAPE_TYPE>
size_t row_major_stride(const SHAPE_TYPE &shape, size_t axis)#
-
template<typename SHAPE_TYPE>
inline bool is_scalar(const SHAPE_TYPE &shape)#
-
template<typename SHAPE_TYPE>
inline bool is_vector(const SHAPE_TYPE &shape)#
- OPENVINO_API std::ostream & operator<< (std::ostream &s, const Shape &shape)
- OPENVINO_API std::ostream & operator<< (std::ostream &s, const Strides &strides)
- OPENVINO_API std::ostream & operator<< (std::ostream &s, const DiscreteTypeInfo &info)
- bool ::type is_type (Value value)
- Type *::type as_type (Value value)
-
template<typename T, typename U>
auto as_type_ptr(const U &value) -> decltype(::ov::util::AsTypePtr<U>::template call<T>(value))# Casts a std::shared_ptr<Value> to a std::shared_ptr<Type> if it is of type Type, nullptr otherwise
- OPENVINO_API std::ostream & operator<< (std::ostream &s, const Version &version)
- OPENVINO_API std::ostream & operator<< (std::ostream &s, const std::map< std::string, Version > &versions)
- OPENVINO_API_C (const Version) get_openvino_version() noexcept
Gets the current OpenVINO version.
- Returns:
The current OpenVINO version
- OPENVINO_API std::ostream & operator<< (std::ostream &s, const op::v1::BinaryConvolution::BinaryConvolutionMode &type)
- OPENVINO_API std::ostream & operator<< (std::ostream &s, const op::v0::DepthToSpace::DepthToSpaceMode &type)
- OPENVINO_API std::ostream & operator<< (std::ostream &s, const op::v9::GridSample::InterpolationMode &type)
- OPENVINO_API std::ostream & operator<< (std::ostream &s, const op::v9::GridSample::PaddingMode &type)
- OPENVINO_API std::ostream & operator<< (std::ostream &s, const op::v0::Interpolate::InterpolateMode &type)
- OPENVINO_API std::ostream & operator<< (std::ostream &s, const op::LSTMWeightsFormat &type)
- OPENVINO_API std::ostream & operator<< (std::ostream &s, const op::v8::MatrixNms::DecayFunction &type)
- OPENVINO_API std::ostream & operator<< (std::ostream &s, const op::v8::MatrixNms::SortResultType &type)
- OPENVINO_API std::ostream & operator<< (std::ostream &s, const op::v1::NonMaxSuppression::BoxEncodingType &type)
- OPENVINO_API std::ostream & operator<< (std::ostream &s, const op::v3::NonMaxSuppression::BoxEncodingType &type)
- OPENVINO_API std::ostream & operator<< (std::ostream &s, const op::v5::NonMaxSuppression::BoxEncodingType &type)
- OPENVINO_API std::ostream & operator<< (std::ostream &s, const op::v9::NonMaxSuppression::BoxEncodingType &type)
- OPENVINO_API std::ostream & operator<< (std::ostream &s, const op::v1::Reverse::Mode &type)
- OPENVINO_API std::ostream & operator<< (std::ostream &s, const op::v5::Round::RoundMode &type)
- OPENVINO_API std::ostream & operator<< (std::ostream &s, const op::v15::ScatterNDUpdate::Reduction &reduction)
- OPENVINO_API std::ostream & operator<< (std::ostream &s, const op::v0::SpaceToDepth::SpaceToDepthMode &type)
- OPENVINO_API std::ostream & operator<< (std::ostream &s, const op::util::InterpolateBase::InterpolateMode &type)
- OPENVINO_API std::ostream & operator<< (std::ostream &s, const op::util::InterpolateBase::CoordinateTransformMode &type)
- OPENVINO_API std::ostream & operator<< (std::ostream &s, const op::util::InterpolateBase::NearestMode &type)
- OPENVINO_API std::ostream & operator<< (std::ostream &s, const op::util::InterpolateBase::ShapeCalcMode &type)
- OPENVINO_API std::ostream & operator<< (std::ostream &s, const op::util::MulticlassNmsBase::SortResultType &type)
- void OPENVINO_API mark_as_precision_sensitive (ov::Input< ov::Node > node_input)
- void OPENVINO_API unmark_as_precision_sensitive (ov::Input< ov::Node > node_input)
- bool OPENVINO_API is_precision_sensitive (const ov::Input< ov::Node > &node_input)
- OPENVINO_API void skip_invalidation (const ov::Output< ov::Node > &output)
- OPENVINO_API bool skip_invalidation (const ov::descriptor::Tensor &tensor)
- OPENVINO_API void remove_skip_invalidation_rti (const std::shared_ptr< ov::Model > &model, bool outermost_model=true)
- OPENVINO_API void populate_tensor_with_missing_symbols (ov::descriptor::Tensor &tensor)
- const OPENVINO_API OpSet & get_opset1 ()
Returns opset1.
- const OPENVINO_API OpSet & get_opset2 ()
Returns opset2.
- const OPENVINO_API OpSet & get_opset3 ()
Returns opset3.
- const OPENVINO_API OpSet & get_opset4 ()
Returns opset4.
- const OPENVINO_API OpSet & get_opset5 ()
Returns opset5.
- const OPENVINO_API OpSet & get_opset6 ()
Returns opset6.
- const OPENVINO_API OpSet & get_opset7 ()
Returns opset7.
- const OPENVINO_API OpSet & get_opset8 ()
Returns opset8.
- const OPENVINO_API OpSet & get_opset9 ()
Returns opset9.
- const OPENVINO_API OpSet & get_opset10 ()
Returns opset10.
- const OPENVINO_API OpSet & get_opset11 ()
Returns opset11.
- const OPENVINO_API OpSet & get_opset12 ()
Returns opset12.
- const OPENVINO_API OpSet & get_opset13 ()
Returns opset13.
- const OPENVINO_API OpSet & get_opset14 ()
Returns opset14.
- const OPENVINO_API OpSet & get_opset15 ()
Returns map of available opsets.
- const OPENVINO_API std::map< std::string, std::function< const ov::OpSet &()> > & get_available_opsets ()
Returns map of available opsets.
-
template<class ElementIter>
constexpr bool is_nf4_iterator()#
-
std::size_t coordinate_index(const Coordinate &c, const Shape &s)#
-
size_t coordinate_offset(const std::vector<size_t> &coordinate, const std::vector<size_t> &strides)#
Calculate offset from begin of buffer based on coordinate and strides.
If coordinates and strides have different sizes then result is undefined behaviour.
- Parameters:
coordinate – Vector with multi-dimension coordinates.
strides – Vector with multi-dimension strides
- Returns:
Offset of element from start of buffer.
-
template<class T>
constexpr bool is_floating_point()# Check if T is OpenVINO floating point precision.
- Returns:
True if OpenVino floating point precision.
-
OV_ITT_DOMAIN(OV_PP_CAT(TYPE_LIST_, ov_eval))#
-
template<class TContainer>
constexpr auto make_tensor_accessor(const TContainer &c) -> TensorAccessor<TContainer># Makes TensorAccessor for specific tensor container.
See also
TensorAccessor for supported types.
- Template Parameters:
TContainer – Type of tensor containers
- Parameters:
c – Container of tensors.
- Returns:
TensorContainer for specific type.
-
auto make_tensor_accessor() -> const TensorAccessor<void>&#
Makes empty TensorAccessor which return empty tensor for any port number.
- Returns:
TensorAccessor to return empty tensor.
-
template<class T, class TResult = std::vector<T>, class UnaryOperation>
TResult get_raw_data_as(const element::Type_t et, const void *const ptr, const size_t size, UnaryOperation &&func)# Get the raw data as TResult object.
- Template Parameters:
T – TResult data type.
TResult – Type of return object, must support creation of std::inserter. Default std::vector<T>.
UnaryOperation – Unary function object applied on data with signature (T f(const U u)).
- Parameters:
et – Element type of input data.
ptr – Pointer to data of type et.
size – Data size as number of elements.
func – Unary operation function object.
- Throws:
ov::AssertionFailure – for not supported element type.
- Returns:
Object of TResult with data from input pointer and transformed by unary operation.
-
template<class T, class TResult = std::vector<T>, class UnaryOperation = ov::util::Cast<T>>
TResult get_tensor_data_as(const Tensor &t, UnaryOperation &&func = ov::util::Cast<T>())# Get data from ov:tensor as object TResult.
- Template Parameters:
T – TResult data type.
TResult – Type of return object, must support creation of std::inserter. Default std::vector<T>.
UnaryOperation – Unary function object applied on data with signature (T f(const U u)).
- Parameters:
t – Input tensor.
func – Unary operation function object.
- Returns:
Object of TResult with data from tensor.
-
void shutdown()#
Shut down the OpenVINO by deleting all static-duration objects allocated by the library and releasing dependent resources.
You might want to use this function if you are developing a dynamically-loaded library which should clean up all resources after itself when the library is unloaded.
Note
This function should be used by advanced user to control unload the resources.
Returns set of nodes from original model which are determined as supported after applied transformation pipeline.
- Parameters:
model – Original model
transform – Transformation pipeline function
is_node_supported – Function returning whether node is supported or not
query_model_ratio – The percentage of the model can be queried during query model (0 if not query)
- Returns:
Set of strings which contains supported node names
-
std::shared_ptr<ITensor> make_tensor(const element::Type type, const Shape &shape, const Allocator &allocator = {})#
Constructs Tensor using element type and shape. Allocate internal host storage using default allocator.
-
std::shared_ptr<ITensor> make_tensor(const element::Type type, const Shape &shape, void *host_ptr, const Strides &strides = {})#
Constructs Tensor using element type and shape. Wraps allocated host memory.
Note
Does not perform memory allocation internally
Constructs region of interest (ROI) tensor form another tensor.
Note
Does not perform memory allocation internally
Note
A Number of dimensions in
beginandendmust match number of dimensions inother.get_shape()- Parameters:
other – original tensor
begin – start coordinate of ROI object inside of the original object.
end – end coordinate of ROI object inside of the original object.
-
bool check_open_mp_env_vars(bool include_omp_num_threads = true)#
Checks whether OpenMP environment variables are defined.
- Parameters:
include_omp_num_threads – [in] Indicates if the omp number threads is included
- Returns:
Trueif any OpenMP environment variable is defined,falseotherwise
-
std::vector<int> get_available_numa_nodes()#
Returns available CPU NUMA nodes (on Linux, and Windows [only with TBB], single node is assumed on all other OSes)
- Returns:
NUMA nodes
-
std::vector<int> get_available_cores_types()#
Returns available CPU cores types (on Linux, and Windows) and ONLY with TBB, single core type is assumed otherwise.
- Returns:
Vector of core types
-
int get_number_of_cpu_cores(bool big_cores_only = false)#
Returns number of CPU physical cores on Linux/Windows (which is considered to be more performance friendly for servers) (on other OSes it simply relies on the original parallel API of choice, which usually uses the logical cores). call function with ‘false’ to get #phys cores of all types call function with ‘true’ to get #phys ‘Big’ cores number of ‘Little’ = ‘all’ - ‘Big’.
- Parameters:
big_cores_only – [in] Additionally limits the number of reported cores to the ‘Big’ cores only.
- Returns:
Number of physical CPU cores.
-
int get_number_of_logical_cpu_cores(bool big_cores_only = false)#
Returns number of CPU logical cores on Linux/Windows (on other OSes it simply relies on the original parallel API of choice, which uses the ‘all’ logical cores). call function with ‘false’ to get #logical cores of all types call function with ‘true’ to get #logical ‘Big’ cores number of ‘Little’ = ‘all’ - ‘Big’.
- Parameters:
big_cores_only – [in] Additionally limits the number of reported cores to the ‘Big’ cores only.
- Returns:
Number of logical CPU cores.
-
int get_number_of_blocked_cores()#
Returns number of blocked CPU cores. Please note that this is a temporary interface for performance optimization on a specific platform. May be removed in future release.
- Returns:
Number of blocked CPU cores.
-
bool with_cpu_x86_sse42()#
Checks whether CPU supports SSE 4.2 capability.
- Returns:
Trueis SSE 4.2 instructions are available,falseotherwise
-
bool with_cpu_x86_avx()#
Checks whether CPU supports AVX capability.
- Returns:
Trueis AVX instructions are available,falseotherwise
-
bool with_cpu_x86_avx2()#
Checks whether CPU supports AVX2 capability.
- Returns:
Trueis AVX2 instructions are available,falseotherwise
-
bool with_cpu_x86_avx2_vnni()#
Checks whether CPU supports AVX2_VNNI capability.
- Returns:
Trueis AVX2_VNNI instructions are available,falseotherwise
-
bool with_cpu_x86_avx512f()#
Checks whether CPU supports AVX 512 capability.
- Returns:
Trueis AVX512F (foundation) instructions are available,falseotherwise
-
bool with_cpu_x86_avx512_core()#
Checks whether CPU supports AVX 512 capability.
- Returns:
Trueis AVX512F, AVX512BW, AVX512DQ instructions are available,falseotherwise
-
bool with_cpu_x86_avx512_core_vnni()#
Checks whether CPU supports AVX 512 VNNI capability.
- Returns:
Trueis AVX512F, AVX512BW, AVX512DQ, AVX512_VNNI instructions are available,falseotherwise
-
bool with_cpu_x86_bfloat16()#
Checks whether CPU supports BFloat16 capability.
- Returns:
Trueis tAVX512_BF16 instructions are available,falseotherwise
-
bool with_cpu_x86_avx512_core_fp16()#
Checks whether CPU supports fp16 capability.
- Returns:
Trueis tAVX512_FP16 instructions are available,falseotherwise
-
bool with_cpu_x86_avx512_core_amx_int8()#
Checks whether CPU supports AMX int8 capability.
- Returns:
Trueis tAMX_INT8 instructions are available,falseotherwise
-
bool with_cpu_x86_avx512_core_amx_bf16()#
Checks whether CPU supports AMX bf16 capability.
- Returns:
Trueis tAMX_BF16 instructions are available,falseotherwise
-
bool with_cpu_x86_avx512_core_amx()#
Checks whether CPU supports AMX capability.
- Returns:
Trueis tAMX_INT8 or tAMX_BF16 instructions are available,falseotherwise
-
bool is_cpu_map_available()#
Checks whether cpu_mapping Available.
- Returns:
Trueis CPU mapping is available,falseotherwise
-
int get_num_numa_nodes()#
Get number of numa nodes.
- Returns:
Number of numa nodes
-
int get_num_sockets()#
Get number of sockets.
- Returns:
Number of sockets
-
std::vector<std::vector<int>> get_proc_type_table()#
Returns a table of number of processor types on Linux/Windows.
Processor table of one socket CPU desktop ALL_PROC | MAIN_CORE_PROC | EFFICIENT_CORE_PROC | HYPER_THREADING_PROC 32 8 16 8 // Total number of one socket
- Returns:
A table about number of CPU cores of different types defined with ColumnOfProcessorTypeTable The following are two example of processor type table.
Processor table of two socket CPUs XEON server ALL_PROC | MAIN_CORE_PROC | EFFICIENT_CORE_PROC | HYPER_THREADING_PROC 96 48 0 48 // Total number of two sockets 48 24 0 24 // Number of socket one 48 24 0 24 // Number of socket two
-
int get_current_socket_id()#
Returns the socket ID in cpu mapping table of the currently running thread.
- Returns:
socket ID in cpu mapping
-
std::vector<std::vector<int>> get_org_proc_type_table()#
Returns a table of original number of processor types without filtering other plugins occupying CPU resources. The difference from get_proc_type_table: This is used to get the configuration of current machine. For example, GPU plugin occupies all Pcores, there is only one type core in proc_type_table from get_proc_type_table(). If user wants to get the real configuration of this machine which should be got from get_org_proc_type_table.
- Returns:
A table about number of CPU cores of different types defined with ColumnOfProcessorTypeTable
-
void reserve_available_cpus(const std::vector<std::vector<int>> streams_info_table, std::vector<std::vector<int>> &stream_processors, const int cpu_status = NOT_USED)#
Get and reserve available cpu ids.
- Parameters:
streams_info_table – [in] streams information table.
stream_processors – [in] processors grouped in stream which is used in core binding in cpu streams executor
cpu_status – [in] set cpu status
-
void set_cpu_used(const std::vector<int> &cpu_ids, const int used)#
Set CPU_MAP_USED_FLAG of cpu_mapping.
- Parameters:
cpu_ids – [in] cpus in cpu_mapping.
used – [in] update CPU_MAP_USED_FLAG of cpu_mapping with this flag bit
-
int get_socket_by_numa_node(int numa_node_id)#
Get socket id by current numa node id.
- Parameters:
numa_node_id – [in] numa node id
- Returns:
socket id
-
int get_org_socket_id(int socket_id)#
Get original socket id by current socket id, the input socket id is recalculated after filtering (like numactl), while the original socket id is the original id before filtering.
- Parameters:
socket_id – [in] socket id
- Returns:
socket id
-
int get_org_numa_id(int numa_node_id)#
Get original numa node id by current numa node id, the input numa node id is recalculated after filtering (like numactl), while the original numa node id is the original id before filtering.
- Parameters:
numa_node_id – [in] numa node id
- Returns:
numa node id
-
class Allocator
- #include <allocator.hpp>
Wraps allocator implementation to provide safe way to store allocater loaded from shared library And constructs default based on
newdeletec++ calls allocator if created without parameters Accepts any std::pmr::memory_resource like allocator.Public Functions
-
~Allocator()
Destructor preserves unloading order of implementation object and reference to library.
-
Allocator()
Default constructor.
-
Allocator(const Allocator &other) = default
Default copy constructor.
- Parameters:
other – other Allocator object
-
Allocator &operator=(const Allocator &other) = default
Default copy assignment operator.
- Parameters:
other – other Allocator object
- Returns:
reference to the current object
-
Allocator(Allocator &&other) = default
Default move constructor.
- Parameters:
other – other Allocator object
-
Allocator &operator=(Allocator &&other) = default
Default move assignment operator.
- Parameters:
other – other Allocator object
- Returns:
reference to the current object
Initialize allocator using any allocator like object.
- Template Parameters:
A – Type of allocator
- Parameters:
a – allocator object
-
void *allocate(const size_t bytes, const size_t alignment = alignof(max_align_t))
Allocates memory.
- Parameters:
bytes – The size in bytes at least to allocate
alignment – The alignment of storage
- Throws:
Exception – if specified size and alignment is not supported
- Returns:
Handle to the allocated resource
-
void deallocate(void *ptr, const size_t bytes = 0, const size_t alignment = alignof(max_align_t))
Releases the handle and all associated memory resources which invalidates the handle.
- Parameters:
ptr – The handle to free
bytes – The size in bytes that was passed into allocate() method
alignment – The alignment of storage that was passed into allocate() method
-
bool operator==(const Allocator &other) const
Compares with other Allocator.
- Parameters:
other – Other instance of allocator
- Returns:
trueif and only if memory allocated from one Allocator can be deallocated from the other and vice versa
-
~Allocator()
-
class Any
- #include <any.hpp>
This class represents an object to work with different types.
Public Functions
-
Any() = default
Default constructor.
-
Any &operator=(const Any &other)
Сopy assignment operator.
- Parameters:
other – other Any object
- Returns:
reference to the current object
-
Any &operator=(Any &&other) = default
Default move assignment operator.
- Parameters:
other – other Any object
- Returns:
reference to the current object
-
~Any()
Destructor preserves unloading order of implementation object and reference to library.
-
template<typename T, typename std::enable_if<!std::is_same<decay_t<T>, Any>::value && !std::is_abstract<decay_t<T>>::value && !std::is_convertible<decay_t<T>, Base::Ptr>::value, bool>::type = true>
inline Any(T &&value) Constructor creates any with object.
- Template Parameters:
T – Any type
- Parameters:
value – object
-
Any(const char *str)
Constructor creates string any from char *.
- Parameters:
str – char array
-
Any(const std::nullptr_t)
Empty constructor.
-
const std::type_info &type_info() const
Returns type info
- Returns:
type info
-
bool empty() const
Checks that any contains a value
- Returns:
false if any contains a value else false
-
template<class T>
inline bool is() const Check that stored type can be casted to specified type. If internal type supports Base.
- Template Parameters:
T – Type of value
- Returns:
true if type of value is correct. Return false if any is empty
Dynamic cast to specified type
- Template Parameters:
T – type
- Returns:
casted object
Dynamic cast to specified type
- Template Parameters:
T – type
- Returns:
casted object
Dynamic cast to specified type
- Template Parameters:
T – type
- Returns:
casted object
-
template<class T>
inline std::enable_if<std::is_same<T, std::string>::value, T>::type &as() Dynamic cast to specified type
- Template Parameters:
T – type
- Returns:
casted object
-
template<class T>
inline const T &as() const Dynamic cast to specified type
- Template Parameters:
T – type
- Returns:
const reference to caster object
-
bool operator==(const Any &other) const
The comparison operator for the Any.
- Parameters:
other – object to compare
- Returns:
true if objects are equal
-
bool operator==(const std::nullptr_t&) const
The comparison operator for the Any.
- Parameters:
other – object to compare
- Returns:
true if objects are equal
-
bool operator!=(const Any &other) const
The comparison operator for the Any.
- Parameters:
other – object to compare
- Returns:
true if objects aren’t equal
-
void print(std::ostream &stream) const
Prints underlying object to the given output stream. Uses operator<< if it is defined, leaves stream unchanged otherwise. In case of empty any or nullptr stream immediately returns.
- Parameters:
stream – Output stream object will be printed to.
-
void read(std::istream &stream)
Read into underlying object from the given input stream. Uses operator>> if it is defined, leaves stream unchanged otherwise. In case of empty any or nullptr stream immediately returns.
- Parameters:
stream – Output stream object will be printed to.
-
void *addressof()
Returns address to internal value if any is not empty and
nullptrinstead.- Returns:
address to internal stored value
-
const void *addressof() const
Returns address to internal value if any is not empty and
nullptrinstead.- Returns:
address to internal stored value
-
Any() = default
-
class AssertFailure : public ov::Exception
- #include <except.hpp>
Base class for check failure exceptions.
Subclassed by ov::NodeValidationFailure, ov::NotImplemented, ov::frontend::GeneralFailure, ov::frontend::InitializationFailure, ov::frontend::NotImplementedFailure, ov::frontend::OpConversionFailure, ov::frontend::OpValidationFailure
-
template<typename AT>
class AttributeAdapter - #include <attribute_adapter.hpp>
An AttributeAdapter “captures” an attribute as an AT& and makes it available as a ValueAccessor<VAT>.
-
template<>
class AttributeAdapter<AxisVector> : public ov::IndirectVectorValueAccessor<AxisVector, std::vector<int64_t>># - #include <axis_vector.hpp>
-
template<>
class AttributeAdapter<bool> : public ov::DirectValueAccessor<bool># - #include <attribute_adapter.hpp>
Access a bool as a bool.
-
template<>
class AttributeAdapter<Coordinate> : public ov::IndirectVectorValueAccessor<Coordinate, std::vector<int64_t>># - #include <coordinate.hpp>
-
template<>
class AttributeAdapter<CoordinateDiff> : public ov::IndirectVectorValueAccessor<CoordinateDiff, std::vector<int64_t>># - #include <coordinate_diff.hpp>
-
template<>
class AttributeAdapter<double> : public ov::DirectValueAccessor<double># - #include <attribute_adapter.hpp>
Access a double as a double.
-
template<>
class AttributeAdapter<float> : public ov::IndirectScalarValueAccessor<float, double># - #include <attribute_adapter.hpp>
-
template<>
class AttributeAdapter<int16_t> : public ov::IndirectScalarValueAccessor<int16_t, int64_t># - #include <attribute_adapter.hpp>
Access an int16_t as an int64_t.
-
template<>
class AttributeAdapter<int32_t> : public ov::IndirectScalarValueAccessor<int32_t, int64_t># - #include <attribute_adapter.hpp>
Access an int32_t as an int64_t.
-
template<>
class AttributeAdapter<int64_t> : public ov::DirectValueAccessor<int64_t># - #include <attribute_adapter.hpp>
Access an int64_t as an int64_t.
-
template<>
class AttributeAdapter<int8_t> : public ov::IndirectScalarValueAccessor<int8_t, int64_t># - #include <attribute_adapter.hpp>
Access an int8_t and an int64_t.
-
template<>
class AttributeAdapter<Layout> : public ov::ValueAccessor<std::string># - #include <layout.hpp>
- template<> AutoBroadcastSpec > : public ov::VisitorAdapter
- #include <attr_types.hpp>
- template<> AutoBroadcastType > : public ov::EnumAttributeAdapterBase< op::AutoBroadcastType >
- #include <attr_types.hpp>
- template<> BroadcastModeSpec > : public ov::VisitorAdapter
- #include <attr_types.hpp>
- template<> BroadcastType > : public ov::EnumAttributeAdapterBase< op::BroadcastType >
- #include <attr_types.hpp>
- template<> EpsMode > : public ov::EnumAttributeAdapterBase< op::EpsMode >
- #include <attr_types.hpp>
- template<> GeluApproximationMode > : public ov::EnumAttributeAdapterBase< op::GeluApproximationMode >
- #include <gelu.hpp>
- template<> LSTMWeightsFormat > : public ov::EnumAttributeAdapterBase< op::LSTMWeightsFormat >
- #include <lstm_cell.hpp>
- template<> MVNEpsMode > : public ov::EnumAttributeAdapterBase< op::MVNEpsMode >
- #include <mvn.hpp>
- template<> PadMode > : public ov::EnumAttributeAdapterBase< op::PadMode >
- #include <attr_types.hpp>
- template<> PadType > : public ov::EnumAttributeAdapterBase< op::PadType >
- #include <attr_types.hpp>
- template<> RecurrentSequenceDirection > : public ov::EnumAttributeAdapterBase< op::RecurrentSequenceDirection >
- #include <attr_types.hpp>
- template<> RoundingType > : public ov::EnumAttributeAdapterBase< op::RoundingType >
- #include <attr_types.hpp>
- template<> TopKMode > : public ov::EnumAttributeAdapterBase< op::TopKMode >
- #include <attr_types.hpp>
- template<> TopKSortType > : public ov::EnumAttributeAdapterBase< op::TopKSortType >
- #include <attr_types.hpp>
- template<> CoordinateTransformMode > : public ov::EnumAttributeAdapterBase< op::util::InterpolateBase::CoordinateTransformMode >
- #include <interpolate_base.hpp>
- template<> InterpolateMode > : public ov::EnumAttributeAdapterBase< op::util::InterpolateBase::InterpolateMode >
- #include <interpolate_base.hpp>
- template<> NearestMode > : public ov::EnumAttributeAdapterBase< op::util::InterpolateBase::NearestMode >
- #include <interpolate_base.hpp>
- template<> ShapeCalcMode > : public ov::EnumAttributeAdapterBase< op::util::InterpolateBase::ShapeCalcMode >
- #include <interpolate_base.hpp>
- template<> SortResultType > : public ov::EnumAttributeAdapterBase< op::util::MulticlassNmsBase::SortResultType >
- #include <multiclass_nms_base.hpp>
- template<> DepthToSpaceMode > : public ov::EnumAttributeAdapterBase< op::v0::DepthToSpace::DepthToSpaceMode >
- #include <depth_to_space.hpp>
- template<> InterpolateMode > : public ov::EnumAttributeAdapterBase< op::v0::Interpolate::InterpolateMode >
- #include <interpolate.hpp>
- template<> SpaceToDepthMode > : public ov::EnumAttributeAdapterBase< op::v0::SpaceToDepth::SpaceToDepthMode >
- #include <space_to_depth.hpp>
- template<> Reduction > : public ov::EnumAttributeAdapterBase< op::v12::ScatterElementsUpdate::Reduction >
- #include <scatter_elements_update.hpp>
- template<> Reduction > : public ov::EnumAttributeAdapterBase< op::v15::ScatterNDUpdate::Reduction >
- #include <scatter_nd_update.hpp>
- template<> BinaryConvolutionMode > : public ov::EnumAttributeAdapterBase< op::v1::BinaryConvolution::BinaryConvolutionMode >
- #include <binary_convolution.hpp>
- template<> BoxEncodingType > : public ov::EnumAttributeAdapterBase< op::v1::NonMaxSuppression::BoxEncodingType >
- #include <non_max_suppression.hpp>
- template<> Mode > : public ov::EnumAttributeAdapterBase< op::v1::Reverse::Mode >
- #include <reverse.hpp>
- template<> BoxEncodingType > : public ov::EnumAttributeAdapterBase< op::v3::NonMaxSuppression::BoxEncodingType >
- #include <non_max_suppression.hpp>
- template<> PoolingMode > : public ov::EnumAttributeAdapterBase< op::v3::ROIAlign::PoolingMode >
- #include <roi_align.hpp>
- template<> SpecialBodyPorts > : public ov::DirectValueAccessor< op::v5::Loop::SpecialBodyPorts >
- #include <loop.hpp>
- template<> BoxEncodingType > : public ov::EnumAttributeAdapterBase< op::v5::NonMaxSuppression::BoxEncodingType >
- #include <non_max_suppression.hpp>
- template<> RoundMode > : public ov::EnumAttributeAdapterBase< op::v5::Round::RoundMode >
- #include <round.hpp>
- template<> DecayFunction > : public ov::EnumAttributeAdapterBase< op::v8::MatrixNms::DecayFunction >
- #include <matrix_nms.hpp>
- template<> SortResultType > : public ov::EnumAttributeAdapterBase< op::v8::MatrixNms::SortResultType >
- #include <matrix_nms.hpp>
- template<> InterpolationMode > : public ov::EnumAttributeAdapterBase< op::v9::GridSample::InterpolationMode >
- #include <grid_sample.hpp>
- template<> PaddingMode > : public ov::EnumAttributeAdapterBase< op::v9::GridSample::PaddingMode >
- #include <grid_sample.hpp>
- template<> BoxEncodingType > : public ov::EnumAttributeAdapterBase< op::v9::NonMaxSuppression::BoxEncodingType >
- #include <non_max_suppression.hpp>
- template<> AlignedMode > : public ov::EnumAttributeAdapterBase< op::v9::ROIAlign::AlignedMode >
- #include <roi_align.hpp>
- template<> PoolingMode > : public ov::EnumAttributeAdapterBase< op::v9::ROIAlign::PoolingMode >
- #include <roi_align.hpp>
- template<> AxisSet > : public ov::ValueAccessor< std::vector< int64_t > >
- #include <axis_set.hpp>
- template<> Dimension > : public ov::DirectValueAccessor< ov::Dimension >
- #include <dimension.hpp>
- template<> Type > : public ov::ValueAccessor< std::string >
- #include <element_type.hpp>
Public Functions
-
virtual const std::string &get() override
Returns the value.
-
virtual void set(const std::string &value) override#
Sets the value.
-
virtual const std::string &get() override
- template<> Type_t > : public ov::EnumAttributeAdapterBase< ov::element::Type_t >
- #include <element_type.hpp>
- template<> TypeVector > : public ov::DirectValueAccessor< ov::element::TypeVector >
- #include <element_type.hpp>
- template<> NodeVector > : public ov::VisitorAdapter
- #include <node.hpp>
- template<> FrameworkNodeAttrs > : public ov::DirectValueAccessor< ov::op::util::FrameworkNodeAttrs >
- #include <framework_node.hpp>
- template<> PartialShape > : public ov::DirectValueAccessor< ov::PartialShape >
- #include <partial_shape.hpp>
- template<> Shape > : public ov::IndirectVectorValueAccessor< ov::Shape, std::vector< int64_t > >
- #include <shape.hpp>
-
template<>
class AttributeAdapter<ParameterVector> : public ov::VisitorAdapter# - #include <parameter.hpp>
-
template<>
class AttributeAdapter<ResultVector> : public ov::VisitorAdapter# - #include <result.hpp>
- template<> string > > : public ov::DirectValueAccessor< std::set< std::string > >
- #include <attribute_adapter.hpp>
- template<> Variable > > : public ov::DirectValueAccessor< std::shared_ptr< op::util::Variable > >
- #include <variable.hpp>
- template<> Model > > : public ov::DirectValueAccessor< std::shared_ptr< ov::Model > >
- #include <model.hpp>
- template<> Node > > : public ov::VisitorAdapter
- #include <node.hpp>
Visits a reference to a node that has been registered with the visitor.
- template<> string > : public ov::DirectValueAccessor< std::string >
- #include <attribute_adapter.hpp>
Access a string as a string.
- template<> vector< double > > : public ov::DirectValueAccessor< std::vector< double > >
- #include <attribute_adapter.hpp>
Access a vector<double>
- template<> vector< float > > : public ov::DirectValueAccessor< std::vector< float > >
- #include <attribute_adapter.hpp>
Access a vector<float>
- template<> vector< int16_t > > : public ov::DirectValueAccessor< std::vector< int16_t > >
- #include <attribute_adapter.hpp>
Access a vector<int16_t>
- template<> vector< int32_t > > : public ov::DirectValueAccessor< std::vector< int32_t > >
- #include <attribute_adapter.hpp>
Access a vector<int32_t>
- template<> vector< int64_t > > : public ov::DirectValueAccessor< std::vector< int64_t > >
- #include <attribute_adapter.hpp>
Access a vector<int64_t>
- template<> vector< int8_t > > : public ov::DirectValueAccessor< std::vector< int8_t > >
- #include <attribute_adapter.hpp>
Access a vector<int8_t>
Note: These class bodies cannot be defined with templates because of interactions between dllexport and templates on Windows.
- template<> InputDescription > > > : public ov::DirectValueAccessor< std::vector< std::shared_ptr< op::util::MultiSubGraphOp::InputDescription > > >
- #include <multi_subgraph_base.hpp>
- template<> OutputDescription > > > : public ov::DirectValueAccessor< std::vector< std::shared_ptr< op::util::MultiSubGraphOp::OutputDescription > > >
- #include <multi_subgraph_base.hpp>
- template<> string > > : public ov::DirectValueAccessor< std::vector< std::string > >
- #include <attribute_adapter.hpp>
Access a vector<string>
- template<> vector< uint16_t > > : public ov::DirectValueAccessor< std::vector< uint16_t > >
- #include <attribute_adapter.hpp>
Access a vector<uint16_t>
- template<> vector< uint32_t > > : public ov::DirectValueAccessor< std::vector< uint32_t > >
- #include <attribute_adapter.hpp>
Access a vector<uint32_t>
- template<> vector< uint64_t > > : public ov::DirectValueAccessor< std::vector< uint64_t > >
- #include <attribute_adapter.hpp>
Access a vector<uint64_t>
- template<> vector< uint8_t > > : public ov::DirectValueAccessor< std::vector< uint8_t > >
- #include <attribute_adapter.hpp>
Access a vector<uint8_t>
-
template<>
class AttributeAdapter<Strides> : public ov::IndirectVectorValueAccessor<Strides, std::vector<int64_t>># - #include <strides.hpp>
-
template<>
class AttributeAdapter<uint16_t> : public ov::IndirectScalarValueAccessor<uint16_t, int64_t># - #include <attribute_adapter.hpp>
Access a uint16_t as an int64_t.
-
template<>
class AttributeAdapter<uint32_t> : public ov::IndirectScalarValueAccessor<uint32_t, int64_t># - #include <attribute_adapter.hpp>
Access a uint32_t as an int64_t.
-
template<>
class AttributeAdapter<uint64_t> : public ov::IndirectScalarValueAccessor<uint64_t, int64_t># - #include <attribute_adapter.hpp>
Access a uint64_t as an int64_t.
-
template<>
class AttributeAdapter<uint8_t> : public ov::IndirectScalarValueAccessor<uint8_t, int64_t># - #include <attribute_adapter.hpp>
Access a uint8_t as an int64_t.
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class AttributeVisitor
- #include <attribute_visitor.hpp>
Visits the attributes of a node, primarily for serialization-like tasks.
Attributes are the node parameters that are always compile-time constants. Values computed from the graph topology and attributes during compilation are not attributes.
Attributes have a wide variety of types, but serialization formats are more restricted. We assume serialization easily supports scalar types of bool 64-bit signed, string, and double, and has specialized ways to support numeric arrays and raw data+size. The visitor and adapter convert between the limited serialization types and the unlimited attribute types.
A visitor is passed to an op’s visit_attributes method. The visit_attributes method calls the template method visitor.on_attribute<AT>(const std::string& name, AT& value) on each attribute. The visitor can read or write the attribute’s value. The on_attribute method creates an AttributeAdapter<AT> for the value and passes it to one of the visitors on_adapter methods. The on_adapter methods expect a reference to a ValueAccessor<VAT> or a VisitorAdapter. A ValueAccessor<VAT> has get/set methods that can be used to read/write the attribute value as type VAT. These methods are triggered by deriving AttributeAdapter<AT> from ValueAccessor<VAT>. For more complex cases, such as structs, the on_adapter method for VisitorAdapter passes the name and visitor to the adapter, so that the adapter can perform additional work such as visiting struct members or sequence values.
When a node visits an attribute with structure, the node’s on_attribute passes a name for the entire attribute, but the struct will have its own methods to be visited. Similarly, a vector will have a sequence of members to be visited. The adapter may use the visitor methods start_struct/finish_struct and start_vector/next_vector/finish_vector to inidicate nexted members.
The visitor method get_name_with_context creates a generic nested version of the name. Visitors can override according to their serialization requirements.
Attributes that are shared_ptr<Node> are special. They must have been already been registered with the visitor using register_node, which needs a shared pointer to a node and a string ID. The ID string will be used to serialize the node or find the node during deserialization.
Subclassed by ov::frontend::FWVisitor, ov::frontend::FWVisitorInputAttributes
Public Functions
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virtual void on_adapter(const std::string &name, ValueAccessor<void> &adapter) = 0
handles all specialized on_adapter methods implemented by the visitor.
The adapter implements get_type_info(), which can be used to determine the adapter directly or via is_type and as_type on any platform
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virtual void on_adapter(const std::string &name, VisitorAdapter &adapter)
Hook for adapters that need visitor access.
Provides API to handle openvino Function attribute type, accessed as ValueAccessor.
- Parameters:
name – attribute name
adapter – reference to a Function ValueAccessor<VAT>
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template<typename AT>
inline void on_attribute(const std::string &name, AT &value) The generic visitor. There must be a definition of AttributeAdapter<T> that can convert to a ValueAccessor
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inline const std::vector<std::string> &get_context() const
- Returns:
The nested context of visits
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virtual std::string get_name_with_context()
- Returns:
context prepended to names
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virtual void start_structure(const std::string &name)
Start visiting a nested structure.
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virtual std::string finish_structure()
Finish visiting a nested structure.
Associate a node with an id.
No node may be used as an attribute unless it has already been registered with an ID. References to nodes are visited with a ValueAccessor of their ID.
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virtual std::shared_ptr<Node> get_registered_node(node_id_t id)
Returns the node with the given id, or nullptr if there is no registered node.
Returns the id for the node, or -1 if the node is not registered.
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virtual void on_adapter(const std::string &name, ValueAccessor<void> &adapter) = 0
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class AvgPoolPrecisionPreservedAttribute : public ov::PrecisionPreservedAttribute
- #include <avg_pool_precision_preserved_attribute.hpp>
AvgPoolPrecisionPreservedAttribute is utility attribute which is used only during
AvgPooloperation precision preserved property definition.For more details about the attribute, refer to AvgPoolPrecisionPreservedAttribute page in the OpenVINO Developer Guide.
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class AxisSet : public std::set<size_t>
- #include <axis_set.hpp>
A set of axes.
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class AxisVector : public std::vector<size_t>
- #include <axis_vector.hpp>
A vector of axes.
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class BaseOpExtension : public ov::Extension
- #include <op_extension.hpp>
The base interface for OpenVINO operation extensions.
Subclassed by ov::OpExtension< T >
Public Functions
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virtual const ov::DiscreteTypeInfo &get_type_info() const = 0
Returns the type info of operation.
- Returns:
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virtual ov::OutputVector create(const ov::OutputVector &inputs, ov::AttributeVisitor &visitor) const = 0
Method creates an OpenVINO operation.
- Parameters:
inputs – vector of input ports
visitor – attribute visitor which allows to read necessaty arguments
- Returns:
vector of output ports
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virtual std::vector<ov::Extension::Ptr> get_attached_extensions() const = 0
Returns extensions that should be registered together with this extension class object.
Attached extensions may include frontend extensions that OpenVINO op to framework ops or necessary transformations that should be applied to the network which consist of target op.
- Returns:
-
~BaseOpExtension() override
Destructor.
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virtual const ov::DiscreteTypeInfo &get_type_info() const = 0
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class bfloat16
- #include <bfloat16.hpp>
-
class BiasAttribute : public ov::RuntimeAttribute
- #include <bias_attribute.hpp>
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class Busy : public ov::Exception
- #include <exception.hpp>
Thrown in case of calling the InferRequest methods while the request is busy with compute operation.
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class Cancelled : public ov::Exception
- #include <exception.hpp>
Thrown in case of cancelled asynchronous operation.
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class CompiledModel
- #include <compiled_model.hpp>
This class represents a compiled model.
A model is compiled by a specific device by applying multiple optimization transformations, then mapping to compute kernels.
Public Functions
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CompiledModel() = default
Default constructor.
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~CompiledModel()
Destructor that preserves unloading order of an implementation object and reference to library.
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std::shared_ptr<const Model> get_runtime_model() const
Gets runtime model information from a device. This object represents an internal device-specific model that is optimized for a particular accelerator. It contains device-specific nodes, runtime information and can be used only to understand how the source model is optimized and which kernels, element types, and layouts are selected for optimal inference.
- Returns:
A model containing Executable Graph Info.
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const std::vector<ov::Output<const ov::Node>> &inputs() const
Gets all inputs of a compiled model. Inputs are represented as a vector of outputs of the ov::op::v0::Parameter operations. They contain information about input tensors such as tensor shape, names, and element type.
- Returns:
std::vector of model inputs.
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const ov::Output<const ov::Node> &input() const
Gets a single input of a compiled model. The input is represented as an output of the ov::op::v0::Parameter operation. The input contains information about input tensor such as tensor shape, names, and element type.
Note
If a model has more than one input, this method throws ov::Exception.
- Returns:
Compiled model input.
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const ov::Output<const ov::Node> &input(size_t i) const
Gets input of a compiled model identified by
i. The input contains information about input tensor such as tensor shape, names, and element type.Note
The method throws ov::Exception if input with the specified index
iis not found.- Parameters:
i – Index of input.
- Returns:
Compiled model input.
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const ov::Output<const ov::Node> &input(const std::string &tensor_name) const
Gets input of a compiled model identified by
tensor_name. The input contains information about input tensor such as tensor shape, names, and element type.Note
The method throws ov::Exception if input with the specified tensor name
tensor_nameis not found.- Parameters:
tensor_name – The input tensor name.
- Returns:
Compiled model input.
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const std::vector<ov::Output<const ov::Node>> &outputs() const
Get all outputs of a compiled model. Outputs are represented as a vector of output from the ov::op::v0::Result operations. Outputs contain information about output tensors such as tensor shape, names, and element type.
- Returns:
std::vector of model outputs.
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const ov::Output<const ov::Node> &output() const
Gets a single output of a compiled model. The output is represented as an output from the ov::op::v0::Result operation. The output contains information about output tensor such as tensor shape, names, and element type.
Note
If a model has more than one output, this method throws ov::Exception.
- Returns:
Compiled model output.
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const ov::Output<const ov::Node> &output(size_t i) const
Gets output of a compiled model identified by
index. The output contains information about output tensor such as tensor shape, names, and element type.Note
The method throws ov::Exception if output with the specified index
indexis not found.- Parameters:
i – Index of input.
- Returns:
Compiled model output.
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const ov::Output<const ov::Node> &output(const std::string &tensor_name) const
Gets output of a compiled model identified by
tensor_name. The output contains information about output tensor such as tensor shape, names, and element type.Note
The method throws ov::Exception if output with the specified tensor name
tensor_nameis not found.- Parameters:
tensor_name – Output tensor name.
- Returns:
Compiled model output.
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InferRequest create_infer_request()
Creates an inference request object used to infer the compiled model. The created request has allocated input and output tensors (which can be changed later).
- Returns:
InferRequest object
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void export_model(std::ostream &model_stream)
Exports the current compiled model to an output stream
std::ostream. The exported model can also be imported via the ov::Core::import_model method.See also
- Parameters:
model_stream – Output stream to store the model to.
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void set_property(const AnyMap &properties)
Sets properties for the current compiled model.
- Parameters:
properties – Map of pairs: (property name, property value).
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template<typename ...Properties>
inline util::EnableIfAllStringAny<void, Properties...> set_property(Properties&&... properties) Sets properties for the current compiled model.
- Template Parameters:
Properties – Should be the pack of
std::pair<std::string, ov::Any>types.- Parameters:
properties – Optional pack of pairs: (property name, property value).
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Any get_property(const std::string &name) const
Gets properties for current compiled model.
The method is responsible for extracting information that affects compiled model inference. The list of supported configuration values can be extracted via CompiledModel::get_property with the ov::supported_properties key, but some of these keys cannot be changed dynamically, for example, ov::device::id cannot be changed if a compiled model has already been compiled for a particular device.
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template<typename T, PropertyMutability mutability>
inline T get_property(const ov::Property<T, mutability> &property) const Gets properties related to device behaviour.
The method extracts information that can be set via the set_property method.
- Template Parameters:
T – Type of a returned value.
- Parameters:
property – Property object.
- Returns:
Value of property.
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RemoteContext get_context() const
Returns pointer to device-specific shared context on a remote accelerator device that was used to create this CompiledModel.
- Returns:
A context.
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bool operator!() const noexcept
Checks if the current CompiledModel object is not initialized.
- Returns:
trueif the current CompiledModel object is not initialized;false, otherwise.
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explicit operator bool() const noexcept
Checks if the current CompiledModel object is initialized.
- Returns:
trueif the current CompiledModel object is initialized;false, otherwise.
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CompiledModel() = default
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class Coordinate : public std::vector<size_t>
- #include <coordinate.hpp>
Coordinates for a tensor element.
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class CoordinateDiff : public std::vector<std::ptrdiff_t>
- #include <coordinate_diff.hpp>
A difference (signed) of tensor element coordinates.
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class CoordinateIterator
- #include <coordinate_transform.hpp>
A useful class that allows to iterate over the tensor coordinates. For example, for tensor with dimensions {2, 3} this iterator produces the following coordinates: {0,0}, {0,1}, {0,2}, {1,0}, {1,1}, {2,2}.
Public Functions
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inline CoordinateIterator(const Shape &target_shape)
Coordinates iterator constructor.
- Parameters:
target_shape – The target shape for coordinates iteration
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void operator++()
The postfix operation increment the iterator by one.
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CoordinateIterator operator++(int)
The prefix operation increment the iterator by one.
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void operator+=(size_t n)
Increments iterator n times.
- Parameters:
n – number of elements it should be advanced
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const Coordinate &operator*() const noexcept
Iterator dereferencing operator returns reference to current pointed coordinate.
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bool operator!=(const CoordinateIterator &it) const noexcept
Checks for iterator inequality.
- Parameters:
it – second iterator to compare
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bool operator==(const CoordinateIterator &it) const noexcept
Checks for iterator equality.
- Parameters:
it – second iterator to compare
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size_t advance(size_t axis) noexcept
Increments iterator using specified axis of the shape n times.
- Parameters:
axis – index used for iteration
Public Static Functions
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static const CoordinateIterator &end()
Useful function to build the last iterator. Returns a singleton that points to the last iterator.
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inline CoordinateIterator(const Shape &target_shape)
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class CoordinateTransformBasic
- #include <coordinate_transform.hpp>
Class which allows to calculate item index with given coordinates in tensor and helps to iterate over all coordinates. Tensor items should be placed in memory in row-major order.
Public Functions
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CoordinateIterator begin() const noexcept
Returns an iterator to the first coordinate of the tensor.
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const CoordinateIterator &end() const noexcept
Returns an iterator to the coordinate following the last element of the tensor.
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CoordinateIterator begin() const noexcept
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class Core
- #include <core.hpp>
This class represents an OpenVINO runtime Core entity.
User applications can create several Core class instances, but in this case the underlying plugins are created multiple times and not shared between several Core instances. The recommended way is to have a single Core instance per application.
Unnamed Group
-
CompiledModel compile_model(const std::string &model_path, const AnyMap &properties = {})
Reads and loads a compiled model from the IR/ONNX/PDPD file to the default OpenVINO device selected by the AUTO plugin.
This can be more efficient than using the Core::read_model + Core::compile_model(model_in_memory_object) flow, especially for cases when caching is enabled and a cached model is available.
- Parameters:
model_path – Path to a model.
properties – Optional map of pairs: (property name, property value) relevant only for this load operation.
- Returns:
A compiled model.
Unnamed Group
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template<typename ...Properties>
inline util::EnableIfAllStringAny<CompiledModel, Properties...> compile_model(const std::string &model_path, Properties&&... properties) Reads and loads a compiled model from IR / ONNX / PDPD file to the default OpenVINO device selected by AUTO plugin.
This can be more efficient than using read_model + compile_model(Model) flow especially for cases when caching is enabled and cached model is available
- Template Parameters:
Properties – Should be the pack of
std::pair<std::string, ov::Any>types- Parameters:
model_path – path to model with string or wstring
properties – Optional pack of pairs: (property name, property value) relevant only for this load operation
- Returns:
A compiled model
Unnamed Group
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CompiledModel compile_model(const std::string &model_path, const std::string &device_name, const AnyMap &properties = {})
Reads a model and creates a compiled model from the IR/ONNX/PDPD file.
This can be more efficient than using the Core::read_model + Core::compile_model(model_in_memory_object) flow, especially for cases when caching is enabled and a cached model is available.
- Parameters:
model_path – Path to a model.
device_name – Name of a device to load a model to.
properties – Optional map of pairs: (property name, property value) relevant only for this load operation.
- Returns:
A compiled model.
Unnamed Group
-
template<typename ...Properties>
inline util::EnableIfAllStringAny<CompiledModel, Properties...> compile_model(const std::string &model_path, const std::string &device_name, Properties&&... properties) Reads a model and creates a compiled model from the IR/ONNX/PDPD file.
This can be more efficient than using read_model + compile_model(Model) flow especially for cases when caching is enabled and cached model is available.
- Template Parameters:
Properties – Should be a pack of
std::pair<std::string, ov::Any>types.- Parameters:
model_path – Path to a model.
device_name – Name of a device to load a model to.
properties – Optional pack of pairs: (property name, property value) relevant only for this load operation.
- Returns:
A compiled model.
Public Functions
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explicit Core(const std::string &xml_config_file = {})
Constructs an OpenVINO Core instance with devices and their plugins description.
There are two ways how to configure device plugins:
(default) Use XML configuration file in case of dynamic libraries build;
Use strictly defined configuration in case of static libraries build.
- Parameters:
xml_config_file – Path to the .xml file with plugins to load from. If path contains only file name with extension, file will be searched in a folder with OpenVINO runtime shared library. If the XML configuration file is not specified, default OpenVINO Runtime plugins are loaded from:
(dynamic build) default
plugins.xmlfile located in the same folder as OpenVINO runtime shared library;(static build) statically defined configuration. In this case path to the .xml file is ignored.
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std::map<std::string, Version> get_versions(const std::string &device_name) const
Returns device plugins version information. Device name can be complex and identify multiple devices at once like
HETERO:CPU,GPU; in this case, std::map contains multiple entries, each per device.- Parameters:
device_name – Device name to identify a plugin.
- Returns:
A vector of versions.
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std::shared_ptr<ov::Model> read_model(const std::string &model_path, const std::string &bin_path = {}) const
Reads models from IR / ONNX / PDPD / TF / TFLite file formats.
- Parameters:
model_path – Path to a model.
bin_path – Path to a data file. For IR format (*.bin):
if
bin_pathis empty, will try to read a bin file with the same name as xml andif the bin file with the same name is not found, will load IR without weights. For the following file formats the
bin_pathparameter is not used:ONNX format (*.onnx)
PDPD (*.pdmodel)
TF (*.pb)
TFLite (*.tflite)
- Returns:
A model.
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std::shared_ptr<ov::Model> read_model(const std::string &model, const Tensor &weights) const
Reads models from IR / ONNX / PDPD / TF / TFLite formats.
Note
Created model object shares the weights with the
weightsobject. Thus, do not createweightson temporary data that can be freed later, since the model constant data will point to an invalid memory.- Parameters:
model – String with a model in IR / ONNX / PDPD / TF / TFLite format.
weights – Shared pointer to a constant tensor with weights. Reading ONNX / PDPD / TF / TFLite models does not support loading weights from the
weightstensors.
- Returns:
A model.
Creates and loads a compiled model from a source model to the default OpenVINO device selected by the AUTO plugin.
Users can create as many compiled models as they need and use them simultaneously (up to the limitation of the hardware resources).
- Parameters:
model – Model object acquired from Core::read_model.
properties – Optional map of pairs: (property name, property value) relevant only for this load operation.
- Returns:
A compiled model.
Creates and loads a compiled model from a source model to the default OpenVINO device selected by AUTO plugin.
Users can create as many compiled models as they need and use them simultaneously (up to the limitation of the hardware resources)
- Template Parameters:
Properties – Should be the pack of
std::pair<std::string, ov::Any>types- Parameters:
model – Model object acquired from Core::read_model
properties – Optional pack of pairs: (property name, property value) relevant only for this load operation
- Returns:
A compiled model
Creates a compiled model from a source model object.
Users can create as many compiled models as they need and use them simultaneously (up to the limitation of the hardware resources).
- Parameters:
model – Model object acquired from Core::read_model.
device_name – Name of a device to load a model to.
properties – Optional map of pairs: (property name, property value) relevant only for this load operation.
- Returns:
A compiled model.
Creates a compiled model from a source model object.
Users can create as many compiled models as they need and use them simultaneously (up to the limitation of the hardware resources)
- Template Parameters:
Properties – Should be the pack of
std::pair<std::string, ov::Any>types- Parameters:
model – Model object acquired from Core::read_model
device_name – Name of device to load model to
properties – Optional pack of pairs: (property name, property value) relevant only for this load operation
- Returns:
A compiled model
-
CompiledModel compile_model(const std::string &model, const ov::Tensor &weights, const std::string &device_name, const AnyMap &properties = {})
Reads a model and creates a compiled model from the IR/ONNX/PDPD memory.
Note
Created model object shares the weights with the
weightsobject. Thus, do not createweightson temporary data that can be freed later, since the model constant data will point to an invalid memory.- Parameters:
model – String with a model in IR/ONNX/PDPD format.
weights – Shared pointer to a constant tensor with weights. Reading ONNX/PDPD models does not support loading weights from the
weightstensors.device_name – Name of a device to load a model to.
properties – Optional map of pairs: (property name, property value) relevant only for this load operation.
- Returns:
A compiled model.
-
template<typename ...Properties>
inline util::EnableIfAllStringAny<CompiledModel, Properties...> compile_model(const std::string &model, const ov::Tensor &weights, const std::string &device_name, Properties&&... properties) Reads a model and creates a compiled model from the IR/ONNX/PDPD memory.
Note
Created model object shares the weights with the
weightsobject. Thus, do not createweightson temporary data that can be freed later, since the model constant data will point to an invalid memory.- Parameters:
model – String with a model in IR/ONNX/PDPD format.
weights – Shared pointer to a constant tensor with weights. Reading ONNX/PDPD models does not support loading weights from the
weightstensors.device_name – Name of a device to load a model to.
- Template Parameters:
Properties – Should be a pack of
std::pair<std::string, ov::Any>types.- Returns:
A compiled model.
Creates a compiled model from a source model within a specified remote context.
- Parameters:
model – Model object acquired from Core::read_model.
context – A reference to a RemoteContext object.
properties – Optional map of pairs: (property name, property value) relevant only for this load operation.
- Returns:
A compiled model object.
Creates a compiled model from a source model within a specified remote context.
- Template Parameters:
Properties – Should be the pack of
std::pair<std::string, ov::Any>types- Parameters:
model – Model object acquired from Core::read_model
context – Pointer to RemoteContext object
properties – Optional pack of pairs: (property name, property value) relevant only for this load operation
- Returns:
A compiled model object
-
void add_extension(const std::string &library_path)
Registers an extension to a Core object.
- Parameters:
library_path – Path to the library with ov::Extension.
Registers an extension to a Core object.
- Parameters:
extension – Pointer to the extension.
Registers extensions to a Core object.
- Parameters:
extensions – Vector of loaded extensions.
-
template<class T, typename std::enable_if<std::is_base_of<ov::Extension, T>::value, bool>::type = true>
inline void add_extension(const T &extension) Registers an extension to a Core object.
- Parameters:
extension – Extension class that is inherited from the ov::Extension class.
-
template<class T, class ...Targs, typename std::enable_if<std::is_base_of<ov::Extension, T>::value, bool>::type = true>
inline void add_extension(const T &extension, Targs... args) Registers extensions to a Core object.
- Parameters:
extension – Extension class that is inherited from the ov::Extension class.
args – A list of extensions.
-
template<class T, typename std::enable_if<std::is_base_of<ov::op::Op, T>::value, bool>::type = true>
inline void add_extension() Registers a custom operation inherited from ov::op::Op.
-
template<class T, class ...Targs, typename std::enable_if<std::is_base_of<ov::op::Op, T>::value && sizeof...(Targs), bool>::type = true>
inline void add_extension() Registers custom operations inherited from ov::op::Op.
-
CompiledModel import_model(std::istream &model_stream, const std::string &device_name, const AnyMap &properties = {})
Imports a compiled model from the previously exported one.
- Parameters:
model_stream – std::istream input stream containing a model previously exported using the ov::CompiledModel::export_model method.
device_name – Name of a device to import a compiled model for. Note, if
device_namedevice was not used to compile the original mode, an exception is thrown.properties – Optional map of pairs: (property name, property value) relevant only for this load operation.
- Returns:
A compiled model.
-
template<typename ...Properties>
inline util::EnableIfAllStringAny<CompiledModel, Properties...> import_model(std::istream &model_stream, const std::string &device_name, Properties&&... properties) Imports a compiled model from the previously exported one.
- Template Parameters:
Properties – Should be the pack of
std::pair<std::string, ov::Any>types.- Parameters:
model_stream – Model stream.
device_name – Name of a device to import a compiled model for. Note, if
device_namedevice was not used to compile the original mode, an exception is thrown.properties – Optional pack of pairs: (property name, property value) relevant only for this load operation.
- Returns:
A compiled model.
-
CompiledModel import_model(std::istream &model_stream, const RemoteContext &context, const AnyMap &properties = {})
Imports a compiled model from the previously exported one with the specified remote context.
- Parameters:
model_stream – std::istream input stream containing a model previously exported from ov::CompiledModel::export_model
context – A reference to a RemoteContext object. Note, if the device from
contextwas not used to compile the original mode, an exception is thrown.properties – Optional map of pairs: (property name, property value) relevant only for this load operation.
- Returns:
A compiled model.
-
template<typename ...Properties>
inline util::EnableIfAllStringAny<CompiledModel, Properties...> import_model(std::istream &model_stream, const RemoteContext &context, Properties&&... properties) Imports a compiled model from the previously exported one with the specified remote context.
- Template Parameters:
Properties – Should be the pack of
std::pair<std::string, ov::Any>types.- Parameters:
model_stream – Model stream.
context – Pointer to a RemoteContext object.
properties – Optional pack of pairs: (property name, property value) relevant only for this load operation.
- Returns:
A compiled model.
Query device if it supports the specified model with specified properties.
- Parameters:
device_name – Name of a device to query.
model – Model object to query.
properties – Optional map of pairs: (property name, property value).
- Returns:
An object containing a map of pairs an operation name -> a device name supporting this operation.
Queries a device if it supports the specified model with specified properties.
- Template Parameters:
Properties – Should be the pack of
std::pair<std::string, ov::Any>types.- Parameters:
device_name – Name of a device to query.
model – Model object to query.
properties – Optional pack of pairs: (property name, property value) relevant only for this query operation.
- Returns:
An object containing a map of pairs an operation name -> a device name supporting this operation.
-
void set_property(const AnyMap &properties)
Sets properties for all the registered devices, acceptable keys can be found in openvino/runtime/properties.hpp.
- Parameters:
properties – Map of pairs: (property name, property value).
-
template<typename ...Properties>
inline util::EnableIfAllStringAny<void, Properties...> set_property(Properties&&... properties) Sets properties for all the registered devices, acceptable keys can be found in openvino/runtime/properties.hpp.
- Template Parameters:
Properties – Should be a pack of
std::pair<std::string, ov::Any>types.- Parameters:
properties – Optional pack of pairs: property name, property value.
-
void set_property(const std::string &device_name, const AnyMap &properties)
Sets properties for a device, acceptable keys can be found in openvino/runtime/properties.hpp.
- Parameters:
device_name – Name of a device.
properties – Map of pairs: (property name, property value).
-
template<typename ...Properties>
inline util::EnableIfAllStringAny<void, Properties...> set_property(const std::string &device_name, Properties&&... properties) Sets properties for a device, acceptable keys can be found in openvino/runtime/properties.hpp.
- Template Parameters:
Properties – Should be the pack of
std::pair<std::string, ov::Any>types.- Parameters:
device_name – Name of a device.
properties – Optional pack of pairs: (property name, property value).
-
Any get_property(const std::string &device_name, const std::string &name) const
Gets properties related to device behaviour.
The method extracts information that can be set via the set_property method.
- Parameters:
device_name – Name of a device to get a property value.
name – Property name.
- Returns:
Value of a property corresponding to the property name.
-
Any get_property(const std::string &device_name, const std::string &name, const AnyMap &arguments) const
Gets properties related to device behaviour.
The method extracts information that can be set via the set_property method.
- Parameters:
device_name – Name of a device to get a property value.
name – Property name.
arguments – Additional arguments to get a property.
- Returns:
Value of a property corresponding to the property name.
-
inline Any get_property(const std::string &name) const
Gets properties related to core behaviour.
The method extracts information that can be set via the set_property method.
- Parameters:
name – Property name.
- Returns:
Value of a property corresponding to the property name.
-
template<typename T, PropertyMutability M>
inline T get_property(const std::string &device_name, const ov::Property<T, M> &property) const Gets properties related to device behaviour.
The method is needed to request common device or system properties. It can be device name, temperature, and other devices-specific values.
-
template<typename T, PropertyMutability M>
inline T get_property(const std::string &device_name, const ov::Property<T, M> &property, const AnyMap &arguments) const Gets properties related to device behaviour.
The method is needed to request common device or system properties. It can be device name, temperature, other devices-specific values.
-
template<typename T, PropertyMutability M, typename ...Args>
inline util::EnableIfAllStringAny<T, Args...> get_property(const std::string &device_name, const ov::Property<T, M> &property, Args&&... args) const Gets properties related to device behaviour.
The method is needed to request common device or system properties. It can be device name, temperature, other devices-specific values.
- Template Parameters:
T – Type of a returned value.
M – Property mutability.
Args – Set of additional arguments ended with property object variable.
- Parameters:
device_name – Name of a device to get a property value.
property – Property object.
args – Optional pack of pairs: (argument name, argument value) ended with property object.
- Returns:
Property value.
-
std::vector<std::string> get_available_devices() const
Returns devices available for inference. Core objects go over all registered plugins and ask about available devices.
- Returns:
A vector of devices. The devices are returned as { CPU, GPU.0, GPU.1, NPU }. If there is more than one device of a specific type, they are enumerated with the .# suffix. Such enumerated device can later be used as a device name in all Core methods like Core::compile_model, Core::query_model, Core::set_property and so on.
-
void register_plugin(const std::string &plugin, const std::string &device_name, const ov::AnyMap &config = {})
Register a new device and plugin that enables this device inside OpenVINO Runtime.
Note
For security purposes it suggested to specify absolute path to register plugin.
- Parameters:
plugin – Path (absolute or relative) or name of a plugin. Depending on platform,
pluginis wrapped with shared library suffix and prefix to identify library full name. For example, on Linux platform, plugin name specified asplugin_namewill be wrapped aslibplugin_name.so. Plugin search algorithm:If
pluginpoints to an exact library path (absolute or relative), it will be used.If
pluginspecifies file name (libplugin_name.so) or plugin name (plugin_name), it will be searched by file name (libplugin_name.so) in CWD or in paths pointed by PATH/LD_LIBRARY_PATH/DYLD_LIBRARY_PATH environment variables depending on the platform.
device_name – Device name to register a plugin for.
config – Plugin configuration options
-
void unload_plugin(const std::string &device_name)
Unloads the previously loaded plugin identified by
device_namefrom OpenVINO Runtime. The method is needed to remove loaded plugin instance and free its resources. If plugin for a specified device has not been created before, the method throws an exception.Note
This method does not remove plugin from the plugins known to OpenVINO Core object.
- Parameters:
device_name – Device name identifying plugin to remove from OpenVINO Runtime.
-
void register_plugins(const std::string &xml_config_file)
Registers a device plugin to the OpenVINO Runtime Core instance using an XML configuration file with plugins description.
The XML file has the following structure:
<ie> <plugins> <plugin name="" location=""> <extensions> <extension location=""/> </extensions> <properties> <property key="" value=""/> </properties> </plugin> </plugins> </ie>
nameidentifies name of a device enabled by a plugin.locationspecifies absolute path to dynamic library with a plugin. The path can also be relative to XML file directory. It allows having common config for different systems with different configurations.propertiesare set to a plugin via the ov::Core::set_property method.extensionsare set to a plugin via the ov::Core::add_extension method.
Note
For security purposes it suggested to specify absolute path to register plugin.
- Parameters:
xml_config_file – A path to .xml file with plugins to register.
-
RemoteContext create_context(const std::string &device_name, const AnyMap &remote_properties)
Creates a new remote shared context object on the specified accelerator device using specified plugin-specific low-level device API parameters (device handle, pointer, context, etc.).
- Parameters:
device_name – Name of a device to create a new shared context on.
remote_properties – Map of device-specific shared context remote properties.
- Returns:
Reference to a created remote context.
-
template<typename ...Properties>
inline util::EnableIfAllStringAny<RemoteContext, Properties...> create_context(const std::string &device_name, Properties&&... remote_properties) Creates a new shared context object on specified accelerator device using specified plugin-specific low level device API properties (device handle, pointer, etc.)
- Template Parameters:
Properties – Should be the pack of
std::pair<std::string, ov::Any>types- Parameters:
device_name – Name of a device to create new shared context on.
remote_properties – Pack of device-specific shared context remote properties.
- Returns:
A shared pointer to a created remote context.
-
RemoteContext get_default_context(const std::string &device_name)
Gets a pointer to default (plugin-supplied) shared context object for the specified accelerator device.
- Parameters:
device_name – Name of a device to get a default shared context from.
- Returns:
Reference to a default remote context.
-
CompiledModel compile_model(const std::string &model_path, const AnyMap &properties = {})
-
class Decompression : public ov::RuntimeAttribute
- #include <decompression.hpp>
Decompression class represents runtime info attribute that marks operation as used as decompression for Compressed Only format.
-
class DequantizationNode : public ov::RuntimeAttribute
- #include <dequantization_node.hpp>
DequantizationNode class represents runtime info attribute that marks operation that are part of dequantization subgraph.
-
class DeviceIDParser
- #include <device_id_parser.hpp>
Class parses device name and id.
-
class Dimension
- #include <dimension.hpp>
Class representing a dimension, which may be dynamic (undetermined until runtime), in a shape or shape-like object.
Static dimensions may be implicitly converted from value_type. A dynamic dimension is constructed with Dimension() or Dimension::dynamic().
Public Functions
-
Dimension(value_type dimension)
Construct a static dimension.
- Parameters:
dimension – Value of the dimension.
-
Dimension(value_type min_dimension, value_type max_dimension)
Construct a dynamic dimension with bounded range.
- Parameters:
min_dimension – The lower inclusive limit for the dimension
max_dimension – The upper inclusive limit for the dimension
-
Dimension(const std::string &str)
Construct a dimension from string.
- Parameters:
str – String to parse to dimension.
-
Dimension() = default
Construct a dynamic dimension with range [0, …].
-
inline bool is_static() const
Check whether this dimension is static.
- Returns:
trueif the dimension is static, elsefalse.
-
inline bool is_dynamic() const
Check whether this dimension is dynamic.
- Returns:
falseif the dimension is static, elsetrue.
-
value_type get_length() const
Convert this dimension to
value_type. This dimension must be static and non-negative.- Throws:
std::invalid_argument – If this dimension is dynamic or negative.
-
inline const Interval &get_interval() const
Return the interval of valid lengths.
-
bool same_scheme(const Dimension &dim) const
Check whether this dimension represents the same scheme as the argument (both dynamic, or equal).
- Parameters:
dim – The other dimension to compare this dimension to.
- Returns:
trueif this dimension anddimare both dynamic, or if they are both static and equal; otherwise,false.
-
bool compatible(const Dimension &d) const
Check whether this dimension is capable of being merged with the argument dimension.
Two dimensions are considered compatible if it is possible to merge them. (See Dimension::merge.)
- Parameters:
d – The dimension to compare this dimension with.
- Returns:
trueif this dimension is compatible withd, elsefalse.
-
bool relaxes(const Dimension &d) const
Check whether this dimension is a relaxation of the argument.
A dimension
d1relaxes (or is a relaxation of)d2ifd1andd2are static and equal, ord1is dynamic.d1.relaxes(d2)is equivalent tod2.refines(d1).- Parameters:
d – The dimension to compare this dimension with.
- Returns:
trueif this dimension relaxesd, elsefalse.
-
bool refines(const Dimension &d) const
Check whether this dimension is a refinement of the argument.
A dimension
d2refines (or is a refinement of)d1ifd1andd2are static and equal, ord2is dynamic.d1.refines(d2)is equivalent tod2.relaxes(d1).- Parameters:
d – The dimension to compare this dimension with.
- Returns:
trueif this dimension relaxesd, elsefalse.
-
Dimension operator+(const Dimension &dim) const
Addition operator for Dimension.
- Parameters:
dim – Right operand for addition.
- Returns:
Smallest interval dimension enclosing inputs
-
Dimension operator-(const Dimension &dim) const
Subtraction operator for Dimension.
- Parameters:
dim – Right operand for subtraction.
- Returns:
Smallest interval dimension enclosing inputs
-
Dimension operator/(const value_type divisor) const
Division operator for Dimension divided by a value_type parameter.
- Parameters:
divisor – Right operand for division.
- Returns:
Smallest interval dimension enclosing inputs
-
inline Dimension &operator/=(const value_type divisor)
Divided-into operator for Dimension.
- Parameters:
divisor – Right operand for multiplication.
- Returns:
A reference to
*this, after updating*thisto the value*this * dim.
-
Dimension operator*(const Dimension &dim) const
Multiplication operator for Dimension.
- Parameters:
dim – Right operand for multiplicaiton.
- Returns:
Smallest interval containing all “produces” which are 0 if either of
thisordimhas length0, else unbounded if either is unbounded, else product of lengths.
-
inline Dimension &operator+=(const Dimension &dim)
Add-into operator for Dimension.
- Parameters:
dim – Right operand for addition.
- Returns:
A reference to
*this, after updating*thisto the value*this + dim.
-
inline Dimension &operator*=(const Dimension &dim)
Multiply-into operator for Dimension.
- Parameters:
dim – Right operand for multiplication.
- Returns:
A reference to
*this, after updating*thisto the value*this * dim.
-
std::string to_string() const
String representation of Dimension.
-
bool has_symbol() const
Indicates if meaningful symbol was set to the Dimension.
Sets symbol of the Dimension.
Public Static Functions
-
static bool merge(Dimension &dst, const Dimension &d1, const Dimension &d2)
Try to merge two Dimension objects together.
If
d1is dynamic, writesd2todstand returnstrue.If
d2is dynamic, writesd1todstand returnstrue.If
d1andd2are static and equal, writesd1todstand returnstrue.If
d1andd2are both static and unequal, leavesdstunchanged and returnsfalse.
- Parameters:
dst – [out] Reference to write the merged Dimension into.
d1 – First dimension to merge.
d2 – Second dimension to merge.
- Returns:
trueif merging succeeds, elsefalse.
-
static bool broadcast_merge(Dimension &dst, const Dimension &d1, const Dimension &d2)
Try to merge two Dimension objects together with implicit broadcasting of unit-sized dimension to non unit-sized dimension.
-
static inline Dimension dynamic()
Create a dynamic dimension.
- Returns:
A dynamic dimension.
-
Dimension(value_type dimension)
-
template<typename AT>
class DirectValueAccessor : public ov::ValueAccessor<AT> - #include <attribute_adapter.hpp>
-
class DisableCleanupAttribute : public ov::RuntimeAttribute
- #include <disable_cleanup_attribute.hpp>
-
class DisableFP16Compression : public ov::RuntimeAttribute
- #include <disable_fp16_compression.hpp>
DisableFP16Compression class represents runtime info attribute that marks operation as prohibited to convert to lower precision (e.g. to FP16) and they should be inferred precisely in the original precision.
-
struct DiscreteTypeInfo#
- #include <type.hpp>
Type information for a type system without inheritance; instances have exactly one type not related to any other type.
Supports three functions, ov::is_type<Type>, ov::as_type<Type>, and ov::as_type_ptr<Type> for type-safe dynamic conversions via static_cast/static_ptr_cast without using C++ RTTI. Type must have a static type_info member and a virtual get_type_info() member that returns a reference to its type_info member.
- template<> bf16 >
- #include <element_type_traits.hpp>
- template<> boolean >
- #include <element_type_traits.hpp>
- template<> f16 >
- #include <element_type_traits.hpp>
- template<> f32 >
- #include <element_type_traits.hpp>
- template<> f64 >
- #include <element_type_traits.hpp>
- template<> f8e4m3 >
- #include <element_type_traits.hpp>
- template<> f8e5m2 >
- #include <element_type_traits.hpp>
- template<> i16 >
- #include <element_type_traits.hpp>
- template<> i32 >
- #include <element_type_traits.hpp>
- template<> i4 >
- #include <element_type_traits.hpp>
- template<> i64 >
- #include <element_type_traits.hpp>
- template<> i8 >
- #include <element_type_traits.hpp>
- template<> nf4 >
- #include <element_type_traits.hpp>
- template<> string >
- #include <element_type_traits.hpp>
- template<> u1 >
- #include <element_type_traits.hpp>
- template<> u16 >
- #include <element_type_traits.hpp>
- template<> u2 >
- #include <element_type_traits.hpp>
- template<> u3 >
- #include <element_type_traits.hpp>
- template<> u32 >
- #include <element_type_traits.hpp>
- template<> u4 >
- #include <element_type_traits.hpp>
- template<> u6 >
- #include <element_type_traits.hpp>
- template<> u64 >
- #include <element_type_traits.hpp>
- template<> u8 >
- #include <element_type_traits.hpp>
-
template<typename AT>
class EnumAttributeAdapterBase : public ov::ValueAccessor<std::string> - #include <attribute_adapter.hpp>
Access an enum via a string.
- Template Parameters:
AT – The attribute type enum class
Public Functions
-
inline virtual const std::string &get() override
Returns the value.
-
inline virtual void set(const std::string &value) override
Sets the value.
-
template<typename T>
class EnumMask - #include <enum_mask.hpp>
Public Types
-
using value_type = typename std::underlying_type<T>::type
Make sure the template type is an enum.
Extract the underlying type of the enum.
Public Functions
-
constexpr EnumMask() = default
Some bit operations are not safe for signed values, we require enum type to use unsigned underlying type.
-
inline bool is_any_set(const EnumMask &p) const
Check if any of the input parameter enum bit mask match.
-
inline bool is_set(const EnumMask &p) const
Check if all of the input parameter enum bit mask match.
-
inline bool is_any_clear(const EnumMask &p) const
Check if any of the input parameter enum bit mask does not match.
-
inline bool is_clear(const EnumMask &p) const
Check if all of the input parameter enum bit mask do not match.
-
using value_type = typename std::underlying_type<T>::type
-
template<typename EnumType>
class EnumNames - #include <enum_names.hpp>
Uses a pairings defined by EnumTypes::get() to convert between strings and enum values.
-
class Exception : public std::runtime_error
- #include <except.hpp>
Base error for ov runtime errors.
Subclassed by ov::AssertFailure, ov::Busy, ov::Cancelled, ov::op::util::error::UnknownActivationFunction
-
class Extension
- #include <extension.hpp>
The class provides the base interface for OpenVINO extensions.
Subclassed by ov::BaseOpExtension, ov::frontend::ConversionExtensionBase, ov::frontend::DecoderTransformationExtension, ov::frontend::ProgressReporterExtension, ov::frontend::TelemetryExtension
-
class float16
- #include <float16.hpp>
-
class float8_e4m3
- #include <float8_e4m3.hpp>
Class to represent the f8e4m3 type.
-
class float8_e5m2
- #include <float8_e5m2.hpp>
Class to represent the f8e5m2 type.
-
class FusedNames : public ov::RuntimeAttribute
- #include <fused_names_attribute.hpp>
FusedName class represents runtime info attribute that stores all operation names that was fully or partially fused into node.
Public Functions
-
FusedNames() = default
A default constructor
-
inline explicit FusedNames(const std::string &name)
Constructs a new object consisting of a single name *.
- Parameters:
name – [in] The name
-
void fuseWith(const FusedNames &names)
Unites current set of already fused names with another FusedNames object.
- Parameters:
names – [in] Another object to fuse with
-
std::string getNames() const
return string with operation names separated by coma in alphabetical order
-
std::vector<std::string> getVectorNames() const
return vector of fused names sorted in alphabetical order
- Returns:
vector if strings
-
FusedNames() = default
-
class IAsyncInferRequest : public ov::IInferRequest
- #include <iasync_infer_request.hpp>
Base class with default implementation of asynchronous multi staged inference request. To customize pipeline stages derived class should change the content of IAsyncInferRequest::m_pipeline member container. It consists of pairs of tasks and executors which will run the task. The class is recommended to be used by plugins as a base class for asynchronous inference request implementation.
- Example
Here is an example of asynchronous inference request implementation for some accelerator device. It uses 5 different executors to run different stages of a synchronous inference request.
Note
To synchronize derived context with stages derived class should call IAsyncInferRequest::stop_and_wait() function in destructor.
Public Functions
-
virtual void start_async()
Start inference of specified input(s) in asynchronous mode.
Note
The method returns immediately. Inference starts also immediately.
-
virtual void wait()
Waits for the result to become available.
-
virtual bool wait_for(const std::chrono::milliseconds &timeout)
Waits for the result to become available. Blocks until specified timeout has elapsed or the result becomes available, whichever comes first.
- Parameters:
timeout – - maximum duration in milliseconds to block for
- Returns:
A true if results are ready.
-
virtual void cancel()
Cancel current inference request execution.
-
virtual void set_callback(std::function<void(std::exception_ptr)> callback)
Set callback function which will be called on success or failure of asynchronous request.
- Parameters:
callback – - function to be called with the following description:
-
virtual void infer() override
Infers specified input(s) in synchronous mode.
Note
blocks all method of InferRequest while request is ongoing (running or waiting in queue)
-
virtual std::vector<ov::ProfilingInfo> get_profiling_info() const override
Queries performance measures per layer to identify the most time consuming operation.
Note
Not all plugins provide meaningful data.
- Returns:
Vector of profiling information for operations in a model.
-
virtual ov::SoPtr<ov::ITensor> get_tensor(const ov::Output<const ov::Node> &port) const override
Gets an input/output tensor for inference.
Note
If the tensor with the specified
portis not found, an exception is thrown.- Parameters:
port – Port of the tensor to get.
- Returns:
Tensor for the port
port.
-
virtual void set_tensor(const ov::Output<const ov::Node> &port, const ov::SoPtr<ov::ITensor> &tensor) override
Sets an input/output tensor to infer.
- Parameters:
port – Port of the input or output tensor.
tensor – Reference to a tensor. The element_type and shape of a tensor must match the model’s input/output element_type and size.
-
virtual std::vector<ov::SoPtr<ov::ITensor>> get_tensors(const ov::Output<const ov::Node> &port) const override
Gets a batch of tensors for input data to infer by input port. Model input must have batch dimension, and the number of
tensorsmust match the batch size. The current version supports setting tensors to model inputs only. Ifportis associated with output (or any other non-input node), an exception is thrown.- Parameters:
port – Port of the input tensor.
tensors – Input tensors for batched infer request. The type of each tensor must match the model input element type and shape (except batch dimension). Total size of tensors must match the input size.
- Returns:
vector of tensors
-
virtual void set_tensors(const ov::Output<const ov::Node> &port, const std::vector<ov::SoPtr<ov::ITensor>> &tensors) override
Sets a batch of tensors for input data to infer by input port. Model input must have batch dimension, and the number of
tensorsmust match the batch size. The current version supports setting tensors to model inputs only. Ifportis associated with output (or any other non-input node), an exception is thrown.- Parameters:
port – Port of the input tensor.
tensors – Input tensors for batched infer request. The type of each tensor must match the model input element type and shape (except batch dimension). Total size of tensors must match the input size.
-
virtual std::vector<ov::SoPtr<ov::IVariableState>> query_state() const override
Gets state control interface for the given infer request.
State control essential for recurrent models.
- Returns:
Vector of Variable State objects.
-
virtual const std::shared_ptr<const ov::ICompiledModel> &get_compiled_model() const override
Gets pointer to compiled model (usually synchronous request holds the compiled model)
- Returns:
Pointer to the compiled model
-
class ICompiledModel : public std::enable_shared_from_this<ICompiledModel>
- #include <icompiled_model.hpp>
OpenVINO ICompiledModel interface.
Public Functions
Constructor for ICompiledModel interface.
- Parameters:
model – OpenVINO model representation
plugin – Pointer to plugin
task_executor – Task executor (CPUStreamsExecutor by default)
callback_executor – Callback executor (CPUStreamsExecutor by default)
Constructor for ICompiledModel interface with remote context.
- Parameters:
model – OpenVINO model representation
plugin – Pointer to plugin
context – Remote context
task_executor – Task executor (CPUStreamsExecutor by default)
callback_executor – Callback executor (CPUStreamsExecutor by default)
-
virtual const std::vector<ov::Output<const ov::Node>> &outputs() const
Gets all outputs from compiled model.
- Returns:
model outputs
-
virtual const std::vector<ov::Output<const ov::Node>> &inputs() const
Gets all inputs from compiled model.
- Returns:
model inputs
-
virtual std::shared_ptr<ov::IAsyncInferRequest> create_infer_request() const
Create infer request.
- Returns:
Asynchronous infer request interface
-
virtual void export_model(std::ostream &model) const = 0
Export compiled model to stream.
- Parameters:
model – output stream
-
virtual std::shared_ptr<const ov::Model> get_runtime_model() const = 0
Returns runtime model.
- Returns:
OpenVINO Model which represents runtime graph
-
virtual void set_property(const ov::AnyMap &properties) = 0
Allows to set property.
- Parameters:
properties – new plugin properties
-
ov::SoPtr<ov::IRemoteContext> get_context() const
Creates device specific remote context.
- Returns:
OpenVINO RemoteContext
-
interface ICore
- #include <icore.hpp>
Minimal ICore interface to allow plugin to get information from Core OpenVINO class.
Public Functions
-
virtual std::shared_ptr<ov::Model> read_model(const std::string &model, const ov::Tensor &weights, bool frontend_mode = false) const = 0
Reads IR xml and bin (with the same name) files.
- Parameters:
model – string with IR
weights – shared pointer to constant blob with weights
frontend_mode – read network without post-processing or other transformations
- Returns:
shared pointer to ov::Model
-
virtual std::shared_ptr<ov::Model> read_model(const std::string &model_path, const std::string &bin_path) const = 0
Reads IR xml and bin files.
- Parameters:
model_path – path to IR file
bin_path – path to bin file, if path is empty, will try to read bin file with the same name as xml and if bin file with the same name was not found, will load IR without weights.
- Returns:
shared pointer to ov::Model
Creates a compiled mdel from a model object.
Users can create as many models as they need and use them simultaneously (up to the limitation of the hardware resources)
- Parameters:
model – OpenVINO Model
device_name – Name of device to load model to
config – Optional map of pairs: (config parameter name, config parameter value) relevant only for this load operation
- Returns:
A pointer to compiled model
Creates a compiled model from a model object.
Users can create as many models as they need and use them simultaneously (up to the limitation of the hardware resources)
- Parameters:
model – OpenVINO Model
context – “Remote” (non-CPU) accelerator device-specific execution context to use
config – Optional map of pairs: (config parameter name, config parameter value) relevant only for this load operation
- Returns:
A pointer to compiled model
-
virtual ov::SoPtr<ov::ICompiledModel> compile_model(const std::string &model_path, const std::string &device_name, const ov::AnyMap &config) const = 0
Creates a compiled model from a model file.
Users can create as many models as they need and use them simultaneously (up to the limitation of the hardware resources)
- Parameters:
model_path – Path to model
device_name – Name of device to load model to
config – Optional map of pairs: (config parameter name, config parameter value) relevant only for this load operation
- Returns:
A pointer to compiled model
-
virtual ov::SoPtr<ov::ICompiledModel> compile_model(const std::string &model_str, const ov::Tensor &weights, const std::string &device_name, const ov::AnyMap &config) const = 0
Creates a compiled model from a model memory.
Users can create as many models as they need and use them simultaneously (up to the limitation of the hardware resources)
- Parameters:
model_str – String data of model
weights – Model’s weights
device_name – Name of device to load model to
config – Optional map of pairs: (config parameter name, config parameter value) relevant only for this load operation
- Returns:
A pointer to compiled model
-
virtual ov::SoPtr<ov::ICompiledModel> import_model(std::istream &model, const std::string &device_name, const ov::AnyMap &config = {}) const = 0
Creates a compiled model from a previously exported model.
- Parameters:
model – model stream
device_name – Name of device load executable model on
config – Optional map of pairs: (config parameter name, config parameter value) relevant only for this load operation*
- Returns:
A pointer to compiled model
-
virtual ov::SoPtr<ov::ICompiledModel> import_model(std::istream &modelStream, const ov::SoPtr<ov::IRemoteContext> &context, const ov::AnyMap &config = {}) const = 0
Creates a compiled model from a previously exported model.
- Parameters:
model – model stream
context – Remote context
config – Optional map of pairs: (config parameter name, config parameter value) relevant only for this load operation*
- Returns:
A pointer to compiled model
Query device if it supports specified network with specified configuration.
- Parameters:
model – OpenVINO Model
device_name – A name of a device to query
config – Optional map of pairs: (config parameter name, config parameter value)
- Returns:
An object containing a map of pairs a layer name -> a device name supporting this layer.
-
virtual std::vector<std::string> get_available_devices() const = 0
Returns devices available for neural networks inference.
- Returns:
A vector of devices. The devices are returned as { CPU, GPU.0, GPU.1, MYRIAD } If there more than one device of specific type, they are enumerated with .# suffix.
-
virtual ov::SoPtr<ov::IRemoteContext> create_context(const std::string &device_name, const AnyMap &args) const = 0
Create a new shared context object on specified accelerator device using specified plugin-specific low level device API parameters (device handle, pointer, etc.)
- Parameters:
device_name – Name of a device to create new shared context on.
params – Map of device-specific shared context parameters.
- Returns:
A shared pointer to a created remote context.
-
virtual ov::SoPtr<ov::IRemoteContext> get_default_context(const std::string &device_name) const = 0
Get a pointer to default shared context object for the specified device.
- Parameters:
device_name – - A name of a device to get create shared context from.
- Returns:
A shared pointer to a default remote context.
-
virtual Any get_property(const std::string &device_name, const std::string &name, const AnyMap &arguments) const = 0
Gets properties related to device behaviour.
- Parameters:
device_name – Name of a device to get a property value.
name – Property name.
arguments – Additional arguments to get a property.
- Returns:
Value of a property corresponding to the property name.
-
template<typename T, PropertyMutability M>
inline T get_property(const std::string &device_name, const Property<T, M> &property) const Gets properties related to device behaviour.
-
template<typename T, PropertyMutability M>
inline T get_property(const std::string &device_name, const Property<T, M> &property, const AnyMap &arguments) const Gets properties related to device behaviour.
-
virtual AnyMap get_supported_property(const std::string &full_device_name, const AnyMap &properties, const bool keep_core_property = true) const = 0
Get only properties that are supported by specified device.
- Parameters:
full_device_name – Name of a device (can be either virtual or hardware)
properties – Properties that can contains configs that are not supported by device
keep_core_property – Whether to return core-level properties
- Returns:
map of properties that are supported by device
-
virtual ~ICore()
Default virtual destructor.
-
virtual std::shared_ptr<ov::Model> read_model(const std::string &model, const ov::Tensor &weights, bool frontend_mode = false) const = 0
-
class IInferRequest
- #include <iinfer_request.hpp>
An internal API of inference request to be implemented by plugin.
Subclassed by ov::IAsyncInferRequest, ov::ISyncInferRequest
Public Functions
-
virtual void infer() = 0
Infers specified input(s) in synchronous mode.
Note
blocks all method of InferRequest while request is ongoing (running or waiting in queue)
-
virtual std::vector<ov::ProfilingInfo> get_profiling_info() const = 0
Queries performance measures per layer to identify the most time consuming operation.
Note
Not all plugins provide meaningful data.
- Returns:
Vector of profiling information for operations in a model.
-
virtual ov::SoPtr<ov::ITensor> get_tensor(const ov::Output<const ov::Node> &port) const = 0
Gets an input/output tensor for inference.
Note
If the tensor with the specified
portis not found, an exception is thrown.- Parameters:
port – Port of the tensor to get.
- Returns:
Tensor for the port
port.
-
virtual void set_tensor(const ov::Output<const ov::Node> &port, const ov::SoPtr<ov::ITensor> &tensor) = 0
Sets an input/output tensor to infer.
- Parameters:
port – Port of the input or output tensor.
tensor – Reference to a tensor. The element_type and shape of a tensor must match the model’s input/output element_type and size.
-
virtual std::vector<ov::SoPtr<ov::ITensor>> get_tensors(const ov::Output<const ov::Node> &port) const = 0
Gets a batch of tensors for input data to infer by input port. Model input must have batch dimension, and the number of
tensorsmust match the batch size. The current version supports setting tensors to model inputs only. Ifportis associated with output (or any other non-input node), an exception is thrown.- Parameters:
port – Port of the input tensor.
tensors – Input tensors for batched infer request. The type of each tensor must match the model input element type and shape (except batch dimension). Total size of tensors must match the input size.
- Returns:
vector of tensors
-
virtual void set_tensors(const ov::Output<const ov::Node> &port, const std::vector<ov::SoPtr<ov::ITensor>> &tensors) = 0
Sets a batch of tensors for input data to infer by input port. Model input must have batch dimension, and the number of
tensorsmust match the batch size. The current version supports setting tensors to model inputs only. Ifportis associated with output (or any other non-input node), an exception is thrown.- Parameters:
port – Port of the input tensor.
tensors – Input tensors for batched infer request. The type of each tensor must match the model input element type and shape (except batch dimension). Total size of tensors must match the input size.
-
virtual std::vector<ov::SoPtr<ov::IVariableState>> query_state() const = 0
Gets state control interface for the given infer request.
State control essential for recurrent models.
- Returns:
Vector of Variable State objects.
-
virtual const std::shared_ptr<const ov::ICompiledModel> &get_compiled_model() const = 0
Gets pointer to compiled model (usually synchronous request holds the compiled model)
- Returns:
Pointer to the compiled model
-
virtual void infer() = 0
-
template<typename AT, typename VAT>
class IndirectScalarValueAccessor : public ov::ValueAccessor<VAT> - #include <attribute_adapter.hpp>
-
template<typename AT, typename VAT>
class IndirectVectorValueAccessor : public ov::ValueAccessor<VAT> - #include <attribute_adapter.hpp>
-
class InferRequest
- #include <infer_request.hpp>
This is a class of infer request that can be run in asynchronous or synchronous manners.
Public Functions
-
InferRequest() = default
Default constructor.
-
InferRequest(const InferRequest &other) = default
Default copy constructor.
- Parameters:
other – Another InferRequest object.
-
InferRequest &operator=(const InferRequest &other) = default
Default copy assignment operator.
- Parameters:
other – Another InferRequest object.
- Returns:
Reference to the current object.
-
InferRequest(InferRequest &&other) = default
Default move constructor.
- Parameters:
other – Another InferRequest object.
-
InferRequest &operator=(InferRequest &&other) = default
Default move assignment operator.
- Parameters:
other – Another InferRequest object.
- Returns:
Reference to the current object.
-
~InferRequest()
Destructor that preserves unloading order of implementation object and reference to the library.
Note
To preserve destruction order inside the default generated assignment operator,
_implis stored before_so. Use the destructor to remove implementation object before referencing to the library explicitly.
-
void set_tensor(const std::string &tensor_name, const Tensor &tensor)
Sets an input/output tensor to infer on.
- Parameters:
tensor_name – Name of the input or output tensor.
tensor – Reference to the tensor. The element_type and shape of the tensor must match the model’s input/output element_type and size.
-
void set_tensor(const ov::Output<const ov::Node> &port, const Tensor &tensor)
Sets an input/output tensor to infer.
- Parameters:
port – Port of the input or output tensor. Use the following methods to get the ports:
tensor – Reference to a tensor. The element_type and shape of a tensor must match the model’s input/output element_type and size.
-
void set_tensor(const ov::Output<ov::Node> &port, const Tensor &tensor)
Sets an input/output tensor to infer.
- Parameters:
port – Port of the input or output tensor. Use the following methods to get the ports:
tensor – Reference to a tensor. The element_type and shape of a tensor must match the model’s input/output element_type and size.
-
void set_tensors(const std::string &tensor_name, const std::vector<Tensor> &tensors)
Sets a batch of tensors for input data to infer by tensor name. Model input must have batch dimension, and the number of
tensorsmust match the batch size. The current version supports setting tensors to model inputs only. Iftensor_nameis associated with output (or any other non-input node), an exception is thrown.- Parameters:
tensor_name – Name of the input tensor.
tensors – Input tensors for batched infer request. The type of each tensor must match the model input element type and shape (except batch dimension). Total size of tensors must match the input size.
-
void set_tensors(const ov::Output<const ov::Node> &port, const std::vector<Tensor> &tensors)
Sets a batch of tensors for input data to infer by input port. Model input must have batch dimension, and the number of
tensorsmust match the batch size. The current version supports setting tensors to model inputs only. Ifportis associated with output (or any other non-input node), an exception is thrown.- Parameters:
port – Port of the input tensor.
tensors – Input tensors for batched infer request. The type of each tensor must match the model input element type and shape (except batch dimension). Total size of tensors must match the input size.
-
void set_input_tensor(size_t idx, const Tensor &tensor)
Sets an input tensor to infer.
- Parameters:
idx – Index of the input tensor. If
idxis greater than the number of model inputs, an exception is thrown.tensor – Reference to the tensor. The element_type and shape of the tensor must match the model’s input/output element_type and size.
-
void set_input_tensor(const Tensor &tensor)
Sets an input tensor to infer models with single input.
Note
If model has several inputs, an exception is thrown.
- Parameters:
tensor – Reference to the input tensor.
-
void set_input_tensors(const std::vector<Tensor> &tensors)
Sets a batch of tensors for single input data. Model input must have batch dimension, and the number of
tensorsmust match the batch size.- Parameters:
tensors – Input tensors for batched infer request. The type of each tensor must match the model input element type and shape (except batch dimension). Total size of tensors must match the input size.
-
void set_input_tensors(size_t idx, const std::vector<Tensor> &tensors)
Sets a batch of tensors for input data to infer by input name. Model input must have batch dimension, and number of
tensorsmust match the batch size.- Parameters:
idx – Name of the input tensor.
tensors – Input tensors for batched infer request. The type of each tensor must match the model input element type and shape (except batch dimension). Total size of tensors must match the input size.
-
void set_output_tensor(size_t idx, const Tensor &tensor)
Sets an output tensor to infer.
Note
Index of the input preserved accross ov::Model, ov::CompiledModel, and ov::InferRequest.
- Parameters:
idx – Index of the output tensor.
tensor – Reference to the output tensor. The type of the tensor must match the model output element type and shape.
-
void set_output_tensor(const Tensor &tensor)
Sets an output tensor to infer models with single output.
Note
If model has several outputs, an exception is thrown.
- Parameters:
tensor – Reference to the output tensor.
-
Tensor get_tensor(const std::string &tensor_name)
Gets an input/output tensor for inference by tensor name.
- Parameters:
tensor_name – Name of a tensor to get.
- Returns:
The tensor with name
tensor_name. If the tensor is not found, an exception is thrown.
-
Tensor get_tensor(const ov::Output<const ov::Node> &port)
Gets an input/output tensor for inference.
Note
If the tensor with the specified
portis not found, an exception is thrown.- Parameters:
port – Port of the tensor to get.
- Returns:
Tensor for the port
port.
-
Tensor get_tensor(const ov::Output<ov::Node> &port)
Gets an input/output tensor for inference.
Note
If the tensor with the specified
portis not found, an exception is thrown.- Parameters:
port – Port of the tensor to get.
- Returns:
Tensor for the port
port.
-
Tensor get_input_tensor(size_t idx)
Gets an input tensor for inference.
- Parameters:
idx – Index of the tensor to get.
- Returns:
Tensor with the input index
idx. If the tensor with the specifiedidxis not found, an exception is thrown.
-
Tensor get_input_tensor()
Gets an input tensor for inference.
- Returns:
The input tensor for the model. If model has several inputs, an exception is thrown.
-
Tensor get_output_tensor(size_t idx)
Gets an output tensor for inference.
- Parameters:
idx – Index of the tensor to get.
- Returns:
Tensor with the output index
idx. If the tensor with the specifiedidxis not found, an exception is thrown.
-
Tensor get_output_tensor()
Gets an output tensor for inference.
- Returns:
Output tensor for the model. If model has several outputs, an exception is thrown.
-
void infer()
Infers specified input(s) in synchronous mode.
Note
It blocks all methods of InferRequest while request is ongoing (running or waiting in a queue). Calling any method leads to throwning the ov::Busy exception.
-
void cancel()
Cancels inference request.
-
std::vector<ProfilingInfo> get_profiling_info() const
Queries performance measures per layer to identify the most time consuming operation.
Note
Not all plugins provide meaningful data.
- Returns:
Vector of profiling information for operations in a model.
-
void start_async()
Starts inference of specified input(s) in asynchronous mode.
Note
It returns immediately. Inference starts also immediately. Calling any method while the request in a running state leads to throwning the ov::Busy exception.
-
void wait()
Waits for the result to become available. Blocks until the result becomes available.
-
bool wait_for(const std::chrono::milliseconds timeout)
Waits for the result to become available. Blocks until the specified timeout has elapsed or the result becomes available, whichever comes first.
- Parameters:
timeout – Maximum duration, in milliseconds, to block for.
- Returns:
True if inference request is ready and false, otherwise.
-
void set_callback(std::function<void(std::exception_ptr)> callback)
Sets a callback std::function that is called on success or failure of an asynchronous request.
Warning
Do not capture strong references to OpenVINO runtime objects into callback. Following objects should not be captured like:
ov::ExecutableNetwork
ov::Core As specified objects implement shared reference concept do not capture this objects by value. It can lead to memory leaks or undefined behaviour! Try to use weak references or pointers.
- Parameters:
callback – callback object which will be called on when inference finish.
-
std::vector<VariableState> query_state()
Gets state control interface for the given infer request.
State control essential for recurrent models.
- Returns:
Vector of Variable State objects.
-
void reset_state()
Resets all internal variable states for relevant infer request to a value specified as default for the corresponding
ReadValuenode.
-
CompiledModel get_compiled_model()
Returns a compiled model that creates this inference request.
- Returns:
Compiled model object.
-
bool operator!() const noexcept
Checks if the current InferRequest object is not initialized.
- Returns:
True if the current InferRequest object is not initialized; false, otherwise.
-
explicit operator bool() const noexcept
Checks if the current InferRequest object is initialized.
- Returns:
True if the current InferRequest object is initialized; false, otherwise.
-
bool operator!=(const InferRequest &other) const noexcept
Compares whether this request wraps the same impl underneath.
- Parameters:
other – Another inference request.
- Returns:
True if the current InferRequest object does not wrap the same impl as the operator’s arg.
-
bool operator==(const InferRequest &other) const noexcept
Compares whether this request wraps the same impl underneath.
- Parameters:
other – Another inference request.
- Returns:
True if the current InferRequest object wraps the same impl as the operator’s arg.
-
InferRequest() = default
-
template<typename NodeType>
class Input - #include <node_input.hpp>
-
template<>
class Input<const Node># - #include <node_input.hpp>
A handle for one of a node’s inputs.
Public Functions
-
Input(const Node *node, size_t index)#
Constructs a Input.
- Parameters:
node – Pointer to the node for the input handle.
index – The index of the input.
-
size_t get_index() const#
- Returns:
The index of the input referred to by this input handle.
-
const element::Type &get_element_type() const#
- Returns:
The element type of the input referred to by this input handle.
-
const PartialShape &get_partial_shape() const#
- Returns:
The partial shape of the input referred to by this input handle.
-
Output<Node> get_source_output() const#
- Returns:
A handle to the output that is connected to this input.
-
descriptor::Tensor &get_tensor() const#
- Returns:
A reference to the tensor descriptor for this input.
-
std::shared_ptr<descriptor::Tensor> get_tensor_ptr() const#
- Returns:
A shared pointer to the tensor descriptor for this input.
-
bool get_is_relevant_to_shapes() const#
- Returns:
true if this input is relevant to its node’s output shapes; else false.
-
bool get_is_relevant_to_values() const#
- Returns:
true if this input is relevant to its node’s output values; else false.
-
const RTMap &get_rt_info() const#
- Returns:
The constant reference to runtime info map
-
Input(const Node *node, size_t index)#
-
template<>
class Input<Node># - #include <node_input.hpp>
A handle for one of a node’s inputs.
Public Functions
-
Input(Node *node, size_t index)#
Constructs a Input.
- Parameters:
node – Pointer to the node for the input handle.
index – The index of the input.
-
size_t get_index() const#
- Returns:
The index of the input referred to by this input handle.
-
const element::Type &get_element_type() const#
- Returns:
The element type of the input referred to by this input handle.
-
const PartialShape &get_partial_shape() const#
- Returns:
The partial shape of the input referred to by this input handle.
-
Output<Node> get_source_output() const#
- Returns:
A handle to the output that is connected to this input.
-
descriptor::Tensor &get_tensor() const#
- Returns:
A reference to the tensor descriptor for this input.
-
std::shared_ptr<descriptor::Tensor> get_tensor_ptr() const#
- Returns:
A shared pointer to the tensor descriptor for this input.
-
bool get_is_relevant_to_shapes() const#
- Returns:
true if this input is relevant to its node’s output shapes; else false.
-
bool get_is_relevant_to_values() const#
- Returns:
true if this input is relevant to its node’s output values; else false.
-
void replace_source_output(const Output<Node> &new_source_output) const#
Replaces the source output of this input.
- Parameters:
new_source_output – A handle for the output that will replace this input’s source.
-
RTMap &get_rt_info()#
- Returns:
The reference to runtime info map
-
const RTMap &get_rt_info() const#
- Returns:
The constant reference to runtime info map
-
Input(Node *node, size_t index)#
-
class Interval
- #include <interval.hpp>
Interval arithmetic.
An interval is the set of integers from m_min_val through m_max_val. The value s_max acts like infinity. The addition, subtraction, or multiplication of intervals is the smallest interval containing the sums, differences, or products of elements of the two intervals. An empty interval is canonicalized to [s_max, s_max].
Public Functions
-
Interval() = default
Interval of everything.
-
Interval(const Interval &interval) = default
Copy constructor.
-
Interval(value_type min_val, value_type max_val)
Closed interval {x|min_val <= x <= max_val}.
-
Interval(value_type val)
Single-valued interval; just contains val.
-
inline size_type size() const
The number of elements in the interval. Zero if max < min.
-
inline bool empty() const
Returns true if the interval has no elements.
-
inline value_type get_min_val() const
the inclusive lower bound of the interval
-
inline void set_min_val(value_type val)
Set the inclusive lower bound of the interval.
-
inline value_type get_max_val() const
the inclusive upper bound of the interval
-
inline void set_max_val(value_type val)
Set the inclusive upper bound of the interval.
-
inline bool has_upper_bound() const
True if the upper bound is finite.
-
bool operator==(const Interval &interval) const
True if min and max bounds match.
-
Interval operator+(const Interval &interval) const
The interval whose elements are a sum of an element from each interval.
-
Interval &operator+=(const Interval &interval)
Extend this interval to sums of elements in this interval and interval.
-
Interval operator-(const Interval &interval) const
The interval whose elements are a difference of an element from each interval.
-
Interval &operator-=(const Interval &interval)
Extend this interval to differences of elements in this interval and interval.
-
Interval operator*(const Interval &interval) const
The smallest interval whose elements are a product of an element from each interval.
-
Interval &operator*=(const Interval &interval)
Extend this interval to products of elements in this interval and interval.
-
Interval operator&(const Interval &interval) const
The interval that is the intersection of this interval and interval.
-
Interval &operator&=(const Interval &interval)
Change this interval to only include elements also in interval.
-
inline bool contains(value_type value) const
True if this interval includes value.
-
bool contains(const Interval &interval) const
True if this interval includes all the values in interval.
Public Static Attributes
-
static constexpr value_type s_max = {std::numeric_limits<value_type>::max()}
The value used for no upper bound.
-
Interval() = default
-
class IntervalsAlignmentAttribute : public SharedAttribute<IntervalsAlignmentSharedValue>
- #include <intervals_alignment_attribute.hpp>
IntervalsAlignmentAttribute defines subgraph with the same quantization intervals alignment. FakeQuantize operations are included. The attribute is used by quantization operations.
For more details about the attribute, refer to IntervalsAlignmentAttribute page in the OpenVINO Developer Guide.
- #include <intervals_alignment_attribute.hpp>
IntervalsAlignmentSharedValue is used by IntervalsAlignmentAttribute as attribute shared value.
- #include <intervals_alignment_attribute.hpp>
-
class IPlugin : public std::enable_shared_from_this<IPlugin>
- #include <iplugin.hpp>
OpenVINO Plugin Interface 2.0.
Public Functions
-
void set_version(const Version &version)
Sets a plugin version.
- Parameters:
version – A version to set
-
const Version &get_version() const
Returns a plugin version.
- Returns:
A constant ov::Version object
Compiles model from ov::Model object.
- Parameters:
model – A model object acquired from ov::Core::read_model or source construction
properties – A ov::AnyMap of properties relevant only for this load operation
- Returns:
Created Compiled Model object
-
virtual std::shared_ptr<ov::ICompiledModel> compile_model(const std::string &model_path, const ov::AnyMap &properties) const
Compiles model from ov::Model object.
- Parameters:
model_path – A path to model (path can be converted from unicode representation)
properties – A ov::AnyMap of properties relevant only for this load operation
- Returns:
Created Compiled Model object
Compiles model from ov::Model object, on specified remote context.
- Parameters:
model – A model object acquired from ov::Core::read_model or source construction
properties – A ov::AnyMap of properties relevant only for this load operation
context – A pointer to plugin context derived from RemoteContext class used to execute the model
- Returns:
Created Compiled Model object
-
virtual void set_property(const ov::AnyMap &properties) = 0
Sets properties for plugin, acceptable keys can be found in openvino/runtime/properties.hpp.
- Parameters:
properties – ov::AnyMap of properties
-
virtual ov::Any get_property(const std::string &name, const ov::AnyMap &arguments) const = 0
Gets properties related to plugin behaviour.
- Parameters:
name – Property name.
arguments – Additional arguments to get a property.
- Returns:
Value of a property corresponding to the property name.
-
virtual ov::SoPtr<ov::IRemoteContext> create_context(const ov::AnyMap &remote_properties) const = 0
Creates a remote context instance based on a map of properties.
- Parameters:
remote_properties – Map of device-specific shared context remote properties.
- Returns:
A remote context object
-
virtual ov::SoPtr<ov::IRemoteContext> get_default_context(const ov::AnyMap &remote_properties) const = 0
Provides a default remote context instance if supported by a plugin.
- Parameters:
remote_properties – Map of device-specific shared context remote properties.
- Returns:
The default context.
-
virtual std::shared_ptr<ov::ICompiledModel> import_model(std::istream &model, const ov::AnyMap &properties) const = 0
Creates an compiled model from an previously exported model using plugin implementation and removes OpenVINO Runtime magic and plugin name.
- Parameters:
model – Reference to model output stream
properties – A ov::AnyMap of properties
- Returns:
An Compiled model
-
virtual std::shared_ptr<ov::ICompiledModel> import_model(std::istream &model, const ov::SoPtr<ov::IRemoteContext> &context, const ov::AnyMap &properties) const = 0
Creates an compiled model from an previously exported model using plugin implementation and removes OpenVINO Runtime magic and plugin name.
- Parameters:
model – Reference to model output stream
context – A pointer to plugin context derived from RemoteContext class used to execute the network
properties – A ov::AnyMap of properties
- Returns:
An Compiled model
Queries a plugin about supported layers in model.
- Parameters:
model – Model object to query.
properties – Optional map of pairs: (property name, property value).
- Returns:
An object containing a map of pairs an operation name -> a device name supporting this operation.
-
void set_core(const std::weak_ptr<ov::ICore> &core)
Sets pointer to ICore interface.
- Parameters:
core – Pointer to Core interface
-
std::shared_ptr<ov::ICore> get_core() const
Gets reference to ICore interface.
- Returns:
Reference to ICore interface
-
const std::shared_ptr<ov::threading::ExecutorManager> &get_executor_manager() const
Gets reference to tasks execution manager.
- Returns:
Reference to ExecutorManager interface
-
void set_version(const Version &version)
-
class IRemoteContext : public std::enable_shared_from_this<IRemoteContext>
- #include <iremote_context.hpp>
Public Functions
-
virtual const ov::AnyMap &get_property() const = 0
Returns a map of device-specific parameters required for low-level operations with underlying object. Parameters include device/context handles, access flags, etc. Contents of the map returned depend on remote execution context that is currently set on the device (working scenario). Abstract method.
- Returns:
A map of name/Any elements.
-
virtual ov::SoPtr<ov::IRemoteTensor> create_tensor(const ov::element::Type &type, const ov::Shape &shape, const ov::AnyMap ¶ms = {}) = 0
Allocates memory tensor in device memory or wraps user-supplied memory handle using the specified tensor description and low-level device-specific parameters. Returns a pointer to the object that implements the RemoteTensor interface.
- Parameters:
type – Defines the element type of the tensor.
shape – Defines the shape of the tensor.
params – Map of the low-level tensor object parameters.
- Returns:
Pointer to a plugin object that implements the RemoteTensor interface.
-
virtual ov::SoPtr<ov::ITensor> create_host_tensor(const ov::element::Type type, const ov::Shape &shape)
This method is used to create a host tensor object friendly for the device in current context. For example, GPU context may allocate USM host memory (if corresponding extension is available), which could be more efficient than regular host memory.
-
virtual const ov::AnyMap &get_property() const = 0
-
class IRemoteTensor : public ITensor
- #include <iremote_tensor.hpp>
Public Functions
-
virtual const AnyMap &get_properties() const = 0
Returns additional information associated with tensor.
- Returns:
Map of property names to properties
-
virtual const AnyMap &get_properties() const = 0
-
class ISyncInferRequest : public ov::IInferRequest
- #include <isync_infer_request.hpp>
Interface for syncronous infer request.
Public Functions
Constructs syncronous inference request.
- Parameters:
compiled_model – pointer to compiled model
-
virtual ov::SoPtr<ov::ITensor> get_tensor(const ov::Output<const ov::Node> &port) const override
Gets an input/output tensor for inference.
Note
If the tensor with the specified
portis not found, an exception is thrown.- Parameters:
port – Port of the tensor to get.
- Returns:
Tensor for the port
port.
-
virtual void set_tensor(const ov::Output<const ov::Node> &port, const ov::SoPtr<ov::ITensor> &tensor) override
Sets an input/output tensor to infer.
- Parameters:
port – Port of the input or output tensor.
tensor – Reference to a tensor. The element_type and shape of a tensor must match the model’s input/output element_type and size.
-
virtual std::vector<ov::SoPtr<ov::ITensor>> get_tensors(const ov::Output<const ov::Node> &port) const override
Gets a batch of tensors for input data to infer by input port. Model input must have batch dimension, and the number of
tensorsmust match the batch size. The current version supports setting tensors to model inputs only. Ifportis associated with output (or any other non-input node), an exception is thrown.- Parameters:
port – Port of the input tensor.
tensors – Input tensors for batched infer request. The type of each tensor must match the model input element type and shape (except batch dimension). Total size of tensors must match the input size.
- Returns:
vector of tensors
-
virtual void set_tensors(const ov::Output<const ov::Node> &port, const std::vector<ov::SoPtr<ov::ITensor>> &tensors) override
Sets a batch of tensors for input data to infer by input port. Model input must have batch dimension, and the number of
tensorsmust match the batch size. The current version supports setting tensors to model inputs only. Ifportis associated with output (or any other non-input node), an exception is thrown.- Parameters:
port – Port of the input tensor.
tensors – Input tensors for batched infer request. The type of each tensor must match the model input element type and shape (except batch dimension). Total size of tensors must match the input size.
-
virtual const std::vector<ov::Output<const ov::Node>> &get_inputs() const override
Gets inputs for infer request.
- Returns:
vector of input ports
-
virtual const std::vector<ov::Output<const ov::Node>> &get_outputs() const override
Gets outputs for infer request.
- Returns:
vector of output ports
-
virtual const std::shared_ptr<const ov::ICompiledModel> &get_compiled_model() const override
Gets pointer to compiled model (usually synchronous request holds the compiled model)
- Returns:
Pointer to the compiled model
-
class ITensorAccessor
- #include <tensor_data_accessor.hpp>
Interface for data accessor.
Subclassed by ov::TensorAccessor< TContainer >
-
interface IVariableState : public std::enable_shared_from_this<IVariableState>
- #include <ivariable_state.hpp>
Minimal interface for variable state implementation.
Public Functions
-
virtual const std::string &get_name() const
Gets a variable state name.
- Returns:
A string representing variable state name
-
virtual void reset()
Reset internal variable state for relevant infer request, to a value specified as default for according
ReadValuenode.
-
virtual const std::string &get_name() const
-
class KeepConstPrecision : public ov::RuntimeAttribute
- #include <keep_const_precision.hpp>
KeepConstPrecision class represents runtime info attribute that marks a Constant as prohibitted to fuse precision in ConvertPrecision.
-
class Layout
- #include <layout.hpp>
ov::Layout represents the text information of tensor’s dimensions/axes. E.g. layout
NCHWmeans that 4D tensor{-1, 3, 480, 640}will have:0:
N = -1: batch dimension is dynamic1:
C = 3: number of channels is ‘3’2:
H = 480: image height is 4803:
W = 640: image width is 640
Examples:
ov::Layoutcan be specified for:Preprocessing purposes. E.g.
To apply normalization (means/scales) it is usually required to set ‘C’ dimension in a layout.
To resize the image to specified width/height it is needed to set ‘H’ and ‘W’ dimensions in a layout
To transpose image - source and target layout can be set (see
ov::preprocess::PreProcessSteps::convert_layout)
To set/get model’s batch (see
ov::get_batch/ov::set_batch) it is required in general to specify ‘N’ dimension in layout for appropriate inputs
Refer also to
ov::layoutnamespace for various additional helper functions ofov::LayoutPublic Functions
-
Layout()
Constructs a dynamic Layout with no layout information.
-
inline Layout(const char *layoutStr)
Constructs a Layout with static or dynamic layout information based on string representation.
- Parameters:
layoutStr – The string used to construct Layout from. The string representation can be in the following form:
can define order and meaning for dimensions “NCHW”
partial layout specialization:
”NC?” defines 3 dimensional layout, first two NC, 3rd one is not defined
”N…C” defines layout with dynamic rank where 1st dimension is N, last one is C
”NC…” defines layout with dynamic rank where first two are NC, others are not defined
only order of dimensions “adbc” (0312)
Advanced syntax can be used for multi-character names like “[N,C,H,W,…,CustomName]”
-
bool operator==(const Layout &rhs) const
Comparison operator (equal)
-
bool operator!=(const Layout &rhs) const
Comparison operator (not equal)
-
bool has_name(const std::string &dimensionName) const
Checks if dimension with specified name is in layout.
- Returns:
trueif layout has information about dimension index with a given name
-
std::int64_t get_index_by_name(const std::string &dimensionName) const
Gets index of dimension with a specified name.
- Throws:
ov::AssertFailure – if dimension name is not found in a layout
- Returns:
Index of given dimension name
-
std::string to_string() const
String representation of Layout.
-
inline bool empty() const
Returns ‘true’ if layout has no information, i.e. equals to Layout()
Public Static Functions
-
static Layout scalar()
Constructs layout representing scalar.
-
class LayoutAttribute : public ov::RuntimeAttribute
- #include <layout.hpp>
-
class MappedMemory
- #include <mmap_object.hpp>
This class represents a mapped memory. Instead of reading files, we can map the memory via mmap for Linux or MapViewOfFile for Windows. The MappedMemory class is a abstraction to handle such memory with os-dependent details.
-
class Mask : public std::vector<std::set<uint64_t>>, public std::enable_shared_from_this<Mask>
- #include <mask_attribute.hpp>
each element in vector represents dimension and each element in set is an id of dimensions which contains zeros.
-
struct MemBandwidthPressure#
- #include <performance_heuristics.hpp>
-
class MemorySolver
- #include <memory_solver.hpp>
Helps to solve issue of optimal memory allocation only for particular execution order.
It works with abstract data description where
Example:
Mem(offset) | |____| Box {4, 5} | |_____________| Box {2, 6} | |____| Box {3, 4} | |____| Box {2, 3} | |____| Box {6, 7} |_____________________________________ 1 2 3 4 5 6 7 8 9 ExecOrder
Boxes which has an ExecOrder-axis intersection should have no Mem-axis intersections. The goal is to define a minimal required memory blob to store all boxes with such constraints and specify all corresponding position on Mem axis(through offset field).
NOTE! Exec order is predefined.
Public Functions
-
inline int64_t solve()
Solve memory location with maximal reuse.
- Returns:
Size of common memory blob required for storing all
-
inline int64_t get_offset(int id) const
Provides calculated offset for specified box id
-
inline int64_t max_depth()
Additional info. Max sum of box sizes required for any time stamp.
-
inline int64_t max_top_depth()
Additional info. Max num of boxes required for any time stamp.
Public Static Functions
-
static inline int normalize_boxes(std::vector<Box> &boxes)
Performes inplace normalization of the input boxes.
- Returns:
lifespan of all boxes
-
struct Box
- #include <memory_solver.hpp>
Representation of edge (size and live time)
Public Members
-
int start
Execution order index of first use. The data will be produced here.
-
int finish
The execution order index of last use. After that data will be released. -1 is a reserved value for “till to end”. The data will be alive to very end of execution.
-
int64_t size
Size of data. In abstract unit of measure (byte, simd, cache line, …)
-
int64_t id
Box identifier, unique for each box. Will be used to querying calculated offset.
-
int start
-
inline int64_t solve()
-
class Model : public std::enable_shared_from_this<Model>
- #include <model.hpp>
A user-defined model.
Public Functions
-
explicit Model(const ov::OutputVector &results, const std::string &name = "")
Constructs a Model. Lists of parameters and variables will be generated automatically based on traversing the graph from the results.
-
Model(const ov::OutputVector &results, const ov::SinkVector &sinks, const std::string &name = "")
Constructs a Model. Lists of parameters and variables will be generated automatically based on traversing the graph from the results and the sinks.
-
size_t get_output_size() const
Return the number of outputs for this Model.
-
const ov::element::Type &get_output_element_type(size_t i) const
Return the element type of output i.
-
const Shape &get_output_shape(size_t i) const
Return the shape of element i.
-
const PartialShape &get_output_partial_shape(size_t i) const
Return the partial shape of element i.
-
const std::string &get_name() const
Get the unique name of the model.
- Returns:
A const reference to the model’s unique name.
-
void set_friendly_name(const std::string &name)
Sets a friendly name for a model. This does not overwrite the unique name of the model and is retrieved via get_friendly_name(). Used mainly for debugging.
- Parameters:
name – is the friendly name to set
-
const std::string &get_friendly_name() const
Gets the friendly name for a model. If no friendly name has been set via set_friendly_name then the model’s unique name is returned.
- Returns:
A const reference to the model’s friendly name.
-
size_t get_graph_size() const
Returns the sum of the size of all nodes in the graph plus the size of all constant data. This has little value beyond comparing the relative size of graphs and should not be considered the actual memory consumption of a graph.
-
bool is_dynamic() const
Returns true if any of the op’s defined in the model contains partial shape.
Replace the
parameter_indexth parameter of the model withparameter.All users of the
parameter_indexth parameter are redirected toparameter, and theparameter_indexth entry in the model parameter list is replaced withparameter.- Parameters:
parameter_index – The index of the parameter to replace.
parameter – The parameter to substitute for the
parameter_indexth parameter.
-
inline const ov::ParameterVector &get_parameters() const
Return the model parameters.
-
inline const ov::ResultVector &get_results() const
Return a list of model’s outputs.
Index for parameter, or -1.
-
int64_t get_result_index(const ov::Output<ov::Node> &value) const
Return the index of this model’s Result represented by the “value” Output object. This method returns -1 if an the passed output is not related to the Results of a model.
-
int64_t get_result_index(const ov::Output<const ov::Node> &value) const
Return the index of this model’s Result represented by the “value” Output object. This method returns -1 if an the passed output is not related to the Results of a model.
-
bool evaluate(ov::TensorVector &output_tensors, const ov::TensorVector &input_tensors, ov::EvaluationContext &evaluation_context) const
Evaluate the model on inputs, putting results in outputs.
- Parameters:
output_tensors – Tensors for the outputs to compute. One for each result
input_tensors – Tensors for the inputs. One for each inputs.
evaluation_context – Storage of additional settings and attributes that can be used when evaluating the model. This additional information can be shared across nodes.
-
bool evaluate(ov::TensorVector &output_tensors, const ov::TensorVector &input_tensors) const
Evaluate the model on inputs, putting results in outputs.
- Parameters:
output_tensors – Tensors for the outputs to compute. One for each result
input_tensors – Tensors for the inputs. One for each inputs.
-
inline const ov::SinkVector &get_sinks() const
Return a list of model’s sinks.
-
void add_sinks(const ov::SinkVector &sinks)
Add new sink nodes to the list. Method doesn’t validate graph, it should be done manually after all changes.
- Parameters:
sinks – new sink nodes
Delete sink node from the list of sinks. Method doesn’t delete node from graph.
- Parameters:
sink – Sink to delete
-
void add_results(const ov::ResultVector &results)
Add new Result nodes to the list. Method doesn’t validate graph, it should be done manually after all changes.
- Parameters:
results – new Result nodes
Delete Result node from the list of results. Method will not delete node from graph.
- Parameters:
result – Result node to delete
-
void add_parameters(const ov::ParameterVector ¶ms)
Add new Parameter nodes to the list.
Method doesn’t change or validate graph, it should be done manually. For example, if you want to replace
ReadValuenode byParameter, you should do the following steps:replace node
ReadValuebyParameterin graphcall add_parameter() to add new input to the list
call graph validation to check correctness of changes
- Parameters:
params – new Parameter nodes
Delete Parameter node from the list of parameters. Method will not delete node from graph. You need to replace Parameter with other operation manually. Attention: Indexing of parameters can be changed.
Possible use of method is to replace input by variable. For it the following steps should be done:
Parameternode should be replaced byReadValuecall remove_parameter(param) to remove input from the list
check if any parameter indexes are saved/used somewhere, update it for all inputs because indexes can be changed
call graph validation to check all changes
- Parameters:
param – Parameter node to delete
-
void add_variables(const ov::op::util::VariableVector &variables)
Add new variables to the list. Method doesn’t validate graph, it should be done manually after all changes.
- Parameters:
variables – new variables to add
-
void remove_variable(const ov::op::util::Variable::Ptr &variable)
Delete variable from the list of variables. Method doesn’t delete nodes that used this variable from the graph.
- Parameters:
variable – Variable to delete
-
inline const ov::op::util::VariableVector &get_variables() const
Return a list of model’s variables.
-
ov::op::util::Variable::Ptr get_variable_by_id(const std::string &variable_id) const
Return a variable by specified variable_id.
-
inline RTMap &get_rt_info()
Returns a runtime info.
- Returns:
reference to ov::AnyMap with runtime info
-
inline const RTMap &get_rt_info() const
Returns a constant runtime info.
- Returns:
reference to const ov::AnyMap with runtime info
-
template<class T, class ...Args, typename std::enable_if<!std::is_same<T, ov::Any>::value, bool>::type = true>
inline const T &get_rt_info(Args... args) const Returns a runtime attribute for the path, throws an ov::Exception if path doesn’t exist.
- Template Parameters:
T – the type of returned value
Args – types of variadic arguments
- Parameters:
args – path to the runtime attribute
- Returns:
constant reference to value from runtime info
-
template<class T, class ...Args, typename std::enable_if<std::is_same<T, ov::Any>::value, bool>::type = true>
inline const T &get_rt_info(Args... args) const Returns a runtime attribute for the path, throws an ov::Exception if path doesn’t exist.
- Template Parameters:
T – the type of returned value
Args – types of variadic arguments
- Parameters:
args – path to the runtime attribute
- Returns:
constant reference to value from runtime info
-
template<class T, typename std::enable_if<!std::is_same<T, ov::Any>::value, bool>::type = true>
inline const T &get_rt_info(const std::vector<std::string> &args) const Returns a runtime attribute for the path, throws an ov::Exception if path doesn’t exist.
- Template Parameters:
T – the type of returned value
- Parameters:
args – vector with path to the runtime attribute
- Returns:
constant reference to value from runtime info
-
template<class T, typename std::enable_if<std::is_same<T, ov::Any>::value, bool>::type = true>
inline const T &get_rt_info(const std::vector<std::string> &args) const Returns a runtime attribute for the path, throws an ov::Exception if path doesn’t exist.
- Template Parameters:
T – the type of returned value
- Parameters:
args – vector with path to the runtime attribute
- Returns:
constant reference to value from runtime info
-
template<class ...Args>
inline bool has_rt_info(Args... args) const Checks if given path exists in runtime info.
- Template Parameters:
Args – types of variadic arguments
- Parameters:
args – path to the runtime attribute
- Returns:
true if path exists, otherwise false
-
bool has_rt_info(const std::vector<std::string> &args) const
Checks if given path exists in runtime info.
- Parameters:
args – vector with path to the runtime attribute
- Returns:
true if path exists, otherwise false
-
template<class T, class ...Args>
inline void set_rt_info(const T &argument, Args... args) Add value inside the runtime info.
- Template Parameters:
T – type of new value
Args – types of variadic arguments
- Parameters:
argument – value for the runtime info
args – path to the runtime attribute
-
template<class T>
inline void set_rt_info(const T &argument, const std::vector<std::string> &args) Add value inside the runtime info.
- Template Parameters:
T – type of new value
- Parameters:
argument – value for the runtime info
args – vector with path to the runtime attribute
-
explicit Model(const ov::OutputVector &results, const std::string &name = "")
-
class NmsSelectedIndices : public ov::RuntimeAttribute
- #include <nms_selected_indices.hpp>
-
class Node : public std::enable_shared_from_this<Node>
- #include <node.hpp>
Nodes are the backbone of the graph of Value dataflow. Every node has zero or more nodes as arguments and one value, which is either a tensor or a (possibly empty) tuple of values.
Subclassed by ov::op::Op, ov::pass::pattern::op::Pattern
Public Functions
-
virtual void validate_and_infer_types()
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
virtual const ov::op::AutoBroadcastSpec &get_autob() const
- Returns:
the autobroadcasr spec
-
virtual bool has_evaluate() const
Allows to get information about availability of evaluate method for the current operation.
-
virtual bool evaluate(ov::TensorVector &output_values, const ov::TensorVector &input_values) const
Evaluates the op on input_values putting results in output_values.
- Parameters:
output_values – Tensors for the outputs to compute. One for each result
input_values – Tensors for the inputs. One for each inputs.
- Returns:
true if successful
-
virtual bool evaluate(ov::TensorVector &output_values, const ov::TensorVector &input_values, const ov::EvaluationContext &evaluationContext) const
Evaluates the op on input_values putting results in output_values.
- Parameters:
output_values – Tensors for the outputs to compute. One for each result
input_values – Tensors for the inputs. One for each inputs.
evaluation_context – Storage of additional settings and attributes that can be used when evaluating the op.
- Returns:
true if successful
-
inline virtual OutputVector decompose_op() const
Decomposes the FusedOp into a sub-graph consisting of core openvino ops.
- Returns:
A vector of nodes comprising the sub-graph. The order of output tensors must match the match output tensors of the FusedOp
-
virtual const type_info_t &get_type_info() const = 0
Returns the NodeTypeInfo for the node’s class. During transition to type_info, returns a dummy type_info for Node if the class has not been updated yet.
-
void set_arguments(const NodeVector &arguments)
Sets/replaces the arguments with new arguments.
-
void set_arguments(const OutputVector &arguments)
Sets/replaces the arguments with new arguments.
-
void set_argument(size_t position, const Output<Node> &argument)
Sets/replaces the arguments with new arguments.
-
void set_output_size(size_t output_size)
Sets the number of outputs.
-
virtual std::string description() const
Get the string name for the type of the node, such as
AddorMultiply. The class name, must not contain spaces as it is used for codegen.- Returns:
A const reference to the node’s type name
-
const std::string &get_name() const
Get the unique name of the node.
- Returns:
A const reference to the node’s unique name.
-
void set_friendly_name(const std::string &name)
Sets a friendly name for a node. This does not overwrite the unique name of the node and is retrieved via get_friendly_name(). Used mainly for debugging. The friendly name may be set exactly once.
- Parameters:
name – is the friendly name to set
-
const std::string &get_friendly_name() const
Gets the friendly name for a node. If no friendly name has been set via set_friendly_name then the node’s unique name is returned.
- Returns:
A const reference to the node’s friendly name.
-
virtual std::ostream &write_description(std::ostream &os, uint32_t depth = 0) const
Writes a description of a node to a stream.
- Parameters:
os – The stream; should be returned
depth – How many levels of inputs to describe
- Returns:
The stream os
-
const std::vector<std::shared_ptr<Node>> &get_control_dependencies() const
Get control dependencies registered on the node.
-
const std::vector<Node*> &get_control_dependents() const
Get nodes dependent on this node.
This node cannot execute until node executes.
Remove the dependency of this node on node.
-
void clear_control_dependencies()
Remove all dependencies from this node.
-
void clear_control_dependents()
Remove this node as a dependency from all dependent nodes.
This node absorbs the control dependencies of source_node.
This node becomes a dependent of every node dependent on source_node.
This node’s control dependencies are replaced by replacement.
-
size_t get_output_size() const
Returns the number of outputs from the node.
-
const element::Type &get_element_type() const
Checks that there is exactly one output and returns its element type.
-
const Shape &get_output_shape(size_t i) const
Returns the shape for output i.
-
const PartialShape &get_output_partial_shape(size_t i) const
Returns the partial shape for output i.
-
Output<const Node> get_default_output() const
Return the output to use when converting to an Output<Node> with no index specified. Throws when not supported.
-
virtual size_t get_default_output_index() const
Returns the output of the default output, or throws if there is none.
-
size_t no_default_index() const
Throws no default.
-
const Shape &get_shape() const
Checks that there is exactly one output and returns its shape.
-
descriptor::Tensor &get_output_tensor(size_t i) const
Returns the tensor for output or input i.
-
size_t get_input_size() const
Returns the number of inputs for the op.
-
const Shape &get_input_shape(size_t i) const
Returns the shape of input i.
-
const PartialShape &get_input_partial_shape(size_t i) const
Returns the partial shape of input i.
True if this and node have one output with same element type and shape.
-
NodeVector get_users(bool check_is_used = false) const
Get all the nodes that uses the current node.
-
inline bool operator<(const Node &other) const
Use instance ids for comparison instead of memory addresses to improve determinism.
-
std::vector<Input<Node>> inputs()
- Returns:
A vector containing a handle for each of this node’s inputs, in order.
-
std::vector<Input<const Node>> inputs() const
- Returns:
A vector containing a handle for each of this node’s inputs, in order.
-
std::vector<Output<Node>> input_values() const
- Returns:
A vector containing the values for each input
-
std::vector<Output<Node>> outputs()
- Returns:
A vector containing a handle for each of this node’s outputs, in order.
-
std::vector<Output<const Node>> outputs() const
- Returns:
A vector containing a handle for each of this node’s outputs, in order.
-
Input<Node> input(size_t input_index)
- Throws:
std::out_of_range – if the node does not have at least
input_index+1inputs.- Returns:
A handle to the
input_indexth input of this node.
-
Input<const Node> input(size_t input_index) const
- Throws:
std::out_of_range – if the node does not have at least
input_index+1inputs.- Returns:
A handle to the
input_indexth input of this node.
-
virtual void validate_and_infer_types()
-
class NodeValidationFailure : public ov::AssertFailure
- #include <node.hpp>
Public Functions
- template<> OPENVINO_API void create (const char *file, int line, const char *check_string, std::pair< const Node *, const std::vector< PartialShape > * > &&ctx, const std::string &explanation)
Specialization to throw the
NodeValidationFailurefor shape inference usingPartialShape- Parameters:
check_loc_info – Exception location details to print.
ctx – NodeValidationFailure context which got pointer to node and input shapes used for shape inference.
explanation – Exception explanation string.
-
class NonconvertibleDivide : public ov::RuntimeAttribute
- #include <nonconvertible_divide.hpp>
NonconvertibleDivide class represents runtime info attribute that marks a Divide as prohibitted to transform it to power.
-
class NotImplemented : public ov::AssertFailure
- #include <except.hpp>
Exception class to be thrown on not implemented code.
-
class NoTransposeSinkingAttr : public ov::RuntimeAttribute
- #include <transpose_sinking_attr.hpp>
NoTransposeSinkingAttr class represents runtime info attribute that marks transpose operation should not be moved be backward sinking propagation.
-
class OldApiMapElementType : public ov::RuntimeAttribute
- #include <old_api_map_element_type_attribute.hpp>
OldApiMapElementType class represents runtime info attribute that stores legacy type that is required for obtaining IR in old API.
Public Functions
-
OldApiMapElementType() = default
A default constructor
-
inline OldApiMapElementType(const ov::element::Type &value)
Constructs a new OldApiMapElementType object.
- Parameters:
value – [in] The object that stores values of OldApiMapElementType.
-
OldApiMapElementType() = default
-
class OldApiMapOrder : public ov::RuntimeAttribute
- #include <old_api_map_order_attribute.hpp>
OldApiMapOrder class represents runtime info attribute that stores order of the transpose that is required for obtaining IR in old API.
OldApiMapOrder stores the following information. Parameter: Order of the transpose which should be applied to Parameter with old API layout to obtain Parameter with new API layout.
Result: Order of the transpose which should be applied to Result with new API layout to obtain Result with old API layout.
Public Functions
-
OldApiMapOrder() = default
A default constructor
-
inline OldApiMapOrder(const std::vector<uint64_t> &value)
Constructs a new OldApiMapOrder object.
- Parameters:
value – [in] The object that stores values of OldApiMapOrder.
-
OldApiMapOrder() = default
-
template<class T>
class OpExtension : public ov::BaseOpExtension - #include <op_extension.hpp>
The default implementation of OpenVINO operation extensions.
Public Functions
-
inline OpExtension()
Default constructor.
-
inline virtual const ov::DiscreteTypeInfo &get_type_info() const override
Returns the type info of operation.
- Returns:
-
inline virtual ov::OutputVector create(const ov::OutputVector &inputs, ov::AttributeVisitor &visitor) const override
Method creates an OpenVINO operation.
- Parameters:
inputs – vector of input ports
visitor – attribute visitor which allows to read necessaty arguments
- Returns:
vector of output ports
-
inline virtual std::vector<ov::Extension::Ptr> get_attached_extensions() const override
Returns extensions that should be registered together with this extension class object.
Attached extensions may include frontend extensions that OpenVINO op to framework ops or necessary transformations that should be applied to the network which consist of target op.
- Returns:
-
inline OpExtension()
-
class OpSet
- #include <opset.hpp>
Run-time opset information.
Public Functions
-
template<typename OP_TYPE>
inline void insert(const std::string &name) Insert OP_TYPE into the opset with a special name and the default factory.
-
template<typename OP_TYPE>
inline void insert() Insert OP_TYPE into the opset with the default name and factory.
-
ov::Node *create_insensitive(const std::string &name) const
Create the op named name using it’s factory.
-
bool contains_type(const NodeTypeInfo &type_info) const
Return true if OP_TYPE is in the opset.
-
template<typename OP_TYPE>
inline bool contains_type() const Return true if OP_TYPE is in the opset.
-
bool contains_type(const std::string &name) const
Return true if name is in the opset.
-
bool contains_type_insensitive(const std::string &name) const
Return true if name is in the opset.
-
bool contains_op_type(const Node *node) const
Return true if node’s type is in the opset.
-
template<typename OP_TYPE>
-
template<class T>
class optional - #include <ov_optional.hpp>
Store optional object of type T (basic version of std::optional).
Note
If cpp17 used this class should be replaced by std::optional.
- Template Parameters:
T – Type of stored object.
-
class OriginalPrecisionAttribute : public ov::RuntimeAttribute
- #include <original_precision_attribute.hpp>
OriginalPrecisionAttribute stores the original precision of the node to pass this information to plugins.
-
template<typename NodeType>
class Output - #include <node_output.hpp>
-
template<>
class Output<const Node># - #include <node_output.hpp>
A handle for one of a node’s outputs.
Public Functions
-
Output(const Node *node, size_t index)#
Constructs a Output.
- Parameters:
node – A pointer to the node for the output handle.
index – The index of the output.
Constructs a Output.
- Parameters:
node – A
shared_ptrto the node for the output handle.index – The index of the output.
Constructs a Output, referencing the zeroth output of the node.
- Parameters:
node – A
shared_ptrto the node for the output handle.
-
Output() = default#
A null output.
- Returns:
A
shared_ptrto the node referred to by this output handle.
-
size_t get_index() const#
- Returns:
The index of the output referred to by this output handle.
-
descriptor::Tensor &get_tensor() const#
- Returns:
A reference to the tensor descriptor for this output.
-
std::shared_ptr<descriptor::Tensor> get_tensor_ptr() const#
- Returns:
A shared point to the tensor ptr for this output.
-
const element::Type &get_element_type() const#
- Returns:
The element type of the output referred to by this output handle.
-
const PartialShape &get_partial_shape() const#
- Returns:
The partial shape of the output referred to by this output handle.
-
const RTMap &get_rt_info() const#
- Returns:
The constant reference to runtime info map
-
const std::unordered_set<std::string> &get_names() const#
- Returns:
The tensor names associated with this output
-
Output(const Node *node, size_t index)#
-
template<>
class Output<Node># - #include <node_output.hpp>
A handle for one of a node’s outputs.
Public Functions
-
Output(Node *node, size_t index)#
Constructs a Output.
- Parameters:
node – A pointer to the node for the output handle.
index – The index of the output.
Constructs a Output.
- Parameters:
node – A
shared_ptrto the node for the output handle.index – The index of the output.
Constructs a Output, referencing the zeroth output of the node.
- Parameters:
node – A
shared_ptrto the node for the output handle.
-
Output() = default#
A null output.
- Returns:
A
shared_ptrto the node referred to by this output handle.
-
size_t get_index() const#
- Returns:
The index of the output referred to by this output handle.
-
descriptor::Tensor &get_tensor() const#
- Returns:
A reference to the tensor descriptor for this output.
-
std::shared_ptr<descriptor::Tensor> get_tensor_ptr() const#
- Returns:
A shared point to the tensor ptr for this output.
- Returns:
Set new tensor desc shared pointer to this output
-
const element::Type &get_element_type() const#
- Returns:
The element type of the output referred to by this output handle.
-
const PartialShape &get_partial_shape() const#
- Returns:
The partial shape of the output referred to by this output handle.
-
RTMap &get_rt_info()#
- Returns:
The reference to runtime info map
-
const RTMap &get_rt_info() const#
- Returns:
The constant reference to runtime info map
-
const std::unordered_set<std::string> &get_names() const#
- Returns:
The tensor names associated with this output
-
void set_names(const std::unordered_set<std::string> &names)#
- Returns:
Set tensor names associated with this output
-
void add_names(const std::unordered_set<std::string> &names)#
- Returns:
Add tensor names associated with this output
-
std::set<Input<Node>> get_target_inputs() const#
- Returns:
A set containing handles for all inputs targeted by the output referenced by this output handle.
-
Output(Node *node, size_t index)#
-
class PartialShape
- #include <partial_shape.hpp>
Class representing a shape that may be partially or totally dynamic.
A PartialShape may have:
Dynamic rank. (Informal notation:
?)Static rank, but dynamic dimensions on some or all axes. (Informal notation examples:
{1,2,?,4},{?,?,?})Static rank, and static dimensions on all axes. (Informal notation examples:
{1,2,3,4},{6},{})
Public Functions
-
PartialShape(std::initializer_list<Dimension> init)
Constructs a shape with static rank from an initializer list of Dimension.
Examples:
PartialShape s{2,3,4}; // rank=3, all dimensions static PartialShape s{}; // rank=0 PartialShape s{2,Dimension::dynamic(),3}; // rank=3, dimension 1 dynamic
- Parameters:
init – The Dimension values for the constructed shape.
-
PartialShape(std::vector<Dimension> dimensions)
Constructs a PartialShape with static rank from a vector of Dimension.
- Parameters:
dimensions – The Dimension values for the constructed shape.
-
PartialShape(const std::vector<Dimension::value_type> &dimensions)
Constructs a PartialShape with static rank from a vector of dimensions values.
- Parameters:
dimensions – The Dimension values for the constructed shape.
-
PartialShape()
Constructs a static PartialShape with zero rank (the shape of a scalar).
-
PartialShape(const Shape &shape)
Constructs a static PartialShape from a PartialShape.
- Parameters:
shape – The PartialShape to convert into PartialShape.
-
PartialShape(const std::string &shape)
Constructs a static PartialShape from a string.
- Parameters:
shape – The string to parse into PartialShape.
-
bool is_static() const
Check if this shape is static.
A shape is considered static if it has static rank, and all dimensions of the shape are static.
- Returns:
trueif this shape is static, elsefalse.
-
inline bool is_dynamic() const
Check if this shape is dynamic.
A shape is considered static if it has static rank, and all dimensions of the shape are static.
- Returns:
falseif this shape is static, elsetrue.
-
inline Rank rank() const
Get the rank of the shape.
- Returns:
The rank of the shape. This will be Rank::dynamic() if the rank of the shape is dynamic.
-
bool compatible(const PartialShape &s) const
Check whether this shape is compatible with the argument, i.e., whether it is possible to merge them.
Two shapes are compatible if
one or both of them has dynamic rank, or
both shapes have dynamic and equal rank, and their dimensions are elementwise compatible (see Dimension::compatible()).
- Parameters:
s – The shape to be checked for compatibility with this shape.
- Returns:
trueif this shape is compatible withs, elsefalse.
-
bool same_scheme(const PartialShape &s) const
Check whether this shape represents the same scheme as the argument.
Two shapes
s1ands2represent the same scheme ifthey both have dynamic rank, or
they both have static and equal rank
r, and for everyifrom0tor-1,s1[i]represents the same scheme ass2[i](see Dimension::same_scheme()).
- Parameters:
s – The shape whose scheme is being compared with this shape.
- Returns:
trueif this shape represents the same scheme ass, elsefalse.
-
bool relaxes(const PartialShape &s) const
Check whether this shape is a relaxation of the argument.
Intuitively, a PartialShape
s1is said to relaxs2(or is a relaxation ofs2) if it is “more permissive” thans2. In other words,s1is a relaxation ofs2if anything you can form by plugging things into the dynamic dimensions ofs2is also something you can form by plugging things into the dynamic dimensions ofs1, but not necessarily the other way around.s1.relaxes(s2)is equivalent tos2.refines(s1).Formally, PartialShape
s1is said to relax PartialShapes2if:For every
ifrom0tor-1, eithers1[i]contains s2[i].
- Parameters:
s – The shape which is being compared against this shape.
- Returns:
trueif this shape relaxess, elsefalse.
-
bool refines(const PartialShape &s) const
Check whether this shape is a refinement of the argument.
Intuitively, a PartialShape
s1is said to relaxs2(or is a relaxation ofs2) if it is “less permissive” thans2. In other words,s1is a relaxation ofs2if anything you can form by plugging things into the dynamic dimensions ofs1is also something you can form by plugging things into the dynamic dimensions ofs2, but not necessarily the other way around.s1.refines(s2)is equivalent tos2.relaxes(s1).Formally, PartialShape
s1is said to refine PartialShapes2if:s2has dynamic rank, ors1ands2both have static rankr, and for everyifrom0tor-1, eithers2[i]is dynamic, ors1[i]==s2[i].
- Parameters:
s – The shape which is being compared against this shape.
- Returns:
trueif this shape refiness, elsefalse.
-
bool merge_rank(const Rank &r)
Checks that this shape’s rank is compatible with
r, and, if this shape’s rank is dynamic andris static, updates this shape to have a rank ofrwith dimensions all dynamic.- Returns:
trueif this shape’s rank is compatible withr, elsefalse.
-
Shape to_shape() const
Convert a static PartialShape to a PartialShape.
- Throws:
std::invalid_argument – If this PartialShape is dynamic.
- Returns:
A new PartialShape
swheres[i] = size_t((*this)[i]).
-
bool all_non_negative() const
Returns
trueif all static dimensions of the tensor are non-negative, elsefalse.
-
Dimension &operator[](std::ptrdiff_t i)
Index operator for PartialShape, with bound checking.
- Parameters:
i – The index of the dimension being selected in range [-rank, rank).
- Returns:
A reference to the
ith Dimension of this shape.
-
const Dimension &operator[](std::ptrdiff_t i) const
Index operator for PartialShape, with bound checking.
- Parameters:
i – The index of the dimension being selected in range [-rank, rank).
- Returns:
A reference to the
ith Dimension of this shape.
-
inline explicit operator std::vector<Dimension>() const
Returns a vector of the dimensions. This has no meaning if dynamic.
-
Shape get_max_shape() const
Get the max bounding shape.
-
Shape get_min_shape() const
Get the min bounding shape.
-
Shape get_shape() const
Get the unique shape.
-
inline iterator begin() noexcept
Returns a read/write iterator that points to the first element in the shape. Iteration is done in ordinary element order.
-
inline const_iterator begin() const noexcept
Returns a read-only (constant) iterator that points to the first element in the shape. Iteration is done in ordinary element order.
-
inline iterator end() noexcept
Returns a read/write iterator that points one past the last element in the shape. Iteration is done in ordinary element order.
-
inline const_iterator end() const noexcept
Returns a read-only (constant) iterator that points one past the last element in the shape. Iteration is done in ordinary element order.
-
inline reverse_iterator rbegin() noexcept
Returns a read/write reverse iterator that points to the last element in the shape. Iteration is done in reverse element order.
-
inline const_reverse_iterator rbegin() const noexcept
Returns a read-only (constant) reverse iterator that points to the last element in the shape. Iteration is done in reverse element order.
-
inline reverse_iterator rend() noexcept
Returns a read/write reverse iterator that points to one before the first element in the shape. Iteration is done in reverse element order.
-
inline const_reverse_iterator rend() const noexcept
Returns a read-only (constant) reverse iterator that points to one before the first element in the shape. Iteration is done in reverse element order.
-
inline const_iterator cbegin() const noexcept
Returns a read-only (constant) iterator that points to the first element in the shape. Iteration is done in ordinary element order.
-
inline const_iterator cend() const noexcept
Returns a read-only (constant) iterator that points one past the last element in the shape. Iteration is done in ordinary element order.
-
inline const_reverse_iterator crbegin() const noexcept
Returns a read-only (constant) reverse iterator that points to the last element in the shape. Iteration is done in reverse element order.
-
inline const_reverse_iterator crend() const noexcept
Returns a read-only (constant) reverse iterator that points to one before the first element in the shape. Iteration is done in reverse element order.
-
inline void resize(size_t count)
Resizes dimensions container to contain count elements.
-
inline size_t size() const
Returns size of dimension vector. Requires rank to be static.
-
inline iterator insert(iterator position, const Dimension &val)
Returns a read/write iterator that points to the inserted element in the shape.
-
inline void insert(iterator position, size_t n, const Dimension &val)
Inserts count copies of the value before position.
-
template<class InputIterator>
inline void insert(iterator position, InputIterator first, InputIterator last) Inserts elements from range [first, last) before position.
-
inline void reserve(size_t n)
Requests that the dimensions vector capacity be enough to contain n elements.
-
inline void push_back(const Dimension &val)
push element to the end of partial shape
-
template<class ...Args>
inline void emplace_back(Args&&... args) emplace element to the end of partial shape
-
std::string to_string() const
String representation of PartialShape.
Public Static Functions
-
static PartialShape dynamic(Rank r = Rank::dynamic())
Construct a PartialShape with the given rank and all dimensions (if any) dynamic.
- Returns:
A PartialShape with the given rank, and all dimensions (if any) dynamic.
-
static bool merge_into(PartialShape &dst, const PartialShape &src)
Try to merge one shape into another.
Merges
srcintodst, returningtrueon success andfalseon failure. Iffalseis returned, the effect ondstis unspecified.To merge two partial shapes
s1ands2is to find the most permissive partial shapesthat is no more permissive thans1ors2, ifsexists. For example:merge(?,?) -> ? merge(?,{?,?}) -> {?,?} merge({?,?},{?,?}) -> {?,?} merge({1,2,3,4},?) -> {1,2,3,4} merge({1,2},{1,?}) -> {1,2} merge({1,2,?,?},{1,?,3,?}) -> {1,2,3,?} merge({1,2,3},{1,2,3}) -> {1,2,3} merge({1,?},{2,?}) fails [dimension 0 constraints are inconsistent] merge({?,?},{?,?,?}) fails [ranks are inconsistent]This function (merge_into) performs the “merge” operation described above on
dstandsrc, but overwritesdstwith the result and returnstrueif merging is successful; if merging is unsuccessful, the function returnsfalseand may make unspecified changes todst.- Parameters:
dst – [inout] The shape that
srcwill be merged into.src – The shape that will be merged into
dst.
- Returns:
trueif merging succeeds, elsefalse.
-
static bool broadcast_merge_into(PartialShape &dst, const PartialShape &src, const ov::op::AutoBroadcastSpec &autob)
Try to merge one shape into another along with implicit broadcasting.
Friends
- friend OPENVINO_API std::ostream & operator<< (std::ostream &str, const PartialShape &shape)
Inserts a human-readable representation of a PartialShape into an output stream.
The output to the stream is in “informal” notation. In other words:
If
shapehas dynamic rank, inserts the string?.If
shapehas static rank, inserts the string{, then inserts each dimension ofshapeinto the output stream separated by commas, then inserts}.
PartialShape s1{PartialShape::dynamic())}; PartialShape s2{}; PartialShape s3{1,Dimension::dynamic(),2,3}; PartialShape s4{2,3,4}; std::cout << s1 << std::endl << s2 << std::endl << s3 << std::endl << s4 << std::endl;
? {} {1,?,2,3} {2,3,4}- Parameters:
str – The output stream targeted for insertion.
shape – The shape to be inserted into
str.
- Returns:
A reference to
strafter insertion.
- friend OPENVINO_API PartialShape operator+ (const PartialShape &s1, const PartialShape &s2)
Elementwise addition of two PartialShape objects.
If
s1ors2has dynamic rank, returns PartialShape::dynamic().If
s1 ands2` both have static rank, and their ranks are unequal, throws std::invalid_argument.If
s1ands2both have static rank, and their ranks are equal, returns a new shape whoseith dimension iss1[i] + s2[i].
- Parameters:
s1 – Left operand for addition.
s2 – Right operand for addition.
- Throws:
std::invalid_argument – If
s1ands2have inconsistent ranks.- Returns:
The result of elementwise adding
s1tos2(see description).
-
class PrecisionPreservedAttribute : public SharedAttribute<bool>
- #include <precision_preserved_attribute.hpp>
PrecisionPreservedAttribute defines the precision preserved operation. If the attribute is absent, then an operation is not precision preserved.
For more details about the attribute, refer to PrecisionPreservedAttribute page in the OpenVINO Developer Guide.
Subclassed by ov::AvgPoolPrecisionPreservedAttribute
-
class PrecisionsAttribute : public SharedAttribute<std::vector<ov::element::Type>>
- #include <precisions_attribute.hpp>
PrecisionsAttribute defines precision which is required for input/output port or an operation.
For more details about the attribute, refer to PrecisionsAttribute page in the OpenVINO Developer Guide.
-
class PrecisionSensitive : public ov::RuntimeAttribute
- #include <precision_sensitive_attribute.hpp>
PrecisionSensitive class represents runtime info attribute that marks input to an operation as a precision sensitive and disables compression to FP16 of the subgraph before this input.
-
class PreprocessingAttribute : public ov::RuntimeAttribute
- #include <preprocessing_attribute.hpp>
-
class PrimitivesPriority : public ov::RuntimeAttribute
- #include <primitives_priority_attribute.hpp>
-
struct ProfilingInfo#
- #include <profiling_info.hpp>
Represents basic inference profiling information per operation.
If the operation is executed using tiling, the sum time per each tile is indicated as the total execution time. Due to parallel execution, the total execution time for all nodes might be greater than the total inference time.
Public Types
-
template<typename T, PropertyMutability mutability_ = PropertyMutability::RW>
class Property : public util::BaseProperty<T, PropertyMutability::RW> - #include <properties.hpp>
This class is used to bind property name with value type.
- Template Parameters:
T – type of value used to set or get property
- template<typename T> RO > : public util::BaseProperty< T, PropertyMutability::RO >
- #include <properties.hpp>
This class is used to bind read-only property name with value type.
- Template Parameters:
T – type of value used to pass or get property
-
struct PropertyName : public std::string#
- #include <properties.hpp>
This class is used to return property name and its mutability attribute.
Public Functions
-
inline PropertyName(const std::string &str, PropertyMutability mutability = PropertyMutability::RW)#
Constructs property name object.
- Parameters:
str – property name
mutability – property mutability
-
inline bool is_mutable() const#
check property mutability
- Returns:
true if property is mutable
-
inline PropertyName(const std::string &str, PropertyMutability mutability = PropertyMutability::RW)#
-
class QuantizationAlignmentAttribute : public SharedAttribute<bool>
- #include <quantization_alignment_attribute.hpp>
QuantizationAlignmentAttribute defines subgraph with the same quantization alignment. FakeQuantize operations are not included. The attribute is used by quantization operations.
For more details about the attribute, refer to QuantizationAlignmentAttribute page in the OpenVINO Developer Guide.
-
class QuantizationGranularityAttribute : public ov::RuntimeAttribute
- #include <quantization_granularity_attribute.hpp>
QuantizationGranularityAttribute defines quantization granularity of operation inputs.
For more details about the attribute, refer to QuantizationGranularityAttribute page in the OpenVINO Developer Guide.
-
class QuantizationModeAttribute : public ov::RuntimeAttribute
- #include <quantization_mode_attribute.hpp>
-
struct RawNodeOutput#
- #include <node.hpp>
-
class RemoteContext
- #include <remote_context.hpp>
This class represents an abstraction
for remote (non-CPU) accelerator device-specific inference context. Such context represents a scope on the device within which compiled models and remote memory tensors can exist, function, and exchange data.
Subclassed by ov::intel_gpu::ocl::ClContext
Public Functions
-
RemoteContext() = default
Default constructor.
-
RemoteContext(const RemoteContext &other) = default
Default copy constructor.
- Parameters:
other – Another RemoteContext object.
-
RemoteContext &operator=(const RemoteContext &other) = default
Default copy assignment operator.
- Parameters:
other – Another RemoteContext object.
- Returns:
Reference to the current object.
-
RemoteContext(RemoteContext &&other) = default
Default move constructor.
- Parameters:
other – Another RemoteContext object.
-
RemoteContext &operator=(RemoteContext &&other) = default
Default move assignment operator.
- Parameters:
other – Another RemoteContext object.
- Returns:
Reference to the current object.
-
operator bool() const noexcept
Checks if current RemoteContext object is initialized.
- Returns:
trueif current RemoteContext object is initialized,false- otherwise
-
~RemoteContext()
Destructor that preserves unloading order of implementation object and reference to the library.
-
template<typename T>
inline bool is() const noexcept Checks if the RemoteContext object can be cast to the type T.
- Template Parameters:
T – Type to be checked. Must represent a class derived from RemoteContext.
- Returns:
True if this object can be dynamically cast to the type T*; false, otherwise.
-
template<typename T>
inline const T as() const Casts this RemoteContext object to the type T.
- Template Parameters:
T – Type to cast to. Must represent a class derived from RemoteContext.
- Returns:
T Object.
-
RemoteTensor create_tensor(const element::Type &type, const Shape &shape, const AnyMap ¶ms = {})
Allocates memory tensor in device memory or wraps user-supplied memory handle using the specified tensor description and low-level device-specific parameters. Returns a pointer to the object that implements the RemoteTensor interface.
- Parameters:
type – Defines the element type of the tensor.
shape – Defines the shape of the tensor.
params – Map of the low-level tensor object parameters.
- Returns:
Pointer to a plugin object that implements the RemoteTensor interface.
-
AnyMap get_params() const
Returns a map of device-specific parameters required for low-level operations with the underlying object. Parameters include device/context handles, access flags, etc. Content of the returned map depends on a remote execution context that is currently set on the device (working scenario). Abstract method.
- Returns:
A map of name/parameter elements.
-
Tensor create_host_tensor(const element::Type type, const Shape &shape)
This method is used to create a host tensor object friendly for the device in current context. For example, GPU context may allocate USM host memory (if corresponding extension is available), which could be more efficient than regular host memory.
Public Static Functions
-
static void type_check(const RemoteContext &remote_context, const std::map<std::string, std::vector<std::string>> &type_info = {})
Internal method: checks remote type.
- Parameters:
remote_context – Remote context which type is checked.
type_info – Map with remote object runtime info.
- Throws:
Exception – if type check with the specified parameters failed.
-
RemoteContext() = default
-
class RemoteTensor : public ov::Tensor
- #include <remote_tensor.hpp>
Remote memory access and interoperability API.
Subclassed by ov::intel_gpu::ocl::ClBufferTensor, ov::intel_gpu::ocl::ClImage2DTensor, ov::intel_gpu::ocl::USMTensor
Public Functions
-
void *data(const element::Type) = delete
Access to host memory is not available for RemoteTensor. To access a device-specific memory, cast to a specific RemoteTensor derived object and work with its properties or parse device memory properties via RemoteTensor::get_params.
- Returns:
Nothing, throws an exception.
-
ov::AnyMap get_params() const
Returns a map of device-specific parameters required for low-level operations with underlying object. Parameters include device/context/surface/buffer handles, access flags, etc. Content of the returned map depends on remote execution context that is currently set on the device (working scenario). Abstract method.
- Returns:
A map of name/parameter elements.
Public Static Functions
-
static void type_check(const Tensor &tensor, const std::map<std::string, std::vector<std::string>> &type_info = {})
Checks OpenVINO remote type.
-
void *data(const element::Type) = delete
-
template<class TShape>
struct result_shape# - #include <utils.hpp>
Get correct return type of input shape when call
shape_infer.The input shapes are vector like std::vector<TShape>, where
TShapecan bestd::vector<const size_t>This will provide correct return especially for static shape which can work as reference to dimension or hold them.- Template Parameters:
TShape – Type of input shape.
- template<> Shape >
- #include <utils.hpp>
Get correct result shape for ov::Shape which is same type.
-
template<>
struct result_shape<PartialShape># - #include <utils.hpp>
Get correct result shape for PartialShape which is same type.
-
class RoundingGuard
- #include <rounding_guard.hpp>
Set current round direction for scoped block.
Round direction can be one of:
FE_DOWNWARD
FE_TONEAREST
FE_TOWARDZERO
FE_UPWARD see std <cfenv> header for details.
-
class RuntimeAttribute
- #include <runtime_attribute.hpp>
Subclassed by SharedAttribute< IntervalsAlignmentSharedValue >, SharedAttribute< bool >, SharedAttribute< std::vector< ov::element::Type > >, SharedAttribute< T >, ov::BiasAttribute, ov::Decompression, ov::DequantizationNode, ov::DisableCleanupAttribute, ov::DisableFP16Compression, ov::FusedNames, ov::KeepConstPrecision, ov::LayoutAttribute, ov::NmsSelectedIndices, ov::NoTransposeSinkingAttr, ov::NonconvertibleDivide, ov::OldApiMapElementType, ov::OldApiMapOrder, ov::OriginalPrecisionAttribute, ov::PrecisionSensitive, ov::PreprocessingAttribute, ov::PrimitivesPriority, ov::QuantizationGranularityAttribute, ov::QuantizationModeAttribute, ov::ShapeSubgraph, ov::SkipInvalidation, ov::StridesPropagation, ov::frontend::tensorflow::GraphIterator, ov::pass::DisableConstantFolding, ov::pass::DisableFoldSubgraphEmptyInputs, ov::pass::DisableRemoveConcatZeroDimInput, ov::preprocess::TensorInfoMemoryType
-
template<class T, Direction D = Direction::FORWARD>
class SeqGen - #include <sequence_generator.hpp>
Infinite generator of sequence increasing values.
Start value can be specified.
- Template Parameters:
T – Type of sequence values (must support
++or ‘—’ operators).
-
class Shape : public std::vector<size_t>
- #include <shape.hpp>
Shape for a tensor.
Public Functions
- OPENVINO_API Shape::reference operator[] (std::ptrdiff_t i)
Gets dimension at index.
- Parameters:
i – Index to shape dimension [-rank, rank).
- Returns:
A reference to i-th dimension of this shape.
- OPENVINO_API Shape::const_reference operator[] (std::ptrdiff_t i) const
Gets dimension at index.
- Parameters:
i – Index to shape dimension [-rank, rank).
- Returns:
A const reference to i-th dimension of this shape.
- OPENVINO_API Shape::reference at (std::ptrdiff_t i)
Gets dimension at index, with bounds checking.
- Parameters:
i – Index to shape dimension [-rank, rank).
- Returns:
A reference to i-th dimension of this shape.
- OPENVINO_API Shape::const_reference at (std::ptrdiff_t i) const
Gets dimension at index, with bounds checking.
- Parameters:
i – Index to shape dimension [-rank, rank).
- Returns:
A const reference to i-th dimension of this shape.
-
class ShapeSubgraph : public ov::RuntimeAttribute
- #include <is_shape_subgraph.hpp>
ShapeSubgraph class represents runtime info attribute that marks shape subgraphs. Information whether the node belongs to the shape path or to the data path is needed during evaluate and CF.
-
class SkipInvalidation : public ov::RuntimeAttribute
- #include <symbolic_info.hpp>
SkipInvalidation class represents runtime info attribute that instructs ov::Output objects to skip invalidation of partial values and symbols during partial value propagation.
-
template<class T>
struct SoPtr# - #include <so_ptr.hpp>
This class instantiate object using shared library.
- Template Parameters:
T – An type of object SoPtr can hold
Public Functions
-
SoPtr() = default#
Default constructor.
-
inline ~SoPtr()#
Destructor preserves unloading order of implementation object and reference to library.
Constructs an object with existing shared object reference and loaded pointer.
- Parameters:
ptr – pointer to the loaded object
so – Existing reference to library
Constructs an object with existing shared object reference.
- Parameters:
ptr – pointer to the loaded object
Constructs an object with existing shared object reference.
- Parameters:
ptr – pointer to the loaded object
-
class Strides : public std::vector<size_t>
- #include <strides.hpp>
Strides for a tensor.
-
class StridesPropagation : public ov::RuntimeAttribute
- #include <strides_property.hpp>
-
class Symbol
- #include <symbol.hpp>
Class representing unique symbol for the purpose of symbolic shape inference. Equality of symbols is being tracked by Disjoint-set data structure.
Public Functions
-
Symbol() = default
Default constructs a unique symbol.
-
Symbol() = default
-
class Tensor
- #include <tensor.hpp>
Tensor API holding host memory It can throw exceptions safely for the application, where it is properly handled.
Subclassed by ov::RemoteTensor
Public Functions
-
Tensor() = default
Default constructor.
Copy constructor with adding new shared object.
- Parameters:
other – Original tensor
so – Shared object
-
Tensor(const Tensor &other) = default
Default copy constructor.
- Parameters:
other – other Tensor object
-
Tensor &operator=(const Tensor &other) = default
Default copy assignment operator.
- Parameters:
other – other Tensor object
- Returns:
reference to the current object
-
Tensor &operator=(Tensor &&other) = default
Default move assignment operator.
- Parameters:
other – other Tensor object
- Returns:
reference to the current object
-
~Tensor()
Destructor preserves unloading order of implementation object and reference to library.
-
Tensor(const element::Type &type, const Shape &shape, const Allocator &allocator = {})
Constructs Tensor using element type and shape. Allocate internal host storage using default allocator.
-
Tensor(const element::Type &type, const Shape &shape, void *host_ptr, const Strides &strides = {})
Constructs Tensor using element type and shape. Wraps allocated host memory.
Note
Does not perform memory allocation internally
-
Tensor(const ov::Output<const ov::Node> &port, const Allocator &allocator = {})
Constructs Tensor using port from node. Allocate internal host storage using default allocator.
- Parameters:
port – port from node
allocator – allocates memory for internal tensor storage
-
Tensor(const ov::Output<const ov::Node> &port, void *host_ptr, const Strides &strides = {})
Constructs Tensor using port from node. Wraps allocated host memory.
Note
Does not perform memory allocation internally
- Parameters:
port – port from node
host_ptr – Pointer to pre-allocated host memory with initialized objects
strides – Optional strides parameters in bytes. Strides are supposed to be computed automatically based on shape and element size
-
Tensor(const Tensor &other, const Coordinate &begin, const Coordinate &end)
Constructs region of interest (ROI) tensor form another tensor.
Note
Does not perform memory allocation internally
Note
A Number of dimensions in
beginandendmust match number of dimensions inother.get_shape()- Parameters:
other – original tensor
begin – start coordinate of ROI object inside of the original object.
end – end coordinate of ROI object inside of the original object.
-
void set_shape(const ov::Shape &shape)
Set new shape for tensor, deallocate/allocate if new total size is bigger than previous one.
Note
Memory allocation may happen
- Parameters:
shape – A new shape
-
const Shape &get_shape() const
- Returns:
A tensor shape
-
void copy_to(ov::Tensor dst) const
Copy tensor, destination tensor should have the same element type and shape.
- Parameters:
dst – destination tensor
-
bool is_continuous() const
Reports whether the tensor is continuous or not.
- Returns:
true if tensor is continuous
-
size_t get_size() const
Returns the total number of elements (a product of all the dims or 1 for scalar)
- Returns:
The total number of elements
-
size_t get_byte_size() const
Returns the size of the current Tensor in bytes.
- Returns:
Tensor’s size in bytes
-
void *data(const element::Type &type = {}) const
Provides an access to the underlaying host memory.
Note
If type parameter is specified, the method throws an exception if specified type’s fundamental type does not match with tensor element type’s fundamental type
- Parameters:
type – Optional type parameter.
- Returns:
A host pointer to tensor memory
-
template<typename T, typename datatype = typename std::decay<T>::type>
inline T *data() const Provides an access to the underlaying host memory casted to type
TNote
Throws exception if specified type does not match with tensor element type
- Returns:
A host pointer to tensor memory casted to specified type
T.
-
bool operator!() const noexcept
Checks if current Tensor object is not initialized.
- Returns:
trueif current Tensor object is not initialized,false- otherwise
-
explicit operator bool() const noexcept
Checks if current Tensor object is initialized.
- Returns:
trueif current Tensor object is initialized,false- otherwise
-
template<typename T>
inline std::enable_if<std::is_base_of<Tensor, T>::value, bool>::type is() const noexcept Checks if the Tensor object can be cast to the type T.
- Template Parameters:
T – Type to be checked. Must represent a class derived from the Tensor
- Returns:
true if this object can be dynamically cast to the type const T*. Otherwise, false
-
Tensor() = default
-
template<class TContainer>
class TensorAccessor : public ov::ITensorAccessor - #include <tensor_data_accessor.hpp>
Tensor data accessor functor.
Creates the ov::Tensor found in tensors container. This accessor does not take ownership of tensors container. Supports following containers:
std::unordered_map<size_t, ov::Tensor>
- Template Parameters:
TContainer – Type of tensor container.
Public Functions
-
inline constexpr TensorAccessor(const TContainer *tensors)
Construct a new Tensor Accessor object for tensors container.
- Parameters:
tensors – Pointer to container with tensors.
-
virtual Tensor operator()(size_t port) const override
Get tensor for given port number.
- Parameters:
port – Port number to get data.
- Returns:
Tensor to data or empty tensor if data not found.
-
virtual Tensor operator()(size_t port) const
Get tensor at port.
- Parameters:
port – Number of data port (operator input) to get tensor.
- Returns:
Tensor to data at port.
-
struct TensorTransform : public ov::element::NotSupported<void>#
- #include <utils.hpp>
-
template<typename VAT>
class ValueAccessor - #include <attribute_adapter.hpp>
Provides access to an attribute of type AT as a value accessor type VAT.
Provides access to values via get/set methods from an m_value, typically from ValueReference.
The m_buffer holds a VAT, which may be wider than the attribute AT. For example, serializers that only support int64_t integers would use a ValueAccessor<vector<int64_t>> to reference a vector<int8_t> attribute. Destruction moves the value back to the attribute if it was changed.
- Template Parameters:
VAT – The adapter value type; may be wider than the value being accessed.
Subclassed by ov::DirectValueAccessor< bool >, ov::DirectValueAccessor< double >, ov::DirectValueAccessor< int64_t >, ov::DirectValueAccessor< op::v5::Loop::SpecialBodyPorts >, ov::DirectValueAccessor< ov::Dimension >, ov::DirectValueAccessor< ov::PartialShape >, ov::DirectValueAccessor< ov::element::TypeVector >, ov::DirectValueAccessor< ov::op::util::FrameworkNodeAttrs >, ov::DirectValueAccessor< std::set< std::string > >, ov::DirectValueAccessor< std::shared_ptr< op::util::Variable > >, ov::DirectValueAccessor< std::shared_ptr< ov::Model > >, ov::DirectValueAccessor< std::vector< double > >, ov::DirectValueAccessor< std::vector< float > >, ov::DirectValueAccessor< std::vector< int16_t > >, ov::DirectValueAccessor< std::vector< int32_t > >, ov::DirectValueAccessor< std::vector< int8_t > >, ov::DirectValueAccessor< std::vector< std::shared_ptr< op::util::MultiSubGraphOp::InputDescription > > >, ov::DirectValueAccessor< std::vector< std::shared_ptr< op::util::MultiSubGraphOp::OutputDescription > > >, ov::DirectValueAccessor< std::vector< std::string > >, ov::DirectValueAccessor< std::vector< uint16_t > >, ov::DirectValueAccessor< std::vector< uint32_t > >, ov::DirectValueAccessor< std::vector< uint64_t > >, ov::DirectValueAccessor< std::vector< uint8_t > >, ov::IndirectScalarValueAccessor< float, double >, ov::IndirectScalarValueAccessor< int16_t, int64_t >, ov::IndirectScalarValueAccessor< int32_t, int64_t >, ov::IndirectScalarValueAccessor< int8_t, int64_t >, ov::IndirectScalarValueAccessor< uint16_t, int64_t >, ov::IndirectScalarValueAccessor< uint32_t, int64_t >, ov::IndirectScalarValueAccessor< uint64_t, int64_t >, ov::IndirectScalarValueAccessor< uint8_t, int64_t >, ov::IndirectScalarValueAccessor< AT, VAT >, ov::IndirectVectorValueAccessor< AT, VAT >
-
template<>
class ValueAccessor<void*> : public ov::ValueAccessor<void># - #include <attribute_adapter.hpp>
-
template<>
class ValueAccessor<void># - #include <attribute_adapter.hpp>
ValueAccessor<void> provides an accessor for values that do not have get/set methods via AttributeVisitor.on_adapter.
All ValueAccessors must be derived from ValueAccessor<void> so that an AttributeVisitor only needs to implement a subset of the on_adapter methods.
Subclassed by ov::ValueAccessor< void * >, ov::VisitorAdapter
Public Functions
-
virtual const DiscreteTypeInfo &get_type_info() const = 0#
type info enables identification of the value accessor, as well as is_type and as_type.
-
virtual const DiscreteTypeInfo &get_type_info() const = 0#
-
class VariableState
- #include <variable_state.hpp>
VariableState class.
Public Functions
-
VariableState() = default
Default constructor.
-
~VariableState()
Destructor that preserves unloading order of implementation object and reference to the library.
-
void reset()
Resets internal variable state for relevant infer request to a value specified as default for the corresponding ReadValue node.
-
std::string get_name() const
Gets the name of the current variable state. If length of an array is not enough, the name is truncated by len, null terminator is inserted as well.
variable_idfrom the correspondingReadValueis used as variable state name.- Returns:
A string representing state name.
-
Tensor get_state() const
Returns the value of the variable state.
- Returns:
A tensor representing a state.
-
void set_state(const Tensor &state)
Sets the new state for the next inference.
- Parameters:
state – The current state to set.
-
VariableState() = default
-
struct Version#
- #include <version.hpp>
Represents version information that describes plugins and the OpemVINO library.
-
class VisitorAdapter : public ov::ValueAccessor<void>
- #include <attribute_adapter.hpp>
Adapters will see visitor.
Subclassed by ov::AttributeAdapter< ParameterVector >, ov::AttributeAdapter< ResultVector >, ov::AttributeAdapter< op::AutoBroadcastSpec >, ov::AttributeAdapter< op::BroadcastModeSpec >, ov::AttributeAdapter< ov::NodeVector >, ov::AttributeAdapter< std::shared_ptr< ov::Node > >
-
namespace batch_util#
Functions
-
namespace cmp#
Enums
Functions
-
template<class T, class U, typename std::enable_if<((std::is_signed<T>::value || std::is_same<T, float16>::value || std::is_same<T, bfloat16>::value) && (std::is_signed<U>::value || std::is_same<U, float16>::value || std::is_same<U, bfloat16>::value)) || (std::is_unsigned<T>::value && std::is_unsigned<U>::value)>::type* = nullptr>
constexpr bool lt(T a, U b) noexcept# Compare two values (a < b) in safe way against lossy integer conversion.
- Template Parameters:
T – Type of a value.
U – Type of b value.
- Parameters:
a – Value a.
b – Value b.
- Returns:
true if a less b otherwise false.
-
template<class T, class U>
constexpr bool gt(T a, U b) noexcept# Compare two values (a > b) in safe way against lossy integer conversion.
- Template Parameters:
T – Type of a value.
U – Type of b value.
- Parameters:
a – Value a.
b – Value b.
- Returns:
true if a > b otherwise false.
-
template<class T, Bound BMode = Bound::NONE>
class Between - #include <compare.hpp>
Compare if value is between lower and upper bounds.
The Between comparator has four modes to check value:
Bound::None (lower, upper)
Bound::LOWER [lower, upper)
Bound::UPPER (lower, upper]
Bound::BOTH [lower, upper]
- Template Parameters:
T – Value type to compare.
BMode – Compare bounds mode.
-
template<class T>
class Equal - #include <compare.hpp>
Compare if value is equal to expected.
- Template Parameters:
T – Value type to compare.
-
template<class T>
class Less - #include <compare.hpp>
Compare if value is less to expected.
- Template Parameters:
T – Value type to compare.
-
template<class T, class U, typename std::enable_if<((std::is_signed<T>::value || std::is_same<T, float16>::value || std::is_same<T, bfloat16>::value) && (std::is_signed<U>::value || std::is_same<U, float16>::value || std::is_same<U, bfloat16>::value)) || (std::is_unsigned<T>::value && std::is_unsigned<U>::value)>::type* = nullptr>
-
namespace coordinates#
-
namespace descriptor#
Functions
- OPENVINO_API std::ostream & operator<< (std::ostream &, const ov::descriptor::Tensor &)
-
class Input
- #include <input.hpp>
Public Functions
-
Input(Node *node, size_t index, Output &output)
- Parameters:
node – The node that owns this input
index – The position of this tensor in all input tensors
output – The output that supplies a value for this input
-
Input(Node *node, size_t index)
Create an Input that is not connected to an output.
- Parameters:
node – The node that owns this input
index – The position of this tensor in all input tensors
-
std::shared_ptr<Node> get_node() const
- Returns:
the node that this is an input of
-
inline Node *get_raw_pointer_node() const
- Returns:
the raw pointer to the node that this is an input of
-
inline size_t get_index() const
- Returns:
the position within all supplied tensors of this input
-
inline const Output &get_output() const
- Returns:
the connected output
-
inline Output &get_output()
- Returns:
the connected output
-
inline bool has_output() const
- Returns:
true if an output is connected to the input.
-
const Tensor &get_tensor() const
- Returns:
the tensor of the connected output
-
Tensor &get_tensor()
- Returns:
the tensor of the connected output
Replace the current output that supplies a value for this input with output i of node.
-
void replace_output(Output &output)
Replace the current output that supplies a value for this input with output.
-
void remove_output()
Remove the output from this input. The node will not be valid until another output is supplied.
-
inline bool get_is_relevant_to_shape() const
See Node::set_input_is_relevant_to_shape for more details.
- Returns:
true if the value of this input is relevant to the output shapes of the corresponding node. (Usually this is false.)
-
inline bool get_is_relevant_to_value() const
See Node::set_input_is_relevant_to_value for more details.
- Returns:
true if the value of this input is relevant to the output value of the corresponding node. (Usually this is true.)
-
const Shape &get_shape() const
- Returns:
the shape of the connected output
-
const PartialShape &get_partial_shape() const
- Returns:
the partial shape of the connected output
-
Input(Node *node, size_t index, Output &output)
-
class Output
- #include <output.hpp>
Public Functions
- Parameters:
node – Node that owns this output.
index – Position of the output tensor in all output tensors
tensor – The tensor where the value will be written
-
const Shape &get_shape() const
- Returns:
the shape of the output
-
const PartialShape &get_partial_shape() const
- Returns:
the partial shape of the output
-
class Tensor
- #include <tensor.hpp>
Compile-time descriptor of a first-class value that is a tensor.
Public Functions
-
void set_value_symbol(const TensorSymbol &value_symbol)
sets value symbol description
-
void invalidate_values()
unsets bound value descriptions
-
inline TensorSymbol get_value_symbol() const
gets symbol value description
-
inline bool has_and_set_bound() const
checks if lower and upper bound are set and point to the same Tensor
-
void set_value_symbol(const TensorSymbol &value_symbol)
-
namespace detail#
Functions
- template<class T> static auto collect_attached_extensions_onnx (std::vector< ov::Extension::Ptr > &res) -> decltype(typename T::template __openvino_framework_map_helper_onnx< T >().get(), void())
- template<class T> static auto collect_attached_extensions_paddle (std::vector< ov::Extension::Ptr > &res) -> decltype(typename T::template __openvino_framework_map_helper_paddle< T >().get(), void())
- template<class T> static auto collect_attached_extensions_tensorflow (std::vector< ov::Extension::Ptr > &res) -> decltype(typename T::template __openvino_framework_map_helper_tensorflow< T >().get(), void())
- template<class T> static auto collect_attached_extensions_pytorch (std::vector< ov::Extension::Ptr > &res) -> decltype(typename T::template __openvino_framework_map_helper_pytorch< T >().get(), void())
-
namespace device#
Namespace with device properties.
Enums
Variables
-
static constexpr Property<std::string> id = {"DEVICE_ID"}#
the property for setting of required device to execute on values: device id starts from “0” - first device, “1” - second device, etc
-
static constexpr Priorities priorities = {"MULTI_DEVICE_PRIORITIES"}#
Device Priorities config option, with comma-separated devices listed in the desired priority.
-
static constexpr Properties properties = {"DEVICE_PROPERTIES"}#
Property to pass set of property values to specified device
Usage Example:
core.compile_model("HETERO" ov::device::priorities("GPU", "CPU"), ov::device::properties("CPU", ov::enable_profiling(true)), ov::device::properties("GPU", ov::enable_profiling(false)));
-
static constexpr Property<std::string, PropertyMutability::RO> full_name = {"FULL_DEVICE_NAME"}#
Read-only property to get a std::string value representing a full device name.
-
static constexpr Property<std::string, PropertyMutability::RO> architecture = {"DEVICE_ARCHITECTURE"}#
Read-only property which defines the device architecture.
-
static constexpr Property<UUID, PropertyMutability::RO> uuid = {"DEVICE_UUID"}#
Read-only property which defines the UUID of the device.
-
static constexpr Property<LUID, PropertyMutability::RO> luid = {"DEVICE_LUID"}#
Read-only property which defines the LUID of the device.
-
static constexpr Property<Type, PropertyMutability::RO> type = {"DEVICE_TYPE"}#
Read-only property to get a type of device. See Type enum definition for possible return values.
-
static constexpr Property<std::map<element::Type, float>, PropertyMutability::RO> gops = {"DEVICE_GOPS"}#
Read-only property which defines Giga OPS per second count (GFLOPS or GIOPS) for a set of precisions supported by specified device.
-
struct LUID#
- #include <properties.hpp>
Structure which defines format of LUID.
Public Members
-
std::array<uint8_t, MAX_LUID_SIZE> luid#
Array with luid for a device.
Public Static Attributes
-
static const uint64_t MAX_LUID_SIZE = 8#
Max size of luid array (64 bits)
-
std::array<uint8_t, MAX_LUID_SIZE> luid#
-
struct Priorities : public ov::Property<std::string>#
- #include <properties.hpp>
Type for device Priorities config option, with comma-separated devices listed in the desired priority.
Public Functions
-
struct Properties : public ov::Property<std::map<std::string, std::map<std::string, Any>>>#
- #include <properties.hpp>
Type for property to pass set of properties to specified device.
Public Functions
-
inline std::pair<std::string, Any> operator()(const AnyMap &config) const#
Constructs property.
- Parameters:
configs – set of property values with names
- Returns:
Pair of string key representation and type erased property value.
-
inline std::pair<std::string, Any> operator()(const std::string &device_name, const AnyMap &config) const#
Constructs property.
- Parameters:
device_name – device plugin alias
config – set of property values with names
- Returns:
Pair of string key representation and type erased property value.
-
template<typename ...Properties>
inline util::EnableIfAllStringAny<std::pair<std::string, Any>, Properties...> operator()(const std::string &device_name, Properties&&... configs) const# Constructs property.
- Template Parameters:
Properties – Should be the pack of
std::pair<std::string, ov::Any>types- Parameters:
device_name – device plugin alias
configs – Optional pack of pairs: (config parameter name, config parameter value)
- Returns:
Pair of string key representation and type erased property value.
-
inline std::pair<std::string, Any> operator()(const AnyMap &config) const#
-
struct UUID#
- #include <properties.hpp>
Structure which defines format of UUID.
Public Members
-
std::array<uint8_t, MAX_UUID_SIZE> uuid#
Array with uuid for a device.
Public Static Attributes
-
static const uint64_t MAX_UUID_SIZE = 16#
Max size of uuid array (128 bits)
-
std::array<uint8_t, MAX_UUID_SIZE> uuid#
-
namespace capability#
Namespace with possible values for ov::device::capabilities property.
Variables
-
static constexpr const auto FP32 = "FP32"#
Device supports fp32 inference.
-
static constexpr const auto BF16 = "BF16"#
Device supports bf16 inference.
-
static constexpr const auto FP16 = "FP16"#
Device supports fp16 inference.
-
static constexpr const auto INT8 = "INT8"#
Device supports int8 inference.
-
static constexpr const auto INT16 = "INT16"#
Device supports int16 inference.
-
static constexpr const auto BIN = "BIN"#
Device supports binary inference.
-
static constexpr const auto WINOGRAD = "WINOGRAD"#
Device supports winograd optimization.
-
static constexpr const auto EXPORT_IMPORT = "EXPORT_IMPORT"#
Device supports compiled model export and import.
-
static constexpr const auto FP32 = "FP32"#
-
static constexpr Property<std::string> id = {"DEVICE_ID"}#
-
namespace element#
-
Enums
-
enum class Type_t#
Enum to define possible element types.
Values:
-
enumerator undefined#
Undefined element type.
-
enumerator dynamic#
Dynamic element type.
-
enumerator boolean#
boolean element type
-
enumerator bf16#
bf16 element type
-
enumerator f16#
f16 element type
-
enumerator f32#
f32 element type
-
enumerator f64#
f64 element type
-
enumerator i4#
i4 element type
-
enumerator i8#
i8 element type
-
enumerator i16#
i16 element type
-
enumerator i32#
i32 element type
-
enumerator i64#
i64 element type
-
enumerator u1#
binary element type
-
enumerator u2#
u2 element type
-
enumerator u3#
u3 element type
-
enumerator u4#
u4 element type
-
enumerator u6#
u6 element type
-
enumerator u8#
u8 element type
-
enumerator u16#
u16 element type
-
enumerator u32#
u32 element type
-
enumerator u64#
u64 element type
-
enumerator nf4#
nf4 element type
-
enumerator f8e4m3#
f8e4m3 element type
-
enumerator f8e5m2#
f8e5m2 element type
-
enumerator string#
string element type
-
enumerator undefined#
Functions
- template<> OPENVINO_API Type from< char > ()
- template<> OPENVINO_API Type from< bool > ()
- template<> OPENVINO_API Type from< float > ()
- template<> OPENVINO_API Type from< double > ()
- template<> OPENVINO_API Type from< int8_t > ()
- template<> OPENVINO_API Type from< int16_t > ()
- template<> OPENVINO_API Type from< int32_t > ()
- template<> OPENVINO_API Type from< int64_t > ()
- template<> OPENVINO_API Type from< uint8_t > ()
- template<> OPENVINO_API Type from< uint16_t > ()
- template<> OPENVINO_API Type from< uint32_t > ()
- template<> OPENVINO_API Type from< uint64_t > ()
- template<> OPENVINO_API Type from< ov::bfloat16 > ()
- template<> OPENVINO_API Type from< ov::float16 > ()
- template<> OPENVINO_API Type from< ov::float8_e4m3 > ()
- template<> OPENVINO_API Type from< ov::float8_e5m2 > ()
- template<> OPENVINO_API Type from< std::string > ()
- OPENVINO_API Type fundamental_type_for (const Type &type)
- OPENVINO_API std::ostream & operator<< (std::ostream &out, const ov::element::Type &obj)
- OPENVINO_API std::istream & operator>> (std::istream &out, ov::element::Type &obj)
-
template<class T>
bool is_max_of(const element::Type_t &type, const T &value)# Check if value has got maximum value of ov::element::Type_t.
- Template Parameters:
T – Input value type.
- Parameters:
type – ov::element type to get its maximum.
value – Input value for check.
- Returns:
True if input value has got maximum number specified by ov::element type otherwise false.
-
template<class T>
bool is_min_of(const element::Type_t type, const T &value)# Check if value has got minimum value of ov::element::Type_t.
- Template Parameters:
T – Input value type.
- Parameters:
type – ov::element type to get its minimum.
value – Input value for check.
- Returns:
True if input value has got minimum number specified by ov::element type otherwise false.
-
template<class T, class U = T>
U get_value_or_limit_of(const element::Type_t &type, const T &value)# Checks input value for element type maximum or minimum and return limit or value.
- Template Parameters:
- Parameters:
type – Type of ov::element::Type_t
value – Input value for check.
- Returns:
If value is maximum or minimum get limit of U otherwise value as U.
-
template<Type_t...>
struct IfTypeOf# Primary template defines suppoted element types.
The list of element types is used to check if runtime value of element type is one in the list. Base on this check the Visitor::visit function is called for specific element type.
- Template Parameters:
List – of supported ov::element types.
-
template<Type_t ET, Type_t... Others>
struct IfTypeOf<ET, Others...># - #include <element_visitor.hpp>
Applies visitor action for supported element type defined by template parameters.
- Template Parameters:
ET – Current ov::element type used for check with input.
Others – Others supported ov::element.
Public Static Functions
-
template<class Visitor, class ...Args>
static inline auto apply(Type_t et, Args&&... args) -> typename Visitor::result_type# Applies visitor action if input element type is same as ET.
Uses Visitor::visit<ET> function if
et == ET, otherwise check input element type against Others.- Template Parameters:
Visitor – Visitor class implementing visit function.
Args – Types of visit parameters.
- Parameters:
et – Input element type.
args – Visitor arguments.
- Returns:
Value of result type returned by Visitor.
-
template<>
struct IfTypeOf<># - #include <element_visitor.hpp>
Applies visitor action for not supported ov::element type.
Public Static Functions
-
template<class Visitor, class ...Args>
static inline auto apply(Type_t et, Args&&... args) -> typename Visitor::result_type# Applies visitor default action if input element type is not not supported by IfTypeOf.
Uses Visitor::visit non-template function.
- Template Parameters:
Visitor – Visitor class implementing visit function.
Args – Types of visit parameters.
- Parameters:
et – Input element type.
args – Visitor arguments.
- Returns:
Value of result type returned by Visitor.
-
template<class Visitor, class ...Args>
-
template<Type_t ET, class T>
class Iterator
-
template<class R, R... value>
struct NoAction# - #include <element_visitor.hpp>
Helper visitor which defines no action for not supported type.
- Template Parameters:
R – Type of return value.
value – Default value returned.
-
template<>
struct NoAction<void># - #include <element_visitor.hpp>
Helper visitor which defines no action for not supported type if result is void type.
-
template<class R>
struct NotSupported# - #include <element_visitor.hpp>
Helper visitor which throws ov::Exception for not supported element type.
- Template Parameters:
R – Type of return value.
-
class Type
- #include <element_type.hpp>
Base class to define element type.
Public Functions
Public Static Functions
-
static bool merge(element::Type &dst, const element::Type &t1, const element::Type &t2)
Merges two element types t1 and t2, writing the result into dst and returning true if successful, else returning false.
To “merge” two element types t1 and t2 is to find the least restrictive element type t that is no more restrictive than t1 and t2, if t exists. More simply:
merge(dst,element::Type::dynamic,t) writes t to dst and returns true
merge(dst,t,element::Type::dynamic) writes t to dst and returns true
merge(dst,t1,t2) where t1, t2 both static and equal writes t1 to dst and returns true
merge(dst,t1,t2) where t1, t2 both static and unequal does nothing to dst, and returns false
-
static bool merge(element::Type &dst, const element::Type &t1, const element::Type &t2)
-
enum class Type_t#
-
namespace exec_model_info#
Variables
-
static const char ORIGINAL_NAMES[] = "originalLayersNames"#
Used to get a string of layer names separated by a comma from the original IR, which were fused/merged to the current executable primitive.
-
static const char IMPL_TYPE[] = "primitiveType"#
Used to get a type of the executable primitive.
-
static const char OUTPUT_PRECISIONS[] = "outputPrecisions"#
Used to get output precisions of the executable primitive.
-
static const char PERF_COUNTER[] = "execTimeMcs"#
Used to get a value of execution time of the executable primitive.
-
static const char OUTPUT_LAYOUTS[] = "outputLayouts"#
Used to get output layouts of primitive.
-
static const char EXECUTION_ORDER[] = "execOrder"#
Used to get an execution order of primitive.
-
static const char LAYER_TYPE[] = "layerType"#
Used to get a type of primitive.
-
static const char RUNTIME_PRECISION[] = "runtimePrecision"#
Used to get runtime precision of the executable primitive.
-
class ExecutionNode : public ov::op::Op
- #include <exec_model_info.hpp>
The Execution node which is used to represent node in execution graph.
It contains the following type of information in node runtime information:
ExecGraphInfoSerialization::ORIGINAL_NAMES
ExecGraphInfoSerialization::IMPL_TYPE
ExecGraphInfoSerialization::OUTPUT_PRECISIONS
ExecGraphInfoSerialization::PERF_COUNTER
ExecGraphInfoSerialization::OUTPUT_LAYOUTS
ExecGraphInfoSerialization::EXECUTION_ORDER
ExecGraphInfoSerialization::LAYER_TYPE
ExecGraphInfoSerialization::RUNTIME_PRECISION
Public Functions
-
ExecutionNode()
A default constructor with no node inputs and 0 output ports.
-
ExecutionNode(const ov::OutputVector &arguments, size_t output_size = 1)
Constructs a new execution node with a given parameters.
- Parameters:
arguments – [in] Inputs nodes
output_size – [in] A number of output ports
-
std::shared_ptr<ov::Node> clone_with_new_inputs(const ov::OutputVector &inputs) const override
Creates a new execution node with the same state, but different input nodes.
- Parameters:
inputs – [in] The input nodes
- Returns:
A newly created execution node
-
virtual bool visit_attributes(ov::AttributeVisitor&) override
Visits attributes of the node.
- Parameters:
visitor – [in] An attribute visitor
- Returns:
Returns
trueif an operation has completed successfully
-
static const char ORIGINAL_NAMES[] = "originalLayersNames"#
-
namespace frontend#
Typedefs
-
template<typename OVOpType = void>
using OpExtension = ov::frontend::OpExtensionBase<ov::frontend::ConversionExtension, OVOpType>#
-
using FrontEndVersion = uint64_t#
Each frontend plugin is responsible to export get_api_version function returning version of frontend API used for this plugin If version is not matched with OV_FRONTEND_API_VERSION - plugin will not be loaded by FrontEndManager.
-
using NamedOutputVector = std::vector<NamedOutput>#
-
using CreatorFunction = std::function<OutputVector(const NodeContext&)>#
-
using CreatorFunctionNamed = std::function<std::map<std::string, OutputVector>(const NodeContext&)>#
-
using CreatorFunctionNamedAndIndexed = std::function<NamedOutputVector(const NodeContext&)>#
Functions
-
inline const ov::OpSet &get_opset_by_name(const std::string &opset_name)#
The helper function to return an instance of OpSet class initialized with operations from provided opset by name.
- Parameters:
opset_name – Opset name (opsetN) to initialize OpSet class.
-
inline std::shared_ptr<ov::Node> create_ov_node_by_name(const std::string &ov_type_name)#
The helper function to create an instance of ov::Node class initialized by provided type name. Expected formats:
opsetN::OpName
opsetN.OpName
OpName
- Parameters:
ov_type_name – Type name of created ov::Node.
-
inline OutputVector indexed_from_named(const NamedOutputVector &outputs)#
-
inline NamedOutputVector named_from_indexed(const OutputVector &outputs)#
-
class ComplexTypeMark : public ov::op::util::FrameworkNode
- #include <complex_type_mark.hpp>
Public Functions
-
inline virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
inline virtual void validate_and_infer_types() override
-
class ConversionExtension : public ov::frontend::ConversionExtensionBase
- #include <conversion.hpp>
-
class ConversionExtensionBase : public ov::Extension
- #include <conversion.hpp>
Subclassed by ov::frontend::ConversionExtension
-
class DecoderBase : public ov::frontend::IDecoder
- #include <decoder.hpp>
Public Functions
-
virtual ov::Any get_attribute(const std::string &name) const = 0
Get attribute value by name.
- Parameters:
name – Attribute name
- Returns:
Shared pointer to appropriate value converted to openvino data type if it exists, ‘nullptr’ otherwise
-
virtual size_t get_input_size() const = 0
Get a number of inputs.
-
virtual void get_input_node(size_t input_port_idx, std::string &producer_name, std::string &producer_output_port_name, size_t &producer_output_port_index) const = 0
Get a producer name and its output port index.
-
virtual const std::string &get_op_type() const = 0
Get operation type.
-
virtual const std::string &get_op_name() const = 0
Get node name.
-
virtual ~DecoderBase()
Destructor.
-
virtual ov::Any get_attribute(const std::string &name) const = 0
-
class DecoderTransformationExtension : public ov::Extension
- #include <decoder_transformation.hpp>
Holds a transformation that is applied just after the original model graph is decoded. This class is a holder for transformation. The transformation can be specified as FunctionPass or MathcerPass derivatives or as a function that can be used to build corresponding FunctionPass or MatcherPass object. The type of the extension is determined in the moment of creation by calling corresponding ctor.
Public Functions
Create a custom functional pass where code of the pass is implemented as a function.
-
explicit DecoderTransformationExtension(const std::function<void(ov::pass::MatcherPass*)> &matcher_pass_initializer)
Create a custom matcher pass where the code of matcher pass initialization is a given function.
-
template<typename Transformation, typename std::enable_if<std::is_base_of<ov::pass::PassBase, Transformation>::value, bool>::type = true>
inline explicit DecoderTransformationExtension(const Transformation &transformation) Register existing transformation object which will be copied and kept for further registration.
-
struct ExtensionHolder#
- #include <holder.hpp>
-
class FrontEnd
- #include <frontend.hpp>
An interface for identifying a frontend for a particular framework. Provides an ability to load and convert of input model.
Public Functions
-
FrontEnd()
Default constructor.
-
template<typename ...Types>
inline bool supported(const Types&... vars) const Validates if FrontEnd can recognize model with parameters specified. Same parameters should be used to load model.
- Parameters:
vars – Any number of parameters of any type. What kind of parameters are accepted is determined by each FrontEnd individually, typically it is std::string containing path to the model file. For more information please refer to specific FrontEnd documentation.
- Returns:
true if model recognized, false - otherwise.
-
template<typename ...Types>
inline InputModel::Ptr load(const Types&... vars) const Loads an input model by any specified arguments. Each FrontEnd separately defines what arguments it can accept.
- Parameters:
vars – Any number of parameters of any type. What kind of parameters are accepted is determined by each FrontEnd individually, typically it is std::string containing path to the model file. For more information please refer to specific FrontEnd documentation.
- Returns:
Loaded input model.
-
virtual std::shared_ptr<ov::Model> convert(const InputModel::Ptr &model) const
Completely convert and normalize entire Model, throws if it is not possible.
Completely convert the remaining, not converted part of a Model.
- Parameters:
partiallyConverted – partially converted OV Model
-
virtual std::shared_ptr<ov::Model> convert_partially(const InputModel::Ptr &model) const
Convert only those parts of the model that can be converted leaving others as-is wrapped by FrameworkNode. Converted parts are normalized by additional transformations like it is done in convert method. If part of the graph cannot be converted, it is not guaranteed that the converted regions are completely normalized. Normalize should be called for each completely converted parts individually in this case.
-
virtual std::shared_ptr<ov::Model> decode(const InputModel::Ptr &model) const
Convert operations with one-to-one mapping with decoding nodes. Each decoding node is an OV node representing a single FW operation node with all attributes represented in FW-independent way.
Runs normalization passes on Model that was loaded with partial conversion.
- Parameters:
Model – partially converted OV Model
-
virtual std::string get_name() const
Gets name of this FrontEnd. Can be used by clients if frontend is selected automatically by FrontEndManager::load_by_model.
- Returns:
Current frontend name. Empty string if not implemented
Register base extension in the FrontEnd.
- Parameters:
extension – base extension
Register base extensions in the FrontEnd.
- Parameters:
extensions – vector of extensions
-
void add_extension(const std::string &library_path)
Registers extension.
- Parameters:
library_path – path to library with ov::Extension
-
FrontEnd()
-
class FrontEndManager
- #include <manager.hpp>
Frontend management class, loads available frontend plugins on construction Allows load of frontends for particular framework, register new and list available frontends This is a main frontend entry point for client applications.
Public Functions
-
FrontEndManager()
Default constructor. Searches and loads of available frontends.
-
FrontEndManager(FrontEndManager&&) noexcept
Default move constructor.
-
FrontEndManager &operator=(FrontEndManager&&) noexcept
Default move assignment operator.
-
~FrontEndManager()
Default destructor.
-
FrontEnd::Ptr load_by_framework(const std::string &framework)
Loads frontend by name of framework and capabilities.
- Parameters:
framework – Framework name. Throws exception if name is not in list of available frontends
- Returns:
Frontend interface for further loading of models
-
template<typename ...Types>
inline FrontEnd::Ptr load_by_model(const Types&... vars) Loads frontend by model fragments described by each FrontEnd documentation. Selects and loads appropriate frontend depending on model file extension and other file info (header)
- Parameters:
vars – Any number of parameters of any type. What kind of parameters are accepted is determined by each FrontEnd individually, typically it is std::string containing path to the model file. For more information please refer to specific FrontEnd documentation.
- Returns:
Frontend interface for further loading of model. Returns ‘nullptr’ if no suitable frontend is found
-
std::vector<std::string> get_available_front_ends()
Gets list of registered frontends. Any not loaded frontends will be loaded by this call.
-
void register_front_end(const std::string &name, FrontEndFactory creator)
Register frontend with name and factory creation method.
- Parameters:
name – Name of front end
creator – Creation factory callback. Will be called when frontend is about to be created
-
void register_front_end(const std::string &name, const std::string &library_path)
Register frontend with name and factory loaded from provided library.
- Parameters:
name – Name of front end
library_path – Path (absolute or relative) or name of a frontend library. If name is provided, depending on platform, it will be wrapped with shared library suffix and prefix to identify library full name
-
FrontEndManager()
-
struct FrontEndPluginInfo#
- #include <manager.hpp>
Each frontend plugin is responsible to export get_front_end_data function returning heap-allocated pointer to this structure. Will be used by FrontEndManager during loading of plugins.
-
class FWVisitor : public ov::AttributeVisitor
- #include <op.hpp>
Public Functions
-
inline virtual void on_adapter(const std::string &name, ValueAccessor<void> &adapter) override
handles all specialized on_adapter methods implemented by the visitor.
The adapter implements get_type_info(), which can be used to determine the adapter directly or via is_type and as_type on any platform
-
inline virtual void on_adapter(const std::string &name, ValueAccessor<void> &adapter) override
-
class FWVisitorInputAttributes : public ov::AttributeVisitor
- #include <op.hpp>
Public Functions
-
inline virtual void on_adapter(const std::string &name, ValueAccessor<void> &adapter) override
handles all specialized on_adapter methods implemented by the visitor.
The adapter implements get_type_info(), which can be used to determine the adapter directly or via is_type and as_type on any platform
-
inline virtual void on_adapter(const std::string &name, ValueAccessor<void> &adapter) override
-
class GeneralFailure : public ov::AssertFailure
- #include <exception.hpp>
-
class IDecoder
- #include <decoder.hpp>
Plays a role of node, block and module decoder.
Subclassed by ov::frontend::DecoderBase
-
class InitializationFailure : public ov::AssertFailure
- #include <exception.hpp>
-
class InputModel
- #include <input_model.hpp>
InputModel class represents an original, not yet converted model graph in a framework format given services to find places of interest in a graph or specialize/edit the model before conversion.
Editing requests may affect ability to convert the original model to OV Model. Aim to provide these editing capabilities is to unlock conversion for models that are not natively supported “as-is” because of undefined shapes, types or operations.
Specific front-end implementation is supposed to have a lazy implementation for all methods, not doing a complete load of a model without an explicit method call. For example, the list of all inputs are not pre-fetched by InputModel derived class instance creation, but only when get_inputs method is called. But it is not an obligation, the most convenient way should be chosen depending on the framework model representation.
All editing requests affect the model representation that is held behind the scene successive method calls observe a new graph structure.
Note
Class methods are divided into several groups: searching for places, naming and annotation, topology editing, setting tensor properties.
Public Functions
-
virtual std::vector<Place::Ptr> get_inputs() const
Returns all inputs for a model An input is a place in a graph where data is supposed to flow inside graph from outside. It can be a tensor, port, operation; which kind of place can be an output is FW dependent. Usually framework models have a dedicated artifact to code model input, it can be a tensor without producer, that writes to it in ONNX, or a special operation like Placeholder in TensorFlow.
- Returns:
A vector of input place references
-
virtual std::vector<Place::Ptr> get_outputs() const
Returns all output for a model An output is a terminal place in a graph where data escapes the flow. It can be a tensor, port, operation; which kind of place can be an output is FW dependent. In comparison to a graph input, the output is less formally defined thing and determination of initial list of outputs may include some conventions defined by a frontend itself, not a framework. For example, all output ports without consumers may be considered as outputs.
- Returns:
A vector of output place references
-
virtual Place::Ptr get_place_by_tensor_name(const std::string &tensor_name) const
Returns a tensor place by a tensor name following framework conventions, or nullptr if a tensor with this name doesn’t exist.
- Parameters:
tensor_name – Name of tensor
- Returns:
Tensor place corresponding to specified tensor name or nullptr if not exists
-
virtual Place::Ptr get_place_by_operation_name(const std::string &operation_name) const
Returns an operation place by an operation name following framework conventions, or nullptr if an operation with this name doesn’t exist.
- Parameters:
operation_name – Name of operation
- Returns:
Place representing operation or nullptr if not exists
-
virtual Place::Ptr get_place_by_operation_name_and_input_port(const std::string &operation_name, int input_port_index)
Returns an input port place by operation name and appropriate port index.
- Parameters:
operation_name – Name of operation
input_port_index – Index of input port for this operation
- Returns:
Place representing input port of operation or nullptr if not exists
-
virtual Place::Ptr get_place_by_operation_name_and_output_port(const std::string &operation_name, int output_port_index)
Returns an output port place by operation name and appropriate port index.
- Parameters:
operation_name – Name of operation
output_port_index – Index of output port for this operation
- Returns:
Place representing output port of operation or nullptr if not exists
-
virtual void set_name_for_tensor(const Place::Ptr &tensor, const std::string &new_name)
Sets name for tensor. Overwrites existing names of this place.
- Parameters:
tensor – Tensor place
new_name – New name for this tensor
-
virtual void add_name_for_tensor(const Place::Ptr &tensor, const std::string &new_name)
Adds new name for tensor.
- Parameters:
tensor – Tensor place
new_name – New name to be added to this place
-
virtual void set_name_for_operation(const Place::Ptr &operation, const std::string &new_name)
Sets name for operation. Overwrites existing names of this place.
- Parameters:
operation – Operation place
new_name – New name for this operation
-
virtual void free_name_for_tensor(const std::string &name)
Unassign specified name from tensor place(s)
- Parameters:
name – Name of tensor
-
virtual void free_name_for_operation(const std::string &name)
Unassign specified name from operation place(s)
- Parameters:
name – Name of operation
-
virtual void set_name_for_dimension(const Place::Ptr &place, size_t shape_dim_index, const std::string &dim_name)
Set name for a particular dimension of a place (e.g. batch dimension)
-
virtual void cut_and_add_new_input(const Place::Ptr &place, const std::string &new_name_optional = "")
Cut immediately before this place and assign this place as new input; prune all nodes that don’t contribute to any output.
- Parameters:
place – New place to be assigned as input
new_name_optional – Optional new name assigned to this input place
-
virtual void cut_and_add_new_output(const Place::Ptr &place, const std::string &new_name_optional = "")
Cut immediately after this place and assign this place as new output; prune all nodes that don’t contribute to any output.
- Parameters:
place – New place to be assigned as output
new_name_optional – Optional new name assigned to this output place
-
virtual Place::Ptr add_output(const Place::Ptr &place)
Assign this place as new output or add necessary nodes to represent a new output.
- Parameters:
place – Anchor point to add an output
- Returns:
new output place, may be the same as a given place
-
virtual void remove_output(const Place::Ptr &place)
Removes any sinks directly attached to this place with all inbound data flow if it is not required by any other output.
- Parameters:
place – Model place
-
virtual void override_all_outputs(const std::vector<Place::Ptr> &outputs)
Replaces all existing outputs with new ones removing all data flow that is not required for new outputs.
- Parameters:
outputs – Vector with places that will become new outputs; may intersect existing outputs.
outputs – Array of new output places
-
virtual void override_all_inputs(const std::vector<Place::Ptr> &inputs)
Modifies the graph to use new inputs instead of existing ones. New inputs should completely satisfy all existing outputs.
- Parameters:
inputs – Array of new input places
-
virtual void extract_subgraph(const std::vector<Place::Ptr> &inputs, const std::vector<Place::Ptr> &outputs)
Leaves only subgraph that are defined by new inputs and new outputs.
- Parameters:
inputs – Array of new input places
outputs – Array of new output places
-
virtual void set_partial_shape(const Place::Ptr &place, const ov::PartialShape &shape)
Defines all possible shape that may be used for this place; place should be uniquely refer to some data. This partial shape will be converted to corresponding shape of results OV nodes and will define shape inference when the model is converted to OV.
- Parameters:
place – Model place
shape – Partial shape for this place
-
virtual ov::PartialShape get_partial_shape(const Place::Ptr &place) const
Returns current partial shape used for this place.
- Parameters:
place – Model place
- Returns:
Partial shape for this place
-
virtual void set_element_type(const Place::Ptr &place, const ov::element::Type &type)
Sets new element type for a place.
- Parameters:
place – Model place
type – New element type
-
virtual ov::element::Type get_element_type(const Place::Ptr &place) const
Returns current element type used for this place.
- Parameters:
place – Model place
- Returns:
Element type for this place
-
virtual void set_tensor_value(const Place::Ptr &place, const void *value)
Freezes a tensor with statically defined value or replace existing value for already constant node or tensor.
- Parameters:
place – Tensor place
value – Value for tensor place representing a memory buffer
-
virtual void set_tensor_partial_value(const Place::Ptr &place, const void *min_value, const void *max_value)
Defines partial value (lower bound and upper bound) for a tensor place TODO: more details for min_value and max_value format; who defines shape?
- Parameters:
place – Tensor place
min_value – Lower bound of partial value for tensor place
max_value – Upper bound of partial value for tensor place
-
virtual std::vector<Place::Ptr> get_inputs() const
-
struct NamedOutput#
- #include <node_context.hpp>
-
class NodeContext
- #include <node_context.hpp>
Public Functions
-
inline virtual size_t get_input_size() const
Returns a number of inputs.
-
inline virtual size_t get_input_size(const std::string &port_name) const
Returns a number of inputs.
-
inline virtual Output<Node> get_input(int idx) const
Returns exactly one input with a given idx; throws if there is no inputs or there are more than one input.
-
inline virtual Output<Node> get_input(const std::string &name, int idx) const
Returns exactly one input with a given name and idx; throws if there is no inputs or there are more than one input.
-
inline virtual Output<Node> get_input(const std::string &name) const
Returns exactly one input with a given name; throws if there is no inputs or there are more than one input.
-
inline virtual Any get_values_from_const_input(int idx) const
Returns values from Constant input with the given index as ov::Any. Throws an exception if the input cannot be represented as Constant.
-
template<class T>
inline T get_attribute(const std::string &name) const Returns node attribute by name.
-
template<class T>
inline T get_attribute(const std::string &name, const T &def) const Returns node attribute by name. Returns ‘def’ value if attribute does not exist.
-
inline bool has_attribute(const std::string &name) const
Check if an attribute of a given name exist.
-
virtual ov::Any get_attribute_as_any(const std::string &name) const = 0
Returns node attribute by name as ov::Any.
-
inline virtual size_t get_subgraph_size() const
Returns the number of sub-graphs that can be enumerated with get_subgraph.
-
inline virtual std::shared_ptr<Model> get_subgraph(int idx) const
Returns subgraph converted on demand by the first access If there is no query for specific sub-graph it shouldn’t be converted idx should be in range 0..get_subgraph_size()-1.
-
inline virtual size_t get_input_size() const
-
class NotImplementedFailure : public ov::AssertFailure
- #include <exception.hpp>
-
class OpConversionFailure : public ov::AssertFailure
- #include <exception.hpp>
-
class OpConversionFunction
- #include <op.hpp>
-
class OpConversionFunctionInputAttributes
- #include <op.hpp>
-
class OpConversionFunctionNamed
- #include <op.hpp>
-
template<typename BaseConversionType, typename OVOpType = void>
class OpExtensionBase : public BaseConversionType - #include <op.hpp>
-
template<typename BaseConversionType>
class OpExtensionBase<BaseConversionType, void> : public BaseConversionType# - #include <op.hpp>
-
class OpValidationFailure : public ov::AssertFailure
- #include <exception.hpp>
-
class Place
- #include <place.hpp>
An interface for identifying a place in a graph and iterate over it; can refer to an operation node, tensor, port etc.
Place can refer to Tensor, Input Edge, Input Port, Operation, Output Port, Output Edge
[Tensor A] | | [Input Edge] | V ------------------- [ [Input Port 0] ] [ ] [ Operation A ] [ ] [ [Output Port 0] ] ------------------- | | [Output Edge] | V [Tensor B] | | [Input Edge] | V ------------------- [ [Input Port 0] ] [ ] [ Operation B ] [ ] [ [Output Port 0] ] ------------------- | | [Output Edge] | V [Tensor C]
Note
Each front end implementation provides specialization of this interface to represent a place in a model graph. Various methods in the front end classes accept and retrieve instances of Place to point to particular node part which should be modified or satisfies some criteria. For example, this class is used to report model inputs and outputs, for searching operations and tensors by name, for setting shape etc.
Public Functions
-
virtual std::vector<std::string> get_names() const
All associated names (synonyms) that identify this place in the graph in a framework specific way.
- Returns:
A vector of strings each representing a name that identifies this place in the graph. Can be empty if there are no names associated with this place or name cannot be attached.
-
virtual std::vector<Ptr> get_consuming_operations() const
Returns references to all operation nodes that consume data from this place.
Note
It can be called for any kind of graph place searching for the first consuming operations. It is optional if place has only one output port
- Returns:
A vector with all operation node references that consumes data from this place
-
virtual std::vector<Ptr> get_consuming_operations(int output_port_index) const
Returns references to all operation nodes that consume data from this place for specified output port.
Note
It can be called for any kind of graph place searching for the first consuming operations.
- Parameters:
output_port_index – If place is an operational node it specifies which output port should be considered.
- Returns:
A vector with all operation node references that consumes data from this place
-
virtual std::vector<Ptr> get_consuming_operations(const std::string &outputName) const
Returns references to all operation nodes that consume data from this place for specified output port.
Note
It can be called for any kind of graph place searching for the first consuming operations.
- Parameters:
outputName – If a given place is itself an operation node, this specifies name of output port group
- Returns:
A vector with all operation node references that consumes data from this place
-
virtual std::vector<Ptr> get_consuming_operations(const std::string &outputName, int outputPortIndex) const
Returns references to all operation nodes that consume data from this place for specified output port.
Note
It can be called for any kind of graph place searching for the first consuming operations.
- Parameters:
outputName – If a given place is itself an operation node, this specifies name of output port group, each group can have multiple ports
outputPortIndex – If place is an operational node it specifies which output port should be considered.
- Returns:
A vector with all operation node references that consumes data from this place
-
virtual Ptr get_target_tensor() const
Returns a tensor place that gets data from this place; applicable for operations, output ports and output edges which have only one output port.
- Returns:
A tensor place which hold the resulting value for this place
-
virtual Ptr get_target_tensor(const std::string &outputName) const
Returns a tensor place that gets data from this place; applicable for operations.
- Parameters:
outputName – Name of output port group
- Returns:
A tensor place which hold the resulting value for this place
-
virtual Ptr get_target_tensor(const std::string &outputName, int outputPortIndex) const
Returns a tensor place that gets data from this place; applicable for operations.
- Parameters:
outputName – Name of output port group, each group can have multiple ports
outputPortIndex – Output port index if the current place is an operation node and has multiple output ports
- Returns:
A tensor place which hold the resulting value for this place
-
virtual Ptr get_target_tensor(int output_port_index) const
Returns a tensor place that gets data from this place; applicable for operations.
- Parameters:
output_port_index – Output port index if the current place is an operation node and has multiple output ports
- Returns:
A tensor place which hold the resulting value for this place
-
virtual Ptr get_source_tensor() const
Returns a tensor place that supplies data for this place; applicable for operations, input ports and input edges which have only one input port.
- Returns:
A tensor place which supplies data for this place
-
virtual Ptr get_source_tensor(int input_port_index) const
Returns a tensor place that supplies data for this place; applicable for operations.
- Parameters:
input_port_index – Input port index for operational nodes.
- Returns:
A tensor place which supplies data for this place
-
virtual Ptr get_source_tensor(const std::string &inputName) const
Returns a tensor place that supplies data for this place; applicable for operations.
- Parameters:
inputName – Name of input port group
- Returns:
A tensor place which supplies data for this place
-
virtual Ptr get_source_tensor(const std::string &inputName, int inputPortIndex) const
Returns a tensor place that supplies data for this place; applicable for operations.
- Parameters:
inputName – If a given place is itself an operation node, this specifies name of output port group, each group can have multiple ports
inputPortIndex – Input port index for operational nodes.
- Returns:
A tensor place which supplies data for this place
-
virtual Ptr get_producing_operation() const
Get an operation node place that immediately produces data for this place; applicable if place has only one input port.
- Returns:
An operation place that produces data for this place
-
virtual Ptr get_producing_operation(int input_port_index) const
Get an operation node place that immediately produces data for this place.
- Parameters:
input_port_index – If a given place is itself an operation node, this specifies a port index
- Returns:
An operation place that produces data for this place
-
virtual Ptr get_producing_operation(const std::string &inputName) const
Get an operation node place that immediately produces data for this place.
- Parameters:
inputName – If a given place is itself an operation node, this specifies name of output port group
- Returns:
An operation place that produces data for this place
-
virtual Ptr get_producing_operation(const std::string &inputName, int inputPortIndex) const
Get an operation node place that immediately produces data for this place.
- Parameters:
inputName – If a given place is itself an operation node, this specifies name of output port group, each group can have multiple ports
inputPortIndex – If a given place is itself an operation node, this specifies a port index
- Returns:
An operation place that produces data for this place
-
virtual Ptr get_producing_port() const
Returns a port that produces data for this place.
-
virtual Ptr get_input_port() const
For operation node returns reference to an input port; applicable if operation node has only one input port.
- Returns:
Input port place or nullptr if not exists
-
virtual Ptr get_input_port(int input_port_index) const
For operation node returns reference to an input port with specified index.
- Parameters:
input_port_index – Input port index
- Returns:
Appropriate input port place or nullptr if not exists
-
virtual Ptr get_input_port(const std::string &input_name) const
For operation node returns reference to an input port with specified name; applicable if port group has only one input port.
- Parameters:
input_name – Name of port group
- Returns:
Appropriate input port place or nullptr if not exists
-
virtual Ptr get_input_port(const std::string &input_name, int input_port_index) const
For operation node returns reference to an input port with specified name and index.
- Parameters:
input_name – Name of port group, each group can have multiple ports
input_port_index – Input port index in a group
- Returns:
Appropriate input port place or nullptr if not exists
-
virtual Ptr get_output_port() const
For operation node returns reference to an output port; applicable for operations with only one output port.
- Returns:
Appropriate output port place or nullptr if not exists
-
virtual Ptr get_output_port(int output_port_index) const
For operation node returns reference to an output port with specified index.
- Parameters:
output_port_index – Output port index
- Returns:
Appropriate output port place or nullptr if not exists
-
virtual Ptr get_output_port(const std::string &output_name) const
For operation node returns reference to an output port with specified name; applicable if port group has only one output port.
- Parameters:
output_name – Name of output port group
- Returns:
Appropriate output port place or nullptr if not exists
-
virtual Ptr get_output_port(const std::string &output_name, int output_port_index) const
For operation node returns reference to an output port with specified name and index.
- Parameters:
output_name – Name of output port group, each group can have multiple ports
output_port_index – Output port index
- Returns:
Appropriate output port place or nullptr if not exists
-
virtual std::vector<Place::Ptr> get_consuming_ports() const
Returns all input ports that consume data flows through this place.
-
virtual bool is_input() const
Returns true if this place is input for a model.
-
virtual bool is_output() const
Returns true if this place is output for a model.
-
virtual bool is_equal(const Ptr &another) const
Returns true if another place is the same as this place.
- Parameters:
another – Another place object
-
virtual bool is_equal_data(const Ptr &another) const
Returns true if another place points to the same data.
Note
The same data means all places on path: output port -> output edge -> tensor -> input edge -> input port.
- Parameters:
another – Another place object
-
virtual std::vector<std::string> get_names() const
-
class ProgressReporterExtension : public ov::Extension
- #include <progress_reporter.hpp>
Public Types
-
using progress_notifier_callback = std::function<void(float, unsigned int, unsigned int)>
A progress reporting callback signature. A FunctionObject that matches this signature should be passed to the constructor of this extension. The extension will then invoke this as a callback each time the progress needs to be reported. The callback itself is responsible for consuming the reported values.
- Param progress:
A float value in the range [0.0, 1.0] indicating the total progress of an operation.
- Param total_steps:
The total number of steps that a given instance of this extension is tracking
- Param completed_completed:
The current number of completed steps (out of the total number of steps to take)
Public Functions
-
inline ProgressReporterExtension()
The default constructor which creates a reporter that doesn’t report progress.
-
void report_progress(float progress, unsigned int total_steps, unsigned int completed_steps) const
The main method of this extension used to report the progress. This method forwards its arguments to the callback stored in this class.
- Parameters:
progress – A float value in the range [0.0, 1.0] indicating the total progress of an operation.
total_steps – The total number of steps that a given instance of this extension is tracking
completed_steps – The current number of completed steps (out of the total number of steps to take)
-
using progress_notifier_callback = std::function<void(float, unsigned int, unsigned int)>
-
class TelemetryExtension : public ov::Extension
- #include <telemetry.hpp>
Provides callback to report telemetry information back to Python code.
-
namespace tensorflow#
-
class GraphIterator : private ov::RuntimeAttribute
- #include <graph_iterator.hpp>
Abstract representation for an input model graph that gives nodes in topologically sorted order.
Public Functions
-
virtual size_t size() const = 0
Get a number of operation nodes in the graph.
-
virtual void reset() = 0
Set iterator to the start position.
-
virtual void next() = 0
Move to the next node in the graph.
-
virtual bool is_end() const = 0
Returns true if iterator goes out of the range of available nodes.
-
virtual std::shared_ptr<DecoderBase> get_decoder() const = 0
Return a pointer to a decoder of the current node.
-
virtual ~GraphIterator() = default
Destructor.
-
virtual std::shared_ptr<GraphIterator> get_body_graph_iterator(const std::string &func_name) const = 0
Checks if the main model graph contains a function of the requested name in the library Returns GraphIterator to this function and nullptr, if it does not exist.
-
virtual std::vector<std::string> get_input_names() const = 0
Returns a vector of input names in the original order.
-
virtual std::vector<std::string> get_output_names() const = 0
Returns a vector of output names in the original order.
-
inline virtual std::map<std::string, std::string> get_input_names_map() const
Returns a map from internal tensor name to (user-defined) external name for inputs.
-
inline virtual std::map<std::string, std::string> get_output_names_map() const
Returns a map from internal tensor name to (user-defined) external name for outputs.
-
virtual size_t size() const = 0
-
class GraphIterator : private ov::RuntimeAttribute
-
template<typename OVOpType = void>
-
namespace helpers#
Functions
-
template<typename ACT, typename ...T, size_t N_ARGS = NumOfLambdaArgs<ACT>::value>
std::enable_if<N_ARGS == sizeof...(T) + 2, void>::type call_with_args(const ACT &body, size_t g_id, size_t iwork, T... arg)#
-
template<typename T>
struct NumOfLambdaArgs# - #include <parallel.hpp>
- template<typename C, typename R, typename... Args> *)(Args...) const >
- #include <parallel.hpp>
-
template<typename ACT, typename ...T, size_t N_ARGS = NumOfLambdaArgs<ACT>::value>
-
namespace hint#
Namespace with hint properties.
Enums
-
enum class Priority#
Enum to define possible priorities hints.
Values:
-
enumerator LOW#
Low priority.
-
enumerator MEDIUM#
Medium priority.
-
enumerator HIGH#
High priority.
-
enumerator DEFAULT#
Default priority is MEDIUM.
-
enumerator LOW#
-
enum class PerformanceMode#
Enum to define possible performance mode hints.
Values:
-
enumerator LATENCY#
Optimize for latency.
-
enumerator THROUGHPUT#
Optimize for throughput.
-
enumerator CUMULATIVE_THROUGHPUT#
Optimize for cumulative throughput.
-
enumerator LATENCY#
-
enum class SchedulingCoreType#
This enum contains definition of core type can be used for CPU tasks on different devices.
Values:
-
enumerator PCORE_ONLY#
Only processors of performance-cores can be used.
-
enumerator ECORE_ONLY#
Only processors of efficient-cores can be used.
-
enumerator PCORE_ONLY#
Variables
-
static constexpr Property<element::Type, PropertyMutability::RW> inference_precision = {"INFERENCE_PRECISION_HINT"}#
Hint for device to use specified precision for inference.
-
static constexpr Property<Priority> model_priority = {"MODEL_PRIORITY"}#
High-level OpenVINO model priority hint Defines what model should be provided with more performant bounded resource first.
-
static constexpr Property<PerformanceMode> performance_mode = {"PERFORMANCE_HINT"}#
High-level OpenVINO Performance Hints unlike low-level properties that are individual (per-device), the hints are something that every device accepts and turns into device-specific settings.
-
static constexpr Property<SchedulingCoreType> scheduling_core_type = {"SCHEDULING_CORE_TYPE"}#
This property defines CPU core type which can be used during inference.
Developer can use this property to select specific CPU cores for inference. Please refer SchedulingCoreType for all definition of core type.
The following code is an example to only use efficient-cores for inference on hybrid CPU. If user sets this configuration on a platform with only performance-cores, CPU inference will still run on the performance-cores.
ie.set_property(ov::hint::scheduling_core_type(ov::hint::SchedulingCoreType::ECORE_ONLY));
-
static constexpr Property<std::set<ModelDistributionPolicy>> model_distribution_policy = {"MODEL_DISTRIBUTION_POLICY"}#
This property defines model distribution policy for inference with multiple sockets/devices.
This property can be used to select model distribution policy between execution units (e.g. between CPU sockets/NUMA nodes or between different GPUs). — TENSOR_PARALLEL : Distribute tensor to multiple sockets/devices during model compilation. At inference time, sockets/devices process individual tensor in parallel. — PIPELINE_PARALLEL : Distribute tensor to multiple sockets/devices during model compilation. At inference time, sockets/devices process individual tensor one by one. And each socket/device processes a portion of a different tensor in parallel.
The following code is an example how TENSOR_PARALLEL or PIPELINE_PARALLEL model distribution policy might be enabled.
ie.set_property(ov::hint::model_distribution_policy({ov::hint::ModelDistributionPolicy::TENSOR_PARALLEL})); ie.set_property(ov::hint::model_distribution_policy({ov::hint::ModelDistributionPolicy::PIPELINE_PARALLEL}));
-
static constexpr Property<bool> enable_cpu_pinning = {"ENABLE_CPU_PINNING"}#
This property allows CPU pinning during inference.
Developer can use this property to enable or disable CPU pinning during inference on Windows and Linux. MacOS does not support CPU pinning, and this property is always disabled. If user does not explicitly set value for this property, OpenVINO may choose any desired value based on internal logic.
The following is an example of CPU fixed behavior on a hybrid CPU (8 performance cores and 16 efficiency cores). For stream with 4 threads on performance cores, if CPU pinning is enabled, each thread is bound to a specific performance core. If CPU pinning is disabled, OS will schedule 4 threads on performance cores only. For stream with 24 threads on all cores, if CPU pinning is enabled, each thread is bound to a specific performance core. If CPU pinning is disabled, OS will schedule 24 threads on both performance cores and efficiency cores.
The following code is example to use this property.
ie.set_property(ov::hint::enable_cpu_pinning(true)); ie.set_property(ov::hint::enable_cpu_pinning(false));
-
static constexpr Property<bool> enable_hyper_threading = {"ENABLE_HYPER_THREADING"}#
This property define if using hyper threading during inference.
Developer can use this property to use or not use CPU pinning during inference. If user does not explicitly set value for this property, OpenVINO may choose any desired value based on internal logic.
The following code is example to use this property.
ie.set_property(ov::hint::enable_hyper_threading(true)); ie.set_property(ov::hint::enable_hyper_threading(false));
-
static constexpr Property<uint32_t> num_requests = {"PERFORMANCE_HINT_NUM_REQUESTS"}#
(Optional) property that backs the (above) Performance Hints by giving additional information on how many inference requests the application will be keeping in flight usually this value comes from the actual use-case (e.g. number of video-cameras, or other sources of inputs)
-
static constexpr Property<std::shared_ptr<ov::Model>> model = {"MODEL_PTR"}#
This key identifies shared pointer to the ov::Model, required for some properties (ov::max_batch_size and ov::optimal_batch_size)
-
static constexpr Property<bool, PropertyMutability::RW> allow_auto_batching = {"ALLOW_AUTO_BATCHING"}#
Special key for auto batching feature configuration. Enabled by default.
-
static constexpr Property<ExecutionMode> execution_mode = {"EXECUTION_MODE_HINT"}#
High-level OpenVINO Execution hint unlike low-level properties that are individual (per-device), the hints are something that every device accepts and turns into device-specific settings Execution mode hint controls preferred optimization targets (performance or accuracy) for given model.
-
static constexpr Property<uint64_t, PropertyMutability::RW> dynamic_quantization_group_size{"DYNAMIC_QUANTIZATION_GROUP_SIZE"}#
This property defines group size for dynamic quantization optimization.
Dynamic quantization optimization provides an ability to get performance benefit from int8 compute. In contrast with static quantization dynamic approach assumes activations are quantized during inference. Despite the fact dynamic quantization has some runtime overheads, it might provide better accuracy metrics. This property defines granularity (aka block size) for dynamic quantization algorithms. Lower group size values might result in better accuracy, but the drawback is worse performance. Group size equal 0 means dynamic quantization optimization is disabled.
-
enum class Priority#
-
namespace intel_auto#
Namespace with Intel AUTO specific properties.
Enums
Variables
-
static constexpr Property<bool> enable_startup_fallback = {"ENABLE_STARTUP_FALLBACK"}#
auto device setting that enable/disable CPU as acceleration (or helper device) at the beginning
-
static constexpr Property<bool> enable_runtime_fallback = {"ENABLE_RUNTIME_FALLBACK"}#
auto device setting that enable/disable runtime fallback to other devices when infer fails on current selected device
-
static constexpr Property<SchedulePolicy> schedule_policy = {"SCHEDULE_POLICY"}#
High-level OpenVINO model policy hint Defines what scheduling policy should be used in AUTO CUMULATIVE_THROUGHPUT or MULTI case.
-
static constexpr Property<bool> enable_startup_fallback = {"ENABLE_STARTUP_FALLBACK"}#
-
namespace intel_cpu#
Namespace with Intel CPU specific properties.
Variables
-
static constexpr Property<bool> denormals_optimization = {"CPU_DENORMALS_OPTIMIZATION"}#
This property define whether to perform denormals optimization.
Computation with denormals is very time consuming. FTZ(Flushing denormals to zero) and DAZ(Denormals as zero) could significantly improve the performance, but it does not comply with IEEE standard. In most cases, this behavior has little impact on model accuracy. Users could enable this optimization if no or acceptable accuracy drop is seen. The following code enables denormals optimization
ie.set_property(ov::denormals_optimization(true)); // enable denormals optimization
The following code disables denormals optimization
ie.set_property(ov::denormals_optimization(false)); // disable denormals optimization
-
static constexpr Property<float> sparse_weights_decompression_rate = {"CPU_SPARSE_WEIGHTS_DECOMPRESSION_RATE"}#
This property defines threshold for sparse weights decompression feature activation.
Sparse weights decompression feature allows to pack weights for Matrix Multiplication operations directly in the CPU plugin at the model compilation stage and store non-zero values in a special packed format. Then, during the execution of the model, the weights are unpacked and used in the computational kernel. Since the weights are loaded from DDR/L3 cache in the packed format this significantly decreases memory consumption and as a consequence improve inference performance. The following code allows to set the sparse rate value.
core.set_property(ov::intel_cpu::sparse_weights_decompression_rate(0.8));
-
static constexpr Property<bool> denormals_optimization = {"CPU_DENORMALS_OPTIMIZATION"}#
-
namespace intel_gpu#
Namespace with Intel GPU specific properties.
Typedefs
-
using gpu_handle_param = void*#
Enums
-
enum class ContextType#
Enum to define the type of the shared context.
Values:
-
enumerator OCL#
Pure OpenCL context.
-
enumerator VA_SHARED#
Context shared with a video decoding device.
-
enumerator OCL#
Enum to define the type of the shared memory buffer.
Values:
Shared OpenCL buffer blob.
Shared OpenCL 2D image blob.
Shared USM pointer allocated by user.
Shared USM pointer type with host allocation type allocated by plugin.
Shared USM pointer type with device allocation type allocated by plugin.
Shared video decoder surface or D3D 2D texture blob.
Shared D3D buffer blob.
Variables
-
static constexpr Property<uint64_t, PropertyMutability::RO> device_total_mem_size = {"GPU_DEVICE_TOTAL_MEM_SIZE"}#
Read-only property which defines size of memory in bytes available for the device. For iGPU it returns host memory size, for dGPU - dedicated gpu memory size.
-
static constexpr Property<std::string, PropertyMutability::RO> uarch_version = {"GPU_UARCH_VERSION"}#
Read-only property to get microarchitecture identifier in major.minor.revision format.
-
static constexpr Property<int32_t, PropertyMutability::RO> execution_units_count = {"GPU_EXECUTION_UNITS_COUNT"}#
Read-only property to get count of execution units for current GPU.
-
static constexpr Property<std::map<std::string, uint64_t>, PropertyMutability::RO> memory_statistics{"GPU_MEMORY_STATISTICS"}#
Read-only property to get statistics of GPU memory allocated by engine for each allocation type It contains information about current memory usage.
-
static constexpr Property<bool> enable_loop_unrolling = {"GPU_ENABLE_LOOP_UNROLLING"}#
Turning on this key enables to unroll recurrent layers such as TensorIterator or Loop with fixed iteration count. This key is turned on by default. Turning this key on will achieve better inference performance for loops with not too many iteration counts (less than 16, as a rule of thumb). Turning this key off will achieve better performance for both graph loading time and inference time with many iteration counts (greater than 16). Note that turning this key on will increase the graph loading time in proportion to the iteration counts. Thus, this key should be turned off if graph loading time is considered to be most important target to optimize.
-
static constexpr Property<bool> disable_winograd_convolution = {"GPU_DISABLE_WINOGRAD_CONVOLUTION"}#
Turning on this key disables winograd convolution. Winograd convolution has different characteristics for accuracy and performance compared to other convolution implementations.
-
static constexpr Property<ContextType> context_type = {"CONTEXT_TYPE"}#
Shared device context type: can be either pure OpenCL (OCL) or shared video decoder (VA_SHARED) context.
-
static constexpr Property<gpu_handle_param> ocl_context = {"OCL_CONTEXT"}#
This key identifies OpenCL context handle in a shared context or shared memory blob parameter map.
-
static constexpr Property<int> ocl_context_device_id = {"OCL_CONTEXT_DEVICE_ID"}#
This key identifies ID of device in OpenCL context if multiple devices are present in the context.
-
static constexpr Property<int> tile_id = {"TILE_ID"}#
In case of multi-tile system, this key identifies tile within given context.
-
static constexpr Property<gpu_handle_param> ocl_queue = {"OCL_QUEUE"}#
This key identifies OpenCL queue handle in a shared context.
-
static constexpr Property<gpu_handle_param> va_device = {"VA_DEVICE"}#
This key identifies video acceleration device/display handle in a shared context or shared memory blob parameter map.
This key identifies type of internal shared memory in a shared memory blob parameter map.
-
static constexpr Property<gpu_handle_param> mem_handle = {"MEM_HANDLE"}#
This key identifies OpenCL memory handle in a shared memory blob parameter map.
-
static constexpr Property<gpu_handle_param> dev_object_handle = {"DEV_OBJECT_HANDLE"}#
This key identifies video decoder surface handle in a shared memory blob parameter map.
-
namespace capability#
Possible return value for ov::device::capabilities property.
Variables
-
static constexpr const auto HW_MATMUL = "GPU_HW_MATMUL"#
Device has hardware block for matrix multiplication.
-
static constexpr const auto HW_MATMUL = "GPU_HW_MATMUL"#
-
namespace hint#
Typedefs
-
using ThrottleLevel = ov::hint::Priority#
This enum represents the possible value of ov::intel_gpu::hint::queue_throttle property:
LOW is used for CL_QUEUE_THROTTLE_LOW_KHR OpenCL throttle hint
MEDIUM (DEFAULT) is used for CL_QUEUE_THROTTLE_MED_KHR OpenCL throttle hint
HIGH is used for CL_QUEUE_THROTTLE_HIGH_KHR OpenCL throttle hint
Variables
-
static constexpr Property<ThrottleLevel> queue_throttle = {"GPU_QUEUE_THROTTLE"}#
This key instructs the GPU plugin to use OpenCL queue throttle hints as defined in https://www.khronos.org/registry/OpenCL/specs/opencl-2.1-extensions.pdf, chapter 9.19. This option should be used with ov::intel_gpu::hint::ThrottleLevel values.
-
static constexpr Property<ov::hint::Priority> queue_priority = {"GPU_QUEUE_PRIORITY"}#
This key instructs the GPU plugin to use the OpenCL queue priority hint as defined in https://www.khronos.org/registry/OpenCL/specs/opencl-2.1-extensions.pdf. This option should be used with ov::hint::Priority:
LOW is used for CL_QUEUE_PRIORITY_LOW_KHR OpenCL priority hint
MEDIUM (DEFAULT) is used for CL_QUEUE_PRIORITY_MED_KHR OpenCL priority hint
HIGH is used for CL_QUEUE_PRIORITY_HIGH_KHR OpenCL priority hint
-
static constexpr Property<ov::hint::Priority> host_task_priority = {"GPU_HOST_TASK_PRIORITY"}#
This key instructs the GPU plugin which cpu core type of TBB affinity used in load network. This option has 3 types of levels: HIGH, LOW, and ANY. It is only affected on Hybrid CPUs.
LOW - instructs the GPU Plugin to use LITTLE cores if they are available
MEDIUM (DEFAULT) - instructs the GPU Plugin to use any available cores (BIG or LITTLE cores)
HIGH - instructs the GPU Plugin to use BIG cores if they are available
-
using ThrottleLevel = ov::hint::Priority#
-
namespace memory_type#
These keys instruct the GPU plugin to use surface/buffer memory type.
Variables
-
static constexpr auto surface = "GPU_SURFACE"#
Native video decoder surface.
-
static constexpr auto surface = "GPU_SURFACE"#
-
namespace ocl#
Namespace with Intel GPU OpenCL specific remote objects.
Typedefs
-
using gpu_handle_param = void*#
Shortcut for defining a handle parameter.
-
class ClBufferTensor : public ov::RemoteTensor
- #include <ocl.hpp>
This class represents an abstraction for GPU plugin remote tensor which can be shared with user-supplied OpenCL buffer. The plugin object derived from this class can be obtained with ClContext::create_tensor() call.
Note
User can obtain OpenCL buffer handle from this class.
Subclassed by ov::intel_gpu::ocl::D3DBufferTensor
Public Functions
-
inline cl_mem get()
Returns the underlying OpenCL memory object handle.
- Returns:
underlying OpenCL memory object handle
-
inline operator cl_mem()
OpenCL memory handle conversion operator.
- Returns:
cl_mem
-
inline operator cl::Buffer()
Standard Khronos cl::Buffer wrapper conversion operator.
- Returns:
cl::Bufferobject
Public Static Functions
-
static inline void type_check(const Tensor &tensor)
Checks that type defined runtime parameters are presented in remote object.
- Parameters:
tensor – a tensor to check
-
inline cl_mem get()
-
class ClContext : public ov::RemoteContext
- #include <ocl.hpp>
This class represents an abstraction for GPU plugin remote context which is shared with OpenCL context object. The plugin object derived from this class can be obtained either with CompiledModel::get_context() or Core::create_context() calls.
Subclassed by ov::intel_gpu::ocl::D3DContext, ov::intel_gpu::ocl::VAContext
Public Functions
-
inline ClContext(Core &core, cl_context ctx, int ctx_device_id = 0)
Constructs context object from user-supplied OpenCL context handle.
- Parameters:
core – A reference to OpenVINO Runtime Core object
ctx – A OpenCL context to be used to create shared remote context
ctx_device_id – An ID of device to be used from ctx
-
inline ClContext(Core &core, cl_command_queue queue)
Constructs context object from user-supplied OpenCL context handle.
Note
Only latency mode is supported for such context sharing case.
- Parameters:
core – A reference to OpenVINO Runtime Core object
queue – An OpenCL queue to be used to create shared remote context. Queue will be reused inside the plugin.
-
inline cl_context get()
Returns the underlying OpenCL context handle.
- Returns:
cl_context
-
inline operator cl_context()
OpenCL context handle conversion operator for the ClContext object.
- Returns:
cl_context
-
inline operator cl::Context()
Standard Khronos cl::Context wrapper conversion operator for the ClContext object.
- Returns:
cl::Contextobject
-
inline std::pair<ClImage2DTensor, ClImage2DTensor> create_tensor_nv12(const cl::Image2D &nv12_image_plane_y, const cl::Image2D &nv12_image_plane_uv)
This function is used to construct a NV12 compound tensor object from two cl::Image2D wrapper objects. The resulting compound contains two remote tensors for Y and UV planes of the surface.
- Parameters:
nv12_image_plane_y – cl::Image2D object containing Y plane data.
nv12_image_plane_uv – cl::Image2D object containing UV plane data.
- Returns:
A pair of remote tensors for each plane
-
inline ClBufferTensor create_tensor(const element::Type type, const Shape &shape, const cl_mem buffer)
This function is used to obtain remote tensor object from user-supplied cl_mem object.
-
inline ClBufferTensor create_tensor(const element::Type type, const Shape &shape, const cl::Buffer &buffer)
This function is used to obtain remote tensor object from user-supplied cl::Buffer object.
-
inline ClImage2DTensor create_tensor(const element::Type type, const Shape &shape, const cl::Image2D &image)
This function is used to obtain remote tensor object from user-supplied cl::Image2D object.
-
inline USMTensor create_tensor(const element::Type type, const Shape &shape, void *usm_ptr)
This function is used to obtain remote tensor object from user-supplied USM pointer.
-
inline USMTensor create_usm_host_tensor(const element::Type type, const Shape &shape)
This function is used to allocate USM tensor with host allocation type.
-
inline USMTensor create_usm_device_tensor(const element::Type type, const Shape &shape)
This function is used to allocate USM tensor with device allocation type.
-
RemoteTensor create_tensor(const element::Type &type, const Shape &shape, const AnyMap ¶ms = {})
Allocates memory tensor in device memory or wraps user-supplied memory handle using the specified tensor description and low-level device-specific parameters. Returns a pointer to the object that implements the RemoteTensor interface.
- Parameters:
type – Defines the element type of the tensor.
shape – Defines the shape of the tensor.
params – Map of the low-level tensor object parameters.
- Returns:
Pointer to a plugin object that implements the RemoteTensor interface.
Public Static Functions
-
static inline void type_check(const RemoteContext &remote_context)
Checks that type defined runtime parameters are presented in remote object.
- Parameters:
remote_context – A remote context to check
-
inline ClContext(Core &core, cl_context ctx, int ctx_device_id = 0)
-
class ClImage2DTensor : public ov::RemoteTensor
- #include <ocl.hpp>
This class represents an abstraction for GPU plugin remote tensor which can be shared with user-supplied OpenCL 2D Image. The plugin object derived from this class can be obtained with ClContext::create_tensor() call.
Note
User can obtain OpenCL image handle from this class.
Subclassed by ov::intel_gpu::ocl::D3DSurface2DTensor, ov::intel_gpu::ocl::VASurfaceTensor
Public Functions
-
inline cl_mem get()
Returns the underlying OpenCL memory object handle.
- Returns:
underlying OpenCL memory object handle
-
inline operator cl_mem()
OpenCL memory handle conversion operator.
- Returns:
cl_mem
-
inline operator cl::Image2D()
Standard Khronos cl::Image2D wrapper conversion operator for the ClContext object.
- Returns:
cl::Image2Dobject
Public Static Functions
-
static inline void type_check(const Tensor &tensor)
Checks that type defined runtime parameters are presented in remote object.
- Parameters:
tensor – a tensor to check
-
inline cl_mem get()
-
class D3DBufferTensor : public ov::intel_gpu::ocl::ClBufferTensor
- #include <dx.hpp>
This class represents an abstraction for GPU plugin remote tensor which is shared with Direct3D 11 buffer. The plugin object derived from this class can be obtained with D3DContext::create_tensor() call.
Note
User can also obtain OpenCL buffer handle from this class.
Public Functions
-
inline operator ID3D11Buffer*()
ID3D11Buffer conversion operator for the D3DContext object.
- Returns:
Pointer to underlying ID3D11Buffer interface
Public Static Functions
-
static inline void type_check(const Tensor &tensor)
Checks that type defined runtime parameters are presented in remote object.
- Parameters:
tensor – a tensor to check
-
inline operator ID3D11Buffer*()
-
class D3DContext : public ov::intel_gpu::ocl::ClContext
- #include <dx.hpp>
This class represents an abstraction for GPU plugin remote context which is shared with Direct3D 11 device. The plugin object derived from this class can be obtained either with CompiledModel::get_context() or Core::create_context() calls.
Note
User can also obtain OpenCL context handle from this class.
Public Functions
-
inline operator ID3D11Device*()
ID3D11Device conversion operator for the D3DContext object.
- Returns:
Pointer to underlying ID3D11Device interface
-
inline D3DContext(Core &core, ID3D11Device *device, int target_tile_id = -1)
Constructs D3DContext remote context object from ID3D11Device.
- Parameters:
core – OpenVINO Runtime Core object instance
device – A pointer to ID3D11Device to be used to create a remote context
target_tile_id – Desired tile id within given context for multi-tile system. Default value (-1) means that root device should be used
-
inline std::pair<D3DSurface2DTensor, D3DSurface2DTensor> create_tensor_nv12(const size_t height, const size_t width, ID3D11Texture2D *nv12_surf)
This function is used to obtain a NV12 tensor from NV12 DXGI video decoder output. The resulting tensor contains two remote tensors for Y and UV planes of the surface.
- Parameters:
height – Height of Y plane
width – Width of Y plane
nv12_surf – A ID3D11Texture2D instance to create NV12 tensor from
- Returns:
A pair of remote tensors for each plane
-
inline D3DBufferTensor create_tensor(const element::Type type, const Shape &shape, ID3D11Buffer *buffer)
This function is used to obtain remote tensor object from ID3D11Buffer.
-
inline D3DSurface2DTensor create_tensor(const element::Type type, const Shape &shape, ID3D11Texture2D *surface, uint32_t plane = 0)
This function is used to obtain remote tensor object from ID3D11Texture2D.
Note
The underlying ID3D11Texture2D can also be a plane of output surface of DXGI video decoder
- Parameters:
- Returns:
D3DSurface2DTensor tensor
-
inline ClBufferTensor create_tensor(const element::Type type, const Shape &shape, const cl_mem buffer)
This function is used to obtain remote tensor object from user-supplied cl_mem object.
-
inline ClBufferTensor create_tensor(const element::Type type, const Shape &shape, const cl::Buffer &buffer)
This function is used to obtain remote tensor object from user-supplied cl::Buffer object.
-
inline ClImage2DTensor create_tensor(const element::Type type, const Shape &shape, const cl::Image2D &image)
This function is used to obtain remote tensor object from user-supplied cl::Image2D object.
Public Static Functions
-
static inline void type_check(const RemoteContext &remote_context)
Checks that type defined runtime parameters are presented in remote object.
- Parameters:
remote_context – A remote context to check
-
inline operator ID3D11Device*()
-
class D3DSurface2DTensor : public ov::intel_gpu::ocl::ClImage2DTensor
- #include <dx.hpp>
This class represents an abstraction for GPU plugin remote tensor which is shared with Direct3D 11 2D texture. The plugin object derived from this class can be obtained with D3DContext::create_tensor() call.
Note
User can also obtain OpenCL 2D image handle from this class.
Public Functions
-
inline operator ID3D11Texture2D*()
ID3D11Texture2D conversion operator for the D3DContext object.
- Returns:
Pointer to underlying ID3D11Texture2D interface
-
inline uint32_t plane()
Returns plane ID of underlying video decoder surface, or 0 if no video surface was shared.
- Returns:
Plane ID
Public Static Functions
-
static inline void type_check(const Tensor &remote_tensor)
Checks that type defined runtime parameters are presented in remote object.
- Parameters:
remote_tensor – remote tensor to check
-
inline operator ID3D11Texture2D*()
-
class USMTensor : public ov::RemoteTensor
- #include <ocl.hpp>
This class represents an abstraction for GPU plugin remote tensor which can be shared with user-supplied USM device pointer. The plugin object derived from this class can be obtained with ClContext::create_tensor() call.
Note
User can obtain USM pointer from this class.
Public Functions
-
inline void *get()
Returns the underlying USM pointer.
- Returns:
underlying USM pointer
Public Static Functions
-
static inline void type_check(const Tensor &tensor)
Checks that type defined runtime parameters are presented in remote object.
- Parameters:
tensor – a tensor to check
-
inline void *get()
-
class VAContext : public ov::intel_gpu::ocl::ClContext
- #include <va.hpp>
This class represents an abstraction for GPU plugin remote context which is shared with VA display object. The plugin object derived from this class can be obtained either with CompiledModel::get_context() or Core::create_context() calls.
Note
User can also obtain OpenCL context handle from this class.
Public Functions
-
inline operator VADisplay()
VADisplayconversion operator for the VAContext object.- Returns:
Underlying
VADisplayobject handle
-
inline VAContext(Core &core, VADisplay device, int target_tile_id = -1)
Constructs remote context object from VA display handle.
- Parameters:
core – OpenVINO Runtime Core object
device – A
VADisplayto create remote context fromtarget_tile_id – Desired tile id within given context for multi-tile system. Default value (-1) means that root device should be used
-
inline std::pair<VASurfaceTensor, VASurfaceTensor> create_tensor_nv12(const size_t height, const size_t width, const VASurfaceID nv12_surf)
This function is used to obtain a NV12 tensor from NV12 VA decoder output. The resulting tensor contains two remote tensors for Y and UV planes of the surface.
- Parameters:
height – A height of Y plane
width – A width of Y plane
nv12_surf – NV12
VASurfaceIDto create NV12 from
- Returns:
A pair of remote tensors for each plane
-
inline VASurfaceTensor create_tensor(const element::Type type, const Shape &shape, const VASurfaceID surface, const uint32_t plane = 0)
This function is used to create remote tensor from VA surface handle.
-
inline ClBufferTensor create_tensor(const element::Type type, const Shape &shape, const cl_mem buffer)
This function is used to obtain remote tensor object from user-supplied cl_mem object.
-
inline ClBufferTensor create_tensor(const element::Type type, const Shape &shape, const cl::Buffer &buffer)
This function is used to obtain remote tensor object from user-supplied cl::Buffer object.
-
inline ClImage2DTensor create_tensor(const element::Type type, const Shape &shape, const cl::Image2D &image)
This function is used to obtain remote tensor object from user-supplied cl::Image2D object.
-
inline USMTensor create_tensor(const element::Type type, const Shape &shape, void *usm_ptr)
This function is used to obtain remote tensor object from user-supplied USM pointer.
-
RemoteTensor create_tensor(const element::Type &type, const Shape &shape, const AnyMap ¶ms = {})
Allocates memory tensor in device memory or wraps user-supplied memory handle using the specified tensor description and low-level device-specific parameters. Returns a pointer to the object that implements the RemoteTensor interface.
- Parameters:
type – Defines the element type of the tensor.
shape – Defines the shape of the tensor.
params – Map of the low-level tensor object parameters.
- Returns:
Pointer to a plugin object that implements the RemoteTensor interface.
Public Static Functions
-
static inline void type_check(const RemoteContext &remote_context)
Checks that type defined runtime parameters are presented in remote object.
- Parameters:
remote_context – A remote context to check
-
inline operator VADisplay()
-
class VASurfaceTensor : public ov::intel_gpu::ocl::ClImage2DTensor
- #include <va.hpp>
This class represents an abstraction for GPU plugin remote tensor which is shared with VA output surface. The plugin object derived from this class can be obtained with VAContext::create_tensor() call.
Note
User can also obtain OpenCL 2D image handle from this class.
Public Functions
-
inline operator VASurfaceID()
VASurfaceID conversion operator for the VASurfaceTensor object.
- Returns:
VASurfaceIDhandle
-
inline uint32_t plane()
Returns plane ID of underlying video decoder surface.
- Returns:
Plane ID
Public Static Functions
-
static inline void type_check(const Tensor &tensor)
Checks that type defined runtime parameters are presented in remote object.
- Parameters:
tensor – a tensor to check
-
inline operator VASurfaceID()
-
using gpu_handle_param = void*#
-
using gpu_handle_param = void*#
-
namespace intel_npu#
Namespace with Intel NPU specific properties.
Variables
-
static constexpr ov::Property<uint64_t, ov::PropertyMutability::RO> device_alloc_mem_size = {"NPU_DEVICE_ALLOC_MEM_SIZE"}#
[Only for NPU plugin] Type: uint64_t Read-only property to get size of already allocated NPU DDR memory (both for discrete/integrated NPU devices)
Note: Queries driver both for discrete/integrated NPU devices
-
static constexpr ov::Property<uint64_t, ov::PropertyMutability::RO> device_total_mem_size = {"NPU_DEVICE_TOTAL_MEM_SIZE"}#
[Only for NPU plugin] Type: uint64_t Read-only property to get size of available NPU DDR memory (both for discrete/integrated NPU devices)
Note: Queries driver both for discrete/integrated NPU devices
-
static constexpr ov::Property<uint64_t, ov::PropertyMutability::RO> device_alloc_mem_size = {"NPU_DEVICE_ALLOC_MEM_SIZE"}#
-
namespace internal#
Variables
-
static constexpr Property<std::vector<PropertyName>, PropertyMutability::RO> supported_properties{"INTERNAL_SUPPORTED_PROPERTIES"}#
Read-only property to get a std::vector<PropertyName> of supported internal properties.
-
static constexpr Property<std::vector<PropertyName>, PropertyMutability::RO> caching_properties = {"CACHING_PROPERTIES"}#
Read-only property to get a std::vector<PropertyName> of properties which should affect the hash calculation for model cache.
-
static constexpr Property<bool, PropertyMutability::RW> exclusive_async_requests = {"EXCLUSIVE_ASYNC_REQUESTS"}#
Allow to create exclusive_async_requests with one executor.
-
static constexpr Property<std::string, PropertyMutability::WO> config_device_id = {"CONFIG_DEVICE_ID"}#
the property for setting of required device for which config to be updated values: device id starts from “0” - first device, “1” - second device, etc note: plugin may have different devices naming convention
-
static constexpr Property<int32_t, PropertyMutability::RW> threads_per_stream = {"THREADS_PER_STREAM"}#
Limit #threads that are used by IStreamsExecutor to execute
parallel_forcalls.
-
static constexpr Property<std::string, PropertyMutability::RO> compiled_model_runtime_properties{"COMPILED_MODEL_RUNTIME_PROPERTIES"}#
It contains compiled_model_runtime_properties information to make plugin runtime can check whether it is compatible with the cached compiled model, the result is returned by get_property() calling.
The information details are defined by plugin itself, each plugin may require different runtime contents. For example, CPU plugin will contain OV version, while GPU plugin will contain OV and GPU driver version, etc. Core doesn’t understand its content and only read it from plugin and write it into blob header.
-
static constexpr Property<std::vector<PropertyName>, PropertyMutability::RO> supported_properties{"INTERNAL_SUPPORTED_PROPERTIES"}#
-
namespace layout#
Functions
- OPENVINO_API bool has_batch (const Layout &layout)
Checks if layout has ‘batch’ dimension.
- OPENVINO_API std::int64_t batch_idx (const Layout &layout)
Returns ‘batch’ dimension index.
- Throws:
ov::AssertFailure – if dimension doesn’t exist.
- OPENVINO_API bool has_channels (const Layout &layout)
Checks if layout has ‘channels’ dimension.
- Throws:
ov::AssertFailure – if dimension doesn’t exist.
- OPENVINO_API std::int64_t channels_idx (const Layout &layout)
Returns ‘channels’ dimension index.
- Throws:
ov::AssertFailure – if dimension doesn’t exist.
- OPENVINO_API bool has_depth (const Layout &layout)
Checks if layout has ‘depth’ dimension.
- OPENVINO_API std::int64_t depth_idx (const Layout &layout)
Returns ‘depth’ dimension index.
- Throws:
ov::AssertFailure – if dimension doesn’t exist.
- OPENVINO_API bool has_height (const Layout &layout)
Checks if layout has ‘height’ dimension.
- OPENVINO_API std::int64_t height_idx (const Layout &layout)
Returns ‘height’ dimension index.
- Throws:
ov::AssertFailure – if dimension doesn’t exist.
- OPENVINO_API bool has_width (const Layout &layout)
Checks if layout has ‘width’ dimension.
- OPENVINO_API std::int64_t width_idx (const Layout &layout)
Returns ‘width’ dimension index.
- Throws:
ov::AssertFailure – if dimension doesn’t exist.
- OPENVINO_API void set_layout (ov::Output< ov::Node > output, const ov::Layout &layout)
Sets Layout of port.
- OPENVINO_API ov::Layout get_layout (const ov::Output< ov::Node > &output)
Gets Layout of port.
- Returns:
layout from port and empty layout in other case
- OPENVINO_API ov::Layout get_layout (const ov::Output< const ov::Node > &output)
Gets Layout of port.
- Returns:
layout from port and empty layout in other case
-
namespace log#
Namespace with log level property and its possible values.
Enums
-
enum class Level#
Enum to define possible log levels.
Values:
-
enumerator NO#
disable any logging
-
enumerator ERR#
error events that might still allow the application to continue running
-
enumerator WARNING#
potentially harmful situations which may further lead to ERROR
-
enumerator INFO#
informational messages that display the progress of the application at coarse-grained level
-
enumerator DEBUG#
fine-grained events that are most useful to debug an application.
-
enumerator TRACE#
finer-grained informational events than the DEBUG
-
enumerator NO#
-
enum class Level#
-
namespace op#
Enums
-
enum class GeluApproximationMode#
Specifies the approximation to calculate Gelu.
Values:
-
enumerator TANH#
-
enumerator ERF#
-
enumerator TANH#
-
enum class LSTMWeightsFormat#
Values:
-
enumerator FICO#
-
enumerator ICOF#
-
enumerator IFCO#
-
enumerator IFOC#
-
enumerator IOFC#
-
enumerator FICO#
-
enum class MVNEpsMode#
Specifies how eps is applied in MVN.
Values:
-
enumerator INSIDE_SQRT#
-
enumerator OUTSIDE_SQRT#
-
enumerator INSIDE_SQRT#
-
enum class PadMode#
Modes for the
Padoperator.Values:
-
enumerator CONSTANT#
-
enumerator EDGE#
-
enumerator REFLECT#
-
enumerator SYMMETRIC#
-
enumerator CONSTANT#
-
enum class PadType#
Padding Type used for
ConvolutionandPoolingFollows ONNX padding type definitions EXPLICIT - Pad dimensions are explicity specified SAME_LOWER - Pad dimensions computed to match input shape Ceil(num_dims/2) at the beginning and Floor(num_dims/2) at the end SAME_UPPER - Pad dimensions computed to match input shape Floor(num_dims/2) at the beginning and Ceil(num_dims/2) at the end VALID - No padding AUTO - Deprecated. User should not use it in the future NOTSET - Deprecated. User should not use it in the future
Values:
-
enumerator EXPLICIT#
-
enumerator SAME_LOWER#
-
enumerator SAME_UPPER#
-
enumerator VALID#
-
enumerator AUTO#
-
enumerator NOTSET#
-
enumerator EXPLICIT#
-
enum class RoundingType#
Rounding Type used for
Poolingoperators.Values:
-
enumerator FLOOR#
-
enumerator CEIL#
-
enumerator CEIL_TORCH#
-
enumerator FLOOR#
-
enum class AutoBroadcastType#
Specifies the algorithm to use for implicit broadcasting of a tensor to align with another tensor.
NONE - No implicit broadcasting of tensor NUMPY - Numpy-style implicit broadcasting (https://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) Right-align dimensions of the two tensors, with missing dimensions treated as size 1 dimensions. After alignment, for each dimension, their sizes should either match or one of them should be of size 1. Size 1 dimension will be implicitly broadcast to match the other size.
E.g., A: Shape(2, 1, 6) B: Shape( 3, 1) Result: Shape(2, 3, 6)
Result: Shape(2, 3, 6) PDPD - PaddlePaddle-style implicit broadcasting (PaddlePaddle/Paddle fluid/operators/elementwise/elementwise_op.h#L126) Broadcast B to match the shape of A, where axis is the start dimension index to align B with A. If axis is -1 (default), i axis = rank(A) - rank(B). The trailing dimensions of size 1 for B will be ignored.A: Shape(2, 1, 6) B: Shape( 3, 1)
E.g., A: Shape(2, 3, 4, 5) B: Shape( 3, 4 ) with axis =1 Result: Shape(2, 3, 4, 5)
Result: Shape(2, 3, 4, 5)A: Shape(2, 3, 4, 5) B: Shape( 3, 1 ) with axis = 1
Values:
-
enumerator NONE#
-
enumerator EXPLICIT#
-
enumerator NUMPY#
-
enumerator PDPD#
-
enumerator NONE#
-
enum class BroadcastType#
BroadcastType specifies rules used for mapping of input tensor axes to output shape axes.
EXPLICIT - Mapping of the input data shape to output shape based on axes_mapping input. NUMPY - Numpy broadcasting rules, aligned with ONNX Broadcasting. (onnx/onnx) PDPD - PaddlePaddle-style implicit broadcasting. For more informaction see AutoBroadcastType documentation. BIDIRECTIONAL - The broadcast rule is similar to numpy.array(input) * numpy.ones(target_shape). Dimensions are right alignment.
Note
Broadcasting rules are different for Broadcast op and for element-wise ops. AutoBroadcastType::NUMPY is equivalent of BroadcastType::BIDIRECTIONAL according to spec.
Values:
-
enumerator NONE#
-
enumerator EXPLICIT#
-
enumerator NUMPY#
-
enumerator PDPD#
-
enumerator BIDIRECTIONAL#
-
enumerator NONE#
Functions
- std::unordered_map< size_t, std::pair< ov::Tensor, ov::Tensor > > OPENVINO_API convert_input_types (OutputVector &inputs, const element::TypeVector &types)
- ov::TensorVector OPENVINO_API get_output_tensors_of_original_type (const ov::TensorVector &fake_output_tensors, const element::TypeVector &types)
- void OPENVINO_API reset_input_types (const std::unordered_map< size_t, std::pair< ov::Tensor, ov::Tensor > > &original_input_vals, OutputVector &inputs)
- bool OPENVINO_API convert_outputs_to_fake_type (ov::TensorVector &outputs, ov::TensorVector &original_outputs, bool is_upper)
- OPENVINO_API std::ostream & operator<< (std::ostream &s, const GeluApproximationMode &type)
-
ov::op::util::LSTMWeightsFormat convert_lstm_weights_enums(LSTMWeightsFormat format)#
- OPENVINO_API std::ostream & operator<< (std::ostream &s, const MVNEpsMode &type)
- OPENVINO_API std::ostream & operator<< (std::ostream &s, const v12::ScatterElementsUpdate::Reduction &reduction)
- OPENVINO_API std::ostream & operator<< (std::ostream &s, const PadMode &type)
- OPENVINO_API std::ostream & operator<< (std::ostream &s, const PadType &type)
- OPENVINO_API std::ostream & operator<< (std::ostream &s, const RoundingType &type)
- OPENVINO_API std::ostream & operator<< (std::ostream &s, const AutoBroadcastType &type)
- OPENVINO_API std::ostream & operator<< (std::ostream &s, const BroadcastType &type)
- OPENVINO_API std::ostream & operator<< (std::ostream &s, const EpsMode &type)
- OPENVINO_API std::ostream & operator<< (std::ostream &s, const TopKSortType &type)
- OPENVINO_API std::ostream & operator<< (std::ostream &s, const TopKMode &type)
- OPENVINO_API std::ostream & operator<< (std::ostream &s, const RecurrentSequenceDirection &direction)
-
template<class TShape, class TRShape = result_shape_t<TShape>>
std::vector<TRShape> copy_shape_infer(const Node *op, const std::vector<TShape> &input_shapes)#
-
template<class OpType, class T, class TRShape = result_shape_t<T>>
std::vector<TRShape> eltwise_shape_infer(const OpType *op, const std::vector<T> &input_shapes)#
-
template<class T, class TRShape = result_shape_t<T>>
std::vector<TRShape> shape_infer(const util::FFTBase *op, const std::vector<T> &input_shapes, const ITensorAccessor &ta = make_tensor_accessor())#
-
template<class TShape, class TRShape = result_shape_t<TShape>>
std::vector<TRShape> shape_infer(const util::GatherBase *op, const std::vector<TShape> &input_shapes, const ITensorAccessor &tensor_accessor = make_tensor_accessor())#
-
template<class TShape, class TRShape = result_shape_t<TShape>>
std::vector<TRShape> shape_infer(const util::ConvertColorI420Base *op, const std::vector<TShape> &input_shapes)#
-
template<class TShape, class TRShape = result_shape_t<TShape>>
std::vector<TRShape> shape_infer(const util::MulticlassNmsBase *op, const std::vector<TShape> &input_shapes, const bool static_output = !std::is_same<PartialShape, TShape>::value, const bool ignore_bg_class = false)#
-
template<class TShape, class TRShape = result_shape_t<TShape>>
std::vector<TRShape> shape_infer(const util::ConvertColorNV12Base *op, const std::vector<TShape> &input_shapes)#
-
template<class TShape, class TRShape = result_shape_t<TShape>>
std::vector<TRShape> shape_infer(const util::PadBase *op, const std::vector<TShape> &input_shapes, const ITensorAccessor &tensor_accessor = make_tensor_accessor())#
-
template<class TShape, class TRShape = result_shape_t<TShape>>
std::vector<TRShape> reduce_shape_infer(const util::ReductionBase *op, bool keep_dims, const std::vector<TShape> &input_shapes, const ITensorAccessor &tensor_accessor = make_tensor_accessor())#
-
template<class TShape, class TRShape = result_shape_t<TShape>>
std::vector<TRShape> shape_infer(const util::ArithmeticReductionKeepDims *op, const std::vector<TShape> &input_shapes, const ITensorAccessor &tensor_accessor = make_tensor_accessor())#
-
template<class TShape, class TRShape = result_shape_t<TShape>>
std::vector<TRShape> shape_infer(const util::LogicalReductionKeepDims *op, const std::vector<TShape> &input_shapes, const ITensorAccessor &tensor_accessor = make_tensor_accessor())#
-
template<class TShape, class TRShape = result_shape_t<TShape>>
std::vector<TRShape> shape_infer(const util::ScatterElementsUpdateBase *op, const std::vector<TShape> &input_shapes, const ITensorAccessor &ta = make_tensor_accessor())#
-
template<class TShape, class TRShape = result_shape_t<TShape>>
std::vector<TRShape> shape_infer(const util::ScatterNDBase *op, const std::vector<TShape> &input_shapes)#
-
template<class TShape, class TRShape = result_shape_t<TShape>>
std::vector<TRShape> shape_infer(const util::TopKBase *op, const std::vector<TShape> &input_shapes, const ITensorAccessor &tensor_accessor = make_tensor_accessor())# TopK shape inference.
- Template Parameters:
TShape – Type of shape.
- Parameters:
op – Pointer to TopK operator.
input_shapes – Input shapes of TopK.
constant_data – Map of constant data. DEfault empty.
- Returns:
Vector of output shapes for
-
template<class TShape, class TData, class TRes = std::vector<TData>, class UnaryOperation = ov::util::Cast<TData>, typename std::enable_if<!std::is_same<TShape, ov::PartialShape>::value>::type* = nullptr>
std::unique_ptr<TRes> get_input_const_data_as(const ov::Node *op, size_t idx, const ITensorAccessor &tensor_accessor, UnaryOperation &&func = ov::util::Cast<TData>())# Get the operator’s input const as pointer to vector of specified type.
The behaviour depends on shape type. The default output type is std::vector<TData> can be replace by other type which if is possible to construct it from constant data vector.
The behaviour depends on shape type. The default output type is std::vector<TData> can be replace by other type which if is possible to construct it from constant data vector.
- Template Parameters:
TShape – Shape type which enabled this version (not ov::PartialShape)
TData – Type use to cast input’s data.
TRes – Result type which has got default type as std::vector<TData>.
UnaryOperation – Unary function object applied on data with signature (Ret f(const TData &a)).
TShape – Shape type which enabled this version (ov::PartialShape)
TData – Type use to cast input’s data.
TRes – Result type which has got default type as std::vector<TData>.
UnaryOperation – Unary function object applied on data with signature (Ret f(const TData &a)).
- Parameters:
- Returns:
Pointer to constant data or nullptr if input has no constant data.
- Returns:
Pointer to constant data or nullptr if input has no constant data.
-
template<class TShape, class TDimValue = typename TShape::value_type::value_type, class UnaryOperation = ov::util::InTypeRange<TDimValue>, typename std::enable_if<!std::is_same<TShape, ov::PartialShape>::value>::type* = nullptr>
ov::optional<TShape> get_input_const_data_as_shape(const ov::Node *op, size_t port, const ITensorAccessor &tensor_accessor, UnaryOperation &&func = ov::util::InTypeRange<TDimValue>())# Get the input const data as shape object.
The input data can be processed by unary operation. By default is validated and casted to shape’s dimension type.
- Template Parameters:
- Parameters:
- Returns:
Unique pointer to shape created from input data.
-
inline element::Type get_input_const_element_type(const ov::Node *const op, size_t port, const ITensorAccessor &ta)#
-
template<class TShape, class TData, class TResult = std::vector<std::pair<TData, TData>>>
ov::optional<TResult> get_input_bounds(const ov::Node *op, size_t port, const ITensorAccessor &ta)# Get the input bounds from constant input or try evaluate bunds and return them as vector of pairs (lower, upper).
-
ov::Shape infer_broadcast_shape(const ov::Node *const op, const ov::Shape &first, const ov::Shape &second)#
Inference broadcast shape for element wise operator according to broadcast specification stored in operator.
- Parameters:
op – Pointer to operator.
first – First input shape.
second – Second input shape.
- Returns:
Result shape from inputs with applied broadcast specification.
-
ov::Shape infer_broadcast_shape(const ov::Node *const op, const ov::TensorVector &inputs)#
Inference broadcast shape from input tensor shapes for element wise operator according to broadcast specification stored in operator.
- Parameters:
op – Pointer to operator.
inputs – Tensors vector to get theirs shapes.
- Returns:
Result shape from input tensors shape with applied broadcast specification.
-
struct AutoBroadcastSpec#
- #include <attr_types.hpp>
Implicit broadcast specification.
-
struct BroadcastModeSpec#
- #include <attr_types.hpp>
Implicit broadcast specification.
-
class Op : public ov::Node
- #include <op.hpp>
Root of all actual ops.
Subclassed by ov::exec_model_info::ExecutionNode, ov::op::Sink, ov::op::internal::NonMaxSuppressionIEInternal, ov::op::internal::RPE, ov::op::util::AvgPoolBase, ov::op::util::BinaryElementwiseArithmetic, ov::op::util::BinaryElementwiseBitwise, ov::op::util::BinaryElementwiseComparison, ov::op::util::BinaryElementwiseLogical, ov::op::util::BroadcastBase, ov::op::util::ConvertColorI420Base, ov::op::util::ConvertColorNV12Base, ov::op::util::ConvolutionBase, ov::op::util::DetectionOutputBase, ov::op::util::EmbeddingBagOffsetsBase, ov::op::util::EmbeddingBagPackedBase, ov::op::util::FFTBase, ov::op::util::GatherBase, ov::op::util::GatherNDBase, ov::op::util::IndexReduction, ov::op::util::InterpolateBase, ov::op::util::MaxPoolBase, ov::op::util::MulticlassNmsBase, ov::op::util::PadBase, ov::op::util::RNNCellBase, ov::op::util::ROIAlignBase, ov::op::util::ReadValueBase, ov::op::util::ReductionBase, ov::op::util::ScatterBase, ov::op::util::ScatterElementsUpdateBase, ov::op::util::ScatterNDBase, ov::op::util::ShapeOfBase, ov::op::util::TopKBase, ov::op::util::UnaryElementwiseArithmetic, ov::op::v0::BatchNormInference, ov::op::v0::CTCGreedyDecoder, ov::op::v0::Concat, ov::op::v0::Constant, ov::op::v0::Convert, ov::op::v0::CumSum, ov::op::v0::DepthToSpace, ov::op::v0::FakeQuantize, ov::op::v0::HardSigmoid, ov::op::v0::Interpolate, ov::op::v0::LRN, ov::op::v0::MVN, ov::op::v0::MatMul, ov::op::v0::NormalizeL2, ov::op::v0::PRelu, ov::op::v0::PSROIPooling, ov::op::v0::Parameter, ov::op::v0::PriorBox, ov::op::v0::PriorBoxClustered, ov::op::v0::Proposal, ov::op::v0::ROIPooling, ov::op::v0::Range, ov::op::v0::RegionYolo, ov::op::v0::ReorgYolo, ov::op::v0::Result, ov::op::v0::ReverseSequence, ov::op::v0::Selu, ov::op::v0::ShuffleChannels, ov::op::v0::SpaceToDepth, ov::op::v0::Squeeze, ov::op::v0::Tile, ov::op::v0::Unsqueeze, ov::op::v10::IsFinite, ov::op::v10::IsInf, ov::op::v10::IsNaN, ov::op::v10::Unique, ov::op::v12::GroupNormalization, ov::op::v13::BitwiseNot, ov::op::v13::FakeConvert, ov::op::v13::Multinomial, ov::op::v13::NMSRotated, ov::op::v13::ScaledDotProductAttention, ov::op::v14::ConvertPromoteTypes, ov::op::v14::Inverse, ov::op::v14::RMSNorm, ov::op::v1::BatchToSpace, ov::op::v1::ConvertLike, ov::op::v1::DeformablePSROIPooling, ov::op::v1::GatherTree, ov::op::v1::LogicalNot, ov::op::v1::NonMaxSuppression, ov::op::v1::OneHot, ov::op::v1::Reshape, ov::op::v1::Reverse, ov::op::v1::Select, ov::op::v1::Softmax, ov::op::v1::SpaceToBatch, ov::op::v1::Split, ov::op::v1::StridedSlice, ov::op::v1::Transpose, ov::op::v1::VariadicSplit, ov::op::v3::Bucketize, ov::op::v3::EmbeddingSegmentsSum, ov::op::v3::ExtractImagePatches, ov::op::v3::NonMaxSuppression, ov::op::v3::NonZero, ov::op::v4::CTCLoss, ov::op::v4::Range, ov::op::v4::Swish, ov::op::v5::BatchNormInference, ov::op::v5::LogSoftmax, ov::op::v5::NonMaxSuppression, ov::op::v6::CTCGreedyDecoderSeqLen, ov::op::v6::ExperimentalDetectronDetectionOutput, ov::op::v6::ExperimentalDetectronGenerateProposalsSingleImage, ov::op::v6::ExperimentalDetectronPriorGridGenerator, ov::op::v6::ExperimentalDetectronROIFeatureExtractor, ov::op::v6::ExperimentalDetectronTopKROIs, ov::op::v6::GatherElements, ov::op::v6::MVN, ov::op::v7::Einsum, ov::op::v7::Roll, ov::op::v8::AdaptiveAvgPool, ov::op::v8::AdaptiveMaxPool, ov::op::v8::MatrixNms, ov::op::v8::PriorBox, ov::op::v8::RandomUniform, ov::op::v8::Slice, ov::op::v8::Softmax, ov::op::v9::Eye, ov::op::v9::GenerateProposals, ov::op::v9::GridSample, ov::op::v9::NonMaxSuppression
-
class Sink : public ov::op::Op
- #include <sink.hpp>
Root of nodes that can be sink nodes.
Subclassed by ov::op::util::AssignBase, ov::op::util::MultiSubGraphOp
-
class TemporaryReplaceOutputType
- #include <type_relaxed.hpp>
Set another type for a specified output for the period of time when an instance of the class exists. When the execution leaves the scope where an onject of TemporaryReplaceOutputType is defined, the type of the output is set to its original value. Used when initialized TypeRelaxed<BaseOp> operation in case when inputs have types that are not compatible with BaseOp infer function. In this case before TypeRelaxed is constructed the BaseOp contructor requires modified data types. So it should be
-
template<typename BaseOp>
class TypeRelaxed : public BaseOp, public ov::op::TypeRelaxedBase - #include <type_relaxed.hpp>
Relaxes tensor element type requirements for BaseOp inputs and outputs This class template should be used with Node descendant class. Defines a new operation by extending the original BaseOp operation with ability to accept inputs and provide outputs with element type that is unusual for BaseOp. For example, TypeRelaxed<opset1::Add> can accept mixed-precision inputs and provide another type of output. New types are provided as inputs attributes for TypeRelaxed template and fixed. There is no any deduction logic for types are provided as a part of this class and it should be implemented outside if required.
Public Functions
-
template<typename ...Args>
inline TypeRelaxed(const element::TypeVector &_input_data_types, const element::TypeVector &_output_data_types, Args&&... args) Creating a new TypeRelaxed operation by calling one of the original op ctors forwarding arguments directly.
-
template<typename ...Args>
-
class TypeRelaxedBase
- #include <type_relaxed.hpp>
A base class for templated TypeRelaxed that maintains overridden input types and output types for an operation.
Subclassed by ov::op::TypeRelaxed< BaseOp >
Public Functions
-
inline const element::Type &get_overridden_output_type(size_t outputIndex = 0) const
This method may look similar to Node::get_output_element_type, but it is not the same thing, because get_output_element_type returns the result of type inference, so it is completely deduced from an operation inputs and attributes, and get_overridden_output_type returns value of the attribute that is used to deduce output type. In some cases they don’t match: get_overridden_output_type may return element::undefined for some index i, and get_output_element_type will return some real type for the same index i.
- Returns:
Data type that will be set for output with a given index outputIndex. If output with a specified index outputIndex hasn’t been set before, element::undefined will returned. Undefined means no type override happens for a given outputIndex and it will deduced as original operation defineds in its infer function.
-
inline void set_overridden_output_type(const element::Type &element_type, size_t outputIndex = 0)
Set data type that overrides the original data type for output port with outputIndex index In case if outputIndex is out of range of known outputs (and this class cannot detect the real number of outputs for original operation), the number of overridden outputs is changed according to a given outputIndex value.
-
inline const element::Type &get_origin_input_type(size_t inputIndex = 0) const
- Returns:
Data type that will be set for input when original shape/type inference function is called. If index inputIndex hasn’t been set before, element::undefined will returned. Undefined means that the type from input tensor descriptor is used for a given index.
-
inline void set_origin_input_type(const element::Type &element_type, size_t inputIndex = 0)
Set data type that overrides the original data type for input port with inputIndex index. In case if inputIndex is out of range of known inputs (and this class cannot detect the real number of inputs for original operation), the number of overridden inputs is changed according to a given inputIndex value. All new entries except one added at inputIndex position are undefined.
-
inline const element::Type &get_overridden_output_type(size_t outputIndex = 0) const
-
namespace batch_norm#
-
namespace convolution#
Functions
-
template<class TOp, class TShape, typename std::enable_if<std::is_base_of<util::ConvolutionBackPropBase, TOp>::value>::type* = nullptr>
size_t calculate_num_spatial(const TOp *op, const std::vector<TShape> &input_shapes, const result_shape_t<TShape> &out_spatial_shape)#
-
template<class TOp, class TShape, class TIter>
void apply_auto_pad(const TOp *op, const TShape &data_shape, const TShape &filters_shape, const result_shape_t<TShape> &out_spatial_shape, TIter pads_begin, TIter pads_end)# Apply auto padding for backward convolution.
The auto padding can be applied only if inputs and attributes of operator are validated. The input shapes must have got static ranks.
- Parameters:
-
template<class TShape>
void apply_padding(const util::ConvolutionBackPropBase *op, const std::vector<TShape> &input_shapes, const result_shape_t<TShape> &out_spatial_shape, CoordinateDiff &pads_begin, CoordinateDiff &pads_end)# Apply auto padding for back propagation convolutions.
-
template<class TOp, class TShape, class TContainer, typename std::enable_if<std::is_base_of<ov::op::util::ConvolutionBackPropBase, TOp>::value>::type* = nullptr>
void append_spatial_shape(const TOp *op, const TShape &data_shape, const TShape &filters_shape, const TContainer &pads_begin, const TContainer &pads_end, result_shape_t<TShape> &out_shape)# Append spatial dimension at end of output shape of back propagation convolution.
-
template<class TConv>
constexpr size_t filter_non_spatial_dims_count()# Provides convolution filter non spatial dimension count.
Note
If specific convolution operator requires different value provide specialization for this operator.
- Template Parameters:
TConv – Type of convolution operator.
- Returns:
Default value for convolution operators (2).
-
template<class TOp>
bool is_auto_pad(const TOp *op)# Checks if Op property auto_pad is set to same lower or upper.
- Template Parameters:
TOp – Type of operator (must have get_auto_pad member function).
- Parameters:
op – Pointer to operator.
- Returns:
True if auto pad enabled.
-
inline void resize_empty_padding(const size_t num_spatial, CoordinateDiff &pads_begin, CoordinateDiff &pads_end)#
Resize paddings if empty to number of spatial dimensions.
- Parameters:
num_spatial – Number of spatial dimensions.
pads_begin – Begin padding to resize.
pads_end – End padding to resize.
-
inline size_t num_spatial_from_attr(const util::ConvolutionBase *op)#
-
template<class TOp, class TShape, typename std::enable_if<std::is_base_of<util::ConvolutionFwdPropBase, TOp>::value>::type* = nullptr>
size_t calculate_num_spatial(const TOp *op, const std::vector<TShape> &input_shapes)#
-
template<class TOp, class TShape, class TIter, typename std::enable_if<std::is_base_of<util::ConvolutionFwdPropBase, TOp>::value || std::is_base_of<util::DeformableConvolutionBase, TOp>::value>::type* = nullptr>
void apply_auto_pad(const TOp *op, const TShape &data_shape, const TShape &filters_shape, TIter pads_begin, TIter pads_end)# Apply auto padding for forward convolution.
The auto padding can be applied only if inputs and attributes of operator are validated. The input shapes must have got static ranks.
-
template<class TOp, class TShape, typename std::enable_if<std::is_base_of<util::ConvolutionFwdPropBase, TOp>::value || std::is_base_of<util::DeformableConvolutionBase, TOp>::value>::type* = nullptr>
void apply_padding(const TOp *op, const TShape &data_shape, const TShape &filters_shape, CoordinateDiff &pads_begin, CoordinateDiff &pads_end)# Apply padding to forward propagation convolution besed on padding.
-
template<class TOp, class TShape, class TRShape = result_shape_t<TShape>, typename std::enable_if<std::is_base_of<util::ConvolutionFwdPropBase, TOp>::value || std::is_base_of<util::DeformableConvolutionBase, TOp>::value>::type* = nullptr>
void append_spatial_shape(const TOp *op, const TShape &data_shape, const TShape &filters_shape, CoordinateDiff &pads_begin, CoordinateDiff &pads_end, TRShape &out_shape)# Append spatial dimension at end of output shape of forward propagation convolution.
-
template<>
constexpr size_t filter_non_spatial_dims_count<v1::GroupConvolutionBackpropData>()# Defines non-spatial dimension for filters for group convolution back propagation operator.
- Returns:
Value of non-spatial filter dimensions (3).
-
template<>
constexpr size_t filter_non_spatial_dims_count<v1::GroupConvolution>()# Defines non-spatial dimension for filters for group convolution operator.
- Returns:
Value of non-spatial filter dimensions (3).
Variables
-
constexpr auto num_spatial_undefined = util::num_spatial_undefined#
-
constexpr size_t spatial_dim_offset = 2#
-
namespace validate#
Functions
-
template<class TShape>
void data_shape(const v1::BinaryConvolution *op, const TShape &data_shape)# Specific check of data shape for binary convolution data shape must be rank 4.
The shape_infer is same as for Convolution operator except this check.
See also
convolution_shape_inference.hpp
-
template<class TShape>
void filter_shape(const ov::op::util::ConvolutionBackPropBase *op, const TShape &filters_shape, const TShape &data_shape)#
-
template<class TShape>
void data_shape(const ov::op::util::ConvolutionBase *op, const TShape &data_shape)#
-
template<class TShape>
void filter_shape(const ov::op::util::ConvolutionBase *op, const TShape &filters_shape, const TShape &data_shape)#
-
inline void common_attributes(const util::ConvolutionBase *op, const size_t num_spatial, const CoordinateDiff &pads_begin, const CoordinateDiff &pads_end)#
-
inline void common_attributes(const util::ConvolutionBackPropBase *op, const size_t num_spatial, const CoordinateDiff &pads_begin, const CoordinateDiff &pads_end)#
-
template<class TShape>
-
template<class TOp, class TShape, typename std::enable_if<std::is_base_of<util::ConvolutionBackPropBase, TOp>::value>::type* = nullptr>
-
namespace deformable_conv#
Functions
-
template<class TShape>
size_t calculate_num_spatial(const util::DeformableConvolutionBase *op, const std::vector<TShape> &input_shapes)#
-
namespace validate#
Functions
-
template<class TDeformableConv>
void group_attribute(const TDeformableConv *op, int64_t group, const std::string &name)#
-
template<class TDeformableConv, class TDim>
void group_divisible_dimension(const TDeformableConv *op, const TDim &dim, const std::string name)#
-
template<class TDeformableConv, class TDim>
void deformable_group_divisible_dimension(const TDeformableConv *op, const TDim &dim, const std::string name)#
-
template<class TDeformableConv>
-
template<class TShape>
-
namespace eye#
Variables
-
constexpr std::array<char const*, 4> shape_names = {"'num_rows'", "'num_columns'", "'diagonal_index'", "'batch_shape'"}#
-
constexpr std::array<char const*, 4> shape_names = {"'num_rows'", "'num_columns'", "'diagonal_index'", "'batch_shape'"}#
-
namespace fft#
Functions
-
template<class TRShape, typename std::enable_if<std::is_same<TRShape, PartialShape>::value>::type* = nullptr>
void apply_dims_from_sizes(const util::FFTBase *op, TRShape &output_shape, const std::vector<int64_t> &axes, const ITensorAccessor &ta)#
-
template<class TRShape, typename std::enable_if<std::is_same<TRShape, PartialShape>::value>::type* = nullptr>
-
namespace gather_nd#
-
namespace internal#
Functions
-
class AUGRUCell : public ov::op::util::RNNCellBase
- #include <augru_cell.hpp>
AUGRUCell operation.
Public Functions
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
virtual void validate_and_infer_types() override
-
class AUGRUSequence : public ov::op::util::RNNCellBase
- #include <augru_sequence.hpp>
AUGRUSequence operation.
Public Functions
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
virtual void validate_and_infer_types() override
-
class GatherCompressed : public ov::op::v8::Gather
- #include <gather_compressed.hpp>
Public Functions
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
virtual void validate_and_infer_types() override
-
class GenerateProposalsIEInternal : public ov::op::v9::GenerateProposals
- #include <generate_proposals_ie_internal.hpp>
Public Functions
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
virtual void validate_and_infer_types() override
-
class MulticlassNmsIEInternal : public ov::op::v9::MulticlassNms
- #include <multiclass_nms_ie_internal.hpp>
Public Functions
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
virtual void validate_and_infer_types() override
-
template<typename BaseNmsOp>
class NmsStaticShapeIE : public BaseNmsOp - #include <nms_static_shape_ie.hpp>
-
class NonMaxSuppressionIEInternal : public ov::op::Op
- #include <nms_ie_internal.hpp>
Public Functions
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
virtual void validate_and_infer_types() override
-
class RPE : public ov::op::Op
- #include <rotary_positional_embeddings.hpp>
Rotary Positional Embeddings operation Internal operation which may change in the future.
Public Functions
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
virtual void validate_and_infer_types() override
-
class AUGRUCell : public ov::op::util::RNNCellBase
-
namespace interpolate#
Functions
-
template<class T, class U, typename std::enable_if<std::is_same<T, U>::value>::type* = nullptr>
constexpr bool is_same_instance(const T &lhs, const U &rhs)#
-
template<class TContainer>
void resize_padding(const ov::op::util::InterpolateBase *op, size_t input_rank, TContainer &pads_begin, TContainer &pads_end)# Resize padding to input rank.
- Template Parameters:
TContainer – Pads container type.
- Parameters:
op – Pointer to base of interpolate.
input_rank – Expected padding size.
pads_begin – Begin padding container.
pads_end – End padding container.
-
template<class TShape, class TInputIter, class TRShape = result_shape_t<TShape>>
TRShape make_padded_shape(const TShape &input, TInputIter pads_begin, TInputIter pads_end)# Makes padded shape from input shapes and padding values.
Note
The input shape must be static rank and padding count must match input shape rank.
-
template<class TShape, class TRes = std::vector<int64_t>>
std::unique_ptr<TRes> get_axes(const Node *const op, size_t port, bool has_axes, size_t rank, const ITensorAccessor &ta)# Get the axes for interpolate from constant input or default value if op has no axes input.
- Parameters:
op – Pointer to operator.
port – Axes input port number.
has_axes – Flag if op has input with axes.
rank – input shape used for axes values validation.
ta – Tensor accessor for input data.
- Returns:
Not null pointer with axes values or null pointer if can’t get axes from input.
-
template<class TShape, class TContainer>
void set_undefined_dim_on_axes(TShape &out, TContainer &axes)# Set the undefined dimensions on specified axes.
- Parameters:
out – Output shape to update.
axes – List of axes for update.
-
template<class TShape, class TContainer>
void update_dims_with_sizes_on_axes(TShape &out_shape, const TContainer &axes, const Node *const op, const size_t port, const ITensorAccessor &ta)# Update output shape with dimension size from input on specified axes.
-
template<class TShape>
void update_dims_with_scales_on_axes(TShape &out_shape, const std::vector<int64_t> &axes, const Node *const op, const size_t port, const ITensorAccessor &ta)# Update output shape by scaling dimensions on axes.
-
namespace validate#
Functions
-
template<class TShape>
void input_rank_1d(const Node *const op, const std::vector<TShape> &shapes, size_t port)# Validates that input at port number from is 1-D rank.
- Template Parameters:
TShape –
- Parameters:
op – Pointer to operator.
shapes – Vector of op’s input shapes.
port – Port number.
-
template<class TShape>
void are_inputs_except_first_1d(const Node *const op, const std::vector<TShape> &shapes)# Validates that inputs from 2nd to last are compatible 1-D rank.
- Template Parameters:
TShape –
- Parameters:
op – Pointer to operator.
shapes – Vector of op’s input shapes.
-
template<class TContainer>
void axes_values(const Node *const op, const TContainer &axes, size_t rank)# Check if axes values in range [0,rank].
- Template Parameters:
TContainer – Type of axes container.
- Parameters:
op – Pointer to operator.
axes – Container with axes to check.
rank – Maximum value for axes values.
-
inline void input_elements_num(const Node *const op, const std::string &input_name, size_t element_count, size_t exp_count)#
Check if number of elements in input is same as expected.
- Parameters:
op – Pointer to operator.
input_name – Input name.
element_count – Element count in tested input.
exp_count – Expected element count on tested input.
-
template<class TShape>
-
template<class T, class U, typename std::enable_if<std::is_same<T, U>::value>::type* = nullptr>
-
namespace nms#
Functions
-
template<class T, class TRShape = result_shape_t<T>>
std::vector<TRShape> shape_infer(const Node *op, const std::vector<T> &input_shapes, const ITensorAccessor &ta = make_tensor_accessor())#
-
namespace validate#
Functions
-
template<class TShape>
void boxes_shape(const Node *const op, const std::vector<TShape> &input_shapes)#
-
template<class TShape>
void scores_shape(const Node *const op, const std::vector<TShape> &input_shapes)#
-
template<class TShape>
void num_batches(const Node *const op, const std::vector<TShape> &input_shapes)#
-
template<class TShape>
void num_boxes(const Node *const op, const std::vector<TShape> &input_shapes)#
-
template<class TShape>
-
template<class T, class TRShape = result_shape_t<T>>
-
namespace pad#
Functions
-
constexpr bool is_inf_padding(const std::pair<int64_t, int64_t> &pad_bounds)#
-
constexpr bool is_inf_padding(const std::pair<int64_t, int64_t> &pad_bounds)#
-
namespace pooling#
Functions
-
template<>
inline void valid_dilated_kernel_with_padding(const v1::AvgPool *op, const size_t kernel, const size_t pad_begin, const size_t pad_end, const size_t axis)#
-
template<>
inline void valid_dilated_kernel_with_padding(const v14::AvgPool *op, const size_t kernel, const size_t pad_begin, const size_t pad_end, const size_t axis)#
-
template<class TOp, class TShape, class TContainer, class TRShape = result_shape_t<TShape>>
std::vector<TRShape> avg_pool_shape_infer_util(const TOp *op, const std::vector<TShape> &input_shapes, TContainer &pads_begin, TContainer &pads_end)#
-
template<class TOp, class TShape, class TContainer, class TRShape = result_shape_t<TShape>>
std::vector<TRShape> max_pool_shape_infer_util(const TOp *op, const std::vector<TShape> &input_shapes, TContainer &pads_begin, TContainer &pads_end)#
-
template<class TContainer>
void resize_empty_padding(const size_t num_spatial, TContainer &pads_begin, TContainer &pads_end)# Resize paddings if empty to number of spatial dimensions.
- Parameters:
num_spatial – Number of spatial dimensions.
pads_begin – Begin padding to resize.
pads_end – End padding to resize.
-
template<class TOp, class TShape, class TContainer>
void apply_padding(const TOp *op, const TShape &data_shape, const Strides &dilations, TContainer &pads_begin, TContainer &pads_end)# Apply pooling operator padding depends on auto pad value.
- Parameters:
op – Pointer to Pooling operator to apply padding.
data_shape – Shape infer data input shape.
dilations – Kernel dilations.
pads_begin – Padding begin to update.
pads_end – Padding end to update.
-
template<class TOp, class TDim>
void valid_dilated_kernel_with_dim(const TOp *op, const size_t kernel, const TDim &dim, const size_t axis)#
-
template<class TOp>
void valid_dilated_kernel_with_padding(const TOp *op, const size_t kernel, const size_t pad_begin, const size_t pad_end, const size_t axis)#
-
template<class TDim>
void align_ceil_torch_dimension_size(TDim &dim, const size_t last_pooling_start_index, const size_t data_dim_length, const size_t pads_begin)#
-
template<class TDim>
TDim disallow_pooling_start_in_padding(const TDim &dim, const size_t stride, const TDim *data_dim, const size_t pads_begin)#
-
template<class TDim>
TDim allow_pooling_start_in_padding(const TDim &dim, const size_t, const TDim*, const size_t)#
-
template<class TOp, class TShape, class TContainer, class TRShape>
void append_spatial_shape(const TOp *op, const TShape &data_shape, const TContainer &pads_begin, const TContainer &pads_end, const Strides &dilations, TRShape &out_shape)# Append spatial shape to the end of output shape for pooling operator shape inference result.
-
template<class TOp, class TShape, class TContainer, class TRShape = result_shape_t<TShape>>
TRShape out_shape_infer(const TOp *op, const TShape &data_shape, const TContainer &pads_begin, const TContainer &pads_end, const Strides &dilations)# Shape inference helper used for pooling operators such Max Pool, Avg Pool.
-
template<class TShape, class TOp, class TRShape = result_shape_t<TShape>, typename std::enable_if<std::is_same<TOp, v8::AdaptiveAvgPool>::value || std::is_same<TOp, v8::AdaptiveMaxPool>::value>::type* = nullptr>
TRShape out_shape_infer(const TOp *op, const std::vector<TShape> &input_shapes, const ITensorAccessor &ta)# Shape inference helper used for adaptive pooling operators.
Variables
-
constexpr size_t spatial_dim_offset = 2#
-
namespace validate#
Functions
-
template<class TOp, class TContainer>
void padding(const TOp *op, const TContainer &pads_begin, const TContainer &pads_end)#
-
template<class TOp>
constexpr bool has_torch_ceil_mode()#
-
template<class TOp, class TContainer>
-
template<>
-
namespace prior_box#
Functions
-
template<class TDim, class TOp, typename std::enable_if<std::is_same<v0::PriorBox, TOp>::value || std::is_same<v8::PriorBox, TOp>::value>::type* = nullptr>
TDim number_of_priors(const TOp *const op)#
-
template<class TDim>
TDim number_of_priors(const v0::PriorBoxClustered *const op)#
-
template<class TOp, class TShape, class TRShape = result_shape_t<TShape>>
std::vector<TRShape> shape_infer(const TOp *const op, const std::vector<TShape> &input_shapes, const ITensorAccessor &ta = make_tensor_accessor())#
Variables
-
constexpr std::array<char const*, 2> input_names = {"output size", "image"}#
-
namespace validate#
Functions
-
inline std::vector<PartialShape> inputs_et(const Node *const op)#
-
inline std::vector<PartialShape> inputs_et(const Node *const op)#
-
template<class TDim, class TOp, typename std::enable_if<std::is_same<v0::PriorBox, TOp>::value || std::is_same<v8::PriorBox, TOp>::value>::type* = nullptr>
-
namespace proposal#
-
namespace psroi_pooling#
-
namespace validate#
Functions
-
template<class TROIPooling, class TShape>
void feat_input_shape(const TROIPooling *op, const TShape feat_shape)#
-
template<class TROIPooling>
void output_group_attr(const TROIPooling *op)#
-
template<class TROIPooling>
void bins_attr(const TROIPooling *op)#
-
template<class TROIPooling>
void mode_attr(const TROIPooling *op)#
-
template<class TROIPooling, class TShape>
-
namespace validate#
-
namespace reshape#
Functions
-
template<class TDim, typename std::enable_if<std::is_same<typename std::decay<TDim>::type, Dimension>::value>::type* = nullptr>
TDim resolve_minus_one_dim(const Product<TDim> &product)#
-
template<class TShape>
std::pair<TShape, int64_t> get_pattern_and_minus_one_idx(const Node *const op, const std::vector<std::pair<int64_t, int64_t>> &bounds)# Get the pattern and minus one idx from input bounds.
- Parameters:
op – Pointer to reshape node.
bounds – Vector of reshape pattern bounds.
- Returns:
Pair which got bounds converted to shape and
minus_oneindex in pattern (-1 if not found).
-
template<class TShape, typename std::enable_if<std::is_same<TShape, PartialShape>::value>::type* = nullptr>
void set_pattern_symbols(const Node *const op, TShape &shape)# Set the pattern symbols on pattern shape if this input has symbols.
Shapes other than PartialShape have no symbols.
- Parameters:
op – Pointer to reshape node.
shape – Pointer to shape for symbols set.
-
template<class T, class U = void>
struct Product# - #include <reshape_shape_inference.hpp>
- template<class T> type >
- #include <reshape_shape_inference.hpp>
Helper to resolve the input and output product for ov::Dimension (dynamic) dimensions.
- template<class T> type >
- #include <reshape_shape_inference.hpp>
Helper to resolve the input and output product for static dimensions.
-
template<class TDim, typename std::enable_if<std::is_same<typename std::decay<TDim>::type, Dimension>::value>::type* = nullptr>
-
namespace rnn#
Functions
-
template<class TShape>
void validate_inputs_rank(const op::util::RNNCellBase *op, const std::vector<TShape> &input_shapes, const std::vector<Rank> &expected_ranks)#
-
template<class TShape>
-
namespace roi_pooling#
-
namespace validate#
Functions
-
template<class TROIPooling, class TShape>
void feat_intput_shape(const TROIPooling *op, const TShape &feat_shape)#
-
template<class TROIPooling, class TShape>
void rois_input_shape(const TROIPooling *op, const TShape rois_shape)#
-
template<class TROIPooling>
void output_roi_attr(const TROIPooling *op)#
-
template<class TROIPooling>
void scale_attr(const TROIPooling *op)#
-
template<class TROIPooling>
void method_attr(const TROIPooling *op)#
-
template<class TROIPooling, class TShape>
-
namespace validate#
-
namespace shape_of#
-
namespace ShapeInferRange#
-
namespace slice#
Typedefs
-
using Bounds = std::pair<int64_t, int64_t>#
Alias to dimension bounds for slice.
Functions
-
inline int64_t get_sliced_value(const int64_t dim, const int64_t start, const int64_t stop, const int64_t step)#
Get sliced value in step for given dimension value and start, stop, step.
Note
This function cannot be use for step 0 (division by 0)
- Parameters:
dim – Dimension value.
start – Start of slice.
stop – Stop of slice.
step – Step of slice.
- Returns:
-1 for infinite number otherwise [0..int64_max] for finit step.
-
constexpr bool is_bounds_zero_crossing(const Bounds b)#
Check if bounds can cross 0 value (rising edge).
- Parameters:
b – Input interval bounds for check.
- Returns:
True if lower bound is negative and upper is not negative, otherwise false.
-
template<class TDim>
constexpr bool is_lb_within_dim(const int64_t lb, const TDim &dim)# Check if lower bound is within dimension.
Check valid only if bounds can cross zero value (lb is negative).
- Parameters:
lb – Lower bound for check.
dim – Dimension used to check lower bound.
- Returns:
True if lower bound is within dimension length, otherwise false.
-
template<class TDim>
constexpr bool is_ub_within_dim(const int64_t ub, const TDim &dim)# Check if upper bound is within dimension.
Check valid only if bounds can cross zero value (up is not negative).
- Parameters:
ub – Upper bound for check.
dim – Dimension used to check upper bound.
- Returns:
True if upper bound is within dimension length, otherwise false.
Variables
-
constexpr std::array<char const*, 4> shape_names = {"start", "stop", "step", "axes"}#
-
struct AxesMap#
- #include <slice_shape_inference.hpp>
-
using Bounds = std::pair<int64_t, int64_t>#
-
namespace util#
Typedefs
-
using ActivationFunctionType = std::shared_ptr<Node> (*)(const std::shared_ptr<Node>&, float, float)#
-
using InputDescriptionVector = std::vector<util::SubGraphOp::InputDescription::Ptr>#
-
using OutputDescriptionVector = std::vector<util::SubGraphOp::OutputDescription::Ptr>#
-
using VariableMap = std::unordered_map<Variable::Ptr, VariableValue::Ptr>#
Enums
Functions
-
template<class T>
bool normalize_single_value(std::vector<T> vec, float &value, bool check_value_range = true)#
-
float cast_eps_to_float(double eps_d)#
Convert epsilon value from double to float type.
If the value is too large, the epsilon is converted to std::numeric_limits<float>::min() or std::numeric_limits<float>::min(), otherwise static cast to float is called. The adjustment is made for positive values only, for negative it works as static cast.
- Parameters:
eps – Original value of the epsilon (double).
- Returns:
Epsilon value as float.
-
bool check_for_broadcast(const PartialShape &ref_shape, const PartialShape &other_shape)#
-
bool shapes_equal_except_dynamic_expected_batch(const PartialShape &expected, const PartialShape &actual)#
-
void visit_shape_path(ov::Node *node, std::unordered_set<ov::Node*> &visited, std::function<void(ov::Node*)> func)#
Traverses a shapeOf subgraph starting from the node and not including the ShapeOf nodes, and calls “func” for each ov::Node.
- Parameters:
node – The node from which constant path is started.
visited – Set of nodes which were visited.
func – The function which is called for each visited node.
-
void visit_constant_path(ov::Node *node, std::unordered_set<ov::Node*> &visited, std::function<void(ov::Node*)> func)#
Traverses a constant path starting from “node”, and calls “func” for each ov::Node. If the function was called for non-constant subgraph, exception is thrown.
- Parameters:
node – The node from which constant path is started.
visited – Set of nodes which were visited.
func – The function which is called for each visited node.
-
template<typename T>
ov::pass::pattern::op::ValuePredicate constant_predicate(std::function<bool(const std::vector<T>&)> predicate)#
-
ActivationFunction get_activation_func_by_name(const std::string &func_name)#
Gets the activation function by name.
- Parameters:
func_name – [in] The function name
- Throws:
UnknownActivationFunction – When provided func_name is unknown.
- Returns:
The activation function object.
-
AxisSet get_normalized_axes_from_tensor(const Node *const node, const Tensor &tensor, const Rank &rank)#
Get the normalized axes as ov::AxisSet from raw tensor data.
- Parameters:
node – A node pointer used for detailed description if normalization fails.
tensor – Tensor with axes for normalization.
rank – Rank value to normalize axes.
- Returns:
Normalized axes as set.
-
std::tuple<element::Type, PartialShape> validate_and_infer_elementwise_args(Node *node)#
- OPENVINO_API bool is_unary_elementwise_arithmetic (const Node *node)
- OPENVINO_API bool is_binary_elementwise_arithmetic (const Node *node)
- OPENVINO_API bool is_binary_elementwise_comparison (const Node *node)
- OPENVINO_API bool is_binary_elementwise_logical (const Node *node)
- OPENVINO_API bool supports_auto_broadcast (const Node *node)
- OPENVINO_API bool is_op (const Node *node)
- OPENVINO_API bool is_parameter (const Node *node)
- OPENVINO_API bool is_output (const Node *node)
- OPENVINO_API bool is_sink (const Node *node)
- OPENVINO_API bool is_constant (const Node *node)
- OPENVINO_API bool is_commutative (const Node *node)
- OPENVINO_API bool is_unary_elementwise_arithmetic (const std::shared_ptr< Node > &node)
- OPENVINO_API bool is_binary_elementwise_arithmetic (const std::shared_ptr< Node > &node)
- OPENVINO_API bool is_binary_elementwise_comparison (const std::shared_ptr< Node > &node)
- OPENVINO_API bool is_binary_elementwise_logical (const std::shared_ptr< Node > &node)
- OPENVINO_API bool supports_auto_broadcast (const std::shared_ptr< Node > &node)
- OPENVINO_API bool is_op (const std::shared_ptr< Node > &node)
- OPENVINO_API bool is_parameter (const std::shared_ptr< Node > &node)
- OPENVINO_API bool is_output (const std::shared_ptr< Node > &node)
- OPENVINO_API bool is_sink (const std::shared_ptr< Node > &node)
- OPENVINO_API bool is_constant (const std::shared_ptr< Node > &node)
- OPENVINO_API bool is_commutative (const std::shared_ptr< Node > &node)
- OPENVINO_API void validate_seq_input_rank_dimension (const std::vector< ov::PartialShape > &input)
Validates static rank and dimension for provided input parameters. Additionally input_size dimension is checked for X and W inputs. Applies to LSTM, GRU and RNN Sequences.
- Parameters:
input – [in] Vector with RNNSequence-like op inputs in following order: X, initial_hidden_state, sequence_lengths, W, R and B.
- std::shared_ptr< Node > OPENVINO_API convert_lstm_node_format (const Output< Node > &node, LSTMWeightsFormat from_format, LSTMWeightsFormat to_format=LSTMWeightsFormat::FICO, int64_t axis=0)
Change data format of provided node.
- Parameters:
node – [in] The input node to be permuted.
from_format – [in] Original node weights format.
to_format – [in] Weights format to convert to.
- Returns:
Node representing reshaped tensor according to
to_formatweights format.
- std::shared_ptr< Node > OPENVINO_API convert_lstm_peepholes_format (const Output< Node > &node, LSTMPeepholesFormat from_format, LSTMPeepholesFormat to_format=LSTMPeepholesFormat::FIO, int64_t axis=0)
-
template<typename T, class TRShape = result_shape_t<T>>
void validate_target_shape_none(const ov::Node *op, const T &arg_shape, const AxisVector &axes_mapping_val, const TRShape &target_input_shape)#
-
template<typename T, class TRShape = result_shape_t<T>>
void validate_target_shape_numpy(const ov::Node *op, const T &arg_shape, const TRShape &target_input_shape)#
-
template<typename T, class TRShape = result_shape_t<T>>
void set_result_shape_pdpd(const ov::Node *op, const T &arg0_shape, const TRShape &target_input_shape, TRShape &result_shape, const ov::op::BroadcastModeSpec &broadcast_spec)#
-
template<typename T, class TRShape = result_shape_t<T>>
void set_result_shape_bidirectional(const ov::Node *op, const T &arg_shape, TRShape &target_input_shape, TRShape &result_shape)#
-
template<class T, class TRShape = result_shape_t<T>>
std::vector<TRShape> broadcast_base_shape_infer(const ov::op::util::BroadcastBase *op, const std::vector<T> &input_shapes, const ITensorAccessor &ta = make_tensor_accessor())#
-
template<class TShape>
size_t num_spatial_from_shapes(const TShape &data_shape, const TShape &filter_shape, const size_t filter_non_spatial_dims_count)# Get num of spatial form convolution operator.
Tries get value from operator member if is not deduced (has -1 value) then tries evaluate it from input shapes.
- Template Parameters:
TConv – Convolution type (this function must be a friend of TConv to access private member).
TShape – Shape type.
- Parameters:
- Returns:
Value of spatial dimension number or infinite bound (-1) if cannot evaluate.
-
inline bool is_attr_validation_required(const ConvolutionBase *op)#
Checks if validation attributes is required.
- Parameters:
op – Pointer to convolution base operator.
- Returns:
True if internal number of spatial dimension not defined otherwise false.
-
inline size_t get_num_spatial(const ConvolutionBase *op)#
Get the num spatil object.
- Parameters:
op –
- Returns:
size_t
-
template<class TShape, class TRShape = result_shape_t<TShape>>
std::vector<TRShape> shape_infer(const DeformableConvolutionBase *op, const std::vector<TShape> &input_shapes, CoordinateDiff &pads_begin, CoordinateDiff &pads_end)#
-
template<typename T, typename V = typename std::iterator_traits<typename T::iterator>::value_type::value_type>
void compute_num_classes(const DetectionOutputBase *op, const DetectionOutputBase::AttributesBase &attrs, const std::vector<T> &input_shapes, V &num_classes, V &num_prior_boxes)#
-
template<class T, class TRShape = result_shape_t<T>>
std::vector<TRShape> shape_infer_base(const DetectionOutputBase *op, const DetectionOutputBase::AttributesBase &attrs, const std::vector<T> &input_shapes, int64_t attribute_num_classes)#
-
template<class TShape, class TRShape = result_shape_t<TShape>>
std::vector<TRShape> shape_infer(const ov::op::util::EmbeddingBagOffsetsBase *op, const std::vector<TShape> &input_shapes)#
-
template<class TShape, class TRShape = result_shape_t<TShape>>
std::vector<TRShape> shape_infer(const ov::op::util::EmbeddingBagPackedBase *op, const std::vector<TShape> &input_shapes)#
Variables
-
constexpr size_t num_spatial_undefined = std::numeric_limits<size_t>::max()#
-
constexpr size_t spatial_dim_offset = 2#
-
class ActivationFunction
- #include <activation_functions.hpp>
Class representing activation function used in RNN cells.
Public Functions
Calls stored activation function with provided node argument.
-
class ArithmeticReduction : public ov::op::util::ReductionBase
- #include <arithmetic_reduction.hpp>
Abstract base class for arithmetic reduction operations, i.e., operations where chosen axes of the input tensors are eliminated (reduced out) by repeated application of a particular binary arithmetic operation.
Subclassed by ov::op::util::ArithmeticReductionKeepDims
Public Functions
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
virtual void validate_and_infer_types() override
-
class ArithmeticReductionKeepDims : public ov::op::util::ArithmeticReduction
- #include <arithmetic_reductions_keep_dims.hpp>
Subclassed by ov::op::v1::ReduceMax, ov::op::v1::ReduceMean, ov::op::v1::ReduceMin, ov::op::v1::ReduceProd, ov::op::v1::ReduceSum, ov::op::v4::ReduceL1, ov::op::v4::ReduceL2
Public Functions
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
inline virtual bool get_keep_dims() const override
- Returns:
If set to 1 it holds axes that are used for reduction. For each such axis, output dimension is equal to 1.
-
virtual void validate_and_infer_types() override
-
class AssignBase : public ov::op::Sink, public ov::op::util::VariableExtension
- #include <assign_base.hpp>
Subclassed by ov::op::v3::Assign, ov::op::v6::Assign
Public Functions
-
inline explicit AssignBase(const OutputVector &arguments)
Constructs an AssignBase operation.
-
inline explicit AssignBase(const OutputVector &arguments)
-
class AvgPoolBase : public ov::op::Op
- #include <avg_pool_base.hpp>
Subclassed by ov::op::v14::AvgPool, ov::op::v1::AvgPool
Public Functions
-
AvgPoolBase(const Output<Node> &arg, const Strides &strides, const Shape &pads_begin, const Shape &pads_end, const Shape &kernel, bool exclude_pad, RoundingType rounding_type = RoundingType::FLOOR, const PadType &auto_pad = PadType::EXPLICIT)
Constructs a batched average pooling operation.
- Parameters:
arg – The output producing the input data batch tensor.
[d1, dn]strides – The strides.
[n]pads_begin – The beginning of padding shape.
[n]pads_end – The end of padding shape.
[n]kernel – The kernel shape.
[n]exclude_pad – If false then averages include padding elements, each treated as the number zero. If true, padding elements are entirely ignored when computing averages.
rounding_type – Whether to use ceiling or floor rounding type while computing output shape.
auto_pad – Padding type to use for additional padded dimensions
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
const Shape &get_kernel() const
- Returns:
The kernel shape.
-
const Strides &get_strides() const
- Returns:
The strides.
-
const Shape &get_pads_begin() const
- Returns:
The beginning of padding shape.
-
const Shape &get_pads_end() const
- Returns:
The end of padding shape.
-
bool get_exclude_pad() const
- Returns:
Exclude zero-values in padding area.
-
const PadType &get_auto_pad() const
- Returns:
The pad type for pooling.
-
RoundingType get_rounding_type() const
- Returns:
The ceiling mode being used for output shape computations
-
AvgPoolBase(const Output<Node> &arg, const Strides &strides, const Shape &pads_begin, const Shape &pads_end, const Shape &kernel, bool exclude_pad, RoundingType rounding_type = RoundingType::FLOOR, const PadType &auto_pad = PadType::EXPLICIT)
-
class BinaryElementwiseArithmetic : public ov::op::Op
- #include <binary_elementwise_arithmetic.hpp>
Abstract base class for elementwise binary arithmetic operations, i.e., operations where the same scalar binary arithmetic operation is applied to each corresponding pair of elements in the two input tensors. Implicit broadcast of input tensors is supported through one of the AutoBroadcast modes.
For example, if the underlying arithmetic operation (determined by the subclass) is \(\mathit{op}(x,y)\), the input tensors \([[x_0,y_0],[z_0,w_0]]\) and \([[x_1,y_1],[z_1,w_1]]\) will be mapped to \([[\mathit{op}(x_0,x_1),\mathit{op}(y_0,y_1)],[\mathit{op}(z_0,z_1),\mathit{op}(w_0,w_1)]]\).
Inputs
Type
Description
arg0\(N[d_1,\dots,d_n]~(n \geq 0)\)
A tensor of any shape. The element type \(N\) may be any numeric type.
arg1\(N[d_1,\dots,d_n]~(n \geq 0)\)
A tensor of the same element type as
arg0.autobAuto broadcast specification.
Type
Description
\(N[d_1,\dots,d_n]\)
The tensor \(T\), where \(T[i_1,\dots,i_n] = \mathit{op}(\texttt{arg0}[i_1,\dots,i_n],\texttt{arg1}[i_1,\dots,i_n])\). This will always have the same shape and element type as the input tensors (after auto broadcasting).
Subclassed by ov::op::v0::SquaredDifference, ov::op::v1::Add, ov::op::v1::Divide, ov::op::v1::FloorMod, ov::op::v1::Maximum, ov::op::v1::Minimum, ov::op::v1::Mod, ov::op::v1::Multiply, ov::op::v1::Power, ov::op::v1::Subtract
Public Functions
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
inline virtual const AutoBroadcastSpec &get_autob() const override
- Returns:
the autobroadcasr spec
-
virtual void validate_and_infer_types() override
-
class BinaryElementwiseBitwise : public ov::op::Op
- #include <binary_elementwise_bitwise.hpp>
Subclassed by ov::op::v13::BitwiseAnd, ov::op::v13::BitwiseOr, ov::op::v13::BitwiseXor
Public Functions
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
virtual const AutoBroadcastSpec &get_autob() const override
- Returns:
the autobroadcasr spec
-
virtual void validate_and_infer_types() override
-
class BinaryElementwiseComparison : public ov::op::Op
- #include <binary_elementwise_comparison.hpp>
Abstract base class for elementwise binary comparison operations, i.e., operations where the same scalar binary comparison operation is applied to each corresponding pair of elements in two input tensors. Implicit broadcast of input tensors is supported through one of the AutoBroadcast modes.
For example, if the underlying comparison operation (determined by the subclass) is \(\mathit{op}(x,y)\), the input tensors \([[x_0,y_0],[z_0,w_0]]\) and \([[x_1,y_1],[z_1,w_1]]\) will be mapped to \([[\mathit{op}(x_0,x_1),\mathit{op}(y_0,y_1)],[\mathit{op}(z_0,z_1),\mathit{op}(w_0,w_1)]]\).
Inputs
Type
Description
arg0\(E[d_1,\dots,d_n]~(n \geq 0)\)
A tensor of any shape and element type.
arg1\(E[d_1,\dots,d_n]~(n \geq 0)\)
A tensor of the same shape and element type as
arg0.autobAuto broadcast specification.
Type
Description
\(\texttt{bool}[d_1,\dots,d_n]\)
The tensor \(T\), where \(T[i_1,\dots,i_n] = \mathit{op}(\texttt{arg0}[i_1,\dots,i_n],\texttt{arg1}[i_1,\dots,i_n])\). This will always have the same shape as the input tensors, and the element type
bool.Subclassed by ov::op::v1::Equal, ov::op::v1::Greater, ov::op::v1::GreaterEqual, ov::op::v1::Less, ov::op::v1::LessEqual, ov::op::v1::NotEqual
Public Functions
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
inline virtual const AutoBroadcastSpec &get_autob() const override
- Returns:
the autobroadcasr spec
-
virtual void validate_and_infer_types() override
-
class BinaryElementwiseLogical : public ov::op::Op
- #include <binary_elementwise_logical.hpp>
Abstract base class for elementwise binary logical operations, i.e., operations where the same scalar binary logical operation is applied to each corresponding pair of elements in two boolean input tensors. Implicit broadcast of input tensors is supported through one of the AutoBroadcast modes.
For example, if the underlying operation (determined by the subclass) is \(\mathit{op}(x,y)\), the input tensors \([[x_0,y_0],[z_0,w_0]]\) and \([[x_1,y_1],[z_1,w_1]]\) will be mapped to \([[\mathit{op}(x_0,x_1),\mathit{op}(y_0,y_1)],[\mathit{op}(z_0,z_1),\mathit{op}(w_0,w_1)]]\).
Inputs
Type
Description
arg0\(\texttt{bool}[d_1,\dots,d_n]~(n \geq 0)\)
A tensor of any shape, with element type
bool.arg1\(\texttt{bool}[d_1,\dots,d_n]~(n \geq 0)\)
A tensor of the same shape and element type as
arg0.autobAuto broadcast specification.
Type
Description
\(\texttt{bool}[d_1,\dots,d_n]\)
The tensor \(T\), where \(T[i_1,\dots,i_n] = \mathit{op}(\texttt{arg0}[i_1,\dots,i_n],\texttt{arg1}[i_1,\dots,i_n])\). This will always have the same shape as the input tensors, and the element type
bool.Subclassed by ov::op::v0::Xor, ov::op::v1::LogicalAnd, ov::op::v1::LogicalOr, ov::op::v1::LogicalXor
Public Functions
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
inline virtual const AutoBroadcastSpec &get_autob() const override
- Returns:
the autobroadcasr spec
-
virtual void validate_and_infer_types() override
-
class BroadcastBase : public ov::op::Op
- #include <broadcast_base.hpp>
Subclassed by ov::op::v1::Broadcast, ov::op::v3::Broadcast
Public Functions
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
virtual std::pair<bool, AxisSet> get_broadcast_axes() const
- Returns:
true and the AxisSet if broadcast axes can be fully determined.
-
virtual bool evaluate(ov::TensorVector &outputs, const ov::TensorVector &inputs) const override
Evaluates the op on input_values putting results in output_values.
- Parameters:
output_values – Tensors for the outputs to compute. One for each result
input_values – Tensors for the inputs. One for each inputs.
- Returns:
true if successful
-
virtual void validate_and_infer_types() override
-
struct ClipNegative#
- #include <reverse_shape_inference.hpp>
Clip if value type T is less than 0, otherwise cast to AxisSet::value_type.
-
class ConvertColorI420Base : public ov::op::Op
- #include <convert_color_i420_base.hpp>
Base class for color conversion operation from I420 to RGB/BGR format. Input:
Operation expects input shape in NHWC layout.
Input NV12 image can be represented in a two ways: a) Single plane: NV12 height dimension is 1.5x bigger than image height. ‘C’ dimension shall be 1 b) Three separate planes: Y, U and V. In this case b1) Y plane has height same as image height. ‘C’ dimension equals to 1 b2) U plane has dimensions: ‘H’ = image_h / 2; ‘W’ = image_w / 2; ‘C’ = 1. b3) V plane has dimensions: ‘H’ = image_h / 2; ‘W’ = image_w / 2; ‘C’ = 1.
Supported element types: u8 or any supported floating-point type. Output:
Output node will have NHWC layout and shape HxW same as image spatial dimensions.
Number of output channels ‘C’ will be 3
Conversion of each pixel from I420 (YUV) to RGB space is represented by following formulas: R = 1.164 * (Y - 16) + 1.596 * (V - 128) G = 1.164 * (Y - 16) - 0.813 * (V - 128) - 0.391 * (U - 128) B = 1.164 * (Y - 16) + 2.018 * (U - 128) Then R, G, B values are clipped to range (0, 255)
Subclassed by ov::op::v8::I420toBGR, ov::op::v8::I420toRGB
Public Types
-
enum class ColorConversion : int
Exact conversion format details Currently supports conversion from I420 to RGB or BGR.
Values:
-
enumerator I420_TO_RGB
-
enumerator I420_TO_BGR
-
enumerator I420_TO_RGB
Public Functions
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
class ConvertColorNV12Base : public ov::op::Op
- #include <convert_color_nv12_base.hpp>
Base class for color conversion operation from NV12 to RGB/BGR format. Input:
Operation expects input shape in NHWC layout.
Input NV12 image can be represented in a two ways: a) Single plane: NV12 height dimension is 1.5x bigger than image height. ‘C’ dimension shall be 1 b) Two separate planes: Y and UV. In this case b1) Y plane has height same as image height. ‘C’ dimension equals to 1 b2) UV plane has dimensions: ‘H’ = image_h / 2; ‘W’ = image_w / 2; ‘C’ = 2.
Supported element types: u8 or any supported floating-point type. Output:
Output node will have NHWC layout and shape HxW same as image spatial dimensions.
Number of output channels ‘C’ will be 3
Conversion of each pixel from NV12 (YUV) to RGB space is represented by following formulas: R = 1.164 * (Y - 16) + 1.596 * (V - 128) G = 1.164 * (Y - 16) - 0.813 * (V - 128) - 0.391 * (U - 128) B = 1.164 * (Y - 16) + 2.018 * (U - 128) Then R, G, B values are clipped to range (0, 255)
Subclassed by ov::op::v8::NV12toBGR, ov::op::v8::NV12toRGB
Public Types
-
enum class ColorConversion : int
Exact conversion format details Currently supports conversion from NV12 to RGB or BGR, in future can be extended with NV21_to_RGBA/BGRA, etc.
Values:
-
enumerator NV12_TO_RGB
-
enumerator NV12_TO_BGR
-
enumerator NV12_TO_RGB
Public Functions
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
class ConvolutionBackPropBase : public ov::op::util::ConvolutionBase
- #include <convolution_backprop_base.hpp>
Base class for operations like back propagation convolution.
Subclassed by ov::op::v1::ConvolutionBackpropData, ov::op::v1::GroupConvolutionBackpropData
Public Functions
-
ConvolutionBackPropBase() = default
Constructs a conversion operation.
-
inline ConvolutionBackPropBase(const OutputVector &arguments, const Strides &strides, const CoordinateDiff &pads_begin, const CoordinateDiff &pads_end, const Strides &dilations, const PadType &auto_pad = PadType::EXPLICIT, const CoordinateDiff &output_padding = {})
Constructs a conversion operation.
- Parameters:
strides – Convolution strides.
pads_begin – Amount of padding to be added to the beginning along each axis. For example in case of a 2D input the value of (1, 2) means that 1 element will be added to the top and 2 elements to the left.
pads_end – Amount of padding to be added to the end along each axis.
dilations – The distance in width and height between the weights in the filters tensor.
auto_pad – Specifies how the automatic calculation of padding should be done.
output_padding – The output padding adds additional amount of paddings per each spatial axis in the output tensor. clang-format on
-
ConvolutionBackPropBase() = default
-
class ConvolutionBase : public ov::op::Op
- #include <convolution_base.hpp>
Base class for operations like convolutions.
Subclassed by ov::op::util::ConvolutionBackPropBase, ov::op::util::ConvolutionFwdPropBase, ov::op::util::DeformableConvolutionBase
Public Functions
-
ConvolutionBase() = default
Constructs a conversion operation.
-
inline ConvolutionBase(const OutputVector &arguments, const Strides &strides, const CoordinateDiff &pads_begin, const CoordinateDiff &pads_end, const Strides &dilations, const PadType &auto_pad = PadType::EXPLICIT)
Constructs a conversion operation.
- Parameters:
strides – Convolution strides.
pads_begin – Amount of padding to be added to the beginning along each axis. For example in case of a 2D input the value of (1, 2) means that 1 element will be added to the top and 2 elements to the left.
pads_end – Amount of padding to be added to the end along each axis.
dilations – The distance in width and height between the weights in the filters tensor.
auto_pad – Specifies how the automatic calculation of padding should be done.
-
ConvolutionBase() = default
-
class ConvolutionFwdPropBase : public ov::op::util::ConvolutionBase
- #include <convolution_base.hpp>
Base class for operations like back propagation convolution.
Subclassed by ov::op::v1::BinaryConvolution, ov::op::v1::Convolution, ov::op::v1::GroupConvolution
Public Functions
-
ConvolutionFwdPropBase() = default
Constructs a conversion operation.
-
inline ConvolutionFwdPropBase(const OutputVector &arguments, const Strides &strides, const CoordinateDiff &pads_begin, const CoordinateDiff &pads_end, const Strides &dilations, const PadType &auto_pad = PadType::EXPLICIT)
Constructs a conversion operation.
- Parameters:
strides – Convolution strides.
pads_begin – Amount of padding to be added to the beginning along each axis. For example in case of a 2D input the value of (1, 2) means that 1 element will be added to the top and 2 elements to the left.
pads_end – Amount of padding to be added to the end along each axis.
dilations – The distance in width and height between the weights in the filters tensor.
auto_pad – Specifies how the automatic calculation of padding should be done.
-
ConvolutionFwdPropBase() = default
-
class DeformableConvolutionBase : public ov::op::util::ConvolutionBase
- #include <deformable_convolution_base.hpp>
Base class for operations DeformableConvolution v1 and DeformableConvolution v8.
Subclassed by ov::op::v1::DeformableConvolution, ov::op::v8::DeformableConvolution
Public Functions
-
DeformableConvolutionBase() = default
Constructs a conversion operation.
-
DeformableConvolutionBase(const OutputVector &arguments, const Strides &strides, const CoordinateDiff &pads_begin, const CoordinateDiff &pads_end, const Strides &dilations, const PadType &auto_pad = PadType::EXPLICIT, int64_t group = 1, int64_t deformable_group = 1)
Constructs a conversion operation.
- Parameters:
strides – Convolution strides.
pads_begin – Amount of padding to be added to the beginning along each axis. For example in case of a 2D input the value of (1, 2) means that 1 element will be added to the top and 2 elements to the left.
pads_end – Amount of padding to be added to the end along each axis.
dilations – The distance in width and height between the weights in the filters tensor.
auto_pad – Specifies how the automatic calculation of padding should be done.
group – The number of groups which both output and input should be split into.
deformable_group – The number of groups which deformable values and output should be split into along the channel axis.
-
DeformableConvolutionBase() = default
-
class DetectionOutputBase : public ov::op::Op
- #include <detection_output_base.hpp>
DetectionOutputBase basic class for DetectionOutput v0 and v8.
Subclassed by ov::op::v0::DetectionOutput, ov::op::v8::DetectionOutput
-
struct AttributesBase
- #include <detection_output_base.hpp>
Subclassed by ov::op::v0::DetectionOutput::Attributes
-
struct AttributesBase
-
class EmbeddingBagOffsetsBase : public ov::op::Op
- #include <embeddingbag_offsets_base.hpp>
Returns embeddings for given indices.
Subclassed by ov::op::v3::EmbeddingBagOffsetsSum
Public Functions
-
EmbeddingBagOffsetsBase() = default
Constructs a EmbeddingBagOffsetsBase operation.
-
EmbeddingBagOffsetsBase(const Output<Node> &emb_table, const Output<Node> &indices, const Output<Node> &offsets, const Output<Node> &default_index, const Output<Node> &per_sample_weights)
Constructs a EmbeddingBagOffsetsBase operation.
EmbeddingBagOffsetsBase constructs an output tensor by replacing every index in a given input tensor with a row (from the weights matrix) at that index
- Parameters:
emb_table – tensor containing the embedding lookup table of the module of shape [num_emb, emb_dim1, emb_dim2, …] and of type T
indices – tensor of shape [num_indices] and of type T_IND. Required
offsets – tensor of shape [batch] and of type T_IND containing the starting index positions of each “bag” in indices. Required.
per_sample_weigths – tensor of the same shape as indices and of type T. Each value in this tensor are multiplied with each value pooled from embedding table for each index. Optional.
default_index – scalar of type T_IND containing default index in embedding table to fill empty “bags”. If not provided empty “bags” are filled with zeros. Optional.
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
EmbeddingBagOffsetsBase() = default
-
class EmbeddingBagPackedBase : public ov::op::Op
- #include <embeddingbag_packed_base.hpp>
Returns embeddings for given indices.
Subclassed by ov::op::v3::EmbeddingBagPackedSum
Public Functions
-
EmbeddingBagPackedBase() = default
Constructs a EmbeddingBagPackedBase operation.
-
EmbeddingBagPackedBase(const Output<Node> &emb_table, const Output<Node> &indices, const Output<Node> &per_sample_weights)
Constructs a EmbeddingBagPackedBase operation.
EmbeddingBagPackedBase constructs an output tensor by replacing every index in a given input tensor with a row (from the weights matrix) at that index
- Parameters:
emb_table – Tensor containing the embedding lookup table of the module of shape [num_emb, emb_dim1, emb_dim2, …] and of type T
indices – Tensor of shape
[batch, indices_per_bag]and of type T_IND. Required.per_sample_weigths – tensor of the same shape as indices and of type T. Each value in this tensor are multiplied with each value pooled from embedding table for each index. Optional.
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
EmbeddingBagPackedBase() = default
-
class FFTBase : public ov::op::Op
- #include <fft_base.hpp>
Base class for operations DFT and DFT.
Subclassed by ov::op::v7::DFT, ov::op::v7::IDFT, ov::op::v9::IRDFT, ov::op::v9::RDFT
Public Functions
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
virtual void validate_and_infer_types() override
-
class FrameworkNode : public ov::op::util::MultiSubGraphOp
- #include <framework_node.hpp>
Subclassed by ov::frontend::ComplexTypeMark
Public Functions
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
virtual void validate_and_infer_types() override
-
class FrameworkNodeAttrs
- #include <framework_node.hpp>
-
class GatherBase : public ov::op::Op
- #include <gather_base.hpp>
GatherBase basic class for Gather v1 and v7.
Subclassed by ov::op::v1::Gather, ov::op::v7::Gather, ov::op::v8::Gather
Public Functions
-
GatherBase(const Output<Node> &data, const Output<Node> &indices, const Output<Node> &axis, const int64_t batch_dims = 0)
- Parameters:
data – The tensor from which slices are gathered
indices – Tensor with indexes to gather
axis – The tensor is a dimension index to gather data from
batch_dims – The number of batch dimension in data and indices tensors
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
GatherBase(const Output<Node> &data, const Output<Node> &indices, const Output<Node> &axis, const int64_t batch_dims = 0)
-
class GatherNDBase : public ov::op::Op
- #include <gather_nd_base.hpp>
GatherNDBase basic class for GatherND v5 and v8.
Subclassed by ov::op::v5::GatherND, ov::op::v8::GatherND
-
template<class T>
struct GetK# - #include <topk_shape_inference.hpp>
-
template<class T>
struct GetNotNegative# - #include <one_hot_shape_inference.hpp>
-
class IndexReduction : public ov::op::Op
- #include <index_reduction.hpp>
Public Functions
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
virtual void validate_and_infer_types() override
-
class InterpolateBase : public ov::op::Op
- #include <interpolate_base.hpp>
Subclassed by ov::op::v11::Interpolate, ov::op::v4::Interpolate
Public Types
-
enum class ShapeCalcMode
PartialShape calculation mode.
SIZES - output shape for interpolated axes is calculated using input
sizesSCALES - output shape for interpolated axes is calculated using inputscalesValues:
-
enumerator SIZES
-
enumerator SCALES
-
enumerator SIZES
-
enum class InterpolateMode
Interpolation mode.
NEAREST - nearest interpolation LINEAR - linear interpolation as in TensorFlow LINEAR_ONNX - linear interpolation as in ONNX CUBIC - cubic interpolation BILINEAR_PILLOW - bilinear interpolation as in Pillow BICUBIC_PILLOW - bicubic interpolation as in Pillow
Values:
-
enumerator NEAREST
-
enumerator LINEAR
-
enumerator LINEAR_ONNX
-
enumerator CUBIC
-
enumerator BILINEAR_PILLOW
-
enumerator BICUBIC_PILLOW
-
enumerator NEAREST
-
enum class CoordinateTransformMode
Mode of the calculation of the source coordinate from resized one.
These modes are modes from ONNXRuntime.
Values:
-
enumerator HALF_PIXEL
-
enumerator PYTORCH_HALF_PIXEL
-
enumerator ASYMMETRIC
-
enumerator TF_HALF_PIXEL_FOR_NN
-
enumerator ALIGN_CORNERS
-
enumerator HALF_PIXEL
-
enum class NearestMode
Rounding modes for the NEAREST interpolation.
Values:
-
enumerator ROUND_PREFER_FLOOR
-
enumerator ROUND_PREFER_CEIL
-
enumerator FLOOR
-
enumerator CEIL
-
enumerator SIMPLE
-
enumerator ROUND_PREFER_FLOOR
Public Functions
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
struct InterpolateAttrs
- #include <interpolate_base.hpp>
-
enum class ShapeCalcMode
-
class LogicalReduction : public ov::op::util::ReductionBase
- #include <logical_reduction.hpp>
Abstract base class for logical reduction operations, i.e., operations where chosen axes of the input tensors are eliminated (reduced out) by repeated application of a particular binary logical operation.
Subclassed by ov::op::util::LogicalReductionKeepDims
Public Functions
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
virtual void validate_and_infer_types() override
-
class LogicalReductionKeepDims : public ov::op::util::LogicalReduction
- #include <logical_reduction_keep_dims.hpp>
Subclassed by ov::op::v1::ReduceLogicalAnd, ov::op::v1::ReduceLogicalOr
Public Functions
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
inline virtual bool get_keep_dims() const override
- Returns:
If set to 1 it holds axes that are used for reduction. For each such axis, output dimension is equal to 1.
-
virtual void validate_and_infer_types() override
-
class MaxPoolBase : public ov::op::Op
- #include <max_pool_base.hpp>
Subclassed by ov::op::v14::MaxPool, ov::op::v1::MaxPool, ov::op::v8::MaxPool
Public Functions
-
MaxPoolBase(const Output<Node> &arg, const Strides &strides, const Shape &pads_begin, const Shape &pads_end, const Shape &kernel, const op::RoundingType rounding_mode = op::RoundingType::FLOOR, const PadType auto_pad = op::PadType::EXPLICIT)
- Parameters:
arg – The node producing the input data batch tensor.
strides – The strides.
pads_begin – The beginning of padding shape.
pads_end – The end of padding shape.
kernel – The kernel shape.
rounding_mode – Whether to use ceiling or floor rounding type while computing output shape.
auto_pad – The pad type for automatically computing padding sizes.
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
inline const Shape &get_kernel() const
- Returns:
The kernel shape.
-
inline const Strides &get_strides() const
- Returns:
The strides.
-
inline const Shape &get_pads_begin() const
- Returns:
The beginning of padding shape.
-
inline const Shape &get_pads_end() const
- Returns:
The end of padding shape.
-
inline PadType get_auto_pad() const
- Returns:
The pad type for pooling.
-
inline op::RoundingType get_rounding_type() const
- Returns:
The ceiling mode being used for output shape computations
-
MaxPoolBase(const Output<Node> &arg, const Strides &strides, const Shape &pads_begin, const Shape &pads_end, const Shape &kernel, const op::RoundingType rounding_mode = op::RoundingType::FLOOR, const PadType auto_pad = op::PadType::EXPLICIT)
-
class MulticlassNmsBase : public ov::op::Op
- #include <multiclass_nms_base.hpp>
Base class for operations MulticlassNMS v8 and MulticlassNMS v9.
Subclassed by ov::op::v8::MulticlassNms, ov::op::v9::MulticlassNms
Public Functions
-
MulticlassNmsBase() = default
Constructs a conversion operation.
-
MulticlassNmsBase(const OutputVector &arguments, const Attributes &attrs)
Constructs a MulticlassNmsBase operation.
- Parameters:
arguments – Node list producing the box coordinates, scores, etc.
attrs – Attributes of the operation
-
inline const Attributes &get_attrs() const
Returns attributes of the operation MulticlassNmsBase.
-
struct Attributes
- #include <multiclass_nms_base.hpp>
Structure that specifies attributes of the operation.
-
MulticlassNmsBase() = default
-
class MultiSubGraphOp : public ov::op::Sink
- #include <multi_subgraph_base.hpp>
Abstract base class for sub-graph based ops, i.e ops that have some sub-graphs.
Subclassed by ov::op::util::FrameworkNode, ov::op::util::SubGraphOp, ov::op::v8::If
Public Functions
-
inline virtual const std::shared_ptr<Model> &get_function(size_t index) const
Gets internal sub-graph by index in MultiSubGraphOp.
- Parameters:
index – sub-graph’s index in op
- Returns:
pointer to Model with sub-graph
-
inline virtual const std::vector<std::shared_ptr<Model>> &get_functions() const
Gets internal sub-graphs.
- Returns:
a vector of pointers to sub-graph Models
Adds sub-graph to MultiSubGraphOp.
- Parameters:
index – index of new sub-graph
func – func new sub_graph as Model
-
inline const MultiSubgraphInputDescriptionVector &get_input_descriptions(int index) const
Gets vector with connections between operation inputs and internal sub-graph parameters.
- Parameters:
index – index of internal sub-graph
- Returns:
vector of input descriptions
-
inline MultiSubgraphInputDescriptionVector &get_input_descriptions(int index)
Gets vector with connections between operation inputs and internal sub-graph parameters.
- Parameters:
index – index of internal sub-graph
- Returns:
vector of input descriptions
-
inline const MultiSubgraphOutputDescriptionVector &get_output_descriptions(int index) const
Gets vector with connections between operation outputs and internal sub-graph results.
- Parameters:
index – index of internal sub-graph
- Returns:
vector of output descriptions
-
inline MultiSubgraphOutputDescriptionVector &get_output_descriptions(int index)
Gets vector with connections between operation outputs and internal sub-graph results.
- Parameters:
index – index of internal sub-graph
- Returns:
vector of output descriptions
-
inline void set_input_descriptions(int index, const MultiSubgraphInputDescriptionVector &inputs)
Sets vector with connections between operation inputs and internal sub-graph parameters.
- Parameters:
index – index of internal sub-graph
inputs – vector of input descriptions
-
inline void set_output_descriptions(int index, const MultiSubgraphOutputDescriptionVector &outputs)
Sets vector with connections between operation outputs and internal sub-graph results.
- Parameters:
index – index of internal sub-graph
outputs – vector of input descriptions
-
virtual void set_invariant_inputs(const Output<Node> &value, const ov::ParameterVector &bodies_parameters)
Set input decriptions for MultiSubGraphOp input.
- Parameters:
value – The value supplied as an input to the block.
bodies_parameters – vector of bodies parameters.
-
virtual Output<Node> set_body_outputs(const ResultVector &bodies_results)
Set output decriptions for MultiSubGraphOp output.
- Parameters:
bodies_results – vector of bodies results for one output.
- Returns:
value Output node for bodies_results.
-
inline virtual size_t get_internal_subgraphs_size() const
Get number of internal sub-graphs.
- Returns:
Number of sub-graphs.
-
inline virtual size_t get_input_descriptions_size() const
Get number of input descriptions.
- Returns:
Number of input descriptions
-
inline virtual size_t get_output_descriptions_size() const
Get number of output descriptions.
- Returns:
Number of output descriptions
-
class BodyOutputDescription : public ov::op::util::MultiSubGraphOp::OutputDescription
- #include <multi_subgraph_base.hpp>
Produces an output from a specific iteration.
Public Functions
-
BodyOutputDescription(uint64_t body_value_index, uint64_t output_index, int64_t iteration = -1)
Constructs a new instance.
- Parameters:
body_value_index – A body value that produces the output
output_index – The SubGraphOp output index
iteration – which iteration (typically -1, final) will supply the value
-
BodyOutputDescription(uint64_t body_value_index, uint64_t output_index, int64_t iteration = -1)
-
class ConcatOutputDescription : public ov::op::util::MultiSubGraphOp::OutputDescription
- #include <multi_subgraph_base.hpp>
Produces an output by concatenating an output from each iteration.
Public Functions
-
ConcatOutputDescription(uint64_t body_value_index, uint64_t output_index, int64_t start, int64_t stride, int64_t part_size, int64_t end, int64_t axis)
Constructs a new instance.
- Parameters:
body_value_index – A body value that produces the output
output_index – The MultiSubGraphOp output index
start – First index for slices
stride – Step amount for slices
part_size – Width of slices
end – Last index for slices
axis – Axis being sliced
-
ConcatOutputDescription(uint64_t body_value_index, uint64_t output_index, int64_t start, int64_t stride, int64_t part_size, int64_t end, int64_t axis)
-
class InputDescription
- #include <multi_subgraph_base.hpp>
Abstract class describes a connection between a MultiSubGraphOp input and the body.
Subclassed by ov::op::util::MultiSubGraphOp::InvariantInputDescription, ov::op::util::MultiSubGraphOp::MergedInputDescription, ov::op::util::MultiSubGraphOp::SliceInputDescription
-
class InvariantInputDescription : public ov::op::util::MultiSubGraphOp::InputDescription
- #include <multi_subgraph_base.hpp>
Produces an input.
Public Functions
-
InvariantInputDescription(uint64_t input_index, uint64_t body_parameter_index)
Constructs a new instance.
- Parameters:
input_index – Position of the MultiSubGraphOp input
body_parameter_index – Body parameter to receive input
-
InvariantInputDescription(uint64_t input_index, uint64_t body_parameter_index)
-
class MergedInputDescription : public ov::op::util::MultiSubGraphOp::InputDescription
- #include <multi_subgraph_base.hpp>
Describes a body input initialized from a MultiSubGraphOp input on the first iteration, and then a body output thereafter.
Public Functions
-
MergedInputDescription(uint64_t input_index, uint64_t body_parameter_index, uint64_t body_value_index)
Constructs a new instance.
- Parameters:
input_index – Position of the MultiSubGraphOp input supplying a value to body_parameter for the initial iteration.
body_parameter_index – Body parameter position to receive input.
body_value_index – Body value to supply body_parameter for successive iterations.
-
MergedInputDescription(uint64_t input_index, uint64_t body_parameter_index, uint64_t body_value_index)
-
class OutputDescription
- #include <multi_subgraph_base.hpp>
Abstract class describes how a MultiSubGraphOp output is produced from the body.
Subclassed by ov::op::util::MultiSubGraphOp::BodyOutputDescription, ov::op::util::MultiSubGraphOp::ConcatOutputDescription
-
class SliceInputDescription : public ov::op::util::MultiSubGraphOp::InputDescription
- #include <multi_subgraph_base.hpp>
Describes a body input formed from slices of an input to MultiSubGraphOp.
Public Functions
-
SliceInputDescription(uint64_t input_index, uint64_t body_parameter_index, int64_t start, int64_t stride, int64_t part_size, int64_t end, int64_t axis)
Constructs a new instance.
- Parameters:
input_index – Position of the MultiSubGraphOp input
body_parameter_index – Body parameter position to receive input
start – First index for slices
stride – Step amount for slices
part_size – Width of slices
end – Last index for slices
axis – Axis being sliced
-
SliceInputDescription(uint64_t input_index, uint64_t body_parameter_index, int64_t start, int64_t stride, int64_t part_size, int64_t end, int64_t axis)
-
inline virtual const std::shared_ptr<Model> &get_function(size_t index) const
-
class PadBase : public ov::op::Op
- #include <pad_base.hpp>
Subclassed by ov::op::v12::Pad, ov::op::v1::Pad
Public Functions
-
PadBase(const Output<Node> &arg, const Output<Node> &pads_begin, const Output<Node> &pads_end, const Output<Node> &arg_pad_value, PadMode pad_mode)
Constructs a generic padding operation.
- Parameters:
arg – The output producing input tensor to be padded.
pads_begin – The output which specifies the number of padding elements added before position 0 on each axis of arg.
pads_end – The output which specifies the number of padding elements after the last element on each axis.
arg_pad_value – The scalar output with the value used for padding if pad_mode is CONSTANT
pad_mode – The padding mode
-
PadBase(const Output<Node> &arg, const Output<Node> &pads_begin, const Output<Node> &pads_end, PadMode pad_mode)
Constructs a generic padding operation.
- Parameters:
arg – The output producing input tensor to be padded.
pads_begin – The output which specifies the number of padding elements added
pads_end – The output which specifies the number of padding elements after the last element on each axis.
pad_mode – The padding mode
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
CoordinateDiff get_pads_begin() const
return The node which specifies the number of padding elements added at the beginning of each axis
-
CoordinateDiff get_pads_end() const
return The node which specifies the number of padding elements added at the end of each axis
-
inline PadMode get_pad_mode() const
- Returns:
The padding mode.
-
PadBase(const Output<Node> &arg, const Output<Node> &pads_begin, const Output<Node> &pads_end, const Output<Node> &arg_pad_value, PadMode pad_mode)
-
class ReadValueBase : public ov::op::Op, public ov::op::util::VariableExtension
- #include <read_value_base.hpp>
Subclassed by ov::op::v3::ReadValue, ov::op::v6::ReadValue
Public Functions
-
inline explicit ReadValueBase(const OutputVector &arguments)
Constructs an AssignBase operation.
-
inline explicit ReadValueBase(const OutputVector &arguments)
-
class ReductionBase : public ov::op::Op
- #include <reduction_base.hpp>
Subclassed by ov::op::util::ArithmeticReduction, ov::op::util::LogicalReduction
Public Functions
-
bool reduction_axes_constant() const
- Returns:
true if reduction axes are constant else false.
-
const AxisSet get_reduction_axes() const
- Throws:
CheckFailure – if the reduction axes are not constant. (Use reduction_axes_constant to check.)
- Returns:
The axis positions (0-based) to be eliminated through reduction.
-
void set_reduction_axes(const AxisSet &reduction_axes)
Change the reduction axes.
-
bool reduction_axes_constant() const
-
class RNNCellBase : public ov::op::Op
- #include <rnn_cell_base.hpp>
Base class for all recurrent network cells.
Note
It holds all common attributes.
Subclassed by ov::op::internal::AUGRUCell, ov::op::internal::AUGRUSequence, ov::op::v0::LSTMCell, ov::op::v0::LSTMSequence, ov::op::v0::RNNCell, ov::op::v3::GRUCell, ov::op::v4::LSTMCell, ov::op::v5::GRUSequence, ov::op::v5::LSTMSequence, ov::op::v5::RNNSequence
Public Functions
-
RNNCellBase(const OutputVector &args, std::size_t hidden_size, float clip, const std::vector<std::string> &activations, const std::vector<float> &activations_alpha, const std::vector<float> &activations_beta)
Constructs a RNNCellBase class.
- Parameters:
hidden_size – [in] The number of hidden units for recurrent cell.
clip – [in] The value defining clipping range [-clip, clip] on input of activation functions.
activations – [in] The vector of activation functions used inside recurrent cell.
activations_alpha – [in] The vector of alpha parameters for activation functions in order respective to activation list.
activations_beta – [in] The vector of beta parameters for activation functions in order respective to activation list.
-
void validate_input_rank_dimension(const std::vector<PartialShape> &input)
Validates static rank and dimension for provided input parameters. Additionally input_size dimension is checked for X and W inputs.
- Parameters:
input – [in] Vector with RNN-Cell op inputs in following order: X, initial_hidden_state, W, R and B.
-
RNNCellBase(const OutputVector &args, std::size_t hidden_size, float clip, const std::vector<std::string> &activations, const std::vector<float> &activations_alpha, const std::vector<float> &activations_beta)
-
class ROIAlignBase : public ov::op::Op
- #include <roi_align_base.hpp>
Base class for ROIAlignXXX operators.
Subclassed by ov::op::v14::ROIAlignRotated, ov::op::v3::ROIAlign, ov::op::v9::ROIAlign
Public Functions
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
virtual void validate_and_infer_types() override
-
class ScatterBase : public ov::op::Op
- #include <scatter_base.hpp>
Base class for ScatterXXX operators.
Subclassed by ov::op::v3::ScatterUpdate
Public Functions
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
virtual void validate_and_infer_types() override
-
class ScatterElementsUpdateBase : public ov::op::Op
- #include <scatter_elements_update_base.hpp>
Subclassed by ov::op::v12::ScatterElementsUpdate, ov::op::v3::ScatterElementsUpdate
Public Functions
-
ScatterElementsUpdateBase(const Output<Node> &data, const Output<Node> &indices, const Output<Node> &updates, const Output<Node> &axis)
The common base class for all ScatterElementsUpdate operator versions.
- Parameters:
data – Input data
indices – Data entry index that will be updated
updates – Update values
axis – Axis to scatter on
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
ScatterElementsUpdateBase(const Output<Node> &data, const Output<Node> &indices, const Output<Node> &updates, const Output<Node> &axis)
-
class ScatterNDBase : public ov::op::Op
- #include <scatter_nd_base.hpp>
Base class for ScatterNDXXX operators.
Subclassed by ov::op::v15::ScatterNDUpdate, ov::op::v3::ScatterNDUpdate
Public Functions
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
virtual void validate_and_infer_types() override
-
class ShapeOfBase : public ov::op::Op
- #include <shape_of_base.hpp>
Subclassed by ov::op::v0::ShapeOf, ov::op::v3::ShapeOf
Public Functions
-
inline explicit ShapeOfBase(const OutputVector &arguments)
Constructs an ShapeOfBase operation.
-
inline explicit ShapeOfBase(const OutputVector &arguments)
-
class SubGraphOp : public ov::op::util::MultiSubGraphOp
- #include <sub_graph_base.hpp>
Abstract base class for sub-graph based ops, i.e ops that have only one sub-graph.
Subclassed by ov::op::v0::TensorIterator, ov::op::v5::Loop
Public Functions
-
inline const std::vector<std::shared_ptr<InputDescription>> &get_input_descriptions() const
- Returns:
a reference to the input descriptions.
-
inline std::vector<std::shared_ptr<InputDescription>> &get_input_descriptions()
- Returns:
a reference to the input descriptions. Can add input descriptions before validation.
-
inline const std::vector<std::shared_ptr<OutputDescription>> &get_output_descriptions() const
- Returns:
a reference to the output descriptions.
-
inline std::vector<std::shared_ptr<OutputDescription>> &get_output_descriptions()
- Returns:
a reference to the output descriptions. Can add output descriptions before validation.
Indicate that a body parameter comes from slices of a value.
- Parameters:
parameter – The parameter to receive the slices
value – The value to be sliced. This will be added as an input to SubGraphOp.
start – First index on axis of the slicing
stride – Stepping of the slice
part_size – Size of the slice on axis
end – The last index on axis of the slicing
axis – The axis to slice along
Indicates that a body parameter has an initial value in the first iteration and computed value thereafter.
- Parameters:
body_parameter – [in] The body parameter
initial_value – Value for the parameter in first iteration. This will be added as an input to Loop.
successive_value – Value for the parameter in successive iterations. The value is what is active in the most recent completed iteration.
Indicates that a body parameter has an invariant value during iteration that may depend on values computed outside of the iteration.
- Parameters:
body_parameter – The body parameter
value – The value supplied as an input to the block
-
virtual Output<Node> get_iter_value(const Output<Node> &body_value, int64_t iteration = -1)
Gets a value for a particular iteration point.
- Parameters:
body_value – The value
iteration – The iteration that supplies the value. Negative values are from the last iteration. Default value -1 (the last iteration).
- Returns:
The iterator value.
-
virtual Output<Node> get_concatenated_slices(const Output<Node> &value, int64_t start, int64_t stride, int64_t part_size, int64_t end, int64_t axis)
Concatenates slices from all iterations.
- Parameters:
value – The value supplying slice values from each iteration.
start – First index on axis of the slicing
stride – Stepping of the slice
part_size – Size of the slice on axis
end – The last index on axis of the slicing
axis – The axis to slice along
- Returns:
The concatenated slices.
-
inline const std::vector<std::shared_ptr<InputDescription>> &get_input_descriptions() const
-
class TopKBase : public ov::op::Op
- #include <topk_base.hpp>
Subclassed by ov::op::v11::TopK, ov::op::v1::TopK, ov::op::v3::TopK
Public Functions
-
TopKBase(const Output<Node> &data, const Output<Node> &k, const int64_t axis, const std::string &mode, const std::string &sort, const element::Type &index_element_type = element::i32)
The common base class for all TopK operator versions.
- Parameters:
data – The input tensor
k – Specifies how many maximum/minimum elements should be computed
axis – The axis along which TopK should be computed
mode – Specifies whether the maximum or minimum elements are selected
sort – Specifies the order of output elements and/or indices Accepted values: none, index, value
index_element_type – Specifies the type of produced indices
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
uint64_t get_axis() const
Returns axis value after normalization.
Note
If input rank required to normalization is dynamic, the exception is thrown
-
inline int64_t get_provided_axis() const
Returns axis value before normalization.
-
size_t get_k() const
Returns the value of K, if available.
Note
If the second input to this op is a constant, the value is retrieved and returned. If the input is not constant(dynamic) this method returns 0
-
inline virtual size_t get_default_output_index() const override
Returns the output of the default output, or throws if there is none.
-
TopKBase(const Output<Node> &data, const Output<Node> &k, const int64_t axis, const std::string &mode, const std::string &sort, const element::Type &index_element_type = element::i32)
-
class UnaryElementwiseArithmetic : public ov::op::Op
- #include <unary_elementwise_arithmetic.hpp>
Abstract base class for elementwise unary arithmetic operations, i.e., operations where the same scalar arithmetic operation is applied to each element.
For example, if the underlying operation (determined by the subclass) is \(\mathit{op}(x)\), the input tensor \([[x,y],[z,w]]\) will be mapped to \([[\mathit{op}(x),\mathit{op}(y)],[\mathit{op}(z),\mathit{op}(w)]]\).
Inputs
Type
Description
arg\(N[d_1,\dots,d_n]~(n \geq 0)\)
A tensor of any shape. The element type \(N\) may be any numeric type.
Type
Description
\(N[d_1,\dots,d_n]\)
The tensor \(T\), where \(T[i_1,\dots,i_n] = \mathit{op}(\texttt{arg}[i_1,\dots,i_n])\). This will always have the same shape and element type as the input tensor.
Subclassed by ov::op::v0::Abs, ov::op::v0::Acos, ov::op::v0::Asin, ov::op::v0::Atan, ov::op::v0::Ceiling, ov::op::v0::Clamp, ov::op::v0::Cos, ov::op::v0::Cosh, ov::op::v0::Elu, ov::op::v0::Erf, ov::op::v0::Exp, ov::op::v0::Floor, ov::op::v0::GRN, ov::op::v0::Gelu, ov::op::v0::Log, ov::op::v0::Negative, ov::op::v0::Relu, ov::op::v0::Sigmoid, ov::op::v0::Sign, ov::op::v0::Sin, ov::op::v0::Sinh, ov::op::v0::Sqrt, ov::op::v0::Tan, ov::op::v0::Tanh, ov::op::v3::Acosh, ov::op::v3::Asinh, ov::op::v3::Atanh, ov::op::v4::HSwish, ov::op::v4::Mish, ov::op::v4::SoftPlus, ov::op::v5::HSigmoid, ov::op::v5::Round, ov::op::v7::Gelu, ov::op::v9::SoftSign
Public Functions
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
virtual void validate_and_infer_types() override
-
class Variable
- #include <variable.hpp>
-
class VariableContext
- #include <variable_context.hpp>
VariableContext stores and manages a evaluation context for Variables.
Public Functions
-
VariableContext() = default
Constructs an uninitialized VariableContext.
-
inline explicit VariableContext(const VariableMap &variable_values)
Constructor for VariableContext.
- Parameters:
variable_values – The values associated with a particular Variables.
-
inline void reset_variable_context() const
Sets the reset flags for all stored Variables to true.
-
inline void set_variable_values(const VariableMap &variable_values)
Sets the new values for Variables.
- Parameters:
variable_values – The new values associated with a particular Variable.
-
inline void set_variable_value(const Variable::Ptr &variable, const VariableValue::Ptr &variable_value)
Changes/sets the values for Variable.
- Parameters:
variable – New or stored Variable.
variable_value – The values associated with the variable.
-
inline void remove_variable_value(const Variable::Ptr &variable)
Removes context for a particular Variable.
- Parameters:
variable – The variable for which the context will be cleared.
-
inline const VariableMap &get_variable_values() const
Returns the current values for Variables.
-
inline VariableValue::Ptr get_variable_value(const Variable::Ptr &variable) const
Returns the value for specified Variable.
-
VariableContext() = default
-
class VariableExtension
- #include <variable_extension.hpp>
Subclassed by ov::op::util::AssignBase, ov::op::util::ReadValueBase
Public Functions
-
inline virtual std::shared_ptr<Variable> get_variable() const
Returns variable connected to this node.
Sets a new variable to be connected to this node.
- Parameters:
variable – New variable to be connected to this node.
-
inline virtual void set_variable_id(const std::string &variable_id)
Sets the identifier to a variable.
- Parameters:
variable_id – New identifier of the variable.
-
virtual std::string get_variable_id() const = 0
Returns the identifier of corresponding variable.
-
inline virtual std::shared_ptr<Variable> get_variable() const
-
struct VariableInfo#
- #include <variable.hpp>
-
class VariableValue
- #include <variable_value.hpp>
VariableValue stores data and state (reset flag) for a Variable, and provides an interface for changing them.
Public Functions
-
VariableValue()
Constructs an uninitialized VariableValue.
-
void set_reset(bool reset)
Sets the reset flag to a new state.
- Parameters:
reset – The new state of the reset flag.
-
bool get_reset() const
Returns the current reset flag state.
-
VariableValue(const ov::Tensor &value, bool reset)
Constructor for VariableValue.
- Parameters:
value – Data for Variable.
reset – The current state of the reset flag.
-
VariableValue()
-
namespace detail#
Functions
-
namespace embedding#
Functions
-
template<class TShape, class TRShape = result_shape_t<TShape>>
TRShape out_shape_infer(const ov::Node *op, const TShape &emb_table_shape, const TShape &dim_shape_src)# Return a copy of the
emb_table_shapewith the first dimension replaced by the first dimension from thedim_shape_src- Template Parameters:
TShape – Shape type
- Parameters:
op – Pointer to operator.
emb_table_shape – The shape to be copied
dim_shape_src – The shape to copy the first dimension from, with dynamic or static rank > 1
- Returns:
The copy of the
emb_table_shapewith the first dimsnsion overwritten bydim_shape_src[0]if the rank is static, otherwise fully dynamic shape with dynamic rank.
-
template<class TShape, class TRShape = result_shape_t<TShape>>
-
namespace error#
-
namespace fft_common_validation#
-
Functions
-
template<class T>
void validate_input_rank(const ov::op::util::FFTBase *op, const std::vector<T> &input_shapes, const T &input_shape, const T &axes_shape, int64_t input_rank, FFTKind fft_kind)#
-
template<class T>
void validate_axes(const ov::op::util::FFTBase *op, const std::vector<T> &input_shapes, const T &axes_shape, std::vector<int64_t> &axes, int64_t input_rank, FFTKind fft_kind)#
-
template<class T>
-
using ActivationFunctionType = std::shared_ptr<Node> (*)(const std::shared_ptr<Node>&, float, float)#
-
namespace v0#
Functions
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u1 > (const bool &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u1 > (const char &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u1 > (const signed char &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u1 > (const unsigned char &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u1 > (const short &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u1 > (const unsigned short &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u1 > (const int &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u1 > (const unsigned int &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u1 > (const long &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u1 > (const unsigned long &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u1 > (const long long &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u1 > (const unsigned long long &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u1 > (const float8_e4m3 &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u1 > (const float8_e5m2 &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u1 > (const float16 &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u1 > (const bfloat16 &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u1 > (const float &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u1 > (const double &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u2 > (const bool &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u2 > (const char &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u2 > (const signed char &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u2 > (const unsigned char &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u2 > (const short &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u2 > (const unsigned short &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u2 > (const int &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u2 > (const unsigned int &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u2 > (const long &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u2 > (const unsigned long &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u2 > (const long long &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u2 > (const unsigned long long &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u2 > (const float8_e4m3 &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u2 > (const float8_e5m2 &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u2 > (const float16 &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u2 > (const bfloat16 &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u2 > (const float &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u2 > (const double &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u3 > (const bool &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u3 > (const char &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u3 > (const signed char &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u3 > (const unsigned char &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u3 > (const short &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u3 > (const unsigned short &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u3 > (const int &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u3 > (const unsigned int &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u3 > (const long &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u3 > (const unsigned long &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u3 > (const long long &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u3 > (const unsigned long long &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u3 > (const float8_e4m3 &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u3 > (const float8_e5m2 &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u3 > (const float16 &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u3 > (const bfloat16 &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u3 > (const float &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u3 > (const double &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u4 > (const bool &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u4 > (const char &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u4 > (const signed char &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u4 > (const unsigned char &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u4 > (const short &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u4 > (const unsigned short &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u4 > (const int &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u4 > (const unsigned int &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u4 > (const long &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u4 > (const unsigned long &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u4 > (const long long &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u4 > (const unsigned long long &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u4 > (const float8_e4m3 &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u4 > (const float8_e5m2 &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u4 > (const float16 &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u4 > (const bfloat16 &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u4 > (const float &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u4 > (const double &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u6 > (const bool &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u6 > (const char &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u6 > (const signed char &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u6 > (const unsigned char &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u6 > (const short &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u6 > (const unsigned short &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u6 > (const int &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u6 > (const unsigned int &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u6 > (const long &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u6 > (const unsigned long &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u6 > (const long long &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u6 > (const unsigned long long &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u6 > (const float8_e4m3 &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u6 > (const float8_e5m2 &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u6 > (const float16 &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u6 > (const bfloat16 &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u6 > (const float &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::u6 > (const double &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::i4 > (const bool &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::i4 > (const char &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::i4 > (const signed char &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::i4 > (const unsigned char &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::i4 > (const short &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::i4 > (const unsigned short &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::i4 > (const int &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::i4 > (const unsigned int &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::i4 > (const long &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::i4 > (const unsigned long &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::i4 > (const long long &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::i4 > (const unsigned long long &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::i4 > (const float8_e4m3 &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::i4 > (const float8_e5m2 &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::i4 > (const float16 &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::i4 > (const bfloat16 &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::i4 > (const float &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::i4 > (const double &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::nf4 > (const bool &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::nf4 > (const char &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::nf4 > (const signed char &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::nf4 > (const unsigned char &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::nf4 > (const short &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::nf4 > (const unsigned short &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::nf4 > (const int &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::nf4 > (const unsigned int &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::nf4 > (const long &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::nf4 > (const unsigned long &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::nf4 > (const long long &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::nf4 > (const unsigned long long &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::nf4 > (const float8_e4m3 &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::nf4 > (const float8_e5m2 &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::nf4 > (const float16 &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::nf4 > (const bfloat16 &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::nf4 > (const float &value)
- template<> OPENVINO_API void Constant::fill_lp_data< element::Type_t::nf4 > (const double &value)
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::u1 > (std::vector< bool > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::u1 > (std::vector< char > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::u1 > (std::vector< signed char > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::u1 > (std::vector< unsigned char > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::u1 > (std::vector< short > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::u1 > (std::vector< unsigned short > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::u1 > (std::vector< int > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::u1 > (std::vector< unsigned int > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::u1 > (std::vector< long > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::u1 > (std::vector< unsigned long > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::u1 > (std::vector< long long > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::u1 > (std::vector< unsigned long long > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::u1 > (std::vector< float16 > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::u1 > (std::vector< bfloat16 > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::u1 > (std::vector< float > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::u1 > (std::vector< double > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::u2 > (std::vector< bool > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::u2 > (std::vector< char > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::u2 > (std::vector< signed char > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::u2 > (std::vector< unsigned char > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::u2 > (std::vector< short > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::u2 > (std::vector< unsigned short > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::u2 > (std::vector< int > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::u2 > (std::vector< unsigned int > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::u2 > (std::vector< long > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::u2 > (std::vector< unsigned long > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::u2 > (std::vector< long long > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::u2 > (std::vector< unsigned long long > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::u2 > (std::vector< float16 > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::u2 > (std::vector< bfloat16 > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::u2 > (std::vector< float > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::u2 > (std::vector< double > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::u3 > (std::vector< bool > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::u3 > (std::vector< char > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::u3 > (std::vector< signed char > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::u3 > (std::vector< unsigned char > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::u3 > (std::vector< short > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::u3 > (std::vector< unsigned short > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::u3 > (std::vector< int > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::u3 > (std::vector< unsigned int > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::u3 > (std::vector< long > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::u3 > (std::vector< unsigned long > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::u3 > (std::vector< long long > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::u3 > (std::vector< unsigned long long > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::u3 > (std::vector< float16 > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::u3 > (std::vector< bfloat16 > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::u3 > (std::vector< float > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::u3 > (std::vector< double > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::u4 > (std::vector< bool > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::u4 > (std::vector< char > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::u4 > (std::vector< signed char > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::u4 > (std::vector< unsigned char > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::u4 > (std::vector< short > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::u4 > (std::vector< unsigned short > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::u4 > (std::vector< int > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::u4 > (std::vector< unsigned int > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::u4 > (std::vector< long > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::u4 > (std::vector< unsigned long > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::u4 > (std::vector< long long > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::u4 > (std::vector< unsigned long long > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::u4 > (std::vector< float16 > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::u4 > (std::vector< bfloat16 > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::u4 > (std::vector< float > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::u4 > (std::vector< double > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::u6 > (std::vector< bool > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::u6 > (std::vector< char > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::u6 > (std::vector< signed char > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::u6 > (std::vector< unsigned char > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::u6 > (std::vector< short > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::u6 > (std::vector< unsigned short > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::u6 > (std::vector< int > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::u6 > (std::vector< unsigned int > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::u6 > (std::vector< long > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::u6 > (std::vector< unsigned long > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::u6 > (std::vector< long long > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::u6 > (std::vector< unsigned long long > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::u6 > (std::vector< float16 > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::u6 > (std::vector< bfloat16 > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::u6 > (std::vector< float > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::u6 > (std::vector< double > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::i4 > (std::vector< bool > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::i4 > (std::vector< char > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::i4 > (std::vector< signed char > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::i4 > (std::vector< unsigned char > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::i4 > (std::vector< short > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::i4 > (std::vector< unsigned short > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::i4 > (std::vector< int > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::i4 > (std::vector< unsigned int > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::i4 > (std::vector< long > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::i4 > (std::vector< unsigned long > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::i4 > (std::vector< long long > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::i4 > (std::vector< unsigned long long > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::i4 > (std::vector< float16 > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::i4 > (std::vector< bfloat16 > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::i4 > (std::vector< float > &output_vector, size_t num_elements) const
- template<> OPENVINO_API void Constant::cast_lp_vector< element::Type_t::i4 > (std::vector< double > &output_vector, size_t num_elements) const
-
template<class TShape, class TRShape = result_shape_t<TShape>>
std::vector<TRShape> shape_infer(const BatchNormInference *op, const std::vector<TShape> &inputs_shapes)#
-
template<class T, class TRShape = result_shape_t<T>>
std::vector<TRShape> shape_infer(const Concat *op, const std::vector<T> &input_shapes)#
-
template<class TShape, class TRShape = result_shape_t<TShape>>
std::vector<TRShape> shape_infer(const CTCGreedyDecoder *op, const std::vector<TShape> &input_shapes)#
-
template<class TShape, class TRShape = result_shape_t<TShape>>
std::vector<TRShape> shape_infer(const DepthToSpace *op, const std::vector<TShape> &input_shapes)#
-
template<class TShape, class TRShape = result_shape_t<TShape>>
std::vector<TRShape> shape_infer(const DetectionOutput *op, const std::vector<TShape> &input_shapes)#
-
template<class T, class TRShape = result_shape_t<T>>
std::vector<TRShape> shape_infer(const FakeQuantize *op, const std::vector<T> &input_shapes)#
-
template<class TShape, class TRShape = result_shape_t<TShape>>
std::vector<TRShape> shape_infer(const Interpolate *op, const std::vector<TShape> &input_shapes, const ITensorAccessor &tensor_accessor)#
-
template<class T>
std::vector<result_shape_t<T>> shape_infer(const LSTMCell *op, const std::vector<T> &input_shapes)#
-
template<class TShape>
std::vector<result_shape_t<TShape>> shape_infer(const LSTMSequence *op, const std::vector<TShape> &input_shapes)#
-
template<class T, class TRShape = result_shape_t<T>>
std::vector<TRShape> shape_infer(const MatMul *op, const std::vector<T> &input_shapes)#
-
template<class TShape, class TRShape = result_shape_t<TShape>>
std::vector<TRShape> shape_infer(const PriorBoxClustered *const op, const std::vector<TShape> &input_shapes, const ITensorAccessor &ta = make_tensor_accessor())#
-
template<class TShape, class TRShape = result_shape_t<TShape>>
std::vector<TRShape> shape_infer(const PriorBox *const op, const std::vector<TShape> &input_shapes, const ITensorAccessor &ta = make_tensor_accessor())#
-
template<class TShape, class TRShape = result_shape_t<TShape>>
std::vector<TRShape> shape_infer(const Proposal *op, const std::vector<TShape> &input_shapes)#
-
template<class TShape, class TRShape = result_shape_t<TShape>>
std::vector<TRShape> shape_infer(const PSROIPooling *op, const std::vector<TShape> &input_shapes)#
-
template<class T, class TRShape = result_shape_t<T>>
std::vector<TRShape> shape_infer(const Range *op, const std::vector<T> &input_shapes, const ITensorAccessor &tensor_accessor = make_tensor_accessor())#
-
template<class TShape, class TRShape = result_shape_t<TShape>>
std::vector<TRShape> shape_infer(const RegionYolo *op, const std::vector<TShape> &input_shapes)#
-
template<class T, class TRShape = result_shape_t<T>>
std::vector<TRShape> shape_infer(const ReorgYolo *op, const std::vector<T> &input_shapes)#
-
template<class TShape, class TRShape = result_shape_t<TShape>>
std::vector<TRShape> shape_infer(const ReverseSequence *op, const std::vector<TShape> &input_shapes)#
-
template<class TShape>
std::vector<result_shape_t<TShape>> shape_infer(const RNNCell *op, const std::vector<TShape> &input_shapes)#
-
template<class TShape, class TRShape = result_shape_t<TShape>>
std::vector<TRShape> shape_infer(const ROIPooling *op, const std::vector<TShape> &input_shapes)#
-
template<class TShape, class TRShape = result_shape_t<TShape>>
std::vector<TRShape> shape_infer(const ShapeOf *op, const std::vector<TShape> &input_shapes)#
-
template<class TShape, class TRShape = result_shape_t<TShape>>
std::vector<TRShape> shape_infer(const ShuffleChannels *op, const std::vector<TShape> &input_shapes)#
-
template<class TShape, class TRShape = result_shape_t<TShape>>
std::vector<TRShape> shape_infer(const ov::op::v0::SpaceToDepth *op, const std::vector<TShape> &input_shapes)#
-
template<class T, class TRShape = result_shape_t<T>>
std::vector<TRShape> shape_infer(const Squeeze *op, const std::vector<T> &input_shapes, const ITensorAccessor &ta = make_tensor_accessor())# Do Squeeze shape inference.
-
template<class TShape, class TRShape = result_shape_t<TShape>>
std::vector<TRShape> shape_infer(const Tile *op, const std::vector<TShape> &input_shapes, const ITensorAccessor &tensor_accessor = make_tensor_accessor())#
-
template<class TShape, class TRShape = result_shape_t<TShape>>
std::vector<TRShape> shape_infer(const Unsqueeze *op, const std::vector<TShape> &input_shapes, const ITensorAccessor &tensor_accessor = make_tensor_accessor())#
-
class Abs : public ov::op::util::UnaryElementwiseArithmetic
- #include <abs.hpp>
Elementwise absolute value operation.
Public Functions
-
Abs() = default
Constructs an absolute value operation.
-
Abs(const Output<Node> &arg)
Constructs an absolute value operation.
Output
[d1, ...]- Parameters:
arg – Output that produces the input tensor.
[d1, ...]
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
Abs() = default
-
class Acos : public ov::op::util::UnaryElementwiseArithmetic
- #include <acos.hpp>
Elementwise inverse cosine (arccos) operation.
Public Functions
-
Acos() = default
Constructs an arccos operation.
-
Acos(const Output<Node> &arg)
Constructs an arccos operation.
Output
[d1, ...]- Parameters:
arg – Output that produces the input tensor.
[d1, ...]
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
Acos() = default
-
class Asin : public ov::op::util::UnaryElementwiseArithmetic
- #include <asin.hpp>
Elementwise inverse sine (arcsin) operation.
Public Functions
-
Asin() = default
Constructs an arcsin operation.
-
Asin(const Output<Node> &arg)
Constructs an arcsin operation.
Output
[d1, ...]- Parameters:
arg – Output that produces the input tensor.
[d1, ...]
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
Asin() = default
-
class Atan : public ov::op::util::UnaryElementwiseArithmetic
- #include <atan.hpp>
Elementwise inverse tangent (arctan) operation.
Public Functions
-
Atan() = default
Constructs an arctan operation.
-
Atan(const Output<Node> &arg)
Constructs an arctan operation.
Output
[d1, ...]- Parameters:
arg – Output that produces the input tensor.
[d1, ...]
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
Atan() = default
-
class BatchNormInference : public ov::op::Op
- #include <batch_norm.hpp>
BatchNormInference operation.
Public Functions
-
BatchNormInference(const Output<Node> &input, const Output<Node> &gamma, const Output<Node> &beta, const Output<Node> &mean, const Output<Node> &variance, double epsilon)
- Parameters:
input – [., C, …]
gamma – gamma scaling for normalized value. [C]
beta – bias added to the scaled normalized value [C]
mean – value for mean normalization [C]
variance – value for variance normalization [C]
epsilon – Avoids divsion by 0 if input has 0 variance
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
BatchNormInference(const Output<Node> &input, const Output<Node> &gamma, const Output<Node> &beta, const Output<Node> &mean, const Output<Node> &variance, double epsilon)
-
class Ceiling : public ov::op::util::UnaryElementwiseArithmetic
- #include <ceiling.hpp>
Elementwise ceiling operation.
Public Functions
-
Ceiling() = default
Constructs a ceiling operation.
-
Ceiling(const Output<Node> &arg)
Constructs a ceiling operation.
- Parameters:
arg – Node that produces the input tensor.
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
Ceiling() = default
-
class Clamp : public ov::op::util::UnaryElementwiseArithmetic
- #include <clamp.hpp>
Performs a clipping operation on all elements of the input node.
All input values that are outside of the <min;max> range are set to ‘min’ or ‘max’ depending on which side of the <min;max> range they are. The values that fall into this range remain unchanged.
Public Functions
-
Clamp(const Output<Node> &data, const double min, const double max)
Constructs a Clamp node.
- Parameters:
data – - Node producing the input tensor
min – - the lower bound of the <min;max> range
max – - the upper bound of the <min;max> range
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
Clamp(const Output<Node> &data, const double min, const double max)
-
class Concat : public ov::op::Op
- #include <concat.hpp>
Concatenation operation.
Public Functions
-
Concat() = default
Constructs a concatenation operation.
-
Concat(const OutputVector &args, int64_t axis)
Constructs a concatenation operation.
- Parameters:
args – The outputs producing the input tensors.
axis – The axis along which to concatenate the input tensors.
-
Concat(const NodeVector &args, int64_t axis)
Constructs a concatenation operation.
- Parameters:
args – The nodes producing the input tensors.
axis – The axis along which to concatenate the input tensors.
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
inline int64_t get_concatenation_axis() const
- Returns:
The concatenation axis.
-
inline int64_t get_axis() const
- Returns:
The concatenation axis.
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
Concat() = default
-
class Constant : public ov::op::Op
- #include <constant.hpp>
Class for constants.
Public Functions
-
Constant(const ov::Tensor &tensor)
Initialize a constant from ov::Tensor.
- Parameters:
tensor – The ov::Tensor with data
-
template<typename T>
inline Constant(const element::Type &type, const Shape &shape, const std::vector<T> &values) Constructs a tensor constant.
- Parameters:
type – The element type of the tensor constant.
shape – The shape of the tensor constant.
values – A vector of literals for initializing the tensor constant. The size of values must match the size of the shape.
-
template<class T, class = typename std::enable_if<std::is_fundamental<T>::value>::type>
inline Constant(const element::Type &type, const Shape &shape, T value) Constructs a uniform tensor constant.
- Parameters:
type – The element type of the tensor constant.
shape – The shape of the tensor constant.
value – A scalar for initializing the uniform tensor constant. The value is broadcast to the specified shape.
-
Constant(const element::Type &type, const Shape &shape, const std::vector<std::string> &values)
Constructs a tensor constant This constructor is mainly to support deserialization of constants.
- Parameters:
type – The element type of the tensor constant.
shape – The shape of the tensor constant.
values – A list of string values to use as the constant data.
-
Constant(const element::Type &type, const Shape &shape, const void *data)
Constructs a tensor constant with the supplied data.
- Parameters:
type – The element type of the tensor constant.
shape – The shape of the tensor constant.
data – A void* to constant data.
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
virtual bool evaluate(ov::TensorVector &outputs, const ov::TensorVector &inputs) const override
Evaluates the op on input_values putting results in output_values.
- Parameters:
output_values – Tensors for the outputs to compute. One for each result
input_values – Tensors for the inputs. One for each inputs.
- Returns:
true if successful
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
Shape get_shape_val() const
Returns the value of the constant node as a Shape object Can only be used on element::i64 nodes and interprets negative values as zeros.
-
Strides get_strides_val() const
Returns the value of the constant node as a Strides object Can only be used on element::i64 nodes and interprets negative values as zeros.
-
Coordinate get_coordinate_val() const
Returns the value of the constant node as a Coordinate object Can only be used on element::i64 nodes and interprets negative values as zeros.
-
CoordinateDiff get_coordinate_diff_val() const
Returns the value of the constant node as a CoordinateDiff object Can only be used on element::i64 nodes.
-
AxisVector get_axis_vector_val() const
Returns the value of the constant node as an AxisVector object Can only be used on element::i64 nodes and interprets negative values as zeros.
-
AxisSet get_axis_set_val() const
Returns the value of the constant node as an AxisSet object Can only be used on element::i64 nodes and interprets negative values as zeros. Repeated values are allowed.
-
size_t get_byte_size() const
Return data size in bytes.
-
std::vector<std::string> get_value_strings() const
- Returns:
The initialization literals for the tensor constant.
-
template<typename T>
inline std::vector<T> cast_vector(int64_t num_elements = -1) const Return the Constant’s value as a vector cast to type T.
- Template Parameters:
T – Type to which data vector’s entries will be cast.
- Parameters:
num_elements – (Optional) Number of elements to cast. In default case returns all elements
- Returns:
Constant’s data vector.
Public Static Functions
Wrapper around constructing a shared_ptr of a Constant.
- Parameters:
type – The element type of the tensor constant.
shape – The shape of the tensor constant.
values – A vector of values to use as the constant data.
Wrapper around constructing a shared_ptr of a Constant.
- Parameters:
type – The element type of the tensor constant.
shape – The shape of the tensor constant.
values – An initializer_list of values to use as the constant data.
-
static inline std::shared_ptr<Constant> create(const element::Type &type, const Shape &shape, const void *memory)
Wrapper around constructing a shared_ptr of a Constant.
- Parameters:
type – The element type of the tensor constant.
shape – The shape of the tensor constant.
memory – An continues memory chunk which contains the constant data.
-
Constant(const ov::Tensor &tensor)
-
class Convert : public ov::op::Op
- #include <convert.hpp>
Elementwise type conversion operation.
Public Functions
-
Convert() = default
Constructs a conversion operation.
-
Convert(const Output<Node> &arg, const ov::element::Type &destination_type)
Constructs a conversion operation.
- Parameters:
arg – Node that produces the input tensor.
destination_type – Element type for the output tensor.
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
Convert() = default
-
class Cos : public ov::op::util::UnaryElementwiseArithmetic
- #include <cos.hpp>
Elementwise cosine operation.
Public Functions
-
Cos() = default
Constructs a cosine operation.
-
Cos(const Output<Node> &arg)
Constructs a cosine operation.
- Parameters:
arg – Node that produces the input tensor.
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
Cos() = default
-
class Cosh : public ov::op::util::UnaryElementwiseArithmetic
- #include <cosh.hpp>
Elementwise hyperbolic cosine (cosh) operation.
Public Functions
-
Cosh() = default
Constructs a hyperbolic cosine operation.
-
Cosh(const Output<Node> &arg)
Constructs a hyperbolic cosine operation.
- Parameters:
arg – Node that produces the input tensor.
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
Cosh() = default
-
class CTCGreedyDecoder : public ov::op::Op
- #include <ctc_greedy_decoder.hpp>
CTCGreedyDecoder operation.
Public Functions
-
CTCGreedyDecoder(const Output<Node> &input, const Output<Node> &seq_len, const bool ctc_merge_repeated)
Constructs a CTCGreedyDecoder operation.
- Parameters:
input – Logits on which greedy decoding is performed
seq_len – Sequence lengths
ctc_merge_repeated – Whether to merge repeated labels
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
CTCGreedyDecoder(const Output<Node> &input, const Output<Node> &seq_len, const bool ctc_merge_repeated)
-
class CumSum : public ov::op::Op
- #include <cum_sum.hpp>
Tensor cumulative sum operation.
Compute the cumulative sum of the input tensor along the axis specified.
Public Functions
-
CumSum() = default
Constructs a cumulative summation operation.
-
CumSum(const Output<Node> &arg, const Output<Node> &axis, const bool exclusive = false, const bool reverse = false)
Constructs a cumulative summation operation.
- Parameters:
arg – The tensor to be summed.
axis – zero dimension tensor specifying axis position along which cumulative sum must be performed
exclusive – if set to true, the top element is not included
reverse – if set to true, will perform the sums in reverse direction
-
CumSum(const Output<Node> &arg, const bool exclusive = false, const bool reverse = false)
Constructs a cumulative summation operation with axis = 0.
- Parameters:
arg – The tensor to be summed
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
CumSum() = default
-
class DepthToSpace : public ov::op::Op
- #include <depth_to_space.hpp>
DepthToSpace permutes data from the depth dimension of the input blob into spatial dimensions.
Output node produces a tensor with shape: [N, C/(blocksize * blocksize), H * blocksize, W * blocksize]
Note
Values from the depth dimension (assuming NCHW layout) are moved in spatial blocks to the height and width dimensions.
Public Functions
-
DepthToSpace(const Output<Node> &data, const DepthToSpaceMode &mode, std::size_t block_size = 1)
Constructs a DepthToSpace operation.
- Parameters:
data – Node producing the input tensor
mode – Specifies how the input depth dimension is split to block coordinates
block_size – The size of the block of values to be moved
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
DepthToSpace(const Output<Node> &data, const DepthToSpaceMode &mode, std::size_t block_size = 1)
-
class DetectionOutput : public ov::op::util::DetectionOutputBase
- #include <detection_output.hpp>
Layer which performs non-max suppression to generate detection output using location and confidence predictions.
Public Functions
-
DetectionOutput(const Output<Node> &box_logits, const Output<Node> &class_preds, const Output<Node> &proposals, const Output<Node> &aux_class_preds, const Output<Node> &aux_box_preds, const Attributes &attrs)
Constructs a DetectionOutput operation.
- Parameters:
box_logits – Box logits
class_preds – Class predictions
proposals – Proposals
aux_class_preds – Auxilary class predictions
aux_box_preds – Auxilary box predictions
attrs – Detection Output attributes
-
DetectionOutput(const Output<Node> &box_logits, const Output<Node> &class_preds, const Output<Node> &proposals, const Attributes &attrs)
Constructs a DetectionOutput operation.
- Parameters:
box_logits – Box logits
class_preds – Class predictions
proposals – Proposals
attrs – Detection Output attributes
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
struct Attributes : public ov::op::util::DetectionOutputBase::AttributesBase
- #include <detection_output.hpp>
-
DetectionOutput(const Output<Node> &box_logits, const Output<Node> &class_preds, const Output<Node> &proposals, const Output<Node> &aux_class_preds, const Output<Node> &aux_box_preds, const Attributes &attrs)
-
class Elu : public ov::op::util::UnaryElementwiseArithmetic
- #include <elu.hpp>
Exponential Linear Unit x < 0 => f(x) = alpha * (exp(x) - 1.) x >= 0 => f(x) = x.
Public Functions
-
Elu(const Output<Node> &data, const double alpha)
Constructs an Elu operation.
- Parameters:
data – Input tensor
alpha – Multiplier for negative values
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
Elu(const Output<Node> &data, const double alpha)
-
class Erf : public ov::op::util::UnaryElementwiseArithmetic
- #include <erf.hpp>
Elementwise erf operation.
Public Functions
-
Erf() = default
Constructs a floor operation.
-
Erf(const Output<Node> &arg)
Constructs a floor operation.
- Parameters:
arg – Node that produces the input tensor.
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
Erf() = default
-
class Exp : public ov::op::util::UnaryElementwiseArithmetic
- #include <exp.hpp>
Elementwise natural exponential (exp) operation.
Public Functions
-
Exp() = default
Constructs an exponential operation.
-
Exp(const Output<Node> &arg)
Constructs an exponential operation.
- Parameters:
arg – Node that produces the input tensor.
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
Exp() = default
-
class FakeQuantize : public ov::op::Op
- #include <fake_quantize.hpp>
Class performing element-wise linear quantization.
Note
Input floating point values are quantized into a discrete set of floating point values.
Public Functions
-
FakeQuantize(const Output<Node> &data, const Output<Node> &input_low, const Output<Node> &input_high, const Output<Node> &output_low, const Output<Node> &output_high, std::size_t levels, const AutoBroadcastSpec &auto_broadcast = AutoBroadcastSpec(AutoBroadcastType::NUMPY))
Constructs a FakeQuantize operation node.
- Parameters:
data – [in] The input data tensor.
input_low – [in] The minimum limit for input values.
input_high – [in] The maximum limit for input values.
output_low – [in] The minimum quantized value.
output_high – [in] The maximum quantized value.
levels – [in] The number of quantization levels.
auto_broadcast – [in] AutoBroadcast mode to be used for broadcasting limit values
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
FakeQuantize(const Output<Node> &data, const Output<Node> &input_low, const Output<Node> &input_high, const Output<Node> &output_low, const Output<Node> &output_high, std::size_t levels, const AutoBroadcastSpec &auto_broadcast = AutoBroadcastSpec(AutoBroadcastType::NUMPY))
-
class Floor : public ov::op::util::UnaryElementwiseArithmetic
- #include <floor.hpp>
Elementwise floor operation.
Public Functions
-
Floor() = default
Constructs a floor operation.
-
Floor(const Output<Node> &arg)
Constructs a floor operation.
- Parameters:
arg – Node that produces the input tensor.
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
Floor() = default
-
class Gelu : public ov::op::util::UnaryElementwiseArithmetic
- #include <gelu.hpp>
Gaussian Error Linear Unit f(x) = 0.5 * x * (1 + erf( x / sqrt(2) )
Public Functions
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
virtual void validate_and_infer_types() override
-
class GRN : public ov::op::util::UnaryElementwiseArithmetic
- #include <grn.hpp>
Global Response Normalization with L2 norm (across channels only).
Public Functions
-
GRN(const Output<Node> &data, float bias)
Constructs a GRN operation.
- Parameters:
data – - Node producing the input tensor
bias – - The bias added to the variance.
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
GRN(const Output<Node> &data, float bias)
-
class HardSigmoid : public ov::op::Op
- #include <hard_sigmoid.hpp>
Parameterized, bounded sigmoid-like, piecewise linear function. min(max(alpha*x + beta, 0), 1)
Public Functions
-
HardSigmoid(const Output<Node> &data, const Output<Node> &alpha, const Output<Node> &beta)
Constructs a HardSigmoid operation.
- Parameters:
data – Input tensor.
alpha – [in] A scalar value representing the alpha parameter.
beta – [in] A scalar value representing the beta parameter.
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
HardSigmoid(const Output<Node> &data, const Output<Node> &alpha, const Output<Node> &beta)
-
class Interpolate : public ov::op::Op
- #include <interpolate.hpp>
Layer which performs bilinear interpolation.
Public Functions
-
Interpolate(const Output<Node> &image, const Output<Node> &output_shape, const Attributes &attrs)
Constructs a Interpolate operation.
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virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
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struct Attributes
- #include <interpolate.hpp>
Structure that specifies attributes for interpolation.
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Interpolate(const Output<Node> &image, const Output<Node> &output_shape, const Attributes &attrs)
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class Log : public ov::op::util::UnaryElementwiseArithmetic
- #include <log.hpp>
Elementwise natural log operation.
Public Functions
-
Log() = default
Constructs a natural log operation.
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Log(const Output<Node> &arg)
Constructs a natural log operation.
- Parameters:
arg – Node that produces the input tensor.
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virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
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Log() = default
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class LRN : public ov::op::Op
- #include <lrn.hpp>
Elementwise Local Response Normalization (LRN) operation.
Inputs
Type
Description
arg\(N[n, c, d_1,\dots,d_n]~(n \geq 0)\)
A tensor of any shape and numeric element type.
Type
Description
\(N[n, c, d_1,\dots,d_n]\)
The tensor \(T\), where \(T[n, c, d_1,\dots,d_n] = \frac{N[n,i,d_1,\dots,d_n]}{ (bias + alpha * (\sum_{i=max(0,(nsize-1)/2)}^{min(C, (nsize-1)/2)+1} N[n,i,d_1,\dots,d_n]^{2}) ^ {2})}\)
Public Functions
-
LRN() = default
Constructs a LRN operation.
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LRN(const Output<Node> &arg, double alpha, double beta, double bias, size_t size)
Constructs a LRN operation.
- Parameters:
arg – Node that produces the input tensor.
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virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
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LRN() = default
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class LSTMCell : public ov::op::util::RNNCellBase
- #include <lstm_cell.hpp>
Class for single lstm cell node.
See also
LSTMSequence, RNNCell, GRUCell
Note
Following implementation supports:
peepholesGers & Schmidhuber (2000) https://ieeexplore.ieee.org/document/861302Coupling input and forget gates.
Note
It calculates following equations:
it = f(Xt*(Wi^T) + Ht-1*(Ri^T) + Pi (.) Ct-1 + Wbi + Rbi) ft = f(Xt*(Wf^T) + Ht-1*(Rf^T) + Pf (.) Ct-1 + Wbf + Rbf) ct = g(Xt*(Wc^T) + Ht-1*(Rc^T) + Wbc + Rbc) Ct = ft (.) Ct-1 + it (.) ct ot = f(Xt*(Wo^T) + Ht-1*(Ro^T) + Po (.) Ct + Wbo + Rbo) Ht = ot (.) h(Ct) * - Is a dot product, (.) - is a Hadamard product (element-wise), f, g, h - are activation functions.
Note
This class represents only single cell (for current time step) and not the whole LSTM Sequence layer
Public Functions
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LSTMCell(const Output<Node> &X, const Output<Node> &initial_hidden_state, const Output<Node> &initial_cell_state, const Output<Node> &W, const Output<Node> &R, std::size_t hidden_size, LSTMWeightsFormat weights_format = LSTMWeightsFormat::IFCO, const std::vector<std::string> &activations = std::vector<std::string>{"sigmoid", "tanh", "tanh"}, const std::vector<float> &activations_alpha = {}, const std::vector<float> &activations_beta = {}, float clip = 0.f, bool input_forget = false)
Constructs LSTMCell node.
- Parameters:
X – [in] The input tensor with shape: [batch_size, input_size].
initial_hidden_state – [in] The hidden state tensor at current time step with shape: [batch_size, hidden_size].
initial_cell_state – [in] The cell state tensor at current time step with shape: [batch_size, hidden_size].
W – [in] The gate weights tensor with shape: [4*hidden_size, input_size].
R – [in] The recurrence weights tensor with shape: [4*hidden_size, hidden_size].
hidden_size – [in] The number of hidden units for recurrent cell.
weights_format – [in] The order of gates in weights tensors. The default format is IFCO since it is used by DNNL.
activations – [in] The vector of activation functions used inside recurrent cell.
activations_alpha – [in] The vector of alpha parameters for activation functions in order respective to activation list.
activations_beta – [in] The vector of beta parameters for activation functions in order respective to activation list.
clip – [in] The value defining clipping range [-clip, clip] on input of activation functions.
input_forget – [in] Controls coupling input and forget gates.
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LSTMCell(const Output<Node> &X, const Output<Node> &initial_hidden_state, const Output<Node> &initial_cell_state, const Output<Node> &W, const Output<Node> &R, const Output<Node> &B, std::size_t hidden_size, LSTMWeightsFormat weights_format = LSTMWeightsFormat::IFCO, const std::vector<std::string> &activations = std::vector<std::string>{"sigmoid", "tanh", "tanh"}, const std::vector<float> &activations_alpha = {}, const std::vector<float> &activations_beta = {}, float clip = 0.f, bool input_forget = false)
Constructs LSTMCell node.
- Parameters:
X – [in] The input tensor with shape: [batch_size, input_size].
initial_hidden_state – [in] The hidden state tensor at current time step with shape: [batch_size, hidden_size].
initial_cell_state – [in] The cell state tensor at current time step with shape: [batch_size, hidden_size].
W – [in] The weight tensor with shape: [4*hidden_size, input_size].
R – [in] The recurrence weight tensor with shape: [4*hidden_size, hidden_size].
B – [in] The bias tensor for gates with shape: [4*hidden_size].
hidden_size – [in] The number of hidden units for recurrent cell.
weights_format – [in] The order of gates in weights tensors. The default format is IFCO since it is used by DNNL.
activations – [in] The vector of activation functions used inside recurrent cell.
activations_alpha – [in] The vector of alpha parameters for activation functions in order respective to activation list.
activations_beta – [in] The vector of beta parameters for activation functions in order respective to activation list.
clip – [in] The value defining clipping range [-clip, clip] on input of activation functions.
input_forget – [in] Controls coupling input and forget gates.
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LSTMCell(const Output<Node> &X, const Output<Node> &initial_hidden_state, const Output<Node> &initial_cell_state, const Output<Node> &W, const Output<Node> &R, const Output<Node> &B, const Output<Node> &P, std::size_t hidden_size, LSTMWeightsFormat weights_format = LSTMWeightsFormat::IFCO, const std::vector<std::string> &activations = std::vector<std::string>{"sigmoid", "tanh", "tanh"}, const std::vector<float> &activations_alpha = {}, const std::vector<float> &activations_beta = {}, float clip = 0.f, bool input_forget = false)
Constructs LSTMCell node.
- Parameters:
X – [in] The input tensor with shape: [batch_size, input_size].
initial_hidden_state – [in] The hidden state tensor at current time step with shape: [batch_size, hidden_size].
initial_cell_state – [in] The cell state tensor at current time step with shape: [batch_size, hidden_size].
W – [in] The weight tensor with shape: [4*hidden_size, input_size].
R – [in] The recurrence weight tensor with shape: [4*hidden_size, hidden_size].
B – [in] The bias tensor for gates with shape: [4*hidden_size].
P – [in] The weight tensor for peepholes with shape: [3*hidden_size] - 3 equals to only iof gates. The order is: input, output, forget gates.
hidden_size – [in] The number of hidden units for recurrent cell.
weights_format – [in] The order of gates in weights tensors. The default format is IFCO since it is used by DNNL.
activations – [in] The vector of activation functions used inside recurrent cell.
activations_alpha – [in] The vector of alpha parameters for activation functions in order respective to activation list.
activations_beta – [in] The vector of beta parameters for activation functions in order respective to activation list.
clip – [in] The value defining clipping range [-clip, clip] on input of activation functions.
input_forget – [in] Controls coupling input and forget gates.
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virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
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class LSTMSequence : public ov::op::util::RNNCellBase
- #include <lstm_sequence.hpp>
Class for lstm sequence node.
Note
It follows notation and equations defined as in ONNX standard: onnx/onnx
Public Functions
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inline virtual size_t get_default_output_index() const override
Returns the output of the default output, or throws if there is none.
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virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
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inline virtual size_t get_default_output_index() const override
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class MatMul : public ov::op::Op
- #include <matmul.hpp>
Operator performing Matrix Multiplication.
Public Functions
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MatMul(const Output<Node> &A, const Output<Node> &B, const bool &transpose_a = false, const bool &transpose_b = false)
Constructs an Matrix Multiplication operation.
- Parameters:
A – Matrix A
B – Matrix B
transpose_a – If matrix A should be transposed.
transpose_b – If matrix B should be transposed.
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virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
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virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
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MatMul(const Output<Node> &A, const Output<Node> &B, const bool &transpose_a = false, const bool &transpose_b = false)
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class MVN : public ov::op::Op
- #include <mvn.hpp>
Operator performing Mean Variance Normalization.
Public Functions
-
MVN(const Output<Node> &data, bool across_channels = true, bool normalize_variance = true, double eps = 1e-9)
Constructs an MVN operation.
- Parameters:
data – Input tensor with data
normalize_variance – flag that denotes whether to perform variance normalization.
across_channels – flag that denotes if mean values are shared across channels.
eps – the number to be added to the variance to avoid division by zero when normalizing the value
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MVN(const Output<Node> &data, AxisSet reduction_axes, bool normalize_variance = true, double eps = 1e-9)
Constructs an MVN operation.
- Parameters:
data – Input tensor with data
reduction_axes – A list of axes, along which to reduce.
normalize_variance – flag that denotes whether to perform variance normalization.
eps – the number to be added to the variance to avoid division by zero when normalizing the value
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virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
MVN(const Output<Node> &data, bool across_channels = true, bool normalize_variance = true, double eps = 1e-9)
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class Negative : public ov::op::util::UnaryElementwiseArithmetic
- #include <negative.hpp>
Elementwise negative operation.
Public Functions
-
Negative() = default
Constructs a negative operation.
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Negative(const Output<Node> &arg)
Constructs a negative operation.
- Parameters:
arg – Node that produces the input tensor.
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virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
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Negative() = default
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template<class TShape>
struct NegativeToZero# - #include <tile_shape_inference.hpp>
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class NormalizeL2 : public ov::op::Op
- #include <normalize_l2.hpp>
Normalization with L2 norm.
Public Functions
-
NormalizeL2(const Output<Node> &data, const Output<Node> &axes, float eps, EpsMode eps_mode)
Constructs a NormalizeL2 operation.
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
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NormalizeL2(const Output<Node> &data, const Output<Node> &axes, float eps, EpsMode eps_mode)
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class Parameter : public ov::op::Op
- #include <parameter.hpp>
A model parameter.
Parameters are nodes that represent the arguments that will be passed to user-defined models. Model creation requires a sequence of parameters. Basic graph operations do not need parameters attached to a model.
Public Functions
-
Parameter() = default
Constructions a tensor-typed parameter node.
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Parameter(const ov::element::Type &element_type, const PartialShape &pshape)
Constructions a tensor-typed parameter node.
- Parameters:
element_type – The element type of the parameter.
pshape – The partial shape of the parameter.
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virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
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Parameter() = default
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class PRelu : public ov::op::Op
- #include <prelu.hpp>
Parametrized Relu x < 0 => f(x) = x * slope x >= 0 => f(x) = x.
Public Functions
-
PRelu(const Output<Node> &data, const Output<Node> &slope)
Constructs a PRelu operation.
- Parameters:
data – Input tensor
slope – Multipliers for negative values
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
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virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
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PRelu(const Output<Node> &data, const Output<Node> &slope)
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class PriorBox : public ov::op::Op
- #include <prior_box.hpp>
Layer which generates prior boxes of specified sizes normalized to input image size.
Public Functions
-
PriorBox(const Output<Node> &layer_shape, const Output<Node> &image_shape, const Attributes &attrs)
Constructs a PriorBox operation.
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
struct Attributes
- #include <prior_box.hpp>
-
PriorBox(const Output<Node> &layer_shape, const Output<Node> &image_shape, const Attributes &attrs)
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class PriorBoxClustered : public ov::op::Op
- #include <prior_box_clustered.hpp>
Layer which generates prior boxes of specified sizes normalized to input image size.
Public Functions
-
PriorBoxClustered(const Output<Node> &layer_shape, const Output<Node> &image_shape, const Attributes &attrs)
Constructs a PriorBoxClustered operation.
- Parameters:
layer_shape – Shape of layer for which prior boxes are computed
image_shape – Shape of image to which prior boxes are scaled
attrs – PriorBoxClustered attributes
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virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
struct Attributes
- #include <prior_box_clustered.hpp>
-
PriorBoxClustered(const Output<Node> &layer_shape, const Output<Node> &image_shape, const Attributes &attrs)
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class Proposal : public ov::op::Op
- #include <proposal.hpp>
Proposal operation.
Subclassed by ov::op::v4::Proposal
Public Functions
-
Proposal(const Output<Node> &class_probs, const Output<Node> &bbox_deltas, const Output<Node> &image_shape, const Attributes &attrs)
Constructs a Proposal operation.
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
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void set_attrs(Attributes attrs)
Set the Proposal operator attributes.
- Parameters:
attrs – Attributes to be set.
-
struct Attributes
- #include <proposal.hpp>
-
Proposal(const Output<Node> &class_probs, const Output<Node> &bbox_deltas, const Output<Node> &image_shape, const Attributes &attrs)
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class PSROIPooling : public ov::op::Op
- #include <psroi_pooling.hpp>
PSROIPooling operation.
Public Functions
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PSROIPooling(const Output<Node> &input, const Output<Node> &coords, const size_t output_dim, const size_t group_size, const float spatial_scale, int spatial_bins_x, int spatial_bins_y, const std::string &mode)
Constructs a PSROIPooling operation.
- Parameters:
input – Input feature map {N, C, …}
coords – Coordinates of bounding boxes
output_dim – Output channel number
group_size – Number of groups to encode position-sensitive scores
spatial_scale – Ratio of input feature map over input image size
spatial_bins_x – Numbers of bins to divide the input feature maps over width
spatial_bins_y – Numbers of bins to divide the input feature maps over height
mode – Mode of pooling - Avg or Bilinear
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
void set_output_dim(size_t output_dim)
Set the output channel dimension size.
- Parameters:
output_dim – Channel dimension size.
-
void set_group_size(size_t group_size)
Set the output groups number.
- Parameters:
group_size – Number of groups.
-
void set_spatial_scale(float scale)
Set the spatial scale.
- Parameters:
scale – Spatial scale value.
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void set_spatial_bins_x(int x)
Set the number of bins over image width.
- Parameters:
x – Number of bins over width (x) axis.
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void set_spatial_bins_y(int y)
Set the number of bins over image height.
- Parameters:
y – Number of bins over height (y) axis.
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void set_mode(std::string mode)
Set the pooling mode.
- Parameters:
mode – Pooling mode name.
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PSROIPooling(const Output<Node> &input, const Output<Node> &coords, const size_t output_dim, const size_t group_size, const float spatial_scale, int spatial_bins_x, int spatial_bins_y, const std::string &mode)
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class Range : public ov::op::Op
- #include <range.hpp>
Range operation, analogous to
range()in Python.Public Functions
-
Range() = default
Constructs an unitialized range operation.
-
Range(const Output<Node> &start, const Output<Node> &stop, const Output<Node> &step)
Constructs a range operation.
- Parameters:
start – The tensor producing the start value. Must be a scalar of integer element type, and same element type as
stopandstep.stop – The tensor producing the stop value. Must be a scalar of integer element type, and same element type as
startandstep.step – The tensor producing the step value. Must be a scalar of integer element type, and same element type as
startandstop.
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virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
Range() = default
-
class RegionYolo : public ov::op::Op
- #include <region_yolo.hpp>
RegionYolo operation.
Public Functions
-
RegionYolo(const Output<Node> &input, const size_t coords, const size_t classes, const size_t regions, const bool do_softmax, const std::vector<int64_t> &mask, const int axis, const int end_axis, const std::vector<float> &anchors = std::vector<float>{})
Constructs a RegionYolo operation.
- Parameters:
input – [in] Input
coords – [in] Number of coordinates for each region
classes – [in] Number of classes for each region
regions – [in] Number of regions
do_softmax – [in] Compute softmax
mask – [in] Mask
axis – [in] Axis to begin softmax on
end_axis – [in] Axis to end softmax on
anchors – [in] A flattened list of pairs
[width, height]that describes prior box sizes.
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
RegionYolo(const Output<Node> &input, const size_t coords, const size_t classes, const size_t regions, const bool do_softmax, const std::vector<int64_t> &mask, const int axis, const int end_axis, const std::vector<float> &anchors = std::vector<float>{})
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class Relu : public ov::op::util::UnaryElementwiseArithmetic
- #include <relu.hpp>
Elementwise Relu operation.
-
class ReorgYolo : public ov::op::Op
- #include <reorg_yolo.hpp>
ReorgYolo operation.
Public Functions
-
ReorgYolo(const Output<Node> &input, const size_t stride)
Constructs a ReorgYolo operation.
- Parameters:
input – Input
stride – Stride to reorganize input by
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
ReorgYolo(const Output<Node> &input, const size_t stride)
-
class Result : public ov::op::Op
- #include <result.hpp>
Result operation.
Public Functions
-
Result() = default
Allows a value to be used as a function result.
-
Result(const Output<Node> &arg)
Allows a value to be used as a function result.
- Parameters:
arg – Node that produces the input tensor.
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
virtual bool evaluate(ov::TensorVector &outputs, const ov::TensorVector &inputs) const override
Evaluates the op on input_values putting results in output_values.
- Parameters:
output_values – Tensors for the outputs to compute. One for each result
input_values – Tensors for the inputs. One for each inputs.
- Returns:
true if successful
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
Result() = default
-
class ReverseSequence : public ov::op::Op
- #include <reverse_sequence.hpp>
ReverseSequence operation.
Public Functions
-
ReverseSequence(const Output<Node> &arg, const Output<Node> &seq_lengths, int64_t batch_axis = 0, int64_t seq_axis = 1)
Constructs a ReverseSequence operation.
- Parameters:
arg – tensor with input data to reverse
seq_lengths – 1D tensor of integers with sequence lengths in the input tensor.
batch_axis – index of the batch dimension.
seq_axis – index of the sequence dimension.
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
ReverseSequence(const Output<Node> &arg, const Output<Node> &seq_lengths, int64_t batch_axis = 0, int64_t seq_axis = 1)
-
class RNNCell : public ov::op::util::RNNCellBase
- #include <rnn_cell.hpp>
Class for single RNN cell node.
See also
LSTMSequence, LSTMCell, GRUCell
Note
It follows notation and equations defined as in ONNX standard: onnx/onnx
Note
It calculates following equations:
Ht = f(Xt*(Wi^T) + Ht-1*(Ri^T) + Wbi + Rbi) * - Is a dot product, f - is activation functions.
Note
This class represents only single cell (for current time step) and not the whole RNN Sequence layer
Public Functions
-
RNNCell(const Output<Node> &X, const Output<Node> &initial_hidden_state, const Output<Node> &W, const Output<Node> &R, std::size_t hidden_size, const std::vector<std::string> &activations = std::vector<std::string>{"tanh"}, const std::vector<float> &activations_alpha = {}, const std::vector<float> &activations_beta = {}, float clip = 0.f)
Constructs RNNCell node.
- Parameters:
X – [in] The input tensor with shape: [batch_size, input_size].
initial_hidden_state – [in] The hidden state tensor at current time step with shape: [batch_size, hidden_size].
W – [in] The weight tensor with shape: [hidden_size, input_size].
R – [in] The recurrence weight tensor with shape: [hidden_size, hidden_size].
hidden_size – [in] The number of hidden units for recurrent cell.
activations – [in] The vector of activation functions used inside recurrent cell.
activations_alpha – [in] The vector of alpha parameters for activation functions in order respective to activation list.
activations_beta – [in] The vector of beta parameters for activation functions in order respective to activation list.
clip – [in] The value defining clipping range [-clip, clip] on input of activation functions.
-
RNNCell(const Output<Node> &X, const Output<Node> &initial_hidden_state, const Output<Node> &W, const Output<Node> &R, const Output<Node> &B, std::size_t hidden_size, const std::vector<std::string> &activations = std::vector<std::string>{"tanh"}, const std::vector<float> &activations_alpha = {}, const std::vector<float> &activations_beta = {}, float clip = 0.f)
Constructs RNNCell node.
- Parameters:
X – [in] The input tensor with shape: [batch_size, input_size].
initial_hidden_state – [in] The hidden state tensor at current time step with shape: [batch_size, hidden_size].
W – [in] The weight tensor with shape: [hidden_size, input_size].
R – [in] The recurrence weight tensor with shape: [hidden_size, hidden_size].
B – [in] The bias tensor for input gate with shape: [hidden_size].
hidden_size – [in] The number of hidden units for recurrent cell.
activations – [in] The vector of activation functions used inside recurrent cell.
activations_alpha – [in] The vector of alpha parameters for activation functions in order respective to activation list.
activations_beta – [in] The vector of beta parameters for activation functions in order respective to activation list.
clip – [in] The value defining clipping range [-clip, clip] on input of activation functions.
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
RNNCell(const Output<Node> &X, const Output<Node> &initial_hidden_state, const Output<Node> &W, const Output<Node> &R, std::size_t hidden_size, const std::vector<std::string> &activations = std::vector<std::string>{"tanh"}, const std::vector<float> &activations_alpha = {}, const std::vector<float> &activations_beta = {}, float clip = 0.f)
-
class ROIPooling : public ov::op::Op
- #include <roi_pooling.hpp>
ROIPooling operation.
Public Functions
-
ROIPooling(const Output<Node> &input, const Output<Node> &coords, const Shape &output_size, const float spatial_scale, const std::string &method = "max")
Constructs a ROIPooling operation.
- Parameters:
input – Input feature map {N, C, H, W}
coords – Coordinates of bounding boxes
output_size – Height/Width of ROI output features
spatial_scale – Ratio of input feature map over input image size
method – Method of pooling - Max or Bilinear
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
void set_output_roi(Shape output_size)
Set the output ROI feature map (pooled_h, pooled_w).
- Parameters:
output_size – Shape with pooling attributes pooled_h and pooled_w sizes.
-
const Shape &get_output_roi() const
Get the output ROI feature map shape (H x W)
- Returns:
Shape with pooled_h and pooled_w attributes.
-
void set_spatial_scale(float scale)
Set the spatial scale value.
- Parameters:
scale – Scale value to set.
-
void set_method(std::string method_name)
Set the method of pooling.
- Parameters:
method_name – Pooling method name.
-
ROIPooling(const Output<Node> &input, const Output<Node> &coords, const Shape &output_size, const float spatial_scale, const std::string &method = "max")
-
class Selu : public ov::op::Op
- #include <selu.hpp>
Performs a SELU activation function on all elements of the input node.
Public Functions
-
Selu(const Output<Node> &data, const Output<Node> &alpha, const Output<Node> &lambda)
Constructs a Selu node.
- Parameters:
data – - Node producing the input tensor
alpha – - Alpha coefficient of SELU operation
lambda – - Lambda coefficient of SELU operation
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
Selu(const Output<Node> &data, const Output<Node> &alpha, const Output<Node> &lambda)
-
class ShapeOf : public ov::op::util::ShapeOfBase
- #include <shape_of.hpp>
Operation that returns the shape of its input argument as a tensor.
Public Functions
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
virtual void validate_and_infer_types() override
-
class ShuffleChannels : public ov::op::Op
- #include <shuffle_channels.hpp>
Permutes data in the channel dimension of the input.
Public Functions
-
ShuffleChannels(const Output<Node> &data, const int64_t axis = 1, const int64_t group = 1)
Constructs a ShuffleChannels node.
- Parameters:
data – Node producing the input tensor.
axis – Channel dimension index in the data tensor. A negative value means that the index should be calculated from the back of the input data shape.
group – Number of group the channel dimension should be split into.
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
ShuffleChannels(const Output<Node> &data, const int64_t axis = 1, const int64_t group = 1)
-
class Sigmoid : public ov::op::util::UnaryElementwiseArithmetic
- #include <sigmoid.hpp>
Sigmoid operation.
Public Functions
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
virtual bool has_evaluate() const override
-
class Sign : public ov::op::util::UnaryElementwiseArithmetic
- #include <sign.hpp>
Elementwise sign operation.
-
class Sin : public ov::op::util::UnaryElementwiseArithmetic
- #include <sin.hpp>
Elementwise sine operation.
Inputs
Type
Description
arg\(N[d_1,\dots,d_n]~(n \geq 0)\)
A tensor of any shape and numeric element type.
Type
Description
\(N[d_1,\dots,d_n]\)
The tensor \(T\), where \(T[i_1,\dots,i_n] = \sin(\texttt{arg}[i_1,\dots,i_n])\)
-
class Sinh : public ov::op::util::UnaryElementwiseArithmetic
- #include <sinh.hpp>
Elementwise hyperbolic sine (sinh) operation.
-
class SpaceToDepth : public ov::op::Op
- #include <space_to_depth.hpp>
SpaceToDepth permutes input tensor blocks of spatial data into depth dimension.
Note
Values from the height and width dimensions are moved to the depth dimension.
Output node produces a tensor with shape: [N, C * blocksize * blocksize, H / blocksize, W / blocksize]
Public Functions
-
SpaceToDepth(const Output<Node> &data, const SpaceToDepthMode &mode, std::size_t block_size = 1)
Constructs a SpaceToDepth operation.
- Parameters:
data – - Node producing the input tensor
mode – Specifies how the output depth dimension is gathered from block coordinates and the old depth dimension.
block_size – - the size of the block of values to be moved
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
SpaceToDepth(const Output<Node> &data, const SpaceToDepthMode &mode, std::size_t block_size = 1)
-
class Sqrt : public ov::op::util::UnaryElementwiseArithmetic
- #include <sqrt.hpp>
Elementwise square root operation.
Inputs
Type
Description
arg\(N[d_1,\dots,d_n]~(n \geq 0)\)
A tensor of any shape and numeric element type.
Type
Description
\(N[d_1,\dots,d_n]\)
The tensor \(T\), where \(T[i_1,\dots,i_n] = \sqrt{\texttt{arg}[i_1,\dots,i_n]}\)
-
class SquaredDifference : public ov::op::util::BinaryElementwiseArithmetic
- #include <squared_difference.hpp>
Calculates an element-wise squared difference between two tensors.
y[i] = (x1[i] - x2[i])^2
Public Functions
-
inline SquaredDifference()
Constrcuts an uninitialized squared difference operation.
-
SquaredDifference(const Output<Node> &x1, const Output<Node> &x2, const AutoBroadcastSpec &auto_broadcast = AutoBroadcastSpec(AutoBroadcastType::NUMPY))
Constructs the squared difference operation.
- Parameters:
x1 – First input tensor
x2 – Second input tensor
auto_broadcast – Auto broadcast specification
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
inline SquaredDifference()
-
class Squeeze : public ov::op::Op
- #include <squeeze.hpp>
Squeeze operation.
Public Functions
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
virtual void validate_and_infer_types() override
-
class Tan : public ov::op::util::UnaryElementwiseArithmetic
- #include <tan.hpp>
Elementwise tangent operation.
Inputs
Type
Description
arg\(N[d_1,\dots,d_n]~(n \geq 0)\)
A tensor of any shape and numeric element type.
Type
Description
\(N[d_1,\dots,d_n]\)
The tensor \(T\), where \(T[i_1,\dots,i_n] = \tan(\texttt{arg}[i_1,\dots,i_n])\)
-
class Tanh : public ov::op::util::UnaryElementwiseArithmetic
- #include <tanh.hpp>
Elementwise hyperbolic tangent operation.
-
class TensorIterator : public ov::op::util::SubGraphOp
- #include <tensor_iterator.hpp>
Iterate a body over tensors, accumulating into tensors.
Public Functions
-
inline std::shared_ptr<Model> get_body() const
- Returns:
the body of the iteration
- Parameters:
body – set the body of the iteration
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
inline std::shared_ptr<Model> get_body() const
-
class Tile : public ov::op::Op
- #include <tile.hpp>
Dynamic Tiling operation which repeats a tensor multiple times along each dimension.
Public Functions
-
Tile(const Output<Node> &data, const Output<Node> &repeats)
Perform dynamic padding of a tensor.
- Parameters:
data – The node producing input tensor to be padded.
repeats – The node producing the per-dimension replication factor
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
virtual bool evaluate(ov::TensorVector &outputs, const ov::TensorVector &inputs) const override
Evaluates the op on input_values putting results in output_values.
- Parameters:
output_values – Tensors for the outputs to compute. One for each result
input_values – Tensors for the inputs. One for each inputs.
- Returns:
true if successful
-
Tile(const Output<Node> &data, const Output<Node> &repeats)
-
class Unsqueeze : public ov::op::Op
- #include <unsqueeze.hpp>
Unsqueeze operation.
Public Functions
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
virtual bool evaluate(ov::TensorVector &outputs, const ov::TensorVector &inputs) const override
Evaluates the op on input_values putting results in output_values.
- Parameters:
output_values – Tensors for the outputs to compute. One for each result
input_values – Tensors for the inputs. One for each inputs.
- Returns:
true if successful
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
virtual void validate_and_infer_types() override
-
class Xor : public ov::op::util::BinaryElementwiseLogical
- #include <xor.hpp>
Elementwise logical-xor operation.
Public Functions
-
Xor(const Output<Node> &arg0, const Output<Node> &arg1, const AutoBroadcastSpec &auto_broadcast = AutoBroadcastSpec())
Constructs a logical-xor operation.
Output
[d0, ...]
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
Xor(const Output<Node> &arg0, const Output<Node> &arg1, const AutoBroadcastSpec &auto_broadcast = AutoBroadcastSpec())
-
namespace lstm_cell#
-
namespace v1#
Functions
-
template<class TShape, class TContainer, class TRShape = result_shape_t<TShape>>
std::vector<TRShape> shape_infer(const AvgPool *op, const std::vector<TShape> &input_shapes, TContainer &pads_begin, TContainer &pads_end)#
-
template<class TShape, class TRShape = result_shape_t<TShape>>
std::vector<TRShape> shape_infer(const BatchToSpace *op, const std::vector<TShape> &input_shapes, const ITensorAccessor &tensor_accessor = make_tensor_accessor())#
-
template<class T, class TRShape = result_shape_t<T>>
std::vector<TRShape> shape_infer(const ov::op::v1::Broadcast *op, const std::vector<T> &input_shapes, const ITensorAccessor &ta = make_tensor_accessor())#
-
template<class TShape, class TRShape = result_shape_t<TShape>>
std::vector<TRShape> shape_infer(const ConvolutionBackpropData *op, const std::vector<TShape> &input_shapes, CoordinateDiff &pads_begin, CoordinateDiff &pads_end, const ITensorAccessor &ta = make_tensor_accessor())#
-
template<class TOp, class TShape, class TRShape = result_shape_t<TShape>, typename std::enable_if<std::is_same<TOp, Convolution>::value || std::is_same<TOp, BinaryConvolution>::value>::type* = nullptr>
std::vector<TRShape> shape_infer(const TOp *op, const std::vector<TShape> &input_shapes, CoordinateDiff &pads_begin, CoordinateDiff &pads_end)#
-
template<class TShape, class TRShape = result_shape_t<TShape>>
std::vector<TRShape> shape_infer(const DeformableConvolution *op, const std::vector<TShape> &input_shapes, CoordinateDiff &pads_begin, CoordinateDiff &pads_end)#
-
template<class TShape, class TRShape = result_shape_t<TShape>>
std::vector<TRShape> shape_infer(const DeformablePSROIPooling *op, const std::vector<TShape> &input_shapes)#
-
template<class TShape, class TRShape = result_shape_t<TShape>>
std::vector<TRShape> shape_infer(const GatherTree *op, const std::vector<TShape> &input_shapes)#
-
template<class TShape, class TRShape = result_shape_t<TShape>>
std::vector<TRShape> shape_infer(const GroupConvolutionBackpropData *op, const std::vector<TShape> &input_shapes, CoordinateDiff &pads_begin, CoordinateDiff &pads_end, const ITensorAccessor &ta = make_tensor_accessor())#
-
template<class TShape, class TRShape = result_shape_t<TShape>>
std::vector<TRShape> shape_infer(const GroupConvolution *op, const std::vector<TShape> &input_shapes, CoordinateDiff &pads_begin, CoordinateDiff &pads_end)#
-
template<class TShape, class TContainer, class TRShape = result_shape_t<TShape>>
std::vector<TRShape> shape_infer(const MaxPool *op, const std::vector<TShape> &input_shapes, TContainer &pads_begin, TContainer &pads_end)#
-
template<class T, class TRShape = result_shape_t<T>>
std::vector<TRShape> shape_infer(const NonMaxSuppression *op, const std::vector<T> &input_shapes, const ITensorAccessor &ta = make_tensor_accessor())#
-
template<class T, class TRShape = result_shape_t<T>>
std::vector<TRShape> shape_infer(const OneHot *op, const std::vector<T> &input_shapes, const ITensorAccessor &ta = make_tensor_accessor())#
-
template<class T, class TRShape = result_shape_t<T>>
std::vector<TRShape> shape_infer(const Reshape *op, const std::vector<T> &input_shapes, const ITensorAccessor &ta = make_tensor_accessor())#
-
template<class TShape, class TRShape = result_shape_t<TShape>>
std::vector<TRShape> shape_infer(const Reverse *op, const std::vector<TShape> &input_shapes, const ITensorAccessor &tensor_accessor = make_tensor_accessor())# Reverse shape inference.
-
template<class TShape, class TRShape = result_shape_t<TShape>>
std::vector<TRShape> shape_infer(const Select *op, const std::vector<TShape> &input_shapes)#
-
template<class TShape, class TRShape = result_shape_t<TShape>>
std::vector<TRShape> shape_infer(const SpaceToBatch *op, const std::vector<TShape> &input_shapes, const ITensorAccessor &ta = make_tensor_accessor())#
-
template<typename T, class TRShape = result_shape_t<T>>
std::vector<TRShape> shape_infer(const Split *op, const std::vector<T> &input_shapes, const ITensorAccessor &ta = make_tensor_accessor())# Shape inference for Split V1 operator.
Note
The split operation cause label lost on splitted dimension even if number of splits is one, because in this case split will be removed by transformation (as NOP) and in fact label will be propagated.
-
template<class T, class TRShape = result_shape_t<T>>
std::vector<TRShape> shape_infer(const StridedSlice *op, const std::vector<T> &input_shapes, const ITensorAccessor &ta = make_tensor_accessor())#
-
template<class T, class TRShape = result_shape_t<T>>
TRShape calc_output_shape(const Transpose *const op, const T &input_shape, std::vector<int64_t> &axes_order)# Calculate transpose output shape.
-
template<class TShape, class TRShape = result_shape_t<TShape>>
std::vector<TRShape> shape_infer(const Transpose *op, const std::vector<TShape> &input_shapes, const ITensorAccessor &tensor_accessor = make_tensor_accessor())# Do transpose shape inference on input and output shapes.
-
template<typename T, class TRShape = result_shape_t<T>>
std::vector<TRShape> shape_infer(const VariadicSplit *op, const std::vector<T> &input_shapes, const ITensorAccessor &ta = make_tensor_accessor())#
-
class Add : public ov::op::util::BinaryElementwiseArithmetic
- #include <add.hpp>
Elementwise addition operation.
Public Functions
-
inline Add()
Constructs an uninitialized addition operation.
-
Add(const Output<Node> &arg0, const Output<Node> &arg1, const AutoBroadcastSpec &auto_broadcast = AutoBroadcastSpec(AutoBroadcastType::NUMPY))
Constructs an addition operation.
Output
[d0, ...]
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
inline Add()
-
class AvgPool : public ov::op::util::AvgPoolBase
- #include <avg_pool.hpp>
Batched average pooling operation.
Public Functions
-
AvgPool() = default
Constructs a batched average pooling operation.
-
AvgPool(const Output<Node> &arg, const Strides &strides, const Shape &pads_begin, const Shape &pads_end, const Shape &kernel, bool exclude_pad, RoundingType rounding_type = RoundingType::FLOOR, const PadType &auto_pad = PadType::EXPLICIT)
Constructs a batched average pooling operation.
- Parameters:
arg – The output producing the input data batch tensor.
[d1, dn]strides – The strides.
[n]pads_begin – The beginning of padding shape.
[n]pads_end – The end of padding shape.
[n]kernel – The kernel shape.
[n]exclude_pad – If false then averages include padding elements, each treated as the number zero. If true, padding elements are entirely ignored when computing averages.
rounding_type – Whether to use ceiling or floor rounding type while computing output shape.
auto_pad – Padding type to use for additional padded dimensions
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
AvgPool() = default
-
class BatchToSpace : public ov::op::Op
- #include <batch_to_space.hpp>
BatchToSpace permutes data from the batch dimension of the data tensor into spatial dimensions.
Note
Values from the batch dimension are moved in spatial blocks dimensions.
Output node produces a tensor with shape: `[batch / (block_shape[0] * block_shape[1] * ... * block_shape[N - 1]), D_1 * block_shape[1] - crops_begin[1] - crops_end[1], D_2 * block_shape[2] - crops_begin[2] - crops_end[2], ..., D_{N - 1} * block_shape[N - 1] - crops_begin[N - 1] - crops_end[N - 1]` of the same type as `data` input.Public Functions
-
BatchToSpace(const Output<Node> &data, const Output<Node> &block_shape, const Output<Node> &crops_begin, const Output<Node> &crops_end)
Constructs a BatchToSpace operation.
- Parameters:
data – Node producing the data tensor
block_shape – The sizes of the block of values to be moved
crops_begin – Specifies the amount to crop from the beginning along each axis of
datainputcrops_end – Specifies the amount to crop from the ending along each axis of
datainput.
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
BatchToSpace(const Output<Node> &data, const Output<Node> &block_shape, const Output<Node> &crops_begin, const Output<Node> &crops_end)
-
class BinaryConvolution : public ov::op::util::ConvolutionFwdPropBase
- #include <binary_convolution.hpp>
BinaryConvolution operation.
Public Functions
-
BinaryConvolution() = default
Constructs a binary convolution operation.
-
BinaryConvolution(const Output<Node> &data, const Output<Node> &kernel, const Strides &strides, const CoordinateDiff &pads_begin, const CoordinateDiff &pads_end, const Strides &dilations, BinaryConvolutionMode mode, float pad_value, const PadType &auto_pad = PadType::EXPLICIT)
Constructs a binary convolution operation.
Output
[N, C_OUT, R1, ... Rf]- Parameters:
data – The node producing the input data batch tensor.
kernel – The node producing the filters tensor.
strides – The strides.
pads_begin – The beginning of padding shape.
pads_end – The end of padding shape.
dilations – The dilations.
mode – Defines how input tensor 0/1 values and weights 0/1 are interpreted.
pad_value – Floating-point value used to fill pad area.
auto_pad – The pad type for automatically computing padding sizes.
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
inline const BinaryConvolutionMode &get_mode() const
- Returns:
The mode of convolution.
-
inline float get_pad_value() const
- Returns:
The pad value.
-
BinaryConvolution() = default
-
class Broadcast : public ov::op::util::BroadcastBase
- #include <broadcast.hpp>
Operation which “adds” axes to an input tensor, replicating elements from the input as needed along the new axes.
Public Functions
-
Broadcast() = default
Constructs a broadcast operation.
-
Broadcast(const Output<Node> &arg, const Output<Node> &target_shape, const Output<Node> &axes_mapping, const AutoBroadcastSpec &broadcast_spec = AutoBroadcastSpec())
Constructs a broadcast operation.
- Parameters:
arg – The input tensor to be broadcast.
target_shape – The shape of the output tensor.
axes_mapping – The axis positions (0-based) in the result that correspond to input axes. ‘Arg’ tensor is broadcast along the remaining axes. E.g., Input Shape - [3, 4], Target Shape - [3, 5, 4, 4] axes_mapping - [0, 2] => Broadcast along axes 1 and 3. axes_mapping - [0, 3] => Broadcast along axes 1 and 2.
broadcast_spec – Broadcast specification to use for determining broadcast axes. ‘axes_mapping’ is ignored if broadcast_spec is not NONE
-
Broadcast(const Output<Node> &arg, const Output<Node> &target_shape, const AutoBroadcastSpec &broadcast_spec = AutoBroadcastSpec(AutoBroadcastType::NUMPY))
Constructs a broadcast operation.
- Parameters:
arg – The input tensor to be broadcast.
target_shape – The shape of the output tensor.
broadcast_spec – Broadcast specification to use for determining broadcast axes
-
inline const AutoBroadcastSpec &get_broadcast_spec() const
- Returns:
Broadcast Specification.
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
virtual bool evaluate(ov::TensorVector &outputs, const ov::TensorVector &inputs) const override
Evaluates the op on input_values putting results in output_values.
- Parameters:
output_values – Tensors for the outputs to compute. One for each result
input_values – Tensors for the inputs. One for each inputs.
- Returns:
true if successful
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
Broadcast() = default
-
class ConvertLike : public ov::op::Op
- #include <convert_like.hpp>
Elementwise type conversion operation.
Public Functions
-
ConvertLike() = default
Constructs a conversion operation.
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
ConvertLike() = default
-
class Convolution : public ov::op::util::ConvolutionFwdPropBase
- #include <convolution.hpp>
Batched convolution operation, with optional window dilation and stride.
Public Functions
-
Convolution() = default
Constructs a batched convolution operation.
-
Convolution(const Output<Node> &data_batch, const Output<Node> &filters, const Strides &strides, const CoordinateDiff &pads_begin, const CoordinateDiff &pads_end, const Strides &dilations, const PadType &auto_pad = PadType::EXPLICIT)
Constructs a batched convolution operation.
Output
[N, C_OUT, R1, ... Rf]- Parameters:
data_batch – The node producing the input data batch tensor.
[N, C_IN, D1, ... Df]filters – The node producing the filters tensor.
[C_OUT, C_IN, F1, ... Ff]strides – The strides.
[f]dilations – The dilations.
[f]pads_begin – The beginning of padding shape.
[f]pads_end – The end of padding shape.
[f]auto_pad – The pad type for automatically computing padding sizes.
[f]
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
Convolution() = default
-
class ConvolutionBackpropData : public ov::op::util::ConvolutionBackPropBase
- #include <convolution.hpp>
Data batch backprop for batched convolution operation.
Public Functions
-
ConvolutionBackpropData() = default
Constructs a batched-convolution data batch-backprop operation.
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
const PartialShape get_output_shape() const
- Returns:
The output spatial dimensions shape.
-
ConvolutionBackpropData() = default
-
class DeformableConvolution : public ov::op::util::DeformableConvolutionBase
- #include <deformable_convolution.hpp>
DeformableConvolution operation.
Public Functions
-
DeformableConvolution() = default
Constructs a conversion operation.
-
DeformableConvolution(const Output<Node> &arg, const Output<Node> &offsets, const Output<Node> &filters, const Strides &strides, const CoordinateDiff &pads_begin, const CoordinateDiff &pads_end, const Strides &dilations, const PadType &auto_pad = PadType::EXPLICIT, const int64_t group = 1, const int64_t deformable_group = 1)
Constructs a conversion operation.
- Parameters:
arg – Node that produces the input tensor.
offsets – Node producing the deformable values tensor.
filters – Node producing the filters(kernels) tensor with OIZYX layout.
strides – Convolution strides.
pads_begin – Amount of padding to be added to the beginning along each axis. For example in case of a 2D input the value of (1, 2) means that 1 element will be added to the top and 2 elements to the left.
pads_end – Amount of padding to be added to the end along each axis.
dilations – The distance in width and height between the weights in the filters tensor.
auto_pad – Specifies how the automatic calculation of padding should be done.
group – The number of groups which both output and input should be split into.
deformable_group – The number of groups which deformable values and output should be split into along the channel axis.
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
DeformableConvolution() = default
-
class DeformablePSROIPooling : public ov::op::Op
- #include <deformable_psroi_pooling.hpp>
DeformablePSROIPooling operation.
Public Functions
-
DeformablePSROIPooling(const Output<Node> &input, const Output<Node> &coords, const Output<Node> &offsets, const int64_t output_dim, const float spatial_scale, const int64_t group_size = 1, const std::string mode = "bilinear_deformable", int64_t spatial_bins_x = 1, int64_t spatial_bins_y = 1, float trans_std = 1, int64_t part_size = 1)
Constructs a DeformablePSROIPooling operation.
- Parameters:
input – Input tensor with position sensitive score maps
coords – Input tensor with list of five element tuples describing ROI coordinates
offsets – Input tensor with transformation values
output_dim – Pooled output channel number
group_size – Number of horizontal bins per row to divide ROI area, it defines output width and height
spatial_scale – Multiplicative spatial scale factor to translate ROI coordinates from their input scale to the scale used when pooling
mode – Specifies mode for pooling.
spatial_bins_x – Specifies numbers of bins to divide ROI single bin over width
spatial_bins_y – Specifies numbers of bins to divide ROI single bin over height
no_trans – The flag that specifies whenever third input exists and contains transformation (offset) values
trans_std – The value that all transformation (offset) values are multiplied with
part_size – The number of parts the output tensor spatial dimensions are divided into. Basically it is the height and width of the third input
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
DeformablePSROIPooling(const Output<Node> &input, const Output<Node> &coords, const Output<Node> &offsets, const int64_t output_dim, const float spatial_scale, const int64_t group_size = 1, const std::string mode = "bilinear_deformable", int64_t spatial_bins_x = 1, int64_t spatial_bins_y = 1, float trans_std = 1, int64_t part_size = 1)
-
class Divide : public ov::op::util::BinaryElementwiseArithmetic
- #include <divide.hpp>
Elementwise division operation.
Public Functions
-
inline Divide()
Constructs a division operation.
-
Divide(const Output<Node> &arg0, const Output<Node> &arg1, bool pythondiv, const AutoBroadcastSpec &auto_broadcast = AutoBroadcastSpec(AutoBroadcastType::NUMPY))
Constructs a division operation.
-
Divide(const Output<Node> &arg0, const Output<Node> &arg1, const AutoBroadcastSpec &auto_broadcast = AutoBroadcastSpec(AutoBroadcastType::NUMPY))
Constructs a division operation.
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
inline Divide()
-
class Equal : public ov::op::util::BinaryElementwiseComparison
- #include <equal.hpp>
Elementwise is-equal operation.
Inputs
Type
Description
arg0\(E[d_1,\dots,d_n]~(n \geq 0)\)
A tensor of any shape and element type.
arg1\(E[d_1,\dots,d_n]~(n \geq 0)\)
A tensor of the same shape and element type as
arg0.autobAuto broadcast specification.
Type
Description
\(\texttt{bool}[d_1,\dots,d_n]\)
The tensor \(T\), where \(T[i_1,\dots,i_n] = 1\text{ if }\texttt{arg0}[i_1,\dots,i_n] = \texttt{arg1}[i_1,\dots,i_n]\text{, else } 0\)
Public Functions
-
inline Equal()
Constructs an equal operation.
-
Equal(const Output<Node> &arg0, const Output<Node> &arg1, const AutoBroadcastSpec &auto_broadcast = AutoBroadcastSpec(AutoBroadcastType::NUMPY))
Constructs an equal operation.
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
inline Equal()
-
class FloorMod : public ov::op::util::BinaryElementwiseArithmetic
- #include <floor_mod.hpp>
Elementwise FloorMod operation.
Public Functions
-
inline FloorMod()
Constructs an uninitialized addition operation.
-
FloorMod(const Output<Node> &arg0, const Output<Node> &arg1, const AutoBroadcastSpec &auto_broadcast = AutoBroadcastType::NUMPY)
Constructs an Floor Mod operation.
Output
[d0, ...]
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
inline FloorMod()
-
class Gather : public ov::op::util::GatherBase
- #include <gather.hpp>
Gather slices from axis of data according to indices.
-
class GatherTree : public ov::op::Op
- #include <gather_tree.hpp>
Generates the complete beams from the ids per each step and the parent beam ids.
Public Functions
-
GatherTree(const Output<Node> &step_ids, const Output<Node> &parent_idx, const Output<Node> &max_seq_len, const Output<Node> &end_token)
- Parameters:
step_ids – Tensor of shape [MAX_TIME, BATCH_SIZE, BEAM_WIDTH] with indices from per each step
parent_idx – Tensor of shape [MAX_TIME, BATCH_SIZE, BEAM_WIDTH] with parent beam indices
max_seq_len – Tensor of shape [BATCH_SIZE] with maximum lengths for each sequence in the batch
end_token – Tensor of shape [MAX_TIME, BATCH_SIZE, BEAM_WIDTH]
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
GatherTree(const Output<Node> &step_ids, const Output<Node> &parent_idx, const Output<Node> &max_seq_len, const Output<Node> &end_token)
-
class Greater : public ov::op::util::BinaryElementwiseComparison
- #include <greater.hpp>
Elementwise greater-than operation.
Public Functions
-
inline Greater()
Constructs a greater-than operation.
-
Greater(const Output<Node> &arg0, const Output<Node> &arg1, const AutoBroadcastSpec &auto_broadcast = AutoBroadcastSpec(AutoBroadcastType::NUMPY))
Constructs a greater-than operation.
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
inline Greater()
-
class GreaterEqual : public ov::op::util::BinaryElementwiseComparison
- #include <greater_eq.hpp>
Elementwise greater-than-or-equal operation.
Public Functions
-
inline GreaterEqual()
Constructs a greater-than-or-equal operation.
-
GreaterEqual(const Output<Node> &arg0, const Output<Node> &arg1, const AutoBroadcastSpec &auto_broadcast = AutoBroadcastSpec(AutoBroadcastType::NUMPY))
Constructs a greater-than-or-equal operation.
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
inline GreaterEqual()
-
class GroupConvolution : public ov::op::util::ConvolutionFwdPropBase
- #include <group_conv.hpp>
Batched convolution operation, with optional window dilation and stride.
Public Functions
-
GroupConvolution() = default
Constructs a batched convolution operation.
-
GroupConvolution(const Output<Node> &data_batch, const Output<Node> &filters, const Strides &strides, const CoordinateDiff &pads_begin, const CoordinateDiff &pads_end, const Strides &dilations, const PadType &auto_pad = PadType::EXPLICIT)
Constructs a batched convolution operation.
Output
[N, FC_OUT * GROUPS, R1, ... Rf]- Parameters:
data_batch – The node producing the input data batch tensor.
[N, C_IN, D1, ... Df]filters – The node producing the filters tensor.
[GROUPS, FC_OUT, FC_IN, F1, ... Ff]strides – The strides.
[f]dilations – The dilations.
[f]pads_begin – The beginning of padding shape.
[f]pads_end – The end of padding shape.
[f]auto_pad – The pad type for automatically computing padding sizes.
[f]
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
GroupConvolution() = default
-
class GroupConvolutionBackpropData : public ov::op::util::ConvolutionBackPropBase
- #include <group_conv.hpp>
Data batch backprop for batched convolution operation.
Public Functions
-
GroupConvolutionBackpropData()
Constructs a batched-convolution data batch-backprop operation.
-
void infer_conv_backprop_output_spatial_shape(const std::vector<Dimension> &input_data_shape, const std::vector<Dimension> &filters_shape, const Strides &strides, const Strides &dilations, const CoordinateDiff &pads_begin, const CoordinateDiff &pads_end, const CoordinateDiff &output_padding, std::vector<Dimension> &output_spatial_shape)
Calculates output spatial features size.
- Parameters:
input_data_shape – [in] The input data partial shape
filters_shape – [in] The filters partial shape
strides – [in] The strides values.
dilations – [in] The dilations values.
pads_begin – [in] The paddings at the beginning of axis.
pads_end – [in] The paddings at the end of axis.
output_padding – [in] The output padding values.
output_spatial_shape – The placeholder for computed output spatial partial shape.
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
const PartialShape get_convolution_output_shape() const
- Returns:
The spatial shape of the output.
-
GroupConvolutionBackpropData()
-
class Less : public ov::op::util::BinaryElementwiseComparison
- #include <less.hpp>
Elementwise less-than operation.
Public Functions
-
inline Less()
Constructs a less-than operation.
-
Less(const Output<Node> &arg0, const Output<Node> &arg1, const AutoBroadcastSpec &auto_broadcast = AutoBroadcastSpec(AutoBroadcastType::NUMPY))
Constructs a less-than operation.
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
inline Less()
-
class LessEqual : public ov::op::util::BinaryElementwiseComparison
- #include <less_eq.hpp>
Elementwise less-than-or-equal operation.
Public Functions
-
inline LessEqual()
Constructs a less-than-or-equal operation.
-
LessEqual(const Output<Node> &arg0, const Output<Node> &arg1, const AutoBroadcastSpec &auto_broadcast = AutoBroadcastSpec(AutoBroadcastType::NUMPY))
Constructs a less-than-or-equal operation.
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
inline LessEqual()
-
class LogicalAnd : public ov::op::util::BinaryElementwiseLogical
- #include <logical_and.hpp>
Elementwise logical-and operation.
Public Functions
-
LogicalAnd() = default
Constructs a logical-and operation.
-
LogicalAnd(const Output<Node> &arg0, const Output<Node> &arg1, const AutoBroadcastSpec &auto_broadcast = AutoBroadcastSpec(AutoBroadcastType::NUMPY))
Constructs a logical-and operation.
Output
[d0, ...]
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
LogicalAnd() = default
-
class LogicalNot : public ov::op::Op
- #include <logical_not.hpp>
Elementwise logical negation operation.
Public Functions
-
LogicalNot() = default
Constructs a logical negation operation.
-
LogicalNot(const Output<Node> &arg)
Constructs a logical negation operation.
- Parameters:
arg – Node that produces the input tensor.
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
LogicalNot() = default
-
class LogicalOr : public ov::op::util::BinaryElementwiseLogical
- #include <logical_or.hpp>
Elementwise logical-or operation.
Public Functions
-
LogicalOr(const Output<Node> &arg0, const Output<Node> &arg1, const AutoBroadcastSpec &auto_broadcast = AutoBroadcastSpec(AutoBroadcastType::NUMPY))
Constructs a logical-or operation.
Output
[d0, ...]
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
LogicalOr(const Output<Node> &arg0, const Output<Node> &arg1, const AutoBroadcastSpec &auto_broadcast = AutoBroadcastSpec(AutoBroadcastType::NUMPY))
-
class LogicalXor : public ov::op::util::BinaryElementwiseLogical
- #include <logical_xor.hpp>
Elementwise logical-xor operation.
Public Functions
-
LogicalXor(const Output<Node> &arg0, const Output<Node> &arg1, const AutoBroadcastSpec &auto_broadcast = AutoBroadcastSpec(AutoBroadcastType::NUMPY))
Constructs a logical-xor operation.
Output
[d0, ...]
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
LogicalXor(const Output<Node> &arg0, const Output<Node> &arg1, const AutoBroadcastSpec &auto_broadcast = AutoBroadcastSpec(AutoBroadcastType::NUMPY))
-
class Maximum : public ov::op::util::BinaryElementwiseArithmetic
- #include <maximum.hpp>
Elementwise maximum operation.
Public Functions
-
inline Maximum()
Constructs a maximum operation.
-
Maximum(const Output<Node> &arg0, const Output<Node> &arg1, const AutoBroadcastSpec &auto_broadcast = AutoBroadcastSpec(AutoBroadcastType::NUMPY))
Constructs a maximum operation.
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
inline Maximum()
-
class MaxPool : public ov::op::util::MaxPoolBase
- #include <max_pool.hpp>
Batched max pooling operation.
Public Functions
-
MaxPool() = default
Constructs a batched max pooling operation.
-
MaxPool(const Output<Node> &arg, const Strides &strides, const Shape &pads_begin, const Shape &pads_end, const Shape &kernel, const op::RoundingType rounding_type = op::RoundingType::FLOOR, const PadType auto_pad = op::PadType::EXPLICIT)
Constructs a batched max pooling operation.
- Parameters:
arg – The node producing the input data batch tensor.
strides – The strides.
pads_begin – The beginning of padding shape.
pads_end – The end of padding shape.
kernel – The kernel shape.
rounding_type – Whether to use ceiling or floor rounding type while computing output shape.
auto_pad – The pad type for automatically computing padding sizes.
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
MaxPool() = default
-
class Minimum : public ov::op::util::BinaryElementwiseArithmetic
- #include <minimum.hpp>
Elementwise minimum operation.
Public Functions
-
inline Minimum()
Constructs a minimum operation.
-
Minimum(const Output<Node> &arg0, const Output<Node> &arg1, const AutoBroadcastSpec &auto_broadcast = AutoBroadcastSpec(AutoBroadcastType::NUMPY))
Constructs a minimum operation.
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
inline Minimum()
-
class Mod : public ov::op::util::BinaryElementwiseArithmetic
- #include <mod.hpp>
Mod returns an element-wise division reminder with two given tensors applying multi-directional broadcast rules.
Public Functions
-
inline Mod()
Constructs a Mod node.
-
Mod(const Output<Node> &A, const Output<Node> &B, const AutoBroadcastSpec &auto_broadcast = AutoBroadcastSpec(AutoBroadcastType::NUMPY))
- Parameters:
A – - Dividend tensor
B – - Divisor tensor
auto_broadcast – Auto broadcast specification
-
virtual bool evaluate(ov::TensorVector &outputs, const ov::TensorVector &inputs) const override
Evaluates the op on input_values putting results in output_values.
- Parameters:
output_values – Tensors for the outputs to compute. One for each result
input_values – Tensors for the inputs. One for each inputs.
- Returns:
true if successful
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
inline Mod()
-
class Multiply : public ov::op::util::BinaryElementwiseArithmetic
- #include <multiply.hpp>
Elementwise multiplication operation.
Public Functions
-
inline Multiply()
Constructs a multiplication operation.
-
Multiply(const Output<Node> &arg0, const Output<Node> &arg1, const AutoBroadcastSpec &auto_broadcast = AutoBroadcastSpec(AutoBroadcastType::NUMPY))
Constructs a multiplication operation.
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
inline Multiply()
-
class NonMaxSuppression : public ov::op::Op
- #include <non_max_suppression.hpp>
Elementwise addition operation.
Public Functions
-
NonMaxSuppression(const Output<Node> &boxes, const Output<Node> &scores, const Output<Node> &max_output_boxes_per_class, const Output<Node> &iou_threshold, const Output<Node> &score_threshold, const BoxEncodingType box_encoding = BoxEncodingType::CORNER, const bool sort_result_descending = true)
Constructs a NonMaxSuppression operation.
- Parameters:
boxes – Node producing the box coordinates
scores – Node producing the box scores
max_output_boxes_per_class – Node producing maximum number of boxes to be selected per class
iou_threshold – Node producing intersection over union threshold
score_threshold – Node producing minimum score threshold
box_encoding – Specifies the format of boxes data encoding
-
NonMaxSuppression(const Output<Node> &boxes, const Output<Node> &scores, const BoxEncodingType box_encoding = BoxEncodingType::CORNER, const bool sort_result_descending = true)
Constructs a NonMaxSuppression operation with default values for the last 3 inputs.
- Parameters:
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
NonMaxSuppression(const Output<Node> &boxes, const Output<Node> &scores, const Output<Node> &max_output_boxes_per_class, const Output<Node> &iou_threshold, const Output<Node> &score_threshold, const BoxEncodingType box_encoding = BoxEncodingType::CORNER, const bool sort_result_descending = true)
-
class NotEqual : public ov::op::util::BinaryElementwiseComparison
- #include <not_equal.hpp>
Elementwise not-equal operation.
Public Functions
-
inline NotEqual()
Constructs a not-equal operation.
-
NotEqual(const Output<Node> &arg0, const Output<Node> &arg1, const AutoBroadcastSpec &auto_broadcast = AutoBroadcastSpec(AutoBroadcastType::NUMPY))
Constructs a not-equal operation.
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
inline NotEqual()
-
class OneHot : public ov::op::Op
- #include <one_hot.hpp>
OneHot operation.
Public Functions
-
OneHot() = default
Constructs a one-hot operation.
-
OneHot(const Output<Node> &indices, const Output<Node> &depth, const Output<Node> &on_value, const Output<Node> &off_value, int64_t axis)
Constructs a one-hot operation.
- Parameters:
indices – Input tensor containing indices.
depth – Specifies number of classes and the size of one-hot dimension.
on_value – Specifies value that the locations in output tensor represented by indices in input take.
off_value – Specifies value that the locations in output tensor not represented by indices in input take.
axis – Axis along which one-hot representation in added.
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
inline const int64_t &get_axis() const
- Returns:
The index of the one-hot axis.
-
OneHot() = default
-
class Pad : public ov::op::util::PadBase
- #include <pad.hpp>
Generic padding operation.
Public Functions
-
Pad() = default
Constructs a Pad-1 operation.
-
Pad(const Output<Node> &arg, const Output<Node> &pads_begin, const Output<Node> &pads_end, const Output<Node> &arg_pad_value, PadMode pad_mode)
Constructs a Pad-1 operation.
- Parameters:
arg – The output producing input tensor to be padded.
pads_begin – The output which specifies the number of padding elements added before position 0 on each axis of arg.
pads_end – The output which specifies the number of padding elements after the last element on each axis.
arg_pad_value – The scalar output with the value used for padding if pad_mode is CONSTANT
pad_mode – The padding mode: CONSTANT, EDGE, REFLECT or SYMMETRIC. CONSTANT initializes new elements with arg_pad_value, EDGE uses the nearest value from arg. REFLECT and SYMMETRIC tile the background by flipping arg at the edge (SYMMETRIC) or on the last row/column/etc. (REFLECT).
-
Pad(const Output<Node> &arg, const Output<Node> &pads_begin, const Output<Node> &pads_end, PadMode pad_mode)
Constructs a Pad-1 operation.
- Parameters:
arg – The output producing input tensor to be padded.
pads_begin – The output which specifies the number of padding elements added
pads_end – The output which specifies the number of padding elements after the last element on each axis.
pad_mode – The padding mode: CONSTANT, EDGE, REFLECT or SYMMETRIC.
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
virtual bool evaluate(ov::TensorVector &outputs, const ov::TensorVector &inputs) const override
Evaluates the op on input_values putting results in output_values.
- Parameters:
output_values – Tensors for the outputs to compute. One for each result
input_values – Tensors for the inputs. One for each inputs.
- Returns:
true if successful
-
Pad() = default
-
class Power : public ov::op::util::BinaryElementwiseArithmetic
- #include <power.hpp>
Elementwise exponentiation operation.
Inputs
Type
Description
arg0\(N[d_1,\dots,d_n]~(n \geq 0)\)
A tensor of any shape and numeric element type.
arg1\(N[d_1,\dots,d_n]~(n \geq 0)\)
A tensor of the same shape and element type as
arg0.Type
Description
\(N[d_1,\dots,d_n]\)
The tensor \(T\), where \(T[i_1,\dots,i_n] = \texttt{arg0}[i_1,\dots,i_n]^{\texttt{arg1}[i_1,\dots,i_n]}\)
Public Functions
-
Power(const Output<Node> &arg0, const Output<Node> &arg1, const AutoBroadcastSpec &auto_broadcast = AutoBroadcastSpec(AutoBroadcastType::NUMPY))
Constructs an exponentiation operation.
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
Power(const Output<Node> &arg0, const Output<Node> &arg1, const AutoBroadcastSpec &auto_broadcast = AutoBroadcastSpec(AutoBroadcastType::NUMPY))
-
class ReduceLogicalAnd : public ov::op::util::LogicalReductionKeepDims
- #include <reduce_logical_and.hpp>
Performs a reduction using “logical and”.
The reduction is performed over slices of the first input. The slices shape depends on the values passed to the second input - the axes.
Public Functions
-
ReduceLogicalAnd(const Output<Node> &data, const Output<Node> &reduction_axes, const bool keep_dims = false)
Constructs a ReduceLogicalAnd node.
- Parameters:
data – - The input tensor with data to be reduced
reduction_axes – - The input tensor with information about axes over which the first tensor should be sliced prior to the reduction operation
keep_dims – - Indicates if the axes used for reduction should be held/kept
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
ReduceLogicalAnd(const Output<Node> &data, const Output<Node> &reduction_axes, const bool keep_dims = false)
-
class ReduceLogicalOr : public ov::op::util::LogicalReductionKeepDims
- #include <reduce_logical_or.hpp>
Performs a reduction using “logical or”.
The reduction is performed over slices of the first input. The slices shape depends on the values passed to the second input - the axes.
Public Functions
-
ReduceLogicalOr(const Output<Node> &data, const Output<Node> &reduction_axes, const bool keep_dims = false)
Constructs a ReduceLogicalOr node.
- Parameters:
data – - The input tensor with data to be reduced
reduction_axes – - The input tensor with information about axes over which the first tensor should be sliced prior to the reduction operation
keep_dims – - Indicates if the axes used for reduction should be held/kept
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
ReduceLogicalOr(const Output<Node> &data, const Output<Node> &reduction_axes, const bool keep_dims = false)
-
class ReduceMax : public ov::op::util::ArithmeticReductionKeepDims
- #include <reduce_max.hpp>
ReduceMax operation.
Public Functions
-
ReduceMax() = default
Constructs a summation operation.
-
ReduceMax(const Output<Node> &arg, const Output<Node> &reduction_axes, bool keep_dims = false)
Constructs a summation operation.
- Parameters:
arg – The tensor to be summed.
reduction_axes – The axis positions (0-based) to be eliminated.
keep_dims – If set to 1 it holds axes that are used for reduction.
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
ReduceMax() = default
-
class ReduceMean : public ov::op::util::ArithmeticReductionKeepDims
- #include <reduce_mean.hpp>
ReduceMean operation.
Public Functions
-
ReduceMean(const Output<Node> &arg, const Output<Node> &reduction_axes, bool keep_dims = false)
- Parameters:
arg – The tensor to be summed.
reduction_axes – The axis positions (0-based) to be eliminated.
keep_dims – If set to 1 it holds axes that are used for reduction.
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
ReduceMean(const Output<Node> &arg, const Output<Node> &reduction_axes, bool keep_dims = false)
-
class ReduceMin : public ov::op::util::ArithmeticReductionKeepDims
- #include <reduce_min.hpp>
ReduceMin operation.
Public Functions
-
ReduceMin() = default
Constructs a summation operation.
-
ReduceMin(const Output<Node> &arg, const Output<Node> &reduction_axes, bool keep_dims = false)
Constructs a summation operation.
- Parameters:
arg – The tensor to be summed.
reduction_axes – The axis positions (0-based) to be eliminated.
keep_dims – If set to 1 it holds axes that are used for reduction.
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
ReduceMin() = default
-
class ReduceProd : public ov::op::util::ArithmeticReductionKeepDims
- #include <reduce_prod.hpp>
Product reduction operation.
Reduces the tensor, eliminating the specified reduction axes by taking the product.
Public Functions
-
ReduceProd() = default
Constructs a product reduction operation.
-
ReduceProd(const Output<Node> &arg, const Output<Node> &reduction_axes, bool keep_dims = false)
Constructs a product reduction operation.
- Parameters:
arg – The tensor to be reduced.
reduction_axes – The axis positions (0-based) to be eliminated.
keep_dims – If set to true it holds axes that are used for reduction.
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
ReduceProd() = default
-
class ReduceSum : public ov::op::util::ArithmeticReductionKeepDims
- #include <reduce_sum.hpp>
Tensor sum operation.
Element-wise sums the input tensor, eliminating the specified reduction axes. For example:
\[\begin{split} \mathit{sum}\left(\{0\}, \left[ \begin{array}{ccc} 1 & 2 \\ 3 & 4 \\ 5 & 6 \end{array} \right]\right) = \left[ (1 + 3 + 5), (2 + 4 + 6) \right] = \left[ 9, 12 \right]~~~\text{(dimension 0 (rows) is eliminated)} \end{split}\]\[\begin{split} \mathit{sum}\left(\{1\}, \left[ \begin{array}{ccc} 1 & 2 \\ 3 & 4 \\ 5 & 6 \end{array} \right]\right) = \left[ (1 + 2), (3 + 4), (5 + 6) \right] = \left[ 3, 7, 11 \right]~~~\text{(dimension 1 (columns) is eliminated)} \end{split}\]\[\begin{split} \mathit{sum}\left(\{0,1\}, \left[ \begin{array}{ccc} 1 & 2 \\ 3 & 4 \\ 5 & 6 \end{array} \right]\right) = (1 + 2) + (3 + 4) + (5 + 6) = 21~~~\text{(both dimensions (rows and columns) are eliminated)} \end{split}\]Parameters
Description
reduction_axesThe axes to eliminate through summation.
keep_dimsIf set to 1 it holds axes that are used for reduction.
Inputs
Type
Description
arg\(N[d_1,\dots,d_n]~(n \geq 0)\)
An input tensor of any shape and numeric element type.
Type
Description
\(N[\textit{delete}(A,d_1,\dots,d_n)]\)
The tensor \(T\), where \(T\) is the input tensor with the
reduction_axes\(A\) eliminated by summation.Public Functions
-
ReduceSum() = default
Constructs a summation operation.
-
ReduceSum(const Output<Node> &arg, const Output<Node> &reduction_axes, bool keep_dims = false)
Constructs a summation operation.
- Parameters:
arg – The tensor to be summed.
reduction_axes – The axis positions (0-based) to be eliminated.
keep_dims – If set to 1 it holds axes that are used for reduction.
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
ReduceSum() = default
-
class Reshape : public ov::op::Op
- #include <reshape.hpp>
Tensor dynamic reshape operation.
“Converts” an input tensor into a new shape with the same number of elements. This op does not touch the actual data. If needed, use Transpose for that purpose.
Public Functions
-
Reshape(const Output<Node> &arg, const Output<Node> &shape_pattern, bool special_zero)
Constructs a dynamic reshape operation. This operation does not perform transpose.
- Parameters:
arg – The tensor to be reshaped.
shape_pattern – The node that defines output shape shape_pattern. If the input shape is \((a_0,\dots,a_{k-1})\) then the output shape must be of the form \((b_0,\dots,b_{j-1})\) where \(\Pi(a_i) = \Pi(b_i)\). A value of -1 is allowed for at most one dimension, in which case the dimension size is inferred based on element count of input tensor.
special_zero – Treats zeros in
shape_patternas wildcard flags indicating a copy from input shape at the same index.
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
virtual bool evaluate(ov::TensorVector &outputs, const ov::TensorVector &inputs) const override
Evaluates the op on input_values putting results in output_values.
- Parameters:
output_values – Tensors for the outputs to compute. One for each result
input_values – Tensors for the inputs. One for each inputs.
- Returns:
true if successful
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
Reshape(const Output<Node> &arg, const Output<Node> &shape_pattern, bool special_zero)
-
class Reverse : public ov::op::Op
- #include <reverse.hpp>
Reverse operation.
Public Functions
-
Reverse(const Output<Node> &data, const Output<Node> &reversed_axes, const std::string &mode)
Constructs a reverse operation.
- Parameters:
data – The input tensor, some of whose axes are to be reversed.
reversed_axes – The axes to reverse in a form of a set of indices or boolean mask.
mode – The way reversed_axes should be interpreted - a set or a mask.
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
inline Mode get_mode() const
- Returns:
The second input data interpretation mode.
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
Reverse(const Output<Node> &data, const Output<Node> &reversed_axes, const std::string &mode)
-
class Select : public ov::op::Op
- #include <select.hpp>
Elementwise selection operation.
Inputs
Type
Description
arg0\(\texttt{bool}[d_1,\dots,d_n]~(n \geq 0)\)
A tensor of any shape, with element
bool.arg1\(E[d_1,\dots,d_n]~(n \geq 0)\)
A tensor of a shape that is broadcast-compatible with
arg0, with any element type.arg2\(E[d_1,\dots,d_n]~(n \geq 0)\)
A tensor of a shape that is broadcast-compatible with
arg0, and same element type asarg1.auto_broadcastAuto broadcast specification.
Type
Description
\(E[d_1,\dots,d_n]\)
The tensor \(T\), where \(T[i_1,\dots,i_n] = \texttt{arg1}[i_1,\dots,i_n]\text{ if }\texttt{arg0}[i_1,\dots,i_n] \neq 0\text{, else }\texttt{arg2}[i_1,\dots,i_n]\)
Public Functions
-
inline Select()
Constructs a selection operation.
-
Select(const Output<Node> &arg0, const Output<Node> &arg1, const Output<Node> &arg2, const AutoBroadcastSpec &auto_broadcast = AutoBroadcastSpec(AutoBroadcastType::NUMPY))
Constructs a selection operation.
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
inline virtual const AutoBroadcastSpec &get_autob() const override
- Returns:
the autobroadcasr spec
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
inline Select()
-
class Softmax : public ov::op::Op
- #include <softmax.hpp>
Softmax operation.
Public Functions
-
Softmax(const Output<Node> &arg, const size_t axis = 1)
Constructs a softmax operation.
Output
[d0, ...]- Parameters:
arg – Node that produces the first input tensor.
[d0, ...]axis – The axis position (0-based) on which to calculate the softmax.
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
Softmax(const Output<Node> &arg, const size_t axis = 1)
-
class SpaceToBatch : public ov::op::Op
- #include <space_to_batch.hpp>
SpaceToBatch permutes data tensor blocks of spatial data into batch dimension.
Note
Values from spatial blocks dimensions are moved in the batch dimension.
Output node produces a tensor with shape: tensor with shape `[batch * block_shape[0] * block_shape[1] * ... * block_shape[N - 1], (pads_begin[1] + D_1 + pads_end[1]) / block_shape[1], (pads_begin[2] + D_2 + pads_end[2]) / block_shape[2], ..., (pads_begin[N - 1] + D_{N - 1} + pads_end[N - 1]) / block_shape[N - 1]` of the same type as `data` input.Public Functions
-
SpaceToBatch(const Output<Node> &data, const Output<Node> &block_shape, const Output<ov::Node> &pads_begin, const Output<ov::Node> &pads_end)
Constructs a SpaceToBatch operation.
- Parameters:
data – Node producing the data tensor
block_shape – The sizes of the block of values to be moved
pads_begin – Specifies the padding for the beginning along each axis of
datainputpads_end – Specifies the padding for the ending along each axis of
datainput.
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
SpaceToBatch(const Output<Node> &data, const Output<Node> &block_shape, const Output<ov::Node> &pads_begin, const Output<ov::Node> &pads_end)
-
class Split : public ov::op::Op
- #include <split.hpp>
Splits the input tensor into a list of equal sized tensors.
Public Functions
-
Split() = default
Constructs a split operation.
-
Split(const Output<Node> &data, const Output<Node> &axis, const size_t num_splits)
Constructs a split operation.
- Parameters:
data – The tensor to be split.
axis – The index of an axis in “data” along which to perform the split.
num_splits – The number of pieces that the data tensor should be split into.
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
Split() = default
-
class StridedSlice : public ov::op::Op
- #include <strided_slice.hpp>
Takes a slice of an input tensor, i.e., the sub-tensor that resides within a bounding box, optionally with stride.
Public Functions
-
StridedSlice(const Output<Node> &data, const Output<Node> &begin, const Output<Node> &end, const Output<Node> &strides, const std::vector<int64_t> &begin_mask, const std::vector<int64_t> &end_mask, const std::vector<int64_t> &new_axis_mask = std::vector<int64_t>{}, const std::vector<int64_t> &shrink_axis_mask = std::vector<int64_t>{}, const std::vector<int64_t> &ellipsis_mask = std::vector<int64_t>{})
Constructs a dynamic tensor strided slice operation.
- Parameters:
data – The tensor to be sliced.
begin – 1D tensor with begin indexes for input blob slicing.
end – 1D tensor with end indexes for input blob slicing.
strides – The slicing strides; for example, strides of
{n,m}means to take every nth row and every mth column of the input matrix.begin_mask – When begin_mask[i] equal to 1 means that the corresponding dimension of the begin input is ignored.
end_mask – When end_mask[i] is 1, the corresponding dimension of the end input is ignored.
new_axis_mask – If new_axis_mask[i] is 1, a length 1 dimension is inserted on the i-th position.
shrink_axis_mask – If shrink_axis_mask[i] is 1, the dimension on the i-th position is deleted.
ellipsis_mask – It inserts missing dimensions on a position of a non-zero bit.
-
StridedSlice(const Output<Node> &data, const Output<Node> &begin, const Output<Node> &end, const std::vector<int64_t> &begin_mask, const std::vector<int64_t> &end_mask, const std::vector<int64_t> &new_axis_mask = std::vector<int64_t>{}, const std::vector<int64_t> &shrink_axis_mask = std::vector<int64_t>{}, const std::vector<int64_t> &ellipsis_mask = std::vector<int64_t>{})
Constructs a dynamic tensor strided slice operation.
- Parameters:
data – The tensor to be sliced.
begin – 1D tensor with begin indexes for input blob slicing.
end – 1D tensor with end indexes for input blob slicing.
begin_mask – When begin_mask[i] equal to 1 means that the corresponding dimension of the begin input is ignored.
end_mask – When end_mask[i] is 1, the corresponding dimension of the end input is ignored.
new_axis_mask – If new_axis_mask[i] is 1, a length 1 dimension is inserted on the i-th position.
shrink_axis_mask – If shrink_axis_mask[i] is 1, the dimension on the i-th position is deleted.
ellipsis_mask – It inserts missing dimensions on a position of a non-zero bit.
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
StridedSlice(const Output<Node> &data, const Output<Node> &begin, const Output<Node> &end, const Output<Node> &strides, const std::vector<int64_t> &begin_mask, const std::vector<int64_t> &end_mask, const std::vector<int64_t> &new_axis_mask = std::vector<int64_t>{}, const std::vector<int64_t> &shrink_axis_mask = std::vector<int64_t>{}, const std::vector<int64_t> &ellipsis_mask = std::vector<int64_t>{})
-
class Subtract : public ov::op::util::BinaryElementwiseArithmetic
- #include <subtract.hpp>
Elementwise subtraction operation.
Public Functions
-
Subtract(const Output<Node> &arg0, const Output<Node> &arg1, const AutoBroadcastSpec &auto_broadcast = AutoBroadcastSpec(AutoBroadcastType::NUMPY))
Constructs a subtraction operation.
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
Subtract(const Output<Node> &arg0, const Output<Node> &arg1, const AutoBroadcastSpec &auto_broadcast = AutoBroadcastSpec(AutoBroadcastType::NUMPY))
-
class TopK : public ov::op::util::TopKBase
- #include <topk.hpp>
Computes indices and values of the k maximum/minimum values for each slice along specified axis.
Public Functions
-
TopK() = default
Constructs a TopK operation.
-
TopK(const Output<Node> &data, const Output<Node> &k, const int64_t axis, const std::string &mode, const std::string &sort, const element::Type &index_element_type = element::i32)
Constructs a TopK operation with two outputs: values and indices. By default the indices output is described by i32 data type.
- Parameters:
data – The input tensor
k – Specifies how many maximum/minimum elements should be computed (note: scalar input tensor)
axis – The axis along which to compute top k indices
mode – Specifies which operation (min or max) is used to select the biggest element of two.
sort – Specifies order of output elements and/or indices Accepted values: none, index, value
index_element_type – Specifies type of produced indices
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
TopK() = default
-
class Transpose : public ov::op::Op
- #include <transpose.hpp>
Tensor transpose operation.
Public Types
-
enum Ins
Inputs indexes and count.
Values:
-
enumerator ARG
-
enumerator ORDER
-
enumerator IN_COUNT
-
enumerator ARG
-
enum Outs
Outputs indexes and count.
Values:
-
enumerator ARG_T
-
enumerator OUT_COUNT
-
enumerator ARG_T
Public Functions
-
Transpose(const Output<Node> &arg, const Output<Node> &input_order)
Constructs a transpose operation.
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
enum Ins
-
class VariadicSplit : public ov::op::Op
- #include <variadic_split.hpp>
VariadicSplit operation splits an input tensor into pieces along some axis. The pieces may have variadic lengths depending on “split_lengths” attribute.
Public Functions
-
VariadicSplit() = default
Constructs a variadic split operation.
-
VariadicSplit(const Output<Node> &data, const Output<Node> &axis, const Output<Node> &split_lengths)
Constructs a variadic split operation.
outputs. The sum of split_lengths must match data.shape[axis]
- Parameters:
data – The tensor to be split.
axis – The index of an axis in “data” along which to perform the split.
split_lengths – A list containing the sizes of each output tensor along the split “axis”. Size of “split_lengths” should be equal to the number of
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
inline virtual size_t get_default_output_index() const override
Returns the output of the default output, or throws if there is none.
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
VariadicSplit() = default
-
template<class TShape, class TContainer, class TRShape = result_shape_t<TShape>>
-
namespace v10#
-
class IsFinite : public ov::op::Op
- #include <is_finite.hpp>
Boolean mask that maps NaN and Infinity values to false and other values to true.
Public Functions
-
IsFinite(const Output<Node> &data)
Constructs a IsFinite operation.
- Parameters:
data – Input data tensor
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
IsFinite(const Output<Node> &data)
-
class IsInf : public ov::op::Op
- #include <is_inf.hpp>
Boolean mask that maps infinite values to true.
Public Functions
-
IsInf(const Output<Node> &data, const Attributes &attributes)
Constructs a IsInf operation.
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
struct Attributes
- #include <is_inf.hpp>
A Structure which contains all IsInf attributes.
-
IsInf(const Output<Node> &data, const Attributes &attributes)
-
class IsNaN : public ov::op::Op
- #include <is_nan.hpp>
Boolean mask that maps NaN values to true and other values to false.
Public Functions
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
virtual void validate_and_infer_types() override
-
class Unique : public ov::op::Op
- #include <unique.hpp>
Operator which selects and returns unique elements or unique slices of the input tensor.
Public Functions
-
Unique(const Output<Node> &data, const bool sorted = true, const element::Type &index_element_type = element::i64, const element::Type &count_element_type = element::i64)
Constructs a Unique operation.
- Parameters:
data – Input data tensor
sorted – Controls the order of the returned unique values (sorts ascendingly when true)
index_element_type – The data type for outputs containing indices
count_element_type – The data type for output containing repetition count
-
Unique(const Output<Node> &data, const Output<Node> &axis, const bool sorted = true, const element::Type &index_element_type = element::i64, const element::Type &count_element_type = element::i64)
Constructs a Unique operation.
- Parameters:
data – Input data tensor
axis – An input tensor containing the axis value
sorted – Controls the order of the returned unique values (sorts ascendingly when true)
index_element_type – The data type for outputs containing indices
count_element_type – The data type for output containing repetition count
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
Unique(const Output<Node> &data, const bool sorted = true, const element::Type &index_element_type = element::i64, const element::Type &count_element_type = element::i64)
-
class IsFinite : public ov::op::Op
-
namespace v11#
Functions
-
template<class TShape, class TContainer, class TRShape = result_shape_t<TShape>>
std::vector<TRShape> shape_infer(const Interpolate *op, const std::vector<TShape> &input_shapes, TContainer &pads_begin, TContainer &pads_end, const ITensorAccessor &tensor_accessor)#
-
class Interpolate : public ov::op::util::InterpolateBase
- #include <interpolate.hpp>
Interpolate operation.
Public Functions
-
Interpolate(const Output<Node> &image, const Output<Node> &scales_or_sizes, const InterpolateAttrs &attrs)
Constructs a Interpolate operation without ‘axes’ input.
- Parameters:
image – Input image
scales_or_sizes – Scales of spatial axes, i.e. output_shape / input_shape
attrs – Interpolation attributes
-
Interpolate(const Output<Node> &image, const Output<Node> &scales_or_sizes, const Output<Node> &axes, const InterpolateAttrs &attrs)
Constructs a Interpolate operation with ‘axes’ input.
- Parameters:
image – Input image
scales_or_sizes – Scales of spatial axes, i.e. output_shape / input_shape
axes – Interpolation axes
attrs – Interpolation attributes
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
inline virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
Interpolate(const Output<Node> &image, const Output<Node> &scales_or_sizes, const InterpolateAttrs &attrs)
-
class TopK : public ov::op::util::TopKBase
- #include <topk.hpp>
Computes the top K elements of a given tensor along the specified axis.
Public Functions
-
TopK() = default
Constructs a TopK operation.
-
TopK(const Output<Node> &data, const Output<Node> &k, const int64_t axis, const std::string &mode, const std::string &sort, const element::Type &index_element_type = element::i32, const bool stable = false)
Constructs a TopK operation with two outputs: values and indices.
- Parameters:
data – The input tensor
k – Specifies how many maximum/minimum elements should be computed
axis – The axis along which the TopK operation should be executed
mode – Specifies whether TopK selects the largest or the smallest elements from each slice
sort – Specifies the order of corresponding elements of the output tensor
index_element_type – Specifies the data type of the elements in the ‘indices’ output tensor.
stable – Specifies whether the equivalent elements should maintain their relative order from the input tensor during sorting.
-
TopK(const Output<Node> &data, const Output<Node> &k, const int64_t axis, const TopKMode mode, const TopKSortType sort, const element::Type &index_element_type = element::i32, const bool stable = false)
Constructs a TopK operation with two outputs: values and indices.
- Parameters:
data – The input tensor
k – Specifies how many maximum/minimum elements should be computed
axis – The axis along which the TopK operation should be executed
mode – Specifies whether TopK selects the largest or the smallest elements from each slice
sort – Specifies the order of corresponding elements of the output tensor
index_element_type – Specifies the data type of the elements in the ‘indices’ output tensor.
stable – Specifies whether the equivalent elements should maintain their relative order from the input tensor during sorting.
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
TopK() = default
-
template<class TShape, class TContainer, class TRShape = result_shape_t<TShape>>
-
namespace v12#
Functions
-
template<class TShape>
std::vector<TShape> shape_infer(const GroupNormalization *op, const std::vector<TShape> &input_shapes)#
-
template<class TShape, class TRShape = result_shape_t<TShape>>
std::vector<TRShape> shape_infer(const ScatterElementsUpdate *op, const std::vector<TShape> &input_shapes, const ITensorAccessor &ta = make_tensor_accessor())#
-
class GroupNormalization : public ov::op::Op
- #include <group_normalization.hpp>
GroupNormalization operation over the input tensor.
Public Functions
-
GroupNormalization(const Output<Node> &data, const Output<Node> &scale, const Output<Node> &bias, int64_t num_groups, double epsilon)
- Parameters:
data – The input tensor to be normalized
scale – The tensor containing scale values for each channel
bias – The tensor containing bias values for each channel
num_groups – The number of groups that the channel dimension will be divided into
epsilon – The value that prevents divisions by zero in GroupNormalization formula
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
GroupNormalization(const Output<Node> &data, const Output<Node> &scale, const Output<Node> &bias, int64_t num_groups, double epsilon)
-
class Pad : public ov::op::util::PadBase
- #include <pad.hpp>
Generic padding operation.
Public Functions
-
Pad() = default
Constructs a Pad-12 operation.
-
Pad(const Output<Node> &arg, const Output<Node> &pads_begin, const Output<Node> &pads_end, PadMode pad_mode)
Constructs a Pad-12 operation.
- Parameters:
arg – The output producing input tensor to be padded.
pads_begin – The output which specifies the number of padding elements to add (or remove) before position 0 on each axis of arg.
pads_end – The output which specifies the number of padding elements to add (or remove) after the last element on each axis.
pad_mode – The padding mode: CONSTANT, EDGE, REFLECT or SYMMETRIC.
-
Pad(const Output<Node> &arg, const Output<Node> &pads_begin, const Output<Node> &pads_end, const Output<Node> &arg_pad_value, PadMode pad_mode)
Constructs a Pad-12 operation.
- Parameters:
arg – The output producing input tensor to be padded.
pads_begin – The output which specifies the number of padding elements to add (or remove) before position 0 on each axis of arg.
pads_end – The output which specifies the number of padding elements to add (or remove) after the last element on each axis.
arg_pad_value – The scalar output with the value used for padding if pad_mode is CONSTANT
pad_mode – The padding mode: CONSTANT, EDGE, REFLECT or SYMMETRIC.
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
virtual bool evaluate(ov::TensorVector &outputs, const ov::TensorVector &inputs) const override
Evaluates the op on input_values putting results in output_values.
- Parameters:
output_values – Tensors for the outputs to compute. One for each result
input_values – Tensors for the inputs. One for each inputs.
- Returns:
true if successful
-
Pad() = default
-
class ScatterElementsUpdate : public ov::op::util::ScatterElementsUpdateBase
- #include <scatter_elements_update.hpp>
Public Types
-
enum class Reduction
Lists the supported reduction types for this version of the operator. See the specification for the description of how reduction works with ScatterElementsUpdate.
Values:
-
enumerator NONE
-
enumerator SUM
-
enumerator PROD
-
enumerator MIN
-
enumerator MAX
-
enumerator MEAN
-
enumerator NONE
Public Functions
-
ScatterElementsUpdate(const Output<Node> &data, const Output<Node> &indices, const Output<Node> &updates, const Output<Node> &axis, const Reduction reduction = Reduction::NONE, const bool use_init_val = true)
Constructs a ScatterElementsUpdate node.
- Parameters:
data – Input data
indices – Data entry index that will be updated
updates – Update values
axis – Axis to scatter on
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
enum class Reduction
-
template<class TShape>
-
namespace v13#
Functions
-
template<class T, class TRShape = result_shape_t<T>>
std::vector<TRShape> shape_infer(const FakeConvert *op, const std::vector<T> &input_shapes)#
-
template<class TShape, class TRShape = result_shape_t<TShape>>
std::vector<TRShape> shape_infer(const Multinomial *op, const std::vector<TShape> &input_shapes, const ITensorAccessor &ta = make_tensor_accessor())#
-
template<class T, class TRShape = result_shape_t<T>>
std::vector<TRShape> shape_infer(const NMSRotated *op, const std::vector<T> &input_shapes, const ITensorAccessor &ta = make_tensor_accessor())#
-
template<class T, class TRShape = result_shape_t<T>>
std::vector<TRShape> shape_infer(const ScaledDotProductAttention *op, const std::vector<T> &input_shapes, const ITensorAccessor &ta = make_tensor_accessor())#
-
class BitwiseAnd : public ov::op::util::BinaryElementwiseBitwise
- #include <bitwise_and.hpp>
Elementwise bitwise AND operation.
Public Functions
-
BitwiseAnd() = default
Constructs a bitwise AND operation.
-
BitwiseAnd(const Output<Node> &arg0, const Output<Node> &arg1, const AutoBroadcastSpec &auto_broadcast = AutoBroadcastSpec(AutoBroadcastType::NUMPY))
Constructs a bitwise AND operation.
Output
[d0, ...]
-
BitwiseAnd() = default
-
class BitwiseNot : public ov::op::Op
- #include <bitwise_not.hpp>
Elementwise bitwise negation operation.
Public Functions
-
BitwiseNot() = default
Constructs a bitwise negation operation.
-
BitwiseNot(const Output<Node> &arg)
Constructs a bitwise negation operation.
- Parameters:
arg – Node that produces the input tensor.
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
BitwiseNot() = default
-
class BitwiseOr : public ov::op::util::BinaryElementwiseBitwise
- #include <bitwise_or.hpp>
Elementwise bitwise OR operation.
Public Functions
-
BitwiseOr() = default
Constructs a bitwise OR operation.
-
BitwiseOr(const Output<Node> &arg0, const Output<Node> &arg1, const AutoBroadcastSpec &auto_broadcast = AutoBroadcastSpec(AutoBroadcastType::NUMPY))
Constructs a bitwise OR operation.
Output
[d0, ...]
-
BitwiseOr() = default
-
class BitwiseXor : public ov::op::util::BinaryElementwiseBitwise
- #include <bitwise_xor.hpp>
Elementwise bitwise XOR operation.
Public Functions
-
BitwiseXor() = default
Constructs a bitwise XOR operation.
-
BitwiseXor(const Output<Node> &arg0, const Output<Node> &arg1, const AutoBroadcastSpec &auto_broadcast = AutoBroadcastSpec(AutoBroadcastType::NUMPY))
Constructs a bitwise XOR operation.
Output
[d0, ...]
-
BitwiseXor() = default
-
class FakeConvert : public ov::op::Op
- #include <fake_convert.hpp>
FakeConvert performs element-wise quantization of input values into a set of values corresponding to a target low-precision type.
Note
FakeConvert is an experimental operation and subject to change.
Public Functions
-
FakeConvert(const ov::Output<ov::Node> &data, const ov::Output<ov::Node> &scale, std::string destination_type = "f8e4m3")
Constructs FakeConvert operation (default shift).
- Parameters:
data – The input data tensor.
scale – Tensor with a scale factor for the data input.
destination_type – The low precision type to be emulated.
-
FakeConvert(const ov::Output<ov::Node> &data, const ov::Output<ov::Node> &scale, const ov::Output<ov::Node> &shift, std::string destination_type = "f8e4m3")
Constructs FakeConvert operation.
-
FakeConvert(const ov::Output<ov::Node> &data, const ov::Output<ov::Node> &scale, const ov::element::Type &destination_type)
Constructs FakeConvert operation (default shift).
- Parameters:
data – The input data tensor.
scale – Tensor with a scale factor for the data input.
destination_type – The low precision type to be emulated.
-
FakeConvert(const ov::Output<ov::Node> &data, const ov::Output<ov::Node> &scale, const ov::Output<ov::Node> &shift, const ov::element::Type &destination_type)
Constructs FakeConvert operation.
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
FakeConvert(const ov::Output<ov::Node> &data, const ov::Output<ov::Node> &scale, std::string destination_type = "f8e4m3")
-
class Multinomial : public ov::op::Op
- #include <multinomial.hpp>
Multinomial operation creates a sequence of indices of classes sampled from the multinomial distribution.
Public Functions
-
Multinomial(const Output<Node> &input, const Output<Node> &num_samples, const ov::element::Type_t convert_type, const bool with_replacement, const bool log_probs, const uint64_t global_seed = 0, const uint64_t op_seed = 0)
Multinomial operation creates a sequence of indices of classes sampled from the multinomial distribution.
- Parameters:
probs – Input tensor containing at each index poisition probability/log probability of sampling a given class. Any floating-point precision values are allowed.
num_samples – Scalar or 1D tensor with a single value that determines the number of samples to generate per batch. Values should be of an integer type.
convert_type – Data type to which to convert the output class indices. Allowed values: i32/i64
with_replacement – Boolean that determines whether a sampled class can appear more than once in the output.
log_probs – Boolean that determines whether to treat input probabilities as log probabilities.
global_seed – First seed value (key) of Phillox random number generation algorithm. (See RandomUniform for details)
op_seed – Second seed value (counter) of Phillox random number generation algorithm. (See RandomUniform for details)
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
Multinomial(const Output<Node> &input, const Output<Node> &num_samples, const ov::element::Type_t convert_type, const bool with_replacement, const bool log_probs, const uint64_t global_seed = 0, const uint64_t op_seed = 0)
-
class NMSRotated : public ov::op::Op
- #include <nms_rotated.hpp>
NMSRotated operation.
Public Functions
-
NMSRotated(const Output<Node> &boxes, const Output<Node> &scores, const Output<Node> &max_output_boxes_per_class, const Output<Node> &iou_threshold, const Output<Node> &score_threshold, const bool sort_result_descending = true, const ov::element::Type &output_type = ov::element::i64, const bool clockwise = true)
Constructs a NMSRotated operation.
- Parameters:
boxes – Node containing the coordinates of the bounding boxes
scores – Node containing the scores of the bounding boxes
max_output_boxes_per_class – Node containing maximum number of boxes to be selected per class
iou_threshold – Node containing intersection over union threshold
score_threshold – Node containing minimum score threshold
sort_result_descending – Specifies whether it is necessary to sort selected boxes across batches
output_type – Specifies the output type of the first and third output
clockwise – Specifies the direction of the rotation
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
NMSRotated(const Output<Node> &boxes, const Output<Node> &scores, const Output<Node> &max_output_boxes_per_class, const Output<Node> &iou_threshold, const Output<Node> &score_threshold, const bool sort_result_descending = true, const ov::element::Type &output_type = ov::element::i64, const bool clockwise = true)
-
class ScaledDotProductAttention : public ov::op::Op
- #include <scaled_dot_product_attention.hpp>
Scaled dot product attention operation from PyTorch.
Public Functions
-
ScaledDotProductAttention() = default
Constructs a ScaledDotProductAttention operation.
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
ScaledDotProductAttention() = default
-
template<class T, class TRShape = result_shape_t<T>>
-
namespace v14#
Functions
-
template<class TShape, class TContainer, class TRShape = result_shape_t<TShape>>
std::vector<TRShape> shape_infer(const AvgPool *op, const std::vector<TShape> &input_shapes, TContainer &pads_begin, TContainer &pads_end)#
-
template<class TShape, class TRShape = result_shape_t<TShape>>
std::vector<TRShape> shape_infer(const Inverse *op, const std::vector<TShape> &input_shapes)#
-
template<class TShape, class TContainer, class TRShape = result_shape_t<TShape>>
std::vector<TRShape> shape_infer(const MaxPool *op, const std::vector<TShape> &input_shapes, TContainer &pads_begin, TContainer &pads_end)#
-
template<class TShape, class TRShape = result_shape_t<TShape>>
std::vector<TRShape> shape_infer(const RMSNorm *op, const std::vector<TShape> &input_shapes, const ITensorAccessor &tensor_accessor = make_tensor_accessor())#
-
template<class TShape, class TRShape = result_shape_t<TShape>>
std::vector<TRShape> shape_infer(const ROIAlignRotated *op, const std::vector<TShape> &input_shapes)#
-
class AvgPool : public ov::op::util::AvgPoolBase
- #include <avg_pool.hpp>
Batched average pooling operation.
Public Functions
-
AvgPool() = default
Constructs a batched average pooling operation.
-
AvgPool(const Output<Node> &arg, const Strides &strides, const Shape &pads_begin, const Shape &pads_end, const Shape &kernel, bool exclude_pad, RoundingType rounding_type = RoundingType::FLOOR, const PadType &auto_pad = PadType::EXPLICIT)
Constructs a batched average pooling operation.
- Parameters:
arg – The output producing the input data batch tensor.
[d1, dn]strides – The strides.
[n]pads_begin – The beginning of padding shape.
[n]pads_end – The end of padding shape.
[n]kernel – The kernel shape.
[n]exclude_pad – If false then averages include padding elements, each treated as the number zero. If true, padding elements are entirely ignored when computing averages.
rounding_type – Whether to use ceiling or floor rounding type while computing output shape.
auto_pad – Padding type to use for additional padded dimensions
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
AvgPool() = default
-
class ConvertPromoteTypes : public ov::op::Op
- #include <convert_promote_types.hpp>
Elementwise operation that promote and convert input types to one common datatype.
Public Functions
-
ConvertPromoteTypes() = default
Constructs operation that promote and convert input types to one common datatype.
-
ConvertPromoteTypes(const Output<Node> &input_0, const Output<Node> &input_1, const bool promote_unsafe = false, const bool pytorch_scalar_promotion = false, const element::Type &u64_integer_promotion_target = element::f32)
Constructs operation that promote and convert input types to one common datatype.
- Parameters:
input_0 – Node with datatype to be promoted.
input_1 – Node with datatype to be promoted.
promote_unsafe – Bool attribute whether to allow promotions that might result in bit-widening, precision loss and undefined behaviors.
pytorch_scalar_promotion – Bool attribute whether to promote scalar input to type provided by non-scalar input when number format is matching.
u64_integer_promotion_target – Element type attribute to select promotion result for u64 and signed integers.
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
bool get_pytorch_scalar_promotion() const
Get bool attribute whether to promote scalar input to type provided by non-scalar input when number format is matching.
-
void set_pytorch_scalar_promotion(bool pytorch_scalar_promotion)
Set bool attribute whether to promote scalar input to type provided by non-scalar input when number format is matching.
-
bool get_promote_unsafe() const
Get bool attribute whether to allow promotions that might result in bit-widening, precision loss and undefined behaviors.
-
void set_promote_unsafe(bool promote_unsafe)
Set bool attribute whether to allow promotions that might result in bit-widening, precision loss and undefined behaviors.
-
ConvertPromoteTypes() = default
-
class Inverse : public ov::op::Op
- #include <inverse.hpp>
Inverse operation computes the inverse of the input tensor.
Public Functions
-
Inverse(const Output<Node> &data, const bool adjoint = false)
Inverse operation computes the inverse of the input matrices. The inverse is computed for each MxM matrix separetely, preserving all batch dimensions.
- Parameters:
data – Input matrices to compute the inverse for. Last two tensor dimensions must be of the same size.
adjoint – Boolean that determines whether to return a normal inverse or adjoint (conjugate transpose) of the input matrices.
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
Inverse(const Output<Node> &data, const bool adjoint = false)
-
class MaxPool : public ov::op::util::MaxPoolBase
- #include <max_pool.hpp>
MaxPooling operation with values and indices calculated as individual outputs.
Public Functions
-
MaxPool() = default
Constructs an empty MaxPool operation.
-
MaxPool(const Output<Node> &arg, const Strides &strides, const Strides &dilations, const Shape &pads_begin, const Shape &pads_end, const Shape &kernel, const op::RoundingType rounding_type = op::RoundingType::FLOOR, const PadType auto_pad = op::PadType::EXPLICIT, const element::Type index_element_type = element::i64, const int64_t axis = 0)
Constructs a parametrized MaxPool operation.
- Parameters:
arg – Output of a node producing the feature tensor to be pooled.
strides – The strides of the pooling filter.
dilations – The dilations of the pooling filter.
pads_begin – Paddings at the beginning of each spatial axis.
pads_end – Paddings at the end of each spatial axis.
kernel – The kernel shape.
rounding_type – Whether to use ceiling or floor rounding type while computing the output shape.
auto_pad – The pad type for automatic calculation of the padding sizes.
index_element_type – The data type used by the second output tensor containing the selected indices.
axis – Indicates a dimension in the input data shape which should be used as a starting point for calculation of the upper bound of allowed values of the indices output.
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
const Strides &get_dilations() const noexcept
- Returns:
The pooling filter’s dilations.
-
element::Type get_index_element_type() const noexcept
- Returns:
The data type of the second output tensor (indices).
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
MaxPool() = default
-
class RMSNorm : public ov::op::Op
- #include <rms_norm.hpp>
Operator performing Root Mean Square Normalization.
Public Functions
-
RMSNorm(const Output<Node> &data, const Output<Node> &axes, double epsilson, const ov::element::Type &compute_type = ov::element::undefined)
Constructs an RMSNorm operation without scaling.
- Parameters:
data – Input tensor with data
axes – Axes for reduce mean calculation
eps – Epsilon for not dividing by zero while normalizing the value
compute_type – Precision for the internal computation, if undefined it’s the same as the input type
-
RMSNorm(const Output<Node> &data, const Output<Node> &axes, const Output<Node> &scale, double epsilson, const ov::element::Type &compute_type = ov::element::undefined)
Constructs an RMSNorm operation with scaling.
- Parameters:
data – Input tensor with data
axes – Axes for reduce mean calculation
scale – Scale values for weight
eps – Epsilon for not dividing by zero while normalizing the value
compute_type – Precision for the internal computation, if undefined it’s the same as the input type
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
RMSNorm(const Output<Node> &data, const Output<Node> &axes, double epsilson, const ov::element::Type &compute_type = ov::element::undefined)
-
class ROIAlignRotated : public ov::op::util::ROIAlignBase
- #include <roi_align_rotated.hpp>
ROIAlignRotated operation.
Public Functions
-
ROIAlignRotated(const Output<Node> &input, const Output<Node> &rois, const Output<Node> &batch_indices, const int pooled_h, const int pooled_w, const int sampling_ratio, const float spatial_scale, const bool clockwise_mode)
Constructs a ROIAlignRotated operation.
- Parameters:
input – Input feature map {N, C, H, W}
rois – Regions of interest to pool over
batch_indices – Indices of images in the batch matching the number or ROIs
pooled_h – Height of the ROI output features
pooled_w – Width of the ROI output features
sampling_ratio – Number of sampling points used to compute an output element
spatial_scale – Spatial scale factor used to translate ROI coordinates
clockwise_mode – If true, rotation angle is interpreted as clockwise, otherwise as counterclockwise
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
ROIAlignRotated(const Output<Node> &input, const Output<Node> &rois, const Output<Node> &batch_indices, const int pooled_h, const int pooled_w, const int sampling_ratio, const float spatial_scale, const bool clockwise_mode)
-
template<class TShape, class TContainer, class TRShape = result_shape_t<TShape>>
-
namespace v15#
-
class ScatterNDUpdate : public ov::op::util::ScatterNDBase
- #include <scatter_nd_update.hpp>
Add updates to slices from inputs addressed by indices.
Public Types
-
enum class Reduction
Lists the supported reduction types for this version of the operator. See the specification for the description of how reduction works with ScatterNDUpdate.
Values:
-
enumerator NONE
-
enumerator SUM
-
enumerator SUB
-
enumerator PROD
-
enumerator MIN
-
enumerator MAX
-
enumerator NONE
Public Functions
-
ScatterNDUpdate(const Output<Node> &inputs, const Output<Node> &indices, const Output<Node> &updates, const Reduction reduction = Reduction::NONE)
- Parameters:
inputs – Tensor
indices – Index tensor: Data type must be
element::i32orelement::i64updates – Tensor: Must have same type as inputs
reduction – Reduction: Type of operation to perform on inputs
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
enum class Reduction
-
class ScatterNDUpdate : public ov::op::util::ScatterNDBase
-
namespace v3#
Functions
-
template<class TShape, class TRShape = result_shape_t<TShape>>
std::vector<TRShape> shape_infer(const Assign *op, const std::vector<TShape> &input_shapes)#
-
template<class T, class TRShape = result_shape_t<T>>
std::vector<TRShape> shape_infer(const ov::op::v3::Broadcast *op, const std::vector<T> &input_shapes, const ITensorAccessor &ta = make_tensor_accessor())#
-
template<class TShape, class TRShape = result_shape_t<TShape>>
std::vector<TRShape> shape_infer(const Bucketize *op, const std::vector<TShape> &input_shapes)#
-
template<class TShape, class TRShape = result_shape_t<TShape>>
std::vector<TRShape> shape_infer(const EmbeddingSegmentsSum *op, const std::vector<TShape> &input_shapes, const ITensorAccessor &ta = make_tensor_accessor())#
-
template<class T, class TRShape = result_shape_t<T>>
std::vector<TRShape> shape_infer(const ExtractImagePatches *op, const std::vector<T> &input_shapes)#
-
template<class TShape, class TRShape = result_shape_t<TShape>>
std::vector<TRShape> shape_infer(const GRUCell *op, const std::vector<TShape> &input_shapes)#
-
template<class T, class TRShape = result_shape_t<T>>
std::vector<TRShape> shape_infer(const NonMaxSuppression *op, const std::vector<T> &input_shapes, const ITensorAccessor &ta = make_tensor_accessor())#
-
class Acosh : public ov::op::util::UnaryElementwiseArithmetic
- #include <acosh.hpp>
Elementwise inverse hyperbolic cos operation.
Public Functions
-
Acosh() = default
Constructs an Acosh operation.
-
Acosh(const Output<Node> &arg)
Constructs an Acosh operation.
Output
[d1, ...]- Parameters:
arg – Output that produces the input tensor.
[d1, ...]
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
Acosh() = default
-
class Asinh : public ov::op::util::UnaryElementwiseArithmetic
- #include <asinh.hpp>
Elementwise inverse hyperbolic sin operation.
Public Functions
-
Asinh() = default
Constructs an Asinh operation.
-
Asinh(const Output<Node> &arg)
Constructs an Asinh operation.
Output
[d1, ...]- Parameters:
arg – Output that produces the input tensor.
[d1, ...]
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
Asinh() = default
-
class Assign : public ov::op::util::AssignBase
- #include <assign.hpp>
Assign operation sets an input value to the variable with
variable_idPublic Functions
-
Assign(const Output<Node> &new_value, const std::string &variable_id)
Constructs an Assign operation.
- Parameters:
new_value – Node that produces the input tensor.
variable_id – identifier of the variable to be updated.
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
inline virtual std::string get_variable_id() const override
Returns the identifier of corresponding variable.
-
Assign(const Output<Node> &new_value, const std::string &variable_id)
-
class Atanh : public ov::op::util::UnaryElementwiseArithmetic
- #include <atanh.hpp>
Elementwise inverse hyperbolic tangent operation.
Public Functions
-
Atanh() = default
Constructs an Atanh operation.
-
Atanh(const Output<Node> &arg)
Constructs an Atanh operation.
Output
[d1, ...]- Parameters:
arg – Output that produces the input tensor.
[d1, ...]
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
Atanh() = default
-
class Broadcast : public ov::op::util::BroadcastBase
- #include <broadcast.hpp>
Operation which “adds” axes to an input tensor, replicating elements from the input as needed along the new axes.
Public Functions
-
Broadcast() = default
Constructs a broadcast operation.
-
Broadcast(const Output<Node> &arg, const Output<Node> &target_shape, const Output<Node> &axes_mapping, const BroadcastModeSpec &broadcast_spec = BroadcastType::EXPLICIT)
Constructs a broadcast operation.
- Parameters:
arg – The input tensor to be broadcast.
target_shape – The shape of the output tensor.
axes_mapping – The axis positions (0-based) in the result that correspond to input axes. ‘Arg’ tensor is broadcast along the remaining axes. E.g., Input Shape - [3, 4], Target Shape - [3, 5, 4, 4] axes_mapping - [0, 2] => Broadcast along axes 1 and 3. axes_mapping - [0, 3] => Broadcast along axes 1 and 2.
broadcast_spec – Broadcast specification to use for determining broadcast axes. ‘axes_mapping’ should not be provided if mode other than explicit (none) is used.
-
Broadcast(const Output<Node> &arg, const Output<Node> &target_shape, const BroadcastModeSpec &broadcast_spec = BroadcastType::NUMPY)
Constructs a broadcast operation.
- Parameters:
arg – The input tensor to be broadcast.
target_shape – The shape of the output tensor.
broadcast_spec – Broadcast specification to use for determining broadcast axes
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
virtual std::pair<bool, AxisSet> get_broadcast_axes() const override
- Returns:
true and the AxisSet if broadcast axes can be fully determined.
-
virtual bool evaluate(ov::TensorVector &outputs, const ov::TensorVector &inputs) const override
Evaluates the op on input_values putting results in output_values.
- Parameters:
output_values – Tensors for the outputs to compute. One for each result
input_values – Tensors for the inputs. One for each inputs.
- Returns:
true if successful
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
Broadcast() = default
-
class Bucketize : public ov::op::Op
- #include <bucketize.hpp>
Operation that bucketizes the input based on boundaries.
Public Functions
-
Bucketize(const Output<Node> &data, const Output<Node> &buckets, const element::Type output_type = element::i64, const bool with_right_bound = true)
Constructs a Bucketize node.
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
Bucketize(const Output<Node> &data, const Output<Node> &buckets, const element::Type output_type = element::i64, const bool with_right_bound = true)
-
class EmbeddingBagOffsetsSum : public ov::op::util::EmbeddingBagOffsetsBase
- #include <embeddingbag_offsets_sum.hpp>
Returns embeddings for given indices.
Public Functions
-
EmbeddingBagOffsetsSum() = default
Constructs a EmbeddingBagOffsetsSum operation.
-
EmbeddingBagOffsetsSum(const Output<Node> &emb_table, const Output<Node> &indices, const Output<Node> &offsets, const Output<Node> &default_index, const Output<Node> &per_sample_weights)
Constructs a EmbeddingBagOffsetsSum operation.
EmbeddingBagOffsetsSum constructs an output tensor by replacing every index in a given input tensor with a row (from the weights matrix) at that index
- Parameters:
emb_table – tensor containing the embedding lookup table of the module of shape [num_emb, emb_dim1, emb_dim2, …] and of type T
indices – tensor of shape [num_indices] and of type T_IND. Required
offsets – tensor of shape [batch] and of type T_IND containing the starting index positions of each “bag” in indices. Required.
default_index – scalar of type T_IND containing default index in embedding table to fill empty “bags”. If not provided empty “bags” are filled with zeros. Optional.
per_sample_weigths – tensor of the same shape as indices and of type T. Each value in this tensor are multiplied with each value pooled from embedding table for each index. Optional.
-
EmbeddingBagOffsetsSum() = default
-
class EmbeddingBagPackedSum : public ov::op::util::EmbeddingBagPackedBase
- #include <embeddingbag_packedsum.hpp>
Returns embeddings for given indices.
Public Functions
-
EmbeddingBagPackedSum() = default
Constructs a EmbeddingBagPackedSum operation.
-
EmbeddingBagPackedSum(const Output<Node> &emb_table, const Output<Node> &indices, const Output<Node> &per_sample_weights)
Constructs a EmbeddingBagPackedSum operation.
EmbeddingBagPackedSum constructs an output tensor by replacing every index in a given input tensor with a row (from the weights matrix) at that index
- Parameters:
emb_table – Tensor containing the embedding lookup table of the module of shape [num_emb, emb_dim1, emb_dim2, …] and of type T
indices – Tensor of shape
[batch, indices_per_bag]and of type T_IND. Required.per_sample_weigths – tensor of the same shape as indices and of type T. Each value in this tensor are multiplied with each value pooled from embedding table for each index. Optional.
-
EmbeddingBagPackedSum() = default
-
class EmbeddingSegmentsSum : public ov::op::Op
- #include <embedding_segments_sum.hpp>
Returns embeddings for given indices.
Public Functions
-
EmbeddingSegmentsSum() = default
Constructs a EmbeddingSegmentsSum operation.
-
EmbeddingSegmentsSum(const Output<Node> &emb_table, const Output<Node> &indices, const Output<Node> &segment_ids, const Output<Node> &num_segments, const Output<Node> &default_index, const Output<Node> &per_sample_weights)
Constructs a EmbeddingSegmentsSum operation.
EmbeddingSegmentsSum constructs an output tensor by replacing every index in a given input tensor with a row (from the weights matrix) at that index
- Parameters:
'emb_table' – tensor containing the embedding lookup table of the module of shape [num_emb, emb_dim1, emb_dim2, …] and of type T
'indices' – tensor of shape [num_indices] and of type T_IND. Required
<tt>segment_ids</tt> – tensor of shape
[num_indices]and of type T_IND with indices into the output Tensor. Values should be sorted and can be repeated. Required.<tt>num_segments</tt> – scalar of type T_IND indicating the number of segments. Required.
'default_index' – scalar of type T_IND containing default index in embedding table to fill empty “bags”. If not provided empty “bags” are filled with zeros. Optional.
'per_sample_weights' – tensor of the same shape as indices and of type T. Each value in this tensor are multiplied with each value pooled from embedding table for each index. Optional.
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
EmbeddingSegmentsSum() = default
-
class ExtractImagePatches : public ov::op::Op
- #include <extractimagepatches.hpp>
ExtractImagePatches operation.
Public Functions
-
ExtractImagePatches(const Output<Node> &image, const Shape &sizes, const Strides &strides, const Shape &rates, const PadType &auto_pad)
Constructs a ExtractImagePatches operation.
- Parameters:
data – 4-D Input data to extract image patches
sizes – Patch size in the format of [size_rows, size_cols]
strides – Patch movement stride in the format of [stride_rows, stride_cols]
rates – Element seleciton rate for creating a patch. in the format of [rate_rows, rate_cols]
auto_pad – Padding type. it can be any value from valid, same_lower, same_upper
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
ExtractImagePatches(const Output<Node> &image, const Shape &sizes, const Strides &strides, const Shape &rates, const PadType &auto_pad)
-
class GRUCell : public ov::op::util::RNNCellBase
- #include <gru_cell.hpp>
Class for GRU cell node.
Note
Note this class represents only single cell and not whole GRU layer.
Public Functions
-
GRUCell(const Output<Node> &X, const Output<Node> &initial_hidden_state, const Output<Node> &W, const Output<Node> &R, std::size_t hidden_size)
Constructs GRUCell node.
- Parameters:
X – [in] The input tensor with shape: [batch_size, input_size].
initial_hidden_state – [in] The hidden state tensor at current time step with shape: [batch_size, hidden_size].
W – [in] The weight tensor with shape: [gates_count * hidden_size, input_size].
R – [in] The recurrence weight tensor with shape: [gates_count * hidden_size, hidden_size].
hidden_size – [in] The number of hidden units for recurrent cell.
-
GRUCell(const Output<Node> &X, const Output<Node> &initial_hidden_state, const Output<Node> &W, const Output<Node> &R, std::size_t hidden_size, const std::vector<std::string> &activations, const std::vector<float> &activations_alpha, const std::vector<float> &activations_beta, float clip, bool linear_before_reset)
Constructs GRUCell node.
- Parameters:
X – [in] The input tensor with shape: [batch_size, input_size].
initial_hidden_state – [in] The hidden state tensor at current time step with shape: [batch_size, hidden_size].
W – [in] The weight tensor with shape: [gates_count * hidden_size, input_size].
R – [in] The recurrence weight tensor with shape: [gates_count * hidden_size, hidden_size].
hidden_size – [in] The number of hidden units for recurrent cell.
activations – [in] The vector of activation functions used inside recurrent cell.
activations_alpha – [in] The vector of alpha parameters for activation functions in order respective to activation list.
activations_beta – [in] The vector of beta parameters for activation functions in order respective to activation list.
clip – [in] The value defining clipping range [-clip, clip] on input of activation functions.
-
GRUCell(const Output<Node> &X, const Output<Node> &initial_hidden_state, const Output<Node> &W, const Output<Node> &R, const Output<Node> &B, std::size_t hidden_size, const std::vector<std::string> &activations = std::vector<std::string>{"sigmoid", "tanh"}, const std::vector<float> &activations_alpha = {}, const std::vector<float> &activations_beta = {}, float clip = 0.f, bool linear_before_reset = false)
Constructs GRUCell node.
- Parameters:
X – [in] The input tensor with shape: [batch_size, input_size].
initial_hidden_state – [in] The hidden state tensor at current time step with shape: [batch_size, hidden_size].
W – [in] The weight tensor with shape: [gates_count * hidden_size, input_size].
R – [in] The recurrence weight tensor with shape: [gates_count * hidden_size, hidden_size].
hidden_size – [in] The number of hidden units for recurrent cell.
B – [in] The sum of biases (weight and recurrence) for update, reset and hidden gates. If linear_before_reset := true then biases for hidden gates are placed separately (weight and recurrence). Shape: [gates_count * hidden_size] if linear_before_reset := false Shape: [(gates_count + 1) * hidden_size] if linear_before_reset := true
activations – [in] The vector of activation functions used inside recurrent cell.
activations_alpha – [in] The vector of alpha parameters for activation functions in order respective to activation list.
activations_beta – [in] The vector of beta parameters for activation functions in order respective to activation list.
clip – [in] The value defining clipping range [-clip, clip] on input of activation functions.
linear_before_reset – [in] Whether or not to apply the linear transformation before multiplying by the output of the reset gate.
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
GRUCell(const Output<Node> &X, const Output<Node> &initial_hidden_state, const Output<Node> &W, const Output<Node> &R, std::size_t hidden_size)
-
class NonMaxSuppression : public ov::op::Op
- #include <non_max_suppression.hpp>
NonMaxSuppression operation.
Subclassed by ov::op::v4::NonMaxSuppression
Public Functions
-
NonMaxSuppression(const Output<Node> &boxes, const Output<Node> &scores, const Output<Node> &max_output_boxes_per_class, const Output<Node> &iou_threshold, const Output<Node> &score_threshold, const BoxEncodingType box_encoding = BoxEncodingType::CORNER, const bool sort_result_descending = true, const ov::element::Type &output_type = ov::element::i64)
Constructs a NonMaxSuppression operation.
- Parameters:
boxes – Node producing the box coordinates
scores – Node producing the box scores
max_output_boxes_per_class – Node producing maximum number of boxes to be selected per class
iou_threshold – Node producing intersection over union threshold
score_threshold – Node producing minimum score threshold
box_encoding – Specifies the format of boxes data encoding
sort_result_descending – Specifies whether it is necessary to sort selected boxes across batches
output_type – Specifies the output tensor type
-
NonMaxSuppression(const Output<Node> &boxes, const Output<Node> &scores, const BoxEncodingType box_encoding = BoxEncodingType::CORNER, const bool sort_result_descending = true, const ov::element::Type &output_type = ov::element::i64)
Constructs a NonMaxSuppression operation with default values for the last 3 inputs.
- Parameters:
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
NonMaxSuppression(const Output<Node> &boxes, const Output<Node> &scores, const Output<Node> &max_output_boxes_per_class, const Output<Node> &iou_threshold, const Output<Node> &score_threshold, const BoxEncodingType box_encoding = BoxEncodingType::CORNER, const bool sort_result_descending = true, const ov::element::Type &output_type = ov::element::i64)
-
class NonZero : public ov::op::Op
- #include <non_zero.hpp>
NonZero operation returning indices of non-zero elements in the input tensor.
Note
The indices are returned by-dimension in row-major order. For example the following output contains 3 indices of a 3D input tensor elements: [[0, 0, 2], [0, 1, 1], [0, 1, 2]] The values point to input elements at [0,0,0], [0,1,1] and [2,1,2]
Public Functions
-
NonZero() = default
Constructs a NonZero operation.
-
NonZero(const Output<Node> &arg)
Constructs a NonZero operation.
Note
The output type is int64.
- Parameters:
arg – Node that produces the input tensor.
-
NonZero(const Output<Node> &arg, const std::string &output_type)
Constructs a NonZero operation.
- Parameters:
arg – Node that produces the input tensor.
output_type – produce indices. Currently, only ‘int64’ or ‘int32’ are supported
-
NonZero(const Output<Node> &arg, const element::Type &output_type)
Constructs a NonZero operation.
- Parameters:
arg – Node that produces the input tensor.
output_type – produce indices. Currently, only int64 or int32 are supported
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
NonZero() = default
-
class ReadValue : public ov::op::util::ReadValueBase
- #include <read_value.hpp>
ReadValue operation creates the variable with
variable_idand returns value of this variable.Public Functions
-
ReadValue(const Output<Node> &init_value, const std::string &variable_id)
Constructs a ReadValue operation.
- Parameters:
init_value – Node that produces the input tensor.
variable_id – identificator of the variable to create.
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
inline virtual std::string get_variable_id() const override
Returns the identifier of corresponding variable.
-
ReadValue(const Output<Node> &init_value, const std::string &variable_id)
-
class ROIAlign : public ov::op::util::ROIAlignBase
- #include <roi_align.hpp>
ROIAlign operation.
Public Functions
-
ROIAlign(const Output<Node> &input, const Output<Node> &rois, const Output<Node> &batch_indices, const int pooled_h, const int pooled_w, const int sampling_ratio, const float spatial_scale, const std::string &mode)
Constructs a ROIAlign node matching the ONNX ROIAlign specification Check util::ROIAlignBase for description of common params.
- Parameters:
mode – Method of pooling - ‘avg’ or ‘max’
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
ROIAlign(const Output<Node> &input, const Output<Node> &rois, const Output<Node> &batch_indices, const int pooled_h, const int pooled_w, const int sampling_ratio, const float spatial_scale, const std::string &mode)
-
class ScatterElementsUpdate : public ov::op::util::ScatterElementsUpdateBase
- #include <scatter_elements_update.hpp>
ScatterElementsUpdate operation.
Public Functions
-
ScatterElementsUpdate(const Output<Node> &data, const Output<Node> &indices, const Output<Node> &updates, const Output<Node> &axis)
Constructs a ScatterElementsUpdate node.
- Parameters:
data – Input data
indices – Data entry index that will be updated
updates – Update values
axis – Axis to scatter on
-
ScatterElementsUpdate(const Output<Node> &data, const Output<Node> &indices, const Output<Node> &updates, const Output<Node> &axis)
-
class ScatterNDUpdate : public ov::op::util::ScatterNDBase
- #include <scatter_nd_update.hpp>
Add updates to slices from inputs addressed by indices.
Public Functions
-
inline ScatterNDUpdate(const Output<Node> &inputs, const Output<Node> &indices, const Output<Node> &updates)
- Parameters:
inputs – Tensor
indices – Index tensor: Data type must be
element::i32orelement::i64updates – Tensor: Must have same type as inputs
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
inline ScatterNDUpdate(const Output<Node> &inputs, const Output<Node> &indices, const Output<Node> &updates)
-
class ScatterUpdate : public ov::op::util::ScatterBase
- #include <scatter_update.hpp>
Set new values to slices from data addressed by indices.
Public Functions
-
ScatterUpdate(const Output<Node> &data, const Output<Node> &indices, const Output<Node> &updates, const Output<Node> &axis)
Constructs ScatterUpdate operator object.
- Parameters:
data – The input tensor to be updated.
indices – The tensor with indexes which will be updated.
updates – The tensor with update values.
axis – [in] The axis at which elements will be updated.
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
ScatterUpdate(const Output<Node> &data, const Output<Node> &indices, const Output<Node> &updates, const Output<Node> &axis)
-
class ShapeOf : public ov::op::util::ShapeOfBase
- #include <shape_of.hpp>
Operation that returns the shape of its input argument as a tensor.
Public Functions
-
ShapeOf(const Output<Node> &arg, const element::Type output_type = element::i64)
Constructs a shape-of operation.
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
ShapeOf(const Output<Node> &arg, const element::Type output_type = element::i64)
-
class TopK : public ov::op::util::TopKBase
- #include <topk.hpp>
Computes indices and values of the k maximum/minimum values for each slice along specified axis.
Public Functions
-
TopK() = default
Constructs a TopK operation.
-
TopK(const Output<Node> &data, const Output<Node> &k, const int64_t axis, const std::string &mode, const std::string &sort, const element::Type &index_element_type = element::i32)
Constructs a TopK operation with two outputs: values and indices. By default the indices output is described by i32 data type.
- Parameters:
data – The input tensor
k – Specifies how many maximum/minimum elements should be computed (note: scalar input tensor)
axis – The axis along which to compute top k indices
mode – Specifies which operation (min or max) is used to select the biggest element of two.
sort – Specifies order of output elements and/or indices Accepted values: none, index, value
index_element_type – Specifies type of produced indices
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
TopK() = default
-
template<class TShape, class TRShape = result_shape_t<TShape>>
-
namespace v4#
Functions
-
template<class TShape, class TRShape = result_shape_t<TShape>>
std::vector<TRShape> shape_infer(const CTCLoss *op, const std::vector<TShape> &input_shapes)#
-
template<class TShape, class TContainer, class TRShape = result_shape_t<TShape>>
std::vector<TRShape> shape_infer(const Interpolate *op, const std::vector<TShape> &input_shapes, TContainer &pads_begin, TContainer &pads_end, const ITensorAccessor &tensor_accessor)#
-
template<class TShape>
std::vector<result_shape_t<TShape>> shape_infer(const LSTMCell *op, const std::vector<TShape> &input_shapes)#
-
template<class T, class TRShape = result_shape_t<T>>
std::vector<TRShape> shape_infer(const NonMaxSuppression *op, const std::vector<T> &input_shapes, const ITensorAccessor &ta = make_tensor_accessor())#
-
template<class TShape, class TRShape = result_shape_t<TShape>>
std::vector<TRShape> shape_infer(const Proposal *op, const std::vector<TShape> &input_shapes)#
-
template<class T, class TRShape = result_shape_t<T>>
std::vector<TRShape> shape_infer(const Range *op, const std::vector<T> &input_shapes, const ITensorAccessor &tensor_accessor = make_tensor_accessor())#
-
class CTCLoss : public ov::op::Op
- #include <ctc_loss.hpp>
CTCLoss operation.
Public Functions
-
CTCLoss(const Output<Node> &logits, const Output<Node> &logit_length, const Output<Node> &labels, const Output<Node> &label_length, const bool preprocess_collapse_repeated = false, const bool ctc_merge_repeated = true, const bool unique = false)
Constructs a CTCLoss operation.
- Parameters:
logits – 3-D tensor of logits
logit_length – 1-D tensor of length for each object from a batch
labels – 2-D tensor of labels for which likelyhood is estimated using logist
label_length – 1-D tensor of length for each label sequence
blank_index – Scalar used to mark a blank index
preprocess_collapse_repeated – Flag for preprocessing labels before loss calculation
ctc_merge_repeated – Flag for merging repeated characters in a potential alignment
unique – Flag to find unique elements in a target before matching with alignment
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
CTCLoss(const Output<Node> &logits, const Output<Node> &logit_length, const Output<Node> &labels, const Output<Node> &label_length, const bool preprocess_collapse_repeated = false, const bool ctc_merge_repeated = true, const bool unique = false)
-
class HSwish : public ov::op::util::UnaryElementwiseArithmetic
- #include <hswish.hpp>
A HSwish Activation Function f(x) = x * min(max(x + 3, 0), 6) / 6 or f(x) = x * min(ReLU(x + 3), 6) / 6.
-
class Interpolate : public ov::op::util::InterpolateBase
- #include <interpolate.hpp>
Interpolate operation.
Public Functions
-
Interpolate(const Output<Node> &image, const Output<Node> &output_shape, const Output<Node> &scales, const InterpolateAttrs &attrs)
Constructs a Interpolate operation without ‘axes’ input.
-
Interpolate(const Output<Node> &image, const Output<Node> &output_shape, const Output<Node> &scales, const Output<Node> &axes, const InterpolateAttrs &attrs)
Constructs a Interpolate operation with ‘axes’ input.
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
Interpolate(const Output<Node> &image, const Output<Node> &output_shape, const Output<Node> &scales, const InterpolateAttrs &attrs)
-
class LSTMCell : public ov::op::util::RNNCellBase
- #include <lstm_cell.hpp>
Class for single lstm cell node.
See also
LSTMSequence, RNNCell, GRUCell
Note
Following implementation supports:
peepholesGers & Schmidhuber (2000) https://ieeexplore.ieee.org/document/861302Coupling input and forget gates.
Note
It calculates following equations:
it = f(Xt*(Wi^T) + Ht-1*(Ri^T) + Wbi + Rbi) ft = f(Xt*(Wf^T) + Ht-1*(Rf^T) + Wbf + Rbf) ct = g(Xt*(Wc^T) + Ht-1*(Rc^T) + Wbc + Rbc) Ct = ft (.) Ct-1 + it (.) ct ot = f(Xt*(Wo^T) + Ht-1*(Ro^T) + Wbo + Rbo) Ht = ot (.) h(Ct) * - Is a dot product, (.) - is a Hadamard product (element-wise), f, g, h - are activation functions.
Note
This class represents only single cell (for current time step) and not the whole LSTM Sequence layer
Public Functions
-
LSTMCell(const Output<Node> &X, const Output<Node> &initial_hidden_state, const Output<Node> &initial_cell_state, const Output<Node> &W, const Output<Node> &R, std::size_t hidden_size, const std::vector<std::string> &activations = std::vector<std::string>{"sigmoid", "tanh", "tanh"}, const std::vector<float> &activations_alpha = {}, const std::vector<float> &activations_beta = {}, float clip = 0.f)
Constructs LSTMCell node.
- Parameters:
X – [in] The input tensor with shape: [batch_size, input_size].
initial_hidden_state – [in] The hidden state tensor at current time step with shape: [batch_size, hidden_size].
initial_cell_state – [in] The cell state tensor at current time step with shape: [batch_size, hidden_size].
W – [in] The gate weights tensor with shape: [4*hidden_size, input_size].
R – [in] The recurrence weights tensor with shape: [4*hidden_size, hidden_size].
hidden_size – [in] The number of hidden units for recurrent cell.
activations – [in] The vector of activation functions used inside recurrent cell.
activations_alpha – [in] The vector of alpha parameters for activation functions in order respective to activation list.
activations_beta – [in] The vector of beta parameters for activation functions in order respective to activation list.
clip – [in] The value defining clipping range [-clip, clip] on input of activation functions.
-
LSTMCell(const Output<Node> &X, const Output<Node> &initial_hidden_state, const Output<Node> &initial_cell_state, const Output<Node> &W, const Output<Node> &R, const Output<Node> &B, std::size_t hidden_size, const std::vector<std::string> &activations = std::vector<std::string>{"sigmoid", "tanh", "tanh"}, const std::vector<float> &activations_alpha = {}, const std::vector<float> &activations_beta = {}, float clip = 0.f)
Constructs LSTMCell node.
- Parameters:
X – [in] The input tensor with shape: [batch_size, input_size].
initial_hidden_state – [in] The hidden state tensor at current time step with shape: [batch_size, hidden_size].
initial_cell_state – [in] The cell state tensor at current time step with shape: [batch_size, hidden_size].
W – [in] The weight tensor with shape: [4*hidden_size, input_size].
R – [in] The recurrence weight tensor with shape: [4*hidden_size, hidden_size].
B – [in] The bias tensor for gates with shape: [4*hidden_size].
hidden_size – [in] The number of hidden units for recurrent cell.
activations – [in] The vector of activation functions used inside recurrent cell.
activations_alpha – [in] The vector of alpha parameters for activation functions in order respective to activation list.
activations_beta – [in] The vector of beta parameters for activation functions in order respective to activation list.
clip – [in] The value defining clipping range [-clip, clip] on input of activation functions.
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
class Mish : public ov::op::util::UnaryElementwiseArithmetic
- #include <mish.hpp>
A Self Regularized Non-Monotonic Neural Activation Function f(x) = x * tanh(log(exp(x) + 1.))
Public Functions
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
virtual void validate_and_infer_types() override
-
class NonMaxSuppression : public ov::op::v3::NonMaxSuppression
- #include <non_max_suppression.hpp>
NonMaxSuppression operation.
Public Functions
-
NonMaxSuppression(const Output<Node> &boxes, const Output<Node> &scores, const Output<Node> &max_output_boxes_per_class, const Output<Node> &iou_threshold, const Output<Node> &score_threshold, const BoxEncodingType box_encoding = BoxEncodingType::CORNER, const bool sort_result_descending = true, const ov::element::Type &output_type = ov::element::i64)
Constructs a NonMaxSuppression operation.
- Parameters:
boxes – Node producing the box coordinates
scores – Node producing the box scores
max_output_boxes_per_class – Node producing maximum number of boxes to be selected per class
iou_threshold – Node producing intersection over union threshold
score_threshold – Node producing minimum score threshold
box_encoding – Specifies the format of boxes data encoding
sort_result_descending – Specifies whether it is necessary to sort selected boxes across batches
output_type – Specifies the output tensor type
-
NonMaxSuppression(const Output<Node> &boxes, const Output<Node> &scores, const BoxEncodingType box_encoding = BoxEncodingType::CORNER, const bool sort_result_descending = true, const ov::element::Type &output_type = ov::element::i64)
Constructs a NonMaxSuppression operation with default values for the last 3 inputs.
- Parameters:
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
NonMaxSuppression(const Output<Node> &boxes, const Output<Node> &scores, const Output<Node> &max_output_boxes_per_class, const Output<Node> &iou_threshold, const Output<Node> &score_threshold, const BoxEncodingType box_encoding = BoxEncodingType::CORNER, const bool sort_result_descending = true, const ov::element::Type &output_type = ov::element::i64)
-
class Proposal : public ov::op::v0::Proposal
- #include <proposal.hpp>
Proposal operation.
Public Functions
-
Proposal(const Output<Node> &class_probs, const Output<Node> &bbox_deltas, const Output<Node> &image_shape, const Attributes &attrs)
Constructs a Proposal operation.
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
Proposal(const Output<Node> &class_probs, const Output<Node> &bbox_deltas, const Output<Node> &image_shape, const Attributes &attrs)
-
class Range : public ov::op::Op
- #include <range.hpp>
Range operation, analogous to
arange()in Numpy.Public Functions
-
Range() = default
Constructs an unitialized range operation.
-
Range(const Output<Node> &start, const Output<Node> &stop, const Output<Node> &step, element::Type output_type)
Constructs a range operation.
- Parameters:
start – The tensor producing the start value. Must be a scalar of numeric element type.
stop – The tensor producing the stop value. Must be a scalar of numeric element type.
step – The tensor producing the step value. Must be a scalar of numeric element type.
output_type – The type of the output.
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
Range() = default
-
class ReduceL1 : public ov::op::util::ArithmeticReductionKeepDims
- #include <reduce_l1.hpp>
Reduction operation using L1 norm: L1(x) = sum(abs(x)) if all dimensions are specified for the normalisation.
Reduces the tensor, eliminating the specified reduction axes by taking the L1-norm.
Public Functions
-
ReduceL1() = default
Constructs a reducet L1-norm operation.
-
ReduceL1(const Output<Node> &arg, const Output<Node> &reduction_axes, bool keep_dims = false)
Constructs a reduce L1-norm operation.
- Parameters:
arg – The tensor to be reduced.
reduction_axes – The axis positions (0-based) to be eliminated.
keep_dims – If set to true it holds axes that are used for reduction.
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
ReduceL1() = default
-
class ReduceL2 : public ov::op::util::ArithmeticReductionKeepDims
- #include <reduce_l2.hpp>
Reduction operation using L2 norm:
Reduces the tensor, eliminating the specified reduction axes by taking the L2-norm.
Public Functions
-
ReduceL2() = default
Constructs a reducet L2-norm operation.
-
ReduceL2(const Output<Node> &arg, const Output<Node> &reduction_axes, bool keep_dims = false)
Constructs a reduce L2-norm operation.
- Parameters:
arg – The tensor to be reduced.
reduction_axes – The axis positions (0-based) to be eliminated.
keep_dims – If set to true it holds axes that are used for reduction.
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
ReduceL2() = default
-
class SoftPlus : public ov::op::util::UnaryElementwiseArithmetic
- #include <softplus.hpp>
A Self Regularized Non-Monotonic Neural Activation Function f(x) = ln(exp(x) + 1.)
Public Functions
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
virtual void validate_and_infer_types() override
-
class Swish : public ov::op::Op
- #include <swish.hpp>
A Swish Activation Function f(x) = x / (1.0 + exp(-beta * x)) or f(x) = x * sigmoid(beta * x)
Public Functions
-
Swish(const Output<Node> &arg, const Output<Node> &beta)
Constructs an Swish operation.
- Parameters:
data – Input tensor
beta – Scalar with beta value. If the argument is not specified then use the default value 1.0
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
Swish(const Output<Node> &arg, const Output<Node> &beta)
-
namespace ctc_loss#
-
template<class TShape, class TRShape = result_shape_t<TShape>>
-
namespace v5#
Functions
-
template<class TShape, class TRShape = result_shape_t<TShape>>
std::vector<TRShape> shape_infer(const BatchNormInference *op, const std::vector<TShape> &inputs_shapes)#
-
template<class TShape, class TRShape = result_shape_t<TShape>>
std::vector<TRShape> shape_infer(const GatherND *op, const std::vector<TShape> &input_shapes)#
-
template<class TShape, class TRShape = result_shape_t<TShape>>
std::vector<TRShape> shape_infer(const ov::op::v5::GRUSequence *op, const std::vector<TShape> &input_shapes)#
-
template<class TShape>
std::vector<result_shape_t<TShape>> shape_infer(const LSTMSequence *op, const std::vector<TShape> &input_shapes)#
-
template<class T, class TRShape = result_shape_t<T>>
std::vector<TRShape> shape_infer(const NonMaxSuppression *op, const std::vector<T> &input_shapes, const ITensorAccessor &ta = make_tensor_accessor(), const bool static_output = !std::is_same<T, PartialShape>::value)#
-
template<class TShape>
std::vector<result_shape_t<TShape>> shape_infer(const RNNSequence *op, const std::vector<TShape> &input_shapes)#
-
class BatchNormInference : public ov::op::Op
- #include <batch_norm.hpp>
BatchNormInference operation.
Public Functions
-
BatchNormInference(const Output<Node> &input, const Output<Node> &gamma, const Output<Node> &beta, const Output<Node> &mean, const Output<Node> &variance, double epsilon)
- Parameters:
input – [., C, …]
gamma – gamma scaling for normalized value. [C]
beta – bias added to the scaled normalized value [C]
mean – value for mean normalization [C]
variance – value for variance normalization [C]
epsilon – Avoids divsion by 0 if input has 0 variance
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
BatchNormInference(const Output<Node> &input, const Output<Node> &gamma, const Output<Node> &beta, const Output<Node> &mean, const Output<Node> &variance, double epsilon)
-
class GatherND : public ov::op::util::GatherNDBase
- #include <gather_nd.hpp>
GatherND operation.
Public Functions
-
GatherND(const Output<Node> &data, const Output<Node> &indices, const size_t batch_dims = 0)
Constructs a GatherND operation.
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
GatherND(const Output<Node> &data, const Output<Node> &indices, const size_t batch_dims = 0)
-
class GRUSequence : public ov::op::util::RNNCellBase
- #include <gru_sequence.hpp>
GRUSequence operation.
Public Functions
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
virtual void validate_and_infer_types() override
-
class HSigmoid : public ov::op::util::UnaryElementwiseArithmetic
- #include <hsigmoid.hpp>
A HSigmoid Activation Function f(x) = min(max(x + 3, 0), 6) / 6 or f(x) = min(ReLU(x + 3), 6) / 6.
-
class LogSoftmax : public ov::op::Op
- #include <log_softmax.hpp>
LogSoftmax operation.
Public Functions
-
LogSoftmax(const Output<Node> &arg, const int64_t axis)
Constructs a LogSoftmax operation.
Output
[d0, ...]- Parameters:
arg – Node that produces the first input tensor.
[d0, ...]axis – The axis position (0-based) on which to calculate the LogSoftmax.
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
LogSoftmax(const Output<Node> &arg, const int64_t axis)
-
class Loop : public ov::op::util::SubGraphOp
- #include <loop.hpp>
Iterate a body over tensors, accumulating into tensors.
Public Functions
-
Loop() = default
Constructs a Loop operation.
-
Loop(const Output<Node> &trip_count, const Output<Node> &execution_condition)
Constructs a Loop operation.
-
virtual Output<Node> get_concatenated_slices(const Output<Node> &value, int64_t start, int64_t stride, int64_t part_size, int64_t end, int64_t axis) override
Concatenates slices from all iterations.
- Parameters:
value – The value supplying slice values from each iteration.
start – First index on axis of the slicing
stride – Stepping of the slice
part_size – Size of the slice on axis
end – The last index on axis of the slicing
axis – The axis to slice along
- Returns:
The concatenated slices.
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
virtual bool evaluate(ov::TensorVector &outputs, const ov::TensorVector &inputs) const override
Evaluates the op on input_values putting results in output_values.
- Parameters:
output_values – Tensors for the outputs to compute. One for each result
input_values – Tensors for the inputs. One for each inputs.
- Returns:
true if successful
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
struct SpecialBodyPorts
- #include <loop.hpp>
Allows to define the purpose of inputs/outputs in the body.
-
Loop() = default
-
class LSTMSequence : public ov::op::util::RNNCellBase
- #include <lstm_sequence.hpp>
Class for lstm sequence node.
See also
LSTMCell, RNNCell, GRUCell
Note
It follows notation and equations defined as in ONNX standard: onnx/onnx
Public Functions
-
inline virtual size_t get_default_output_index() const override
Returns the output of the default output, or throws if there is none.
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
inline virtual size_t get_default_output_index() const override
-
class NonMaxSuppression : public ov::op::Op
- #include <non_max_suppression.hpp>
NonMaxSuppression operation.
Public Functions
-
NonMaxSuppression(const Output<Node> &boxes, const Output<Node> &scores, const BoxEncodingType box_encoding = BoxEncodingType::CORNER, const bool sort_result_descending = true, const ov::element::Type &output_type = ov::element::i64)
Constructs a NonMaxSuppression operation with default values in the last 4 inputs.
-
NonMaxSuppression(const Output<Node> &boxes, const Output<Node> &scores, const Output<Node> &max_output_boxes_per_class, const BoxEncodingType box_encoding = BoxEncodingType::CORNER, const bool sort_result_descending = true, const ov::element::Type &output_type = ov::element::i64)
Constructs a NonMaxSuppression operation with default values in the last. 3 inputs.
- Parameters:
boxes – Node producing the box coordinates
scores – Node producing the box scores
max_output_boxes_per_class – Node producing maximum number of boxes to be selected per class
box_encoding – Specifies the format of boxes data encoding
sort_result_descending – Specifies whether it is necessary to sort selected boxes across batches
output_type – Specifies the output tensor type
-
NonMaxSuppression(const Output<Node> &boxes, const Output<Node> &scores, const Output<Node> &max_output_boxes_per_class, const Output<Node> &iou_threshold, const BoxEncodingType box_encoding = BoxEncodingType::CORNER, const bool sort_result_descending = true, const ov::element::Type &output_type = ov::element::i64)
Constructs a NonMaxSuppression operation with default values in the last. 2 inputs.
- Parameters:
boxes – Node producing the box coordinates
scores – Node producing the box scores
max_output_boxes_per_class – Node producing maximum number of boxes to be selected per class
iou_threshold – Node producing intersection over union threshold
box_encoding – Specifies the format of boxes data encoding
sort_result_descending – Specifies whether it is necessary to sort selected boxes across batches
output_type – Specifies the output tensor type
-
NonMaxSuppression(const Output<Node> &boxes, const Output<Node> &scores, const Output<Node> &max_output_boxes_per_class, const Output<Node> &iou_threshold, const Output<Node> &score_threshold, const BoxEncodingType box_encoding = BoxEncodingType::CORNER, const bool sort_result_descending = true, const ov::element::Type &output_type = ov::element::i64)
Constructs a NonMaxSuppression operation with default value in the last. input.
- Parameters:
boxes – Node producing the box coordinates
scores – Node producing the box scores
max_output_boxes_per_class – Node producing maximum number of boxes to be selected per class
iou_threshold – Node producing intersection over union threshold
score_threshold – Node producing minimum score threshold
box_encoding – Specifies the format of boxes data encoding
sort_result_descending – Specifies whether it is necessary to sort selected boxes across batches
output_type – Specifies the output tensor type
-
NonMaxSuppression(const Output<Node> &boxes, const Output<Node> &scores, const Output<Node> &max_output_boxes_per_class, const Output<Node> &iou_threshold, const Output<Node> &score_threshold, const Output<Node> &soft_nms_sigma, const BoxEncodingType box_encoding = BoxEncodingType::CORNER, const bool sort_result_descending = true, const ov::element::Type &output_type = ov::element::i64)
Constructs a NonMaxSuppression operation.
- Parameters:
boxes – Node producing the box coordinates
scores – Node producing the box scores
max_output_boxes_per_class – Node producing maximum number of boxes to be selected per class
iou_threshold – Node producing intersection over union threshold
score_threshold – Node producing minimum score threshold
soft_nms_sigma – Node specifying the sigma parameter for Soft-NMS
box_encoding – Specifies the format of boxes data encoding
sort_result_descending – Specifies whether it is necessary to sort selected boxes across batches
output_type – Specifies the output tensor type
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
NonMaxSuppression(const Output<Node> &boxes, const Output<Node> &scores, const BoxEncodingType box_encoding = BoxEncodingType::CORNER, const bool sort_result_descending = true, const ov::element::Type &output_type = ov::element::i64)
-
class RNNSequence : public ov::op::util::RNNCellBase
- #include <rnn_sequence.hpp>
RNNSequence operation.
Public Functions
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
virtual void validate_and_infer_types() override
-
class Round : public ov::op::util::UnaryElementwiseArithmetic
- #include <round.hpp>
Elementwise round operation. The output is round to the nearest integer for each value. In case of halfs, the rule is defined in attribute ‘mode’: ‘HALF_TO_EVEN’ - round halfs to the nearest even integer. ‘HALF_AWAY_FROM_ZERO’: - round in such a way that the result heads away from zero.
Public Functions
-
Round() = default
Constructs a round operation.
-
Round(const Output<Node> &arg, const RoundMode mode)
Constructs a round operation.
- Parameters:
arg – Node that produces the input tensor.
mode – Rule to resolve halfs
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
Round() = default
-
template<class TShape, class TRShape = result_shape_t<TShape>>
-
namespace v6#
Functions
-
template<class TShape, class TRShape = result_shape_t<TShape>>
std::vector<TRShape> shape_infer(const CTCGreedyDecoderSeqLen *op, const std::vector<TShape> &input_shapes)#
-
template<class TShape, class TRShape = result_shape_t<TShape>>
std::vector<TRShape> shape_infer(const ExperimentalDetectronDetectionOutput *op, const std::vector<TShape> &input_shapes)#
-
template<class TShape, class TRShape = result_shape_t<TShape>>
std::vector<TRShape> shape_infer(const ExperimentalDetectronGenerateProposalsSingleImage *op, const std::vector<TShape> &input_shapes)#
-
template<class TShape, class TRShape = result_shape_t<TShape>>
std::vector<TRShape> shape_infer(const ExperimentalDetectronPriorGridGenerator *op, const std::vector<TShape> &input_shapes)#
-
template<class TShape, class TRShape = result_shape_t<TShape>>
std::vector<TRShape> shape_infer(const ExperimentalDetectronROIFeatureExtractor *op, const std::vector<TShape> &input_shapes)#
-
template<class TShape, class TRShape = result_shape_t<TShape>>
std::vector<TRShape> shape_infer(ExperimentalDetectronTopKROIs *op, const std::vector<TShape> &input_shapes)#
-
template<class T, class TRShape = result_shape_t<T>>
std::vector<TRShape> shape_infer(const GatherElements *op, const std::vector<T> &input_shapes)#
-
class Assign : public ov::op::util::AssignBase
- #include <assign.hpp>
Assign operation sets an input value to the variable with
variable_idPublic Functions
Constructs an Assign operation.
- Parameters:
new_value – Node that produces the input tensor.
variable – Class for storing and synchronizing element types, shapes and identifiers between pairs of Assign/ReadValue nodes.
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
inline virtual std::string get_variable_id() const override
Returns the identifier of corresponding variable.
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
class CTCGreedyDecoderSeqLen : public ov::op::Op
- #include <ctc_greedy_decoder_seq_len.hpp>
Operator performing CTCGreedyDecoder.
Public Functions
-
CTCGreedyDecoderSeqLen(const Output<Node> &input, const Output<Node> &seq_len, const bool merge_repeated = true, const element::Type &classes_index_type = element::i32, const element::Type &sequence_length_type = element::i32)
Constructs a CTCGreedyDecoderSeqLen operation.
- Parameters:
input – 3-D tensor of logits on which greedy decoding is performed
seq_len – 1-D tensor of sequence lengths
merge_repeated – Whether to merge repeated labels
classes_index_type – Specifies the output classes_index tensor type
sequence_length_type – Specifies the output sequence_length tensor type
-
CTCGreedyDecoderSeqLen(const Output<Node> &input, const Output<Node> &seq_len, const Output<Node> &blank_index, const bool merge_repeated = true, const element::Type &classes_index_type = element::i32, const element::Type &sequence_length_type = element::i32)
Constructs a CTCGreedyDecoderSeqLen operation.
- Parameters:
input – 3-D tensor of logits on which greedy decoding is performed
seq_len – 1-D tensor of sequence lengths
blank_index – Scalar or 1-D tensor with 1 element used to mark a blank index
merge_repeated – Whether to merge repeated labels
classes_index_type – Specifies the output classes_index tensor type
sequence_length_type – Specifies the output sequence_length tensor type
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
inline bool get_merge_repeated() const
Get merge_repeated attribute.
- Returns:
Current value of merge_repeated attribute
-
inline void set_merge_repeated(bool merge_repeated)
Set merge_repeated attribute.
- Parameters:
merge_repeated – A new value for the attribute
-
inline const element::Type &get_classes_index_type() const
Get classes_index_type attribute.
- Returns:
Current value of classes_index_type attribute
-
inline void set_classes_index_type(const element::Type &classes_index_type)
Set classes_index_type attribute.
- Parameters:
classes_index_type – Type of classes_index
-
CTCGreedyDecoderSeqLen(const Output<Node> &input, const Output<Node> &seq_len, const bool merge_repeated = true, const element::Type &classes_index_type = element::i32, const element::Type &sequence_length_type = element::i32)
-
class ExperimentalDetectronDetectionOutput : public ov::op::Op
- #include <experimental_detectron_detection_output.hpp>
An operation ExperimentalDetectronDetectionOutput performs non-maximum suppression to generate the detection output using information on location and score predictions.
Public Functions
-
ExperimentalDetectronDetectionOutput(const Output<Node> &input_rois, const Output<Node> &input_deltas, const Output<Node> &input_scores, const Output<Node> &input_im_info, const Attributes &attrs)
Constructs a ExperimentalDetectronDetectionOutput operation.
- Parameters:
input_rois – Input rois
input_deltas – Input deltas
input_scores – Input scores
input_im_info – Input image info
attrs – Attributes attributes
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
inline const Attributes &get_attrs() const
Returns attributes of the operation ExperimentalDetectronDetectionOutput.
-
void set_attrs(Attributes attrs)
Set the attributes of the operation ExperimentalDetectronDetectionOutput.
- Parameters:
attrs – Attributes to set.
-
struct Attributes
- #include <experimental_detectron_detection_output.hpp>
Structure that specifies attributes of the operation.
-
ExperimentalDetectronDetectionOutput(const Output<Node> &input_rois, const Output<Node> &input_deltas, const Output<Node> &input_scores, const Output<Node> &input_im_info, const Attributes &attrs)
-
class ExperimentalDetectronGenerateProposalsSingleImage : public ov::op::Op
- #include <experimental_detectron_generate_proposals.hpp>
An operation ExperimentalDetectronGenerateProposalsSingleImage computes ROIs and their scores based on input data.
Public Functions
-
ExperimentalDetectronGenerateProposalsSingleImage(const Output<Node> &im_info, const Output<Node> &anchors, const Output<Node> &deltas, const Output<Node> &scores, const Attributes &attrs)
Constructs a ExperimentalDetectronGenerateProposalsSingleImage operation.
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
struct Attributes
- #include <experimental_detectron_generate_proposals.hpp>
Structure that specifies attributes of the operation.
-
ExperimentalDetectronGenerateProposalsSingleImage(const Output<Node> &im_info, const Output<Node> &anchors, const Output<Node> &deltas, const Output<Node> &scores, const Attributes &attrs)
-
class ExperimentalDetectronPriorGridGenerator : public ov::op::Op
- #include <experimental_detectron_prior_grid_generator.hpp>
An operation ExperimentalDetectronPriorGridGenerator generates prior grids of specified sizes.
Public Functions
-
ExperimentalDetectronPriorGridGenerator(const Output<Node> &priors, const Output<Node> &feature_map, const Output<Node> &im_data, const Attributes &attrs)
Constructs a ExperimentalDetectronDetectionOutput operation.
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
inline const Attributes &get_attrs() const
Returns attributes of this operation.
-
void set_attrs(Attributes attrs)
Set the attributes of the operation ExperimentalDetectronPriorGridGenerator.
- Parameters:
attrs – Attributes to set.
-
struct Attributes
- #include <experimental_detectron_prior_grid_generator.hpp>
Structure that specifies attributes of the operation.
-
ExperimentalDetectronPriorGridGenerator(const Output<Node> &priors, const Output<Node> &feature_map, const Output<Node> &im_data, const Attributes &attrs)
-
class ExperimentalDetectronROIFeatureExtractor : public ov::op::Op
- #include <experimental_detectron_roi_feature.hpp>
An operation ExperimentalDetectronROIFeatureExtractor is the ROIAlign operation applied over a feature pyramid.
Public Functions
-
ExperimentalDetectronROIFeatureExtractor(const OutputVector &args, const Attributes &attrs)
Constructs a ExperimentalDetectronROIFeatureExtractor operation.
- Parameters:
args – Inputs of ExperimentalDetectronROIFeatureExtractor
attrs – Operation attributes
-
ExperimentalDetectronROIFeatureExtractor(const NodeVector &args, const Attributes &attrs)
Constructs a ExperimentalDetectronROIFeatureExtractor operation.
- Parameters:
args – Inputs of ExperimentalDetectronROIFeatureExtractor
attrs – Operation attributes
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
inline const Attributes &get_attrs() const
Returns attributes of the operation.
-
void set_attrs(Attributes attrs)
Set the ExperimentalDetectronROIFeatureExtractor’s attributes.
- Parameters:
attrs – Attributes to set.
-
struct Attributes
- #include <experimental_detectron_roi_feature.hpp>
Structure that specifies attributes of the operation.
-
ExperimentalDetectronROIFeatureExtractor(const OutputVector &args, const Attributes &attrs)
-
class ExperimentalDetectronTopKROIs : public ov::op::Op
- #include <experimental_detectron_topkrois.hpp>
An operation ExperimentalDetectronTopKROIs, according to the repository is TopK operation applied to probabilities of input ROIs.
Public Functions
-
ExperimentalDetectronTopKROIs(const Output<Node> &input_rois, const Output<Node> &rois_probs, size_t max_rois = 0)
Constructs a ExperimentalDetectronTopKROIs operation.
- Parameters:
input_rois – Input rois
rois_probs – Probabilities for input rois
max_rois – Maximal numbers of output rois
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
ExperimentalDetectronTopKROIs(const Output<Node> &input_rois, const Output<Node> &rois_probs, size_t max_rois = 0)
-
class GatherElements : public ov::op::Op
- #include <gather_elements.hpp>
GatherElements operation.
Public Functions
-
GatherElements(const Output<Node> &data, const Output<Node> &indices, const int64_t axis)
Constructs a GatherElements operation.
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
GatherElements(const Output<Node> &data, const Output<Node> &indices, const int64_t axis)
-
class MVN : public ov::op::Op
- #include <mvn.hpp>
Operator performing Mean Variance Normalization.
Public Functions
-
MVN(const Output<Node> &data, const Output<Node> &reduction_axes, bool normalize_variance, float eps, MVNEpsMode eps_mode)
Constructs an MVN operation.
- Parameters:
data – Input tensor with data
reduction_axes – A list of axes, along which to reduce.
normalize_variance – flag that denotes whether to perform variance normalization.
eps – the number to be added to the variance to avoid division by zero when normalizing the value
eps_mode – the mode of applying epsilon
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
virtual bool evaluate(ov::TensorVector &output_values, const ov::TensorVector &input_values) const override
Evaluates the op on input_values putting results in output_values.
- Parameters:
output_values – Tensors for the outputs to compute. One for each result
input_values – Tensors for the inputs. One for each inputs.
- Returns:
true if successful
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
MVN(const Output<Node> &data, const Output<Node> &reduction_axes, bool normalize_variance, float eps, MVNEpsMode eps_mode)
-
class ReadValue : public ov::op::util::ReadValueBase
- #include <read_value.hpp>
ReadValue operation gets an input value from the variable with
variable_idand returns it as an output.Public Functions
Constructs a ReadValue operation.
- Parameters:
variable – Class for storing and synchronizing element types, shapes and identifiers between pairs of Assign/ReadValue nodes.
Constructs a ReadValue operation.
- Parameters:
init_value – Node that produces the input tensor.
variable – Class for storing and synchronizing element types, shapes and identifiers between pairs of Assign/ReadValue nodes.
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
inline virtual std::string get_variable_id() const override
Returns the identifier of corresponding variable.
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
template<class TShape, class TRShape = result_shape_t<TShape>>
-
namespace v7#
Functions
-
template<class T, class TRShape = result_shape_t<T>>
std::vector<TRShape> shape_infer(const Einsum *op, const std::vector<T> &input_shapes)#
-
template<class TShape, class TRShape = result_shape_t<TShape>>
std::vector<TRShape> shape_infer(const Roll *op, const std::vector<TShape> &input_shapes, const ITensorAccessor &ta = make_tensor_accessor())#
-
class DFT : public ov::op::util::FFTBase
- #include <dft.hpp>
An operation DFT that computes the discrete Fourier transformation.
-
class Einsum : public ov::op::Op
- #include <einsum.hpp>
Einsum operation.
Public Functions
-
Einsum(const OutputVector &inputs, const std::string &equation)
Constructs Einsum operation.
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
void set_equation(std::string equation)
Set Einsum equation.
- Parameters:
equation – Equation string to be set.
Public Static Functions
-
static void parse_equation(const std::string &equation, std::vector<std::string> &input_subscripts, std::string &output_subscript)
Check correctness of equation format and extract input subscripts and output subscript.
- Parameters:
equation – Equation to be parsed and checked
input_subscripts – A vector of extracted input subscripts
output_subscript – An output subscript
-
static std::vector<std::string> extract_labels(const std::string &subscript)
Extract labels (from subscript) that can be alphabetic letters or ellipsis.
- Parameters:
subscript – Subscript
- Returns:
A vector of extracted labels from the input subscript in the order of appearence
-
Einsum(const OutputVector &inputs, const std::string &equation)
-
class Gather : public ov::op::util::GatherBase
- #include <gather.hpp>
Gather slices from axis of data according to indices.
Public Functions
-
Gather(const Output<Node> &data, const Output<Node> &indices, const Output<Node> &axis, const int64_t batch_dims = 0)
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
Gather(const Output<Node> &data, const Output<Node> &indices, const Output<Node> &axis, const int64_t batch_dims = 0)
-
class Gelu : public ov::op::util::UnaryElementwiseArithmetic
- #include <gelu.hpp>
Gaussian Error Linear Unit f(x) = 0.5 * x * (1 + erf( x / sqrt(2) ) for “approximation” = “erf” f(x) = 0.5 * x * (1 + tanh([sqrt(2 / pi)] * [x + 0.044715^3]) for “approximation” = “tanh”.
Public Functions
-
Gelu(const Output<Node> &data, GeluApproximationMode mode = GeluApproximationMode::ERF)
Constructs a Gelu operation.
- Parameters:
data – Input tensor
mode – Approximation mode
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
Gelu(const Output<Node> &data, GeluApproximationMode mode = GeluApproximationMode::ERF)
-
class IDFT : public ov::op::util::FFTBase
- #include <idft.hpp>
An operation IDFT that computes the inverse discrete Fourier transformation.
-
class Roll : public ov::op::Op
- #include <roll.hpp>
Tensor roll operation.
Public Functions
-
Roll(const Output<Node> &data, const Output<Node> &shift, const Output<Node> &axes)
Constructs a roll operation.
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
Roll(const Output<Node> &data, const Output<Node> &shift, const Output<Node> &axes)
-
template<class T, class TRShape = result_shape_t<T>>
-
namespace v8#
Functions
-
template<class TShape, class TRShape = result_shape_t<TShape>>
std::vector<TRShape> shape_infer(const AdaptiveAvgPool *op, const std::vector<TShape> &input_shapes, const ITensorAccessor &tensor_accessor = make_tensor_accessor())#
-
template<class TShape, class TRShape = result_shape_t<TShape>>
std::vector<TRShape> shape_infer(const AdaptiveMaxPool *op, const std::vector<TShape> &input_shapes, const ITensorAccessor &tensor_accessor = make_tensor_accessor())#
-
template<class TShape, class TRShape = result_shape_t<TShape>>
std::vector<TRShape> shape_infer(const DeformableConvolution *op, const std::vector<TShape> &input_shapes, CoordinateDiff &pads_begin, CoordinateDiff &pads_end)#
-
template<class TShape, class TRShape = result_shape_t<TShape>>
std::vector<TRShape> shape_infer(const DetectionOutput *op, const std::vector<TShape> &input_shapes)#
-
template<class TShape, class TRShape = result_shape_t<TShape>>
std::vector<TRShape> shape_infer(const GatherND *op, const std::vector<TShape> &input_shapes)#
-
template<class TShape, class TRShape = result_shape_t<TShape>>
std::vector<TRShape> shape_infer(const MatrixNms *op, const std::vector<TShape> &input_shapes)#
-
template<class TShape, class TContainer, class TRShape = result_shape_t<TShape>>
std::vector<TRShape> shape_infer(const MaxPool *op, const std::vector<TShape> &input_shapes, TContainer &pads_begin, TContainer &pads_end)#
-
template<class TShape, class TRShape = result_shape_t<TShape>>
std::vector<TRShape> shape_infer(const PriorBox *const op, const std::vector<TShape> &input_shapes, const ITensorAccessor &ta = make_tensor_accessor())#
-
template<class TShape, class TRShape = result_shape_t<TShape>>
std::vector<TRShape> shape_infer(const RandomUniform *op, const std::vector<TShape> &input_shapes, const ITensorAccessor &ta = make_tensor_accessor())#
-
template<class T, class TRShape = result_shape_t<T>>
std::vector<TRShape> shape_infer(const Slice *op, const std::vector<T> &input_shapes, const ITensorAccessor &ta = make_tensor_accessor())#
-
class AdaptiveAvgPool : public ov::op::Op
- #include <adaptive_avg_pool.hpp>
Adaptive average pooling operation.
Public Functions
-
AdaptiveAvgPool(const Output<Node> &data, const Output<Node> &output_shape)
Constructs adaptive average pooling operation.
- Parameters:
data – Input data
output_shape – 1D tensor describing output shape for spatial dimensions.
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
AdaptiveAvgPool(const Output<Node> &data, const Output<Node> &output_shape)
-
class AdaptiveMaxPool : public ov::op::Op
- #include <adaptive_max_pool.hpp>
Adaptive max pooling operation.
Public Functions
-
AdaptiveMaxPool(const Output<Node> &data, const Output<Node> &output_shape, const ov::element::Type &index_element_type = ov::element::i64)
Constructs adaptive max pooling operation.
- Parameters:
data – Input data
output_shape – 1D tensor describing output shape for spatial dimensions.
index_element_type – Specifies the output tensor type for indices output
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
AdaptiveMaxPool(const Output<Node> &data, const Output<Node> &output_shape, const ov::element::Type &index_element_type = ov::element::i64)
-
class DeformableConvolution : public ov::op::util::DeformableConvolutionBase
- #include <deformable_convolution.hpp>
DeformableConvolution operation.
Public Functions
-
DeformableConvolution() = default
Constructs a conversion operation.
-
DeformableConvolution(const Output<Node> &arg, const Output<Node> &offsets, const Output<Node> &filters, const Strides &strides, const CoordinateDiff &pads_begin, const CoordinateDiff &pads_end, const Strides &dilations, const PadType &auto_pad = PadType::EXPLICIT, const int64_t group = 1, const int64_t deformable_group = 1, const bool bilinear_interpolation_pad = false)
Constructs a conversion operation.
- Parameters:
arg – Node that produces the input tensor.
offsets – Node producing the deformable values tensor.
filters – Node producing the filters(kernels) tensor with OIZYX layout.
strides – Convolution strides.
pads_begin – Amount of padding to be added to the beginning along each axis. For example in case of a 2D input the value of (1, 2) means that 1 element will be added to the top and 2 elements to the left.
pads_end – Amount of padding to be added to the end along each axis.
dilations – The distance in width and height between the weights in the filters tensor.
auto_pad – Specifies how the automatic calculation of padding should be done.
group – The number of groups which both output and input should be split into.
deformable_group – The number of groups which deformable values and output should be split into along the channel axis.
bilinear_interpolation_pad – The flag that determines the mode of bilinear interpolation execution. If the flag is
trueand the sampling location is within one pixel outside of the feature map boundary, then bilinear interpolation is performed on the zero padded feature map. If the flag isfalseand the sampling location is within one pixel outside of the feature map boundary, then the sampling location shifts to the inner boundary of the feature map.`
-
DeformableConvolution(const Output<Node> &arg, const Output<Node> &offsets, const Output<Node> &filters, const Output<Node> &mask, const Strides &strides, const CoordinateDiff &pads_begin, const CoordinateDiff &pads_end, const Strides &dilations, const PadType &auto_pad = PadType::EXPLICIT, const int64_t group = 1, const int64_t deformable_group = 1, const bool bilinear_interpolation_pad = false)
Constructs a conversion operation.
- Parameters:
arg – Node that produces the input tensor.
offsets – Node producing the deformable values tensor.
filters – Node producing the filters(kernels) tensor with OIZYX layout.
mask – Node producing the mask(mask) tensor.
strides – Convolution strides.
pads_begin – Amount of padding to be added to the beginning along each axis. For example in case of a 2D input the value of (1, 2) means that 1 element will be added to the top and 2 elements to the left.
pads_end – Amount of padding to be added to the end along each axis.
dilations – The distance in width and height between the weights in the filters tensor.
auto_pad – Specifies how the automatic calculation of padding should be done.
group – The number of groups which both output and input should be split into.
deformable_group – The number of groups which deformable values and output should be split into along the channel axis.
bilinear_interpolation_pad – The flag that determines the mode of bilinear interpolation execution. If the flag is
trueand the sampling location is within one pixel outside of the feature map boundary, then bilinear interpolation is performed on the zero padded feature map. If the flag isfalseand the sampling location is within one pixel outside of the feature map boundary, then the sampling location shifts to the inner boundary of the feature map.
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
DeformableConvolution() = default
-
class DetectionOutput : public ov::op::util::DetectionOutputBase
- #include <detection_output.hpp>
Layer which performs non-max suppression to generate detection output using location and confidence predictions.
Public Functions
-
DetectionOutput(const Output<Node> &box_logits, const Output<Node> &class_preds, const Output<Node> &proposals, const Output<Node> &aux_class_preds, const Output<Node> &aux_box_preds, const Attributes &attrs)
Constructs a DetectionOutput operation.
- Parameters:
box_logits – Box logits
class_preds – Class predictions
proposals – Proposals
aux_class_preds – Auxilary class predictions
aux_box_preds – Auxilary box predictions
attrs – Detection Output attributes
-
DetectionOutput(const Output<Node> &box_logits, const Output<Node> &class_preds, const Output<Node> &proposals, const Attributes &attrs)
Constructs a DetectionOutput operation.
- Parameters:
box_logits – Box logits
class_preds – Class predictions
proposals – Proposals
attrs – Detection Output attributes
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
DetectionOutput(const Output<Node> &box_logits, const Output<Node> &class_preds, const Output<Node> &proposals, const Output<Node> &aux_class_preds, const Output<Node> &aux_box_preds, const Attributes &attrs)
-
class Gather : public ov::op::util::GatherBase
- #include <gather.hpp>
Gather slices from axis of data according to indices. Negative indices are supported and indicate reverse indexing from the end.
Subclassed by ov::op::internal::GatherCompressed
Public Functions
-
Gather(const Output<Node> &data, const Output<Node> &indices, const Output<Node> &axis, const int64_t batch_dims = 0)
- Parameters:
data – The tensor from which slices are gathered
indices – Tensor with indexes to gather
axis – The tensor is a dimension index to gather data from
batch_dims – The number of batch dimension in data and indices tensors.
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
Gather(const Output<Node> &data, const Output<Node> &indices, const Output<Node> &axis, const int64_t batch_dims = 0)
-
class GatherND : public ov::op::util::GatherNDBase
- #include <gather_nd.hpp>
GatherND operation.
Public Functions
-
GatherND(const Output<Node> &data, const Output<Node> &indices, const size_t batch_dims = 0)
Constructs a GatherND operation.
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
GatherND(const Output<Node> &data, const Output<Node> &indices, const size_t batch_dims = 0)
-
class I420toBGR : public ov::op::util::ConvertColorI420Base
- #include <i420_to_bgr.hpp>
Color conversion operation from I420 to BGR format. Input:
Input NV12 image can be represented in two ways: a) Single plane (as it is in the file): I420 height dimension is 1.5x bigger than image height. ‘C’ dimension shall be 1. b) Three separate planes (used this way in many physical video sources): Y, U and V. In this case b1) Y plane has height same as image height. ‘C’ dimension equals to 1 b2) U plane has dimensions: ‘H’ = image_h / 2; ‘W’ = image_w / 2; ‘C’ = 1. b3) V plane has dimensions: ‘H’ = image_h / 2; ‘W’ = image_w / 2; ‘C’ = 1.
Supported element types: u8 or any supported floating-point type. Output:
Output node will have NHWC layout and shape HxW same as image spatial dimensions.
Number of output channels ‘C’ will be 3, as per interleaved BGR format, first channel is B, last is R
Conversion of each pixel from I420 (YUV) to RGB space is represented by following formulas: R = 1.164 * (Y - 16) + 1.596 * (V - 128) G = 1.164 * (Y - 16) - 0.813 * (V - 128) - 0.391 * (U - 128) B = 1.164 * (Y - 16) + 2.018 * (U - 128) Then R, G, B values are clipped to range (0, 255)
Public Functions
-
explicit I420toBGR(const Output<Node> &arg)
Constructs a conversion operation from input image in I420 format As per I420 format definition, node height dimension shall be 1.5 times bigger than image height so that image (w=640, h=480) is represented by NHWC shape {N,720,640,1} (height*1.5 x width)
-
explicit I420toBGR(const Output<Node> &arg_y, const Output<Node> &arg_u, const Output<Node> &arg_v)
Constructs a conversion operation from 2-plane input image in NV12 format In general case Y channel of image can be separated from UV channel which means that operation needs two nodes for Y and UV planes respectively. Y plane has one channel, and UV has 2 channels, both expect ‘NHWC’ layout.
- Parameters:
arg_y – Node that produces the input tensor for Y plane (NHWC layout). Shall have WxH dimensions equal to image dimensions. ‘C’ dimension equals to 1.
arg_u – Node that produces the input tensor for U plane (NHWC layout). ‘H’ is half of image height, ‘W’ is half of image width, ‘C’ dimension equals to 1.
arg_v – Node that produces the input tensor for V plane (NHWC layout). ‘H’ is half of image height, ‘W’ is half of image width, ‘C’ dimension equals to 1.
-
class I420toRGB : public ov::op::util::ConvertColorI420Base
- #include <i420_to_rgb.hpp>
Color conversion operation from I420 to RGB format. Input:
Input NV12 image can be represented in two ways: a) Single plane (as it is in the file): I420 height dimension is 1.5x bigger than image height. ‘C’ dimension shall be 1. b) Three separate planes (used this way in many physical video sources): Y, U and V. In this case b1) Y plane has height same as image height. ‘C’ dimension equals to 1 b2) U plane has dimensions: ‘H’ = image_h / 2; ‘W’ = image_w / 2; ‘C’ = 1. b3) V plane has dimensions: ‘H’ = image_h / 2; ‘W’ = image_w / 2; ‘C’ = 1.
Supported element types: u8 or any supported floating-point type. Output:
Output node will have NHWC layout and shape HxW same as image spatial dimensions.
Number of output channels ‘C’ will be 3, as per interleaved RGB format, first channel is R, last is B
Conversion of each pixel from I420 (YUV) to RGB space is represented by following formulas: R = 1.164 * (Y - 16) + 1.596 * (V - 128) G = 1.164 * (Y - 16) - 0.813 * (V - 128) - 0.391 * (U - 128) B = 1.164 * (Y - 16) + 2.018 * (U - 128) Then R, G, B values are clipped to range (0, 255)
Public Functions
-
explicit I420toRGB(const Output<Node> &arg)
Constructs a conversion operation from input image in I420 format As per I420 format definition, node height dimension shall be 1.5 times bigger than image height so that image (w=640, h=480) is represented by NHWC shape {N,720,640,1} (height*1.5 x width)
-
explicit I420toRGB(const Output<Node> &arg_y, const Output<Node> &arg_u, const Output<Node> &arg_v)
Constructs a conversion operation from 2-plane input image in NV12 format In general case Y channel of image can be separated from UV channel which means that operation needs two nodes for Y and UV planes respectively. Y plane has one channel, and UV has 2 channels, both expect ‘NHWC’ layout.
- Parameters:
arg_y – Node that produces the input tensor for Y plane (NHWC layout). Shall have WxH dimensions equal to image dimensions. ‘C’ dimension equals to 1.
arg_u – Node that produces the input tensor for U plane (NHWC layout). ‘H’ is half of image height, ‘W’ is half of image width, ‘C’ dimension equals to 1.
arg_v – Node that produces the input tensor for V plane (NHWC layout). ‘H’ is half of image height, ‘W’ is half of image width, ‘C’ dimension equals to 1.
-
class If : public ov::op::util::MultiSubGraphOp
- #include <if.hpp>
If operation.
Public Functions
-
If(const Output<Node> &execution_condition)
Constructs If with condition.
- Parameters:
execution_condition – condition node.
-
inline const std::shared_ptr<Model> &get_then_body() const
gets then_body as ov::Model.
- Returns:
then_body as ov::Model.
-
inline const std::shared_ptr<Model> &get_else_body() const
gets else_body as ov::Model.
- Returns:
else_body as ov::Model.
sets new ov::Model as new then_body.
- Parameters:
body – new body for ‘then’ branch.
sets new ov::Model as new else_body.
- Parameters:
body – new body for ‘else’ branch.
sets new input to the operation associated with parameters of each sub-graphs
- Parameters:
value – input to operation
then_parameter – parameter for then_body or nullptr
else_parameter – parameter for else_body or nullpt
sets new output from the operation associated with results of each sub-graphs
- Parameters:
then_result – result from then_body
else_parameter – result from else_body
- Returns:
output from operation
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
If(const Output<Node> &execution_condition)
-
class MatrixNms : public ov::op::Op
- #include <matrix_nms.hpp>
MatrixNms operation.
Public Functions
-
MatrixNms() = default
Constructs a conversion operation.
-
MatrixNms(const Output<Node> &boxes, const Output<Node> &scores, const Attributes &attrs)
Constructs a MatrixNms operation.
- Parameters:
boxes – Node producing the box coordinates
scores – Node producing the box scores
attrs – Attributes of the operation
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
inline const Attributes &get_attrs() const
Returns attributes of the operation MatrixNms.
-
struct Attributes
- #include <matrix_nms.hpp>
Structure that specifies attributes of the operation.
-
MatrixNms() = default
-
class MaxPool : public ov::op::util::MaxPoolBase
- #include <max_pool.hpp>
MaxPooling operation with values and indices calculated as individual outputs.
Public Functions
-
MaxPool() = default
Constructs an empty MaxPool operation.
-
MaxPool(const Output<Node> &arg, const Strides &strides, const Strides &dilations, const Shape &pads_begin, const Shape &pads_end, const Shape &kernel, const op::RoundingType rounding_type = op::RoundingType::FLOOR, const PadType auto_pad = op::PadType::EXPLICIT, const element::Type index_element_type = element::i64, const int64_t axis = 0)
Constructs a parametrized MaxPool operation.
- Parameters:
arg – Output of a node producing the feature tensor to be pooled.
strides – The strides of the pooling filter.
dilations – The dilations of the pooling filter.
pads_begin – Paddings at the beginning of each spatial axis.
pads_end – Paddings at the end of each spatial axis.
kernel – The kernel shape.
rounding_type – Whether to use ceiling or floor rounding type while computing the output shape.
auto_pad – The pad type for automatic calculation of the padding sizes.
index_element_type – The data type used by the second output tensor containing the selected indices.
axis – Indicates a dimension in the input data shape which should be used as a starting point for calculation of the upper bound of allowed values of the indices output.
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
const Strides &get_dilations() const noexcept
- Returns:
The pooling filter’s dilations.
-
element::Type get_index_element_type() const noexcept
- Returns:
The data type of the second output tensor (indices).
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
MaxPool() = default
-
class MulticlassNms : public ov::op::util::MulticlassNmsBase
- #include <multiclass_nms.hpp>
MulticlassNms operation.
Public Functions
-
MulticlassNms() = default
Constructs a conversion operation.
-
MulticlassNms(const Output<Node> &boxes, const Output<Node> &scores, const Attributes &attrs)
Constructs a MulticlassNms operation.
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
MulticlassNms() = default
-
class NV12toBGR : public ov::op::util::ConvertColorNV12Base
- #include <nv12_to_bgr.hpp>
Color conversion operation from NV12 to RGB format. Input:
Input NV12 image can be represented in two ways: a) Single plane (as it is in the file): NV12 height dimension is 1.5x bigger than image height. ‘C’ dimension shall be 1. b) Two separate planes (used this way in many physical video sources): Y and UV. In this case b1) Y plane has height same as image height. ‘C’ dimension equals to 1 b2) UV plane has dimensions: ‘H’ = image_h / 2; ‘W’ = image_w / 2; ‘C’ = 2.
Supported element types: u8 or any supported floating-point type. Output:
Output node will have NHWC layout and shape HxW same as image spatial dimensions.
Number of output channels ‘C’ will be 3, as per interleaved RGB format, first channel is B, last is R
Conversion of each pixel from NV12 (YUV) to RGB space is represented by following formulas: R = 1.164 * (Y - 16) + 1.596 * (V - 128) G = 1.164 * (Y - 16) - 0.813 * (V - 128) - 0.391 * (U - 128) B = 1.164 * (Y - 16) + 2.018 * (U - 128) Then R, G, B values are clipped to range (0, 255)
Public Functions
-
explicit NV12toBGR(const Output<Node> &arg)
Constructs a conversion operation from input image in NV12 format As per NV12 format definition, node height dimension shall be 1.5 times bigger than image height so that image (w=640, h=480) is represented by NHWC shape {N,720,640,1} (height*1.5 x width)
-
explicit NV12toBGR(const Output<Node> &arg_y, const Output<Node> &arg_uv)
Constructs a conversion operation from 2-plane input image in NV12 format In general case Y channel of image can be separated from UV channel which means that operation needs two nodes for Y and UV planes respectively. Y plane has one channel, and UV has 2 channels, both expect ‘NHWC’ layout.
- Parameters:
arg_y – Node that produces the input tensor for Y plane (NHWC layout). Shall have WxH dimensions equal to image dimensions. ‘C’ dimension equals to 1.
arg_uv – Node that produces the input tensor for UV plane (NHWC layout). ‘H’ is half of image height, ‘W’ is half of image width, ‘C’ dimension equals to 2. Channel 0 represents ‘U’, channel 1 represents ‘V’ channel
-
class NV12toRGB : public ov::op::util::ConvertColorNV12Base
- #include <nv12_to_rgb.hpp>
Color conversion operation from NV12 to RGB format. Input:
Input NV12 image can be represented in two ways: a) Single plane (as it is in the file): NV12 height dimension is 1.5x bigger than image height. ‘C’ dimension shall be 1. b) Two separate planes (used this way in many physical video sources): Y and UV. In this case b1) Y plane has height same as image height. ‘C’ dimension equals to 1 b2) UV plane has dimensions: ‘H’ = image_h / 2; ‘W’ = image_w / 2; ‘C’ = 2.
Supported element types: u8 or any supported floating-point type. Output:
Output node will have NHWC layout and shape HxW same as image spatial dimensions.
Number of output channels ‘C’ will be 3, as per interleaved RGB format, first channel is R, last is B
Conversion of each pixel from NV12 (YUV) to RGB space is represented by following formulas: R = 1.164 * (Y - 16) + 1.596 * (V - 128) G = 1.164 * (Y - 16) - 0.813 * (V - 128) - 0.391 * (U - 128) B = 1.164 * (Y - 16) + 2.018 * (U - 128) Then R, G, B values are clipped to range (0, 255)
Public Functions
-
explicit NV12toRGB(const Output<Node> &arg)
Constructs a conversion operation from input image in NV12 format As per NV12 format definition, node height dimension shall be 1.5 times bigger than image height so that image (w=640, h=480) is represented by NHWC shape {N,720,640,1} (height*1.5 x width)
-
NV12toRGB(const Output<Node> &arg_y, const Output<Node> &arg_uv)
Constructs a conversion operation from 2-plane input image in NV12 format In general case Y channel of image can be separated from UV channel which means that operation needs two nodes for Y and UV planes respectively. Y plane has one channel, and UV has 2 channels, both expect ‘NHWC’ layout.
- Parameters:
arg_y – Node that produces the input tensor for Y plane (NHWC layout). Shall have WxH dimensions equal to image dimensions. ‘C’ dimension equals to 1.
arg_uv – Node that produces the input tensor for UV plane (NHWC layout). ‘H’ is half of image height, ‘W’ is half of image width, ‘C’ dimension equals to 2. Channel 0 represents ‘U’, channel 1 represents ‘V’ channel
-
class PriorBox : public ov::op::Op
- #include <prior_box.hpp>
Layer which generates prior boxes of specified sizes normalized to input image size.
Public Functions
-
PriorBox(const Output<Node> &layer_shape, const Output<Node> &image_shape, const Attributes &attrs)
Constructs a PriorBox operation.
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
struct Attributes
- #include <prior_box.hpp>
-
PriorBox(const Output<Node> &layer_shape, const Output<Node> &image_shape, const Attributes &attrs)
-
class RandomUniform : public ov::op::Op
- #include <random_uniform.hpp>
Tensor RandomUniform operation.
Public Functions
-
RandomUniform(const Output<Node> &out_shape, const Output<Node> &min_val, const Output<Node> &max_val, const ov::element::Type &out_type, uint64_t global_seed = 0, uint64_t op_seed = 0)
Constructs a RandomUniform operation.
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
inline virtual bool constant_fold(OutputVector &output_values, const OutputVector &inputs_values) override
- Returns:
Turns off constant folding for RandomUniform operation.
-
inline uint64_t get_global_seed() const
- Returns:
The global seed value.
-
inline uint64_t get_op_seed() const
- Returns:
The operational seed value.
-
inline std::pair<uint64_t, uint64_t> get_state() const
- Returns:
The state value.
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
RandomUniform(const Output<Node> &out_shape, const Output<Node> &min_val, const Output<Node> &max_val, const ov::element::Type &out_type, uint64_t global_seed = 0, uint64_t op_seed = 0)
-
class Slice : public ov::op::Op
- #include <slice.hpp>
Slice operation.
Public Functions
-
Slice(const Output<Node> &data, const Output<Node> &start, const Output<Node> &stop, const Output<Node> &step)
Constructs Slice operation (default axes).
- Parameters:
data – The tensor to be sliced.
start – 1D tensor with start indices of the slice.
stop – 1D tensor with end indices of the slice.
step – 1D tensor specifies the increment to use in slicing along corresponding axes.
-
Slice(const Output<Node> &data, const Output<Node> &start, const Output<Node> &stop, const Output<Node> &step, const Output<Node> &axes)
Constructs Slice operation.
- Parameters:
data – The tensor to be sliced.
start – 1D tensor with start indices of the slice.
stop – 1D tensor with end indices of the slice.
step – 1D tensor specifies the increment to use in slicing along corresponding axes.
axes – 1D tensor indicating which dimensions the values in the
startandstopapply to.
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
Slice(const Output<Node> &data, const Output<Node> &start, const Output<Node> &stop, const Output<Node> &step)
-
class Softmax : public ov::op::Op
- #include <softmax.hpp>
Softmax operation with negative axis values.
Public Functions
-
Softmax(const Output<Node> &arg, const int64_t axis = 1)
Constructs a softmax operation.
Output
[d0, ...]- Parameters:
arg – Node that produces the first input tensor.
[d0, ...]axis – The axis position (0-based) in range [-rank(arg), rank(arg) - 1] on which to calculate the softmax.
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
Softmax(const Output<Node> &arg, const int64_t axis = 1)
-
template<class TShape, class TRShape = result_shape_t<TShape>>
-
namespace v9#
Functions
-
template<class TShape, class TRShape = result_shape_t<TShape>>
std::vector<TRShape> shape_infer(const Eye *op, const std::vector<TShape> &input_shapes, const ITensorAccessor &ta = make_tensor_accessor())#
-
template<class T, class TRShape = result_shape_t<T>>
std::vector<TRShape> shape_infer(const GenerateProposals *op, const std::vector<T> &input_shapes)#
-
template<class TShape, class TRShape = result_shape_t<TShape>>
std::vector<TRShape> shape_infer(const GridSample *op, const std::vector<TShape> &input_shapes)#
-
template<class T, class TRShape = result_shape_t<T>>
std::vector<TRShape> shape_infer(const IRDFT *op, const std::vector<T> &input_shapes, const ITensorAccessor &ta = make_tensor_accessor())#
-
template<class T, class TRShape = result_shape_t<T>>
std::vector<TRShape> shape_infer(const NonMaxSuppression *op, const std::vector<T> &input_shapes, const ITensorAccessor &ta = make_tensor_accessor(), const bool static_output = !std::is_same<T, PartialShape>::value)#
-
template<class T, class TRShape = result_shape_t<T>>
std::vector<TRShape> shape_infer(const RDFT *op, const std::vector<T> &input_shapes, const ITensorAccessor &ta = make_tensor_accessor())#
-
class Eye : public ov::op::Op
- #include <eye.hpp>
Public Functions
-
Eye(const Output<Node> &num_rows, const Output<Node> &num_columns, const Output<Node> &diagonal_index, const Output<Node> &batch_shape, const ov::element::Type &out_type)
Constructs a Eye operation.
- Parameters:
-
Eye(const Output<Node> &num_rows, const Output<Node> &num_columns, const Output<Node> &diagonal_index, const ov::element::Type &out_type)
Constructs a Eye operation without batch_shape.
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
Eye(const Output<Node> &num_rows, const Output<Node> &num_columns, const Output<Node> &diagonal_index, const Output<Node> &batch_shape, const ov::element::Type &out_type)
-
class GenerateProposals : public ov::op::Op
- #include <generate_proposals.hpp>
An operation GenerateProposals computes ROIs and their scores based on input data.
Subclassed by ov::op::internal::GenerateProposalsIEInternal
Public Functions
-
GenerateProposals(const Output<Node> &im_info, const Output<Node> &anchors, const Output<Node> &deltas, const Output<Node> &scores, const Attributes &attrs, const element::Type &roi_num_type = element::i64)
Constructs a GenerateProposals operation.
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
struct Attributes
- #include <generate_proposals.hpp>
Structure that specifies attributes of the operation.
-
GenerateProposals(const Output<Node> &im_info, const Output<Node> &anchors, const Output<Node> &deltas, const Output<Node> &scores, const Attributes &attrs, const element::Type &roi_num_type = element::i64)
-
class GridSample : public ov::op::Op
- #include <grid_sample.hpp>
Operator performing interpolated sampling of the input tensor.
Public Functions
-
GridSample(const Output<Node> &data, const Output<Node> &grid, const Attributes &attributes)
Constructs a GridSample operation.
- Parameters:
data – Input data tensor (input image)
grid – Normalized interpolation coordinates
attrs – GridSample attributes
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
struct Attributes
- #include <grid_sample.hpp>
A Structure which contains all GridSample attributes.
-
GridSample(const Output<Node> &data, const Output<Node> &grid, const Attributes &attributes)
-
class IRDFT : public ov::op::util::FFTBase
- #include <irdft.hpp>
An operation IRDFT that computes the discrete inverse complex-to-real Fourier transformation.
Public Functions
-
IRDFT(const Output<Node> &data, const Output<Node> &axes)
Constructs a IRDFT operation. IRDFT is performed for full size axes.
-
IRDFT(const Output<Node> &data, const Output<Node> &axes, const Output<Node> &signal_size)
Constructs a IRDFT operation.
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
IRDFT(const Output<Node> &data, const Output<Node> &axes)
-
class MulticlassNms : public ov::op::util::MulticlassNmsBase
- #include <multiclass_nms.hpp>
MulticlassNms operation.
Subclassed by ov::op::internal::MulticlassNmsIEInternal
Public Functions
-
MulticlassNms() = default
Constructs a conversion operation.
-
MulticlassNms(const Output<Node> &boxes, const Output<Node> &scores, const Attributes &attrs)
Constructs a MulticlassNms operation.
-
MulticlassNms(const Output<Node> &boxes, const Output<Node> &scores, const Output<Node> &roisnum, const Attributes &attrs)
Constructs a MulticlassNms operation.
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
MulticlassNms() = default
-
class NonMaxSuppression : public ov::op::Op
- #include <non_max_suppression.hpp>
NonMaxSuppression operation.
Public Functions
-
NonMaxSuppression(const Output<Node> &boxes, const Output<Node> &scores, const BoxEncodingType box_encoding = BoxEncodingType::CORNER, const bool sort_result_descending = true, const ov::element::Type &output_type = ov::element::i64)
Constructs a NonMaxSuppression operation with default values in the last 4 inputs.
-
NonMaxSuppression(const Output<Node> &boxes, const Output<Node> &scores, const Output<Node> &max_output_boxes_per_class, const BoxEncodingType box_encoding = BoxEncodingType::CORNER, const bool sort_result_descending = true, const ov::element::Type &output_type = ov::element::i64)
Constructs a NonMaxSuppression operation with default values in the last 3 inputs.
- Parameters:
boxes – Node producing the box coordinates
scores – Node producing the box scores
max_output_boxes_per_class – Node producing maximum number of boxes to be selected per class
box_encoding – Specifies the format of boxes data encoding
sort_result_descending – Specifies whether it is necessary to sort selected boxes across batches
output_type – Specifies the output tensor type
-
NonMaxSuppression(const Output<Node> &boxes, const Output<Node> &scores, const Output<Node> &max_output_boxes_per_class, const Output<Node> &iou_threshold, const BoxEncodingType box_encoding = BoxEncodingType::CORNER, const bool sort_result_descending = true, const ov::element::Type &output_type = ov::element::i64)
Constructs a NonMaxSuppression operation with default values in the last. 2 inputs.
- Parameters:
boxes – Node producing the box coordinates
scores – Node producing the box scores
max_output_boxes_per_class – Node producing maximum number of boxes to be selected per class
iou_threshold – Node producing intersection over union threshold
box_encoding – Specifies the format of boxes data encoding
sort_result_descending – Specifies whether it is necessary to sort selected boxes across batches
output_type – Specifies the output tensor type
-
NonMaxSuppression(const Output<Node> &boxes, const Output<Node> &scores, const Output<Node> &max_output_boxes_per_class, const Output<Node> &iou_threshold, const Output<Node> &score_threshold, const BoxEncodingType box_encoding = BoxEncodingType::CORNER, const bool sort_result_descending = true, const ov::element::Type &output_type = ov::element::i64)
Constructs a NonMaxSuppression operation with default value in the last. input.
- Parameters:
boxes – Node producing the box coordinates
scores – Node producing the box scores
max_output_boxes_per_class – Node producing maximum number of boxes to be selected per class
iou_threshold – Node producing intersection over union threshold
score_threshold – Node producing minimum score threshold
box_encoding – Specifies the format of boxes data encoding
sort_result_descending – Specifies whether it is necessary to sort selected boxes across batches
output_type – Specifies the output tensor type
-
NonMaxSuppression(const Output<Node> &boxes, const Output<Node> &scores, const Output<Node> &max_output_boxes_per_class, const Output<Node> &iou_threshold, const Output<Node> &score_threshold, const Output<Node> &soft_nms_sigma, const BoxEncodingType box_encoding = BoxEncodingType::CORNER, const bool sort_result_descending = true, const ov::element::Type &output_type = ov::element::i64)
Constructs a NonMaxSuppression operation.
- Parameters:
boxes – Node producing the box coordinates
scores – Node producing the box scores
max_output_boxes_per_class – Node producing maximum number of boxes to be selected per class
iou_threshold – Node producing intersection over union threshold
score_threshold – Node producing minimum score threshold
soft_nms_sigma – Node specifying the sigma parameter for Soft-NMS
box_encoding – Specifies the format of boxes data encoding
sort_result_descending – Specifies whether it is necessary to sort selected boxes across batches
output_type – Specifies the output tensor type
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
NonMaxSuppression(const Output<Node> &boxes, const Output<Node> &scores, const BoxEncodingType box_encoding = BoxEncodingType::CORNER, const bool sort_result_descending = true, const ov::element::Type &output_type = ov::element::i64)
-
class RDFT : public ov::op::util::FFTBase
- #include <rdft.hpp>
An operation RDFT that computes the discrete real-to-complex Fourier transformation.
Public Functions
-
RDFT(const Output<Node> &data, const Output<Node> &axes)
Constructs a RDFT operation. RDFT is performed for full size axes.
-
RDFT(const Output<Node> &data, const Output<Node> &axes, const Output<Node> &signal_size)
Constructs a RDFT operation.
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
RDFT(const Output<Node> &data, const Output<Node> &axes)
-
class ROIAlign : public ov::op::util::ROIAlignBase
- #include <roi_align.hpp>
Public Functions
-
ROIAlign(const Output<Node> &input, const Output<Node> &rois, const Output<Node> &batch_indices, const int pooled_h, const int pooled_w, const int sampling_ratio, const float spatial_scale, const PoolingMode mode, const AlignedMode aligned_mode = AlignedMode::ASYMMETRIC)
Constructs a ROIAlign operation.
- Parameters:
input – Input feature map {N, C, H, W}
rois – Regions of interest to pool over
batch_indices – Indices of images in the batch matching the number or ROIs
pooled_h – Height of the ROI output features
pooled_w – Width of the ROI output features
sampling_ratio – Number of sampling points used to compute an output element
spatial_scale – Spatial scale factor used to translate ROI coordinates
mode – Method of pooling - ‘avg’ or ‘max’
aligned_mode – Method of coordinates alignment - ‘asymmetric’, ‘half_pixel_for_nn’ or ‘half_pixel’
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
ROIAlign(const Output<Node> &input, const Output<Node> &rois, const Output<Node> &batch_indices, const int pooled_h, const int pooled_w, const int sampling_ratio, const float spatial_scale, const PoolingMode mode, const AlignedMode aligned_mode = AlignedMode::ASYMMETRIC)
-
class SoftSign : public ov::op::util::UnaryElementwiseArithmetic
- #include <softsign.hpp>
Public Functions
-
virtual void validate_and_infer_types() override
Verifies that attributes and inputs are consistent and computes output shapes and element types. Must be implemented by concrete child classes so that it can be run any number of times.
Throws if the node is invalid.
-
virtual bool has_evaluate() const override
Allows to get information about availability of evaluate method for the current operation.
-
virtual void validate_and_infer_types() override
-
template<class TShape, class TRShape = result_shape_t<TShape>>
-
namespace validate#
Functions
-
template<class TShape>
std::string shape_infer_explanation_str(const std::vector<TShape> &shapes, const std::string &explanation)# Provides
NodeValidationFailureexception explanation string.- Parameters:
shapes – Vector of shapes used for inference to be printed before explanation.
explanation – String with exception explanation.
- Returns:
Explanation string.
-
template<class TShape>
-
enum class GeluApproximationMode#
-
namespace opset1#
-
namespace opset10#
-
namespace opset11#
-
namespace opset12#
-
namespace opset13#
-
namespace opset14#
-
namespace opset15#
-
namespace opset2#
-
namespace opset3#
-
namespace opset4#
-
namespace opset5#
-
namespace opset6#
-
namespace opset7#
-
namespace opset8#
-
namespace opset9#
-
namespace pass#
Typedefs
-
using TransposeSinkingGeneral = ov::pass::transpose_sinking::TSGeneral#
-
using PassPropertyMask = ov::EnumMask<PassProperty>#
-
using param_callback_map = std::map<ov::DiscreteTypeInfo, param_callback>#
Enums
Functions
- OPENVINO_API void disable_remove_concat_zerodim_input (const std::shared_ptr< Node > &node)
- OPENVINO_API void enable_remove_concat_zerodim_input (const std::shared_ptr< Node > &node)
- OPENVINO_API bool remove_concat_zerodim_input_is_disabled (const std::shared_ptr< Node > &node)
- OPENVINO_API void disable_constant_folding (const std::shared_ptr< Node > &node)
this method disables constant folding for given node. Under constant folding we consider ConstantFolding transformation, so other type of constant folding like
get_constant_from_sourcedoesn’t work with this attribute. Also before using this attribute please consider two corner cases:If for sub-graph like ShapeOf->ShapeOf we disable cf for first ShapeOf node, it doesn’t spread to the second ShapeOf, so the entire sub-graph will be folded. (In case if first ShapeOf has exactly one consumer)
If node with disable_constant_folding was replaced with another node, the attribute will be lost because it is not copyable.
- OPENVINO_API void enable_constant_folding (const std::shared_ptr< Node > &node)
- OPENVINO_API bool constant_folding_is_disabled (const std::shared_ptr< Node > &node)
Check if constant folding is disabled on Node.
- Parameters:
node – Smart pointer to the node.
- Returns:
true if attribute constant folding set otherwise false.
- OPENVINO_API bool constant_folding_is_disabled (const Node *const node)
Check if constant folding is disabled on Node.
- Parameters:
node – Pointer to the node.
- Returns:
true if attribute constant folding set otherwise false.
Variables
-
class OPENVINO_API RemoveConcatZeroDimInput
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class OPENVINO_API DisableRemoveConcatZeroDimInput
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class AdaptivePoolToReduce : public ov::pass::MatcherPass
- #include <adaptive_pool_to_reduce.hpp>
AdaptivePoolToReduce transformation replaces AdaptiveXXXPool with ReduceXXX when possible.
-
class AddAddFusion : public ov::pass::MatcherPass
- #include <lin_op_sequence_fusion.hpp>
-
class AddFakeQuantizeFusion : public ov::pass::MatcherPass
- #include <add_fake_quantize_fusion.hpp>
AddFakeQuantizeFusion transformation replaces following graph: Add->FakeQuantize to a single FakeQuantize Restrictions:
second input to Add is a Constant
-
class AddMultiplyFusion : public ov::pass::MatcherPass
- #include <lin_op_sequence_fusion.hpp>
-
class AlignEltwiseInputRanks : public ov::pass::MatcherPass
- #include <align_eltwise_input_ranks.hpp>
-
class AlignMixedFP32FP16Types : public ov::pass::ModelPass
- #include <align_mixed_fp32_fp16_types.hpp>
AlignMixedFP32FP16Types adds Converts to keep mixed FP16/FP32 graph type consistent.
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class ApplySymbolEquivalence : public ov::pass::ModelPass
- #include <symbol_optimization.hpp>
Resets symbols on output shapes and values according to symbol equivalence. It allows to reduce number of labels used in the model and to disambiguate symbol values.
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class Attributes
- #include <attributes.hpp>
-
class AUGRUCellFusion : public ov::pass::MatcherPass
- #include <augru_cell_fusion.hpp>
AUGRUCellFusion transformation replaces a sequence of operations with AUGRUCell op.
Supported activations: 1st is Sigmoid, 2nd is Tanh Clip attribute is not supported. Linear_before_reset attribute is not supported. Supported weights format: ‘rzh’
-
class BackwardGraphRewrite : public ov::pass::GraphRewrite
- #include <graph_rewrite.hpp>
Subclassed by ov::pass::StridesOptimization
-
class BatchNormDecomposition : public ov::pass::MatcherPass
- #include <batch_norm_decomposition.hpp>
-
class BatchToSpaceFusion : public ov::pass::MatcherPass
- #include <batch_to_space_fusion.hpp>
BatchToSpaceFusion transformation replaces following graph: Transpose (or Reshape) -> DepthToSpace -> StridedSlice -> Transpose (or Reshape) to BatchToSpace Restrictions:
input rank must be 4
Transpose permutation must be [1, 0, 2, 3]
DepthToSpaceMode must be BLOCKS_FIRST
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class BidirectionalGRUSequenceDecomposition : public ov::pass::MatcherPass
- #include <bidirectional_sequences_decomposition.hpp>
Decompose GRUSequence to forward and reverse GRUSequence.
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class BidirectionalLSTMSequenceDecomposition : public ov::pass::MatcherPass
- #include <bidirectional_sequences_decomposition.hpp>
Decompose LSTMSequence to forward and reverse LSTMSequence.
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class BidirectionalRNNSequenceDecomposition : public ov::pass::MatcherPass
- #include <bidirectional_sequences_decomposition.hpp>
Decompose RNNSequence to forward and reverse RNNSequence.
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class BidirectionalSequenceDecomposition : public ov::pass::GraphRewrite
- #include <bidirectional_sequences_decomposition.hpp>
Container for all types of sequences decomposition.
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class BinarizeWeights : public ov::pass::MatcherPass
- #include <binarize_weights.hpp>
This transformation converts weights to -1/+1 form and applies normalization factors to output low/high and after Convolution. For example, following graph.
is transformed to:.... .... out_low out_high weights .. .. out_low out_high | | | | | | | | | +--------------------------+ +--------------------------+ | FakeQuantize (levels==2) | | FakeQuantize (levels==2) | | (on activations) | | (on weights) | +--------------------------+ +--------------------------+ | | | | ----------------- ------------------- | | v v +-------------+ | Convolution | +-------------+ | v
Normalization factors are chosen based output_high value. If it’s zero - norm factor is equal to output_low and output_high otherwisenormalized normalized .... .... out_low out_high | | | | +--------------------------+ +--------------------------+ | FakeQuantize (levels==2) | | Constant | | (on activations) | | (with converted weights) | +--------------------------+ +--------------------------+ | | | | ----------------- ------------------- | | v v +-------------+ | Convolution | +-------------+ | v +------------+ +---------------------------------------------------------------+ | Multiply | <---| Constant (normalization factor coming from FQ on activations) | +------------+ +---------------------------------------------------------------+ | v +------------+ +-----------------------------------------------------------+ | Multiply | <---| Constant (normalization factor coming from FQ on weights) | +------------+ +------------------------------------------------------------ | v
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class BroadcastConstRangeReplacement : public ov::pass::MatcherPass
- #include <broadcast_const_range_replacement.hpp>
BroadcastConstRangeReplacement replaces Constant filled with range values starting from 0 and replaces it with Range op.
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class BroadcastElementwiseFusion : public ov::pass::MatcherPass
- #include <broadcast_elementwise_fusion.hpp>
Removing Broadcast OP before ElementWise if output shape of Broadcast are equal neighboring input shape of ElementWise.
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class BroadcastTransition : public ov::pass::MatcherPass
- #include <broadcast_transition.hpp>
BroadcastTransition transformation moves broadcast through binary eltwise operation.
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class ChainedMaximumOptimization : public ov::pass::MatcherPass
- #include <chained_maximum.hpp>
Optimizes graphs based on value symbols Maximum(Maximum(A, B), B) -> Maximum(A, B) Maximum(Maximum(A, B), A) -> Maximum(A, B)
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class ChangePlaceholderTypes : public ov::pass::ModelPass
- #include <change_placeholder_types.hpp>
Add OldApiMap with legacy type for Parameter node.
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class ClampFusion : public ov::pass::MatcherPass
- #include <clamp_fusion.hpp>
ClampFusion transformation replaces following graph: Maximum->Minimum to Clamp Restrictions:
one of the parameters to Maximum is a scalar constant
one of the parameters to Minimum is a scalar constant
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class CompressFloatConstants : public ov::pass::GraphRewrite
- #include <compress_float_constants.hpp>
CompressFloatConstants transformation replaces FP32/FP64 Constants with FP16 ones.
Public Functions
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inline CompressFloatConstants(bool postponed = false)
Transformation constructor.
- Parameters:
postponed – Postponed compression, see ov::pass::CompressFloatConstantsImpl for details.
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inline CompressFloatConstants(bool postponed = false)
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class CompressFloatConstantsImpl : public ov::pass::MatcherPass
- #include <compress_float_constants.hpp>
CompressFloatConstantsImpl transformation replaces FP32/FP64 Constants with FP16 ones.
Public Functions
-
CompressFloatConstantsImpl(bool postponed = false)
Transformation constructor.
- Parameters:
postponed – If true then the transformation won’t compress the constants keeping them in the original type but still will insert Converts. This is a special mode of operation that requires another transformation to apply a real compression on constants. Constants eligible for postponed compression are marked with a special rt_info tag.
-
CompressFloatConstantsImpl(bool postponed = false)
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class CompressQuantizeWeights : public ov::pass::GraphRewrite
- #include <compress_quantize_weights.hpp>
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class CompressWeightsWithFakeConvert : public ov::pass::MatcherPass
- #include <compress_quantize_weights.hpp>
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class CompressWeightsWithFakeQuantize : public ov::pass::MatcherPass
- #include <compress_quantize_weights.hpp>
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class ConcatFusion : public ov::pass::MatcherPass
- #include <concat_fusion.hpp>
ConcatFusion transformation fuses sequence of Concats.
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class ConcatReduceFusion : public ov::pass::GraphRewrite
- #include <concat_reduce_fusion.hpp>
ConcatReduceFusion pass replaces the following graph:
by a single Minimum/Maximum with 2 inputs and tries to eliminate Squeeze/Unsqueeze layers before and after Min/Max.+---------------+ +---------------+ │ │ | | │ input │ | input | │ │ | | +---------------+ +---------------- | | | | \ / \ / \ / \ / \ / \ / \ / \ / \ / +---------------+ | | | Concat | | | +---------------- | v +---------------+ | | | ReduceMin/ | | ReduceMax | +----------------
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class ConstantFolding : public ov::pass::ModelPass
- #include <constant_folding.hpp>
Constant folding iterates over the function and tries to evaluate nodes with constant inputs. Such nodes are then replaced with new Constants containing the result of a folded operation.
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class ConvertBatchToSpace : public ov::pass::MatcherPass
- #include <convert_batch_to_space.hpp>
ConvertBatchToSpace transformation decomposes BatchToSpace layer to Reshape->Transpose->Reshape->Crop.
- Param convert_by_elements:
- reduces the maximum number of dimensions that arise during the transformation if enabled. Default value: true. false - BatchToSpace decomposes to Reshape->Transpose->Reshape->Crop. During transformation, the number of tensor dimensions can be increased by length of block_shape input of BatchToSpace layer. true - BatchToSpace decomposes to N x (Reshape->Transpose->Reshape)->Crop, where N = length of block_shape input of BatchToSpace layer. During transformation, the number of tensor dimensions can be increased by 1.
-
class ConvertBitwiseAndToLogicalAnd : public ov::pass::MatcherPass
- #include <convert_bitwise_to_logical_bool.hpp>
-
class ConvertBitwiseNotToLogicalNot : public ov::pass::MatcherPass
- #include <convert_bitwise_to_logical_bool.hpp>
-
class ConvertBitwiseOrToLogicalOr : public ov::pass::MatcherPass
- #include <convert_bitwise_to_logical_bool.hpp>
-
class ConvertBitwiseXorToLogicalXor : public ov::pass::MatcherPass
- #include <convert_bitwise_to_logical_bool.hpp>
-
class ConvertBroadcast3 : public ov::pass::MatcherPass
- #include <convert_broadcast3.hpp>
-
class ConvertBroadcastToTiles : public ov::pass::MatcherPass
- #include <convert_broadcast_to_tiles.hpp>
-
class ConvertCompressedOnlyToLegacy : public ov::pass::ModelPass
- #include <convert_compression_only_to_legacy.hpp>
ConvertCompressedOnlyToLegacy transformation converts compression only FP16 format to legacy FP16 format.
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class ConvertConvertLike : public ov::pass::MatcherPass
- #include <convert_convertlike.hpp>
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class ConvertConvertPromoteTypes : public ov::pass::MatcherPass
- #include <convert_convertpromotetypes.hpp>
Transformation to replace ConvertPromoteTypes with pair of Convert ops for each input to evaluated common element type.
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class ConvertDeformableConv8To1 : public ov::pass::MatcherPass
- #include <convert_deformable_conv_v8_to_v1.hpp>
ConvertDeformableConv8To1 converts v8::DeformableConvolution into v1::DeformableConvolution.
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class ConvertDepthToSpace : public ov::pass::MatcherPass
- #include <convert_depth_to_space.hpp>
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class ConvertDetectionOutput1ToDetectionOutput8 : public ov::pass::MatcherPass
- #include <detection_output_upgrade.hpp>
ConvertDetectionOutput1ToDetectionOutput8 converts v0::DetectionOutput into v8::DetectionOutput.
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class ConvertDetectionOutput8ToDetectionOutput1 : public ov::pass::MatcherPass
- #include <detection_output_downgrade.hpp>
ConvertDetectionOutput8ToDetectionOutput1 converts v8::DetectionOutput into v0::DetectionOutput.
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class ConvertDivide : public ov::pass::MatcherPass
- #include <convert_divide.hpp>
-
class ConvertDivideWithConstant : public ov::pass::MatcherPass
- #include <convert_divide.hpp>
-
class ConvertFP32ToFP16 : public ov::pass::ModelPass
- #include <convert_fp32_to_fp16.hpp>
ConvertFP32ToFP16 transformation.
-
class ConvertGather0D : public ov::pass::MatcherPass
- #include <convert_gather_0d.hpp>
ConvertGather0D decomposes v1::Gather operation into v0::Unsqueeze + v1::Gather + v0::Squeeze pattern when gather indices is scalar.
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class ConvertGather1ToGather7 : public ov::pass::MatcherPass
- #include <convert_gather_upgrade.hpp>
ConvertGather1ToGather7 converts v1::Gather into v7::Gather.
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class ConvertGather7ToGather1 : public ov::pass::MatcherPass
- #include <convert_gather_downgrade.hpp>
ConvertGather7ToGather1 converts v7::Gather into v1::Gather.
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class ConvertGather7ToGather8 : public ov::pass::MatcherPass
- #include <convert_gather_upgrade.hpp>
ConvertGather7ToGather8 converts v7::Gather into v8::Gather.
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class ConvertGather8ToGather7 : public ov::pass::MatcherPass
- #include <convert_gather_downgrade.hpp>
ConvertGather8ToGather7 converts v8::Gather into v7::Gather.
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class ConvertGatherToGatherCompressed : public ov::pass::MatcherPass
- #include <convert_gather_to_compressed.hpp>
-
class ConvertGELU : public ov::pass::MatcherPass
- #include <convert_gelu.hpp>
-
class ConvertGP9ToGPIEInternal : public ov::pass::MatcherPass
- #include <convert_gp9_to_gp_ie_internal.hpp>
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class ConvertGRUSequenceToTensorIterator : public ov::pass::MatcherPass
- #include <convert_sequences_to_tensor_iterator.hpp>
ConvertGRUSequenceToTensorIterator transformation converts GRUSequence layer to TensorIterator *.
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class ConvertInterpolate11ToInterpolate4 : public ov::pass::MatcherPass
- #include <convert_interpolate11_downgrade.hpp>
Converts Interpolate version 11 to Interpolate version 4 if the new op uses any of the v4 allowed interpolation modes.
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class ConvertInterpolate1ToInterpolate4 : public ov::pass::MatcherPass
- #include <convert_interpolate1_to_interpolate4.hpp>
ConvertInterpolate1ToInterpolate4 covert v0:interpolate into v4::Interpolate.
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class ConvertLoopToLSTMSequence : public ov::pass::MatcherPass
- #include <convert_ti_to_sequences.hpp>
Replaces Loop with LSTMCell inside to LSTMSequence.
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class ConvertLSTMSequenceToTensorIterator : public ov::pass::MatcherPass
- #include <convert_sequences_to_tensor_iterator.hpp>
ConvertLSTMSequenceToTensorIterator transformation converts LSTMSequence layer to TensorIterator *.
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class ConvertMatrixNmsToMatrixNmsIE : public ov::pass::MatcherPass
- #include <convert_matrix_nms_to_matrix_nms_ie.hpp>
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class ConvertMaxPool1ToMaxPool8 : public ov::pass::MatcherPass
- #include <convert_maxpool_upgrade.hpp>
ConvertMaxPool1ToMaxPool8 converts v1::MaxPool into v8::MaxPool.
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class ConvertMaxPool8ToMaxPool1 : public ov::pass::MatcherPass
- #include <convert_maxpool_downgrade.hpp>
ConvertMaxPool8ToMaxPool1 converts v8::MaxPool into v1::MaxPool.
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class ConvertMinimum : public ov::pass::MatcherPass
- #include <convert_minimum_to_power_and_max.hpp>
-
class ConvertMod : public ov::pass::MatcherPass
- #include <convert_mod.hpp>
-
class ConvertMulticlassNms8ToMulticlassNms9 : public ov::pass::MatcherPass
- #include <convert_multiclass_nms_upgrade.hpp>
-
class ConvertMulticlassNmsToMulticlassNmsIE : public ov::pass::MatcherPass
- #include <convert_multiclass_nms_to_multiclass_nms_ie.hpp>
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class ConvertMVN1ToMVN6 : public ov::pass::MatcherPass
- #include <convert_mvn1_to_mvn6.hpp>
ConvertMVN1ToMVN6 covert v0:MVN into v6::MVN.
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class ConvertNegative : public ov::pass::MatcherPass
- #include <convert_negative.hpp>
-
class ConvertNMS1ToNMS5 : public ov::pass::MatcherPass
- #include <convert_previous_nms_to_nms_5.hpp>
-
class ConvertNMS1ToNMS9 : public ov::pass::MatcherPass
- #include <convert_previous_nms_to_nms_9.hpp>
-
class ConvertNMS3ToNMS5 : public ov::pass::MatcherPass
- #include <convert_previous_nms_to_nms_5.hpp>
-
class ConvertNMS3ToNMS9 : public ov::pass::MatcherPass
- #include <convert_previous_nms_to_nms_9.hpp>
-
class ConvertNMS4ToNMS5 : public ov::pass::MatcherPass
- #include <convert_previous_nms_to_nms_5.hpp>
-
class ConvertNMS4ToNMS9 : public ov::pass::MatcherPass
- #include <convert_previous_nms_to_nms_9.hpp>
-
class ConvertNMS5ToNMS9 : public ov::pass::MatcherPass
- #include <convert_previous_nms_to_nms_9.hpp>
-
class ConvertNMS9ToNMSIEInternal : public ov::pass::MatcherPass
- #include <convert_nms9_to_nms_ie_internal.hpp>
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class ConvertNmsGatherPathToUnsigned : public ov::pass::GraphRewrite
- #include <convert_nms_gather_path_to_unsigned.hpp>
Converts Gather indices to unsigned if indices are from NMS selected indices output. NMS returns -1 for not selected boxes, old version of Gather fill corresponding output for such indices with zero. But new Gather-8 has support of negative indices indicating counting from the end. In order to keep such behaviour (until dynamism is not supported) instead of -1 new Gather-8 will accept UINT32_MAX which is always outside of the bounds and corresponding output for such indices in gather always will be filled with zeros.
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class ConvertNMSRotatedToNMSIEInternal : public ov::pass::MatcherPass
- #include <convert_nms_rotated_to_nms_ie_internal.hpp>
-
class ConvertNMSToNMSIEInternal : public ov::pass::MatcherPass
- #include <convert_nms_to_nms_ie_internal.hpp>
-
class ConvertPad12ToPad1 : public ov::pass::MatcherPass
- #include <convert_pad12_downgrade.hpp>
Converts Pad v12 to Pad v1.
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class ConvertPadToGroupConvolution : public ov::pass::MatcherPass
- #include <convert_pad_to_group_conv.hpp>
ConvertPadToGroupConvolution transformation replaces Pad operation with GroupConvolution but has some restrictions on Pad parameters:
PadMode must be Constant and value is equal to 0
Padding must be applied only for spatial dimensions
Input shape rank must be static and greater than 3
Padding values must be non-negative
-
class ConvertPriorBox8To0 : public ov::pass::MatcherPass
- #include <convert_prior_box_v8_to_v0.hpp>
ConvertPriorBox8To1 converts v8::PriorBox into v0::PriorBox.
-
class ConvertQuantizeDequantize : public ov::pass::MatcherPass
- #include <convert_quantize_dequantize.hpp>
ConvertQuantizeDequantize transformation replaces following graph: FakeQuantize->Convert->Convert->Subtract->Multiply with a single FakeQuantize. Restrictions:
quantized data type must be i8 or u8
’levels’ attribute to FakeQuantize must be equal to 256
(output_low, output_high) must be (-128, 127) or (0, 256) (depends on sign of quantized data type)
’zero_point’ and ‘scale’ must be broadcastable to FakeQuantize’s output
-
class ConvertReduceLogicalAndToReshape : public CvtReduceBase
- #include <convert_reduce_to_reshape.hpp>
-
class ConvertReduceLogicalOrToReshape : public CvtReduceBase
- #include <convert_reduce_to_reshape.hpp>
-
class ConvertReduceMaxToPooling : public ConvertReduceBase
- #include <convert_reduce_to_pooling.hpp>
-
class ConvertReduceMaxToReshape : public CvtReduceBase
- #include <convert_reduce_to_reshape.hpp>
-
class ConvertReduceMeanToPooling : public ConvertReduceBase
- #include <convert_reduce_to_pooling.hpp>
-
class ConvertReduceMeanToReshape : public CvtReduceBase
- #include <convert_reduce_to_reshape.hpp>
-
class ConvertReduceMinToReshape : public CvtReduceBase
- #include <convert_reduce_to_reshape.hpp>
-
class ConvertReduceProdToReshape : public CvtReduceBase
- #include <convert_reduce_to_reshape.hpp>
-
class ConvertReduceSumToPooling : public ConvertReduceBase
- #include <convert_reduce_to_pooling.hpp>
-
class ConvertReduceSumToReshape : public CvtReduceBase
- #include <convert_reduce_to_reshape.hpp>
-
class ConvertReduceToPooling : public ov::pass::GraphRewrite
- #include <convert_reduce_to_pooling.hpp>
-
class ConvertReduceToReshape : public ov::pass::GraphRewrite
- #include <convert_reduce_to_reshape.hpp>
-
class ConvertRNNSequenceToTensorIterator : public ov::pass::MatcherPass
- #include <convert_sequences_to_tensor_iterator.hpp>
ConvertRNNSequenceToTensorIterator transformation converts RNNSequence layer to TensorIterator *.
-
class ConvertROIAlign3To9 : public ov::pass::MatcherPass
- #include <convert_roi_align_v3_to_v9.hpp>
ConvertROIAlign3To9 converts v3::ROIAlign into v9::ROIAlign.
-
class ConvertROIAlign9To3 : public ov::pass::MatcherPass
- #include <convert_roi_align_v9_to_v3.hpp>
ConvertROIAlign9To3 converts v9::ROIAlign into v3::ROIAlign.
-
class ConvertScatterElementsToScatter : public ov::pass::MatcherPass
- #include <convert_scatter_elements_to_scatter.hpp>
ConvertScatterElementsToScatter convert opset3::ScatterElementsUpdate to opset3::ScatterUpdate.
-
class ConvertScatterElementsUpdate12ToScatterElementsUpdate3 : public ov::pass::MatcherPass
- #include <convert_scatter_elements_update12_downgrade.hpp>
Converts Pad v12 to Pad v1.
-
class ConvertSequenceToTensorIterator : public ov::pass::GraphRewrite
- #include <convert_sequences_to_tensor_iterator.hpp>
-
class ConvertShapeOf3 : public ov::pass::MatcherPass
- #include <convert_shapeof3.hpp>
-
class ConvertShuffleChannels3 : public ov::pass::MatcherPass
- #include <convert_shuffle_channels3.hpp>
-
class ConvertSoftMax1ToSoftMax8 : public ov::pass::MatcherPass
- #include <convert_softmax_upgrade.hpp>
ConvertSoftMax1ToSoftMax8 converts v1::SoftMax into v8::SoftMax.
-
class ConvertSoftMax8ToSoftMax1 : public ov::pass::MatcherPass
- #include <convert_softmax_downgrade.hpp>
ConvertSoftMax8ToSoftMax1 converts v8::SoftMax into v1::SoftMax.
-
class ConvertSpaceToBatch : public ov::pass::MatcherPass
- #include <convert_space_to_batch.hpp>
ConvertSpaceToBatch transformation decomposes SpaceToBatch layer to Pad->Reshape->Transpose->Reshape.
- Param convert_by_elements:
- reduces the maximum number of dimensions that arise during the transformation if enabled. Default value: true. false - SpaceToBatch decomposes to Pad->Reshape->Transpose->Reshape. During transformation, the number of tensor dimensions can be increased by length of block_shape input of SpaceToBatch layer. true - SpaceToBatch decomposes to Pad-> N x (Reshape->Transpose->Reshape), where N = length of block_shape input of SpaceToBatch layer. During transformation, the number of tensor dimensions can be increased by 1.
-
class ConvertSpaceToDepth : public ov::pass::MatcherPass
- #include <convert_space_to_depth.hpp>
-
class ConvertSubtract : public ov::pass::MatcherPass
- #include <convert_subtract.hpp>
-
class ConvertSubtractWithConstant : public ov::pass::MatcherPass
- #include <convert_subtract.hpp>
-
class ConvertTensorIteratorToGRUSequence : public ov::pass::MatcherPass
- #include <convert_ti_to_sequences.hpp>
Finds all TensorIterator layers, detects the pattern Squeeze->GRUCell->Unsqueeze in the TensorIterator body, converts this pattern to GRUSequence layer and replaces them TensorIterator.
-
class ConvertTensorIteratorToLSTMSequence : public ov::pass::MatcherPass
- #include <convert_ti_to_sequences.hpp>
Finds all TensorIterator layers, detects the pattern Squeeze->LSTMCell->Unsqueeze in the TensorIterator body, converts this pattern to LSTMSequence layer and replaces them TensorIterator.
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class ConvertTensorIteratorToRNNSequence : public ov::pass::MatcherPass
- #include <convert_ti_to_sequences.hpp>
Finds all TensorIterator layers, detects the pattern Squeeze->RNNCell->Unsqueeze in the TensorIterator body, converts this pattern to RNNSequence layer and replaces them TensorIterator.
-
class ConvertTensorIteratorToSequence : public ov::pass::GraphRewrite
- #include <convert_ti_to_sequences.hpp>
-
class ConvertTopK11ToTopK3 : public ov::pass::MatcherPass
- #include <convert_topk11_downgrade.hpp>
Converts TopK version 11 to TopK version 3 if TopK 11 stable attribute is set to false.
-
class ConvertTopK3 : public ov::pass::MatcherPass
- #include <convert_topk3.hpp>
-
class ConvertU4WeightsZeroPointToScalar : public ov::pass::MatcherPass
- #include <convert_u4_weights_zero_point_to_scalar.hpp>
Converts U4 weights zero point to scalar if all values are equal.
-
class ConvertXorToLogicalXor : public ov::pass::MatcherPass
- #include <convert_xor_to_logical_xor.hpp>
ConvertXorToLogicalXor converts v0::Xor to v1::LogicalXor.
-
class ConvolutionBackpropDataMultiplyFusion : public ov::pass::MatcherPass
- #include <conv_mul_fusion.hpp>
-
class ConvolutionMultiplyFusion : public ov::pass::MatcherPass
- #include <conv_mul_fusion.hpp>
-
class ConvolutionToGroupConvolutionFusion : public ov::pass::MatcherPass
- #include <convolution_to_group_convolution_fusion.hpp>
ConvolutionToGroupConvolutionFusion transformation replaces following graph: Split (or VariadicSplit) / \ Conv … Conv \ / \ / Concat.
to GroupConvolution
-
class ConvStridesPropagation : public ov::pass::MatcherPass
- #include <strides_optimization.hpp>
ConvStridesPropagation either propagates stride (greater than 1) from Convolution up through the graph or inserts pooling between current node and its consumers if the consumers have different StridesProp attributes. Strides can be propagated if Convolution kernel is {1, 1, …}.
-
class ConvToBinaryConv : public ov::pass::MatcherPass
- #include <conv_to_binary_conv.hpp>
This transformation converts Convolution to BinaryConvolution under following conditions:
first input to Convolution is a FakeQuantize with levels==2 with output low,high being either (0, 1) or (-1, 1)
second input (weights) is a constant with values -1 or 1 The transformation also converts weights to binary Constant (with ‘u1’ type) For example, when output_low is equal to 0 and output_high is equal to 1, following graph
is transformed to:.... .... out_low out_high | | | | +--------------------------+ +-------------------------------------+ | FakeQuantize (levels==2) | | Constant | | (on activations) | | (weights containing -1 or 1 values) | +--------------------------+ +-------------------------------------+ | | | | ----------------- ------------------- | | v v +-------------+ | Convolution | +-------------+ | v
.... .... out_low out_high | | | | +--------------------------+ +---------------------------------+ | FakeQuantize (levels==2) | | Constant (with u1 type) | | (on activations) | | (with u1 type - binary weights) | +--------------------------+ +---------------------------------+ | | | | ----------------- ------------------- | | v v +-------------------+ | BinaryConvolution | +-------------------+ | v +------------+ +----------------------------------------------------+ | | | Constant | | Add | <---| (weights from original graph, | | | | sum-reduced over [1,..., len(weights.shape)] axes | +------------+ +----------------------------------------------------+ | v +------------+ +-----+ | Multiply | <---| 0.5 | +------------+ +-----+ | v
-
class DepthToSpaceFusion : public ov::pass::MatcherPass
- #include <depth_to_space_fusion.hpp>
DepthToSpaceFusion transformation detects Reshape-Transpose-Reshape pattern and tries to fuse it into a single DepthToSpace layer.
DepthToSpaceFusion transformation is optional and disabled by default. The transformation can be enabled with callback using setCallback method. See the example below.
Callback example:
// This callback enables DepthToSpaceFusion transformation auto callback = [](const std::shared_ptr<const ov::Node> & node) -> bool { return std::dynamic_pointer_cast<const ov::opset3::DepthToSpace>(node) != nullptr; }; auto p = ov::pass::DepthToSpaceFusion(); p.setCallback(callback); p.run_on_function(f);
-
class DeReshapeFullyConnected : public ov::pass::MatcherPass
- #include <dereshape_matmul.hpp>
Transformation uses symbol information to optimize out Reshape operations surrounding special cases of MatMul. It checks that surrounding Reshapes are only manipulating with batch dimensions of tensor in a do-undo kind of way. The difference with previous optimization is that this case has Reshape only on one input of MatMul and the other input is strictly 2D. Such MatMuls are also called FullyConnected.
Example: Before: [A,B,4096] -> Reshape -> [A*B,4096] MatMul [A*B,4608] -> Reshape -> [A,B,4608] [4096,4608]
After: [A,B,4096] -> MatMul -> [A,B,4608] [4096,4608] ->
-
class DeReshapeMatMul : public ov::pass::MatcherPass
- #include <dereshape_matmul.hpp>
Transformation uses symbol information to optimize out Reshape operations surrounding MatMul. It checks that surrounding Reshapes are only manipulating with batch dimensions of tensor in a do-undo kind of way.
Example: Before: [A,B,C,D] -> Reshape -> [A*B,C,D] MatMul [A*B,C,E] -> Reshape -> [A,B,C,E] [A,B,D,E] -> Reshape -> [A*B,D,E]
After: [A,B,C,D] -> MatMul -> [A,B,C,E] [A,B,D,E] ->
Transformation allows slightly different variations of the pattern on inputs of MatMul.
Simplest pattern contains only Reshape operation on MatMul input: Reshape -> MatMul
The next acceptable variation is Concat of two inputs on MatMul input: Reshape -[-> Concat -]-> MatMul This variation would be transformed with realignment of the other input of Concat and the other outputs of Concat with the help of Reshape operations
The most complex variation on the MatMul input pattern is with Binary Elementwise Operation with scalar second input: Reshape -[-> Concat -]-[-> BEA (scalar) -]-> MatMul
Additionally, transformation supports variation of the pattern on output of MatMul. It allows for Binary Elementwise Arithmetic operation without second input scalar restriction. MatMul -[-> BEA -]-> Reshape this pattern variation is only applicable for the case when input reshapes are 4D -> 3D and output reshape is 3D -> 4D. Additionally, shape symbols on output of MatMul should be equal to the input shape symbols of the last Reshape, meaning that this Binary Elementwise Arithmetic doesn’t perform any broadcasting of input coming from MatMul — only other input may be broadcasted to the MatMul input of this BEA. This effect (equality of MatMul output shape symbols and output shape of BEA) is being handled by LabelResolvingThroughSelect transformation in the particular models that this variation targets.
Full pattern this transformation searches for: -> Reshape -[-> Concat -]-[-> BEA (scalar) -]-> MatMul -[-> BEA -]-> Reshape -> -> Reshape -[-> Concat -]-[-> BEA (scalar) -]->
NOTE: input branches could be (and in observed model cases are) asymmetrical, meaning that the presence of Concat on one input of MatMul doesn’t require the other input to also have Concat
-
class DilatedConvolutionConverter : public ov::pass::MatcherPass
- #include <dilated_convolution_converter.hpp>
DilatedConvolutionConverter transformation replaces following graph: SpaceToBatch -> Convolution(GroupConvolution) -> BatchToSpace to a single Convolution(GroupConvolution) node with updated pads and dilations Restrictions:
pads in SpaceToBatch must have 0 on first and second position
-
class DisableConstantFolding : public ov::RuntimeAttribute
- #include <constant_folding.hpp>
-
class DisableDecompressionConvertConstantFolding : public ov::pass::MatcherPass
- #include <mark_decompression_convert_constant_folding.hpp>
Disables ConstantFolding for Convert operation in compressed function.
-
class DisableFoldSubgraphEmptyInputs : public ov::RuntimeAttribute
- #include <fold_subgraph_empty_inputs.hpp>
-
class DisableRandomUniformConstantFolding : public ov::pass::MatcherPass
- #include <disable_random_uniform_constant_folding.hpp>
Disables ConstantFolding for RandomUniform operation. It is required as RandomUniform should generate new sequence each run.
-
class DisableRemoveConcatZeroDimInput : public ov::RuntimeAttribute
- #include <remove_concat_zero_dim_input.hpp>
-
class DisableShapeOfConstantFolding : public ov::pass::MatcherPass
- #include <disable_shapeof_constant_folding.hpp>
-
class DivideFusion : public ov::pass::MatcherPass
- #include <divide_fusion.hpp>
DivideFusion transformation replaces a sub-graph Pow(y, -1) * x or x * Pow(y, -1) with Divide(x,y)
-
class DropoutWithRandomUniformReplacer : public ov::pass::MatcherPass
- #include <dropout_with_random_uniform_replacer.hpp>
This transformation replaces possible Dropout block (in inference mode) with RandomUniform to Broadcast of half-ones in a sub-graph.
Dropout block: RandomUniform ——-—> Add —> Floor /\ /\ /\ | | | Const(0) Const(1) Const(1) min_val max_val
Resulted block: Broadcast —-—> Add —> Floor /\ /\ | | Const(0.5) Const(1)
-
class EinsumDecomposition : public ov::pass::MatcherPass
- #include <einsum_decomposition.hpp>
EinsumDecomposition transformation decomposes Einsum-7 operation into a sub-graph with more simple operations: Transpose, Reshape, MatMul, ReduceSum, Unsqueeze, ShapeOf, ReduceProd, StridedSlice, and Concat.
-
class EliminateConcat : public ov::pass::MatcherPass
- #include <nop_elimination.hpp>
EliminateConcat eliminates concat that does nothing.
-
class EliminateConvert : public ov::pass::MatcherPass
- #include <nop_elimination.hpp>
EliminateConvert eliminates convert that does nothing.
-
class EliminateConvertNonZero : public ov::pass::MatcherPass
- #include <nop_elimination.hpp>
EliminateConvertNonZero eliminates convert before NonZero.
-
class EliminateDuplicateTIInputs : public ov::pass::MatcherPass
- #include <eliminate_duplicate_ti_inputs.hpp>
-
class EliminateEltwise : public ov::pass::MatcherPass
- #include <nop_elimination.hpp>
EliminateEltwise eliminates eltwise ops that do nothing.
-
class EliminateGatherUnsqueeze : public ov::pass::MatcherPass
- #include <eliminate_unsqueeze_gather.hpp>
Matches Gather ->[Binary Operation]-> Unsqueeze If axis for Gather and Unsqueeze is the same and Gather indices are scalar Unsqueeze is being removed and indices become 1D. Must be executed after SharedOpOptimization — It is possible to have multiple similar Unsqueeze operations after Gather, so they must be optimized beforehand.
-
class EliminateNopBroadcast : public ov::pass::MatcherPass
- #include <nop_elimination.hpp>
EliminateNopBroadcast eliminates broadcast or tile with all ones on the second input.
-
class EliminatePad : public ov::pass::MatcherPass
- #include <nop_elimination.hpp>
EliminatePad eliminates pad that does nothing.
-
class EliminateScatterUpdate : public ov::pass::MatcherPass
- #include <nop_elimination.hpp>
EliminateScatterUpdate eliminates scatter ops that do nothing (updates/indices are empty)
-
class EliminateSplit : public ov::pass::MatcherPass
- #include <nop_elimination.hpp>
EliminateSplit eliminates split that does nothing.
-
class EliminateSplitConcat : public ov::pass::MatcherPass
- #include <nop_elimination.hpp>
EliminateSplit eliminates split+concat pairs which do nothing.
-
class EliminateSqueeze : public ov::pass::MatcherPass
- #include <nop_elimination.hpp>
EliminateSqueeze eliminates squeeze that does nothing.
-
class EliminateTranspose : public ov::pass::MatcherPass
- #include <nop_elimination.hpp>
EliminateTranspose eliminates transpose that does nothing.
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class EliminateUnsqueezeGather : public ov::pass::MatcherPass
- #include <eliminate_unsqueeze_gather.hpp>
Remove Unsqueeze + Gather pair, if Gather gathers data by dimension that was previously added by Unsqueeze.
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class EnableDecompressionConvertConstantFolding : public ov::pass::MatcherPass
- #include <mark_decompression_convert_constant_folding.hpp>
Enables ConstantFolding for Convert operation in compressed function.
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class EnableShapeOfConstantFolding : public ov::pass::MatcherPass
- #include <enable_shapeof_constant_folding.hpp>
This transformation enables constfoldability for ShapeOf nodes that was disabled by DisableShapeOfConstantFolding.
-
class EyeDecomposition : public ov::pass::MatcherPass
- #include <eye_decomposition.hpp>
Do eye decomposition to sub-graph (model).
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class FakeQuantizeDecomposition : public ov::pass::MatcherPass
- #include <fq_decomposition.hpp>
FakeQuantizeDecomposition transformation decomposes FakeQuantize layer.
Expression from specification: if x <= min(input_low, input_high): output = output_low elif x > max(input_low, input_high): output = output_high else: output = round((x - input_low) / (input_high - input_low) * (levels-1)) / (levels-1) * (output_high - output_low) + output_low
expand brackets into round: round(x * (levels-1) / (input_high - input_low) - input_low * (levels-1) / (input_high - input_low)) div on (levels-1) and mult on (output_high - output_low) => mult on (output_high - output_low) / (levels-1)
=> round(x * (levels-1) / (input_high - input_low) - input_low * (levels-1) / (input_high - input_low)) * (output_high - output_low) / (levels-1) + output_low
This transformation doesn’t support following cases:
At least one ‘range’ input is not Constant
At least one ‘input_low’ input value greater or equal than ‘input_high’ input value
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class FakeQuantizeMulFusion : public ov::pass::MatcherPass
- #include <fq_mul_fusion.hpp>
This transformation looks for a FQ + Mul pair in the graph and moves the Mul operation above the FQ node. The last two inputs of FQ are multiplied by the value that was originally below the FQ node.
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class FakeQuantizeReshapeFusion : public ov::pass::MatcherPass
- #include <fq_reshape_fusion.hpp>
This transformation looks for a FQ + Reshape pair in the graph and moves the Reshape operation above the FQ node. Shapes of limit inputs are updated following FQ broadcasting semantics.
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class FlushFP32SubnormalsToZero : public ov::pass::MatcherPass
- #include <flush_fp32_subnormals_to_zero.hpp>
-
class FoldSubgraphEmptyInputs : public ov::pass::MatcherPass
- #include <fold_subgraph_empty_inputs.hpp>
-
class FusedNamesCleanup : public ov::pass::ModelPass
- #include <fused_names_cleanup.hpp>
FusedNamesCleanup removes fused_names attribute.
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class FuseFilteringBoxesBySize : public ov::pass::GraphRewrite
- #include <remove_filtering_boxes_by_size.hpp>
-
class FuseLSTMSequencesToBidirectionalLSTMSequence : public ov::pass::MatcherPass
- #include <convert_ti_to_sequences.hpp>
Replaces two LSTMSequences to one bidirectional LSTMSequence.
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class FuseReverseLSTMSequence : public ov::pass::MatcherPass
- #include <convert_ti_to_sequences.hpp>
Fuses ReverseSequence->LSTM->ReverseSequence to LSTMSequence with REVERSE direction flag.
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class GatherNopElimination : public ov::pass::MatcherPass
- #include <simplify_shape_of_sub_graph.hpp>
GatherNopElimination transformation optimizes out useless Gather operations.
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class Gelu7Downgrade : public ov::pass::MatcherPass
- #include <gelu7_downgrade.hpp>
Gelu7Downgrade converts v7::Gelu operation to v2::Gelu unconditionally. This is done because only limited set of plugins support v7::Gelu which has an attribute specifying approximation mode. For other plugins the behaviour is to use v2 version of the operation which does not support the approximation mode.
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class GeluFusion : public ov::pass::GraphRewrite
- #include <gelu_fusion.hpp>
GeluFusion transformation replaces various sub-graphs with a Gelu op.
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class GeluFusionWithErfFour : public ov::pass::MatcherPass
- #include <gelu_fusion.hpp>
GeluFusion transformation replaces a sub-graph x * (0.5 + 0.5 * erf(x * (1 / sqrt(2)))) with a Gelu op.
-
class GeluFusionWithErfOne : public ov::pass::MatcherPass
- #include <gelu_fusion.hpp>
GeluFusion transformation replaces a sub-graph (0.5 * x) * (1 + erf(x / sqrt(2))) with a Gelu op.
-
class GeluFusionWithErfThree : public ov::pass::MatcherPass
- #include <gelu_fusion.hpp>
GeluFusion transformation replaces a sub-graph x * (0.5 * (1 + erf(x / sqrt(2)))) with a Gelu op.
-
class GeluFusionWithErfTwo : public ov::pass::MatcherPass
- #include <gelu_fusion.hpp>
GeluFusion transformation replaces a sub-graph 0.5 * (x * (1 + erf(x / sqrt(2)))) with a Gelu op.
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class GeluFusionWithTanh : public ov::pass::MatcherPass
- #include <gelu_fusion.hpp>
GeluFusion transformation replaces a sub-graph x * (0.5 * (1 + tanh([sqrt(2 / pi)] * [x + 0.044715^3]))) with a Gelu (Tanh) op.
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class GeluFusionWithTanhNoPower : public ov::pass::MatcherPass
- #include <gelu_fusion.hpp>
GeluFusion transformation replaces a sub-graph x * 0.5 * (1 + tanh((x * 0.044715 * x + 1) * x * sqrt(2 / pi))) with a Gelu (Tanh) op.
-
class GraphRewrite : public ov::pass::ModelPass
- #include <graph_rewrite.hpp>
GraphRewrite is a container for MatcherPasses that allows to run them on Function in efficient way.
Graph rewrite pass is used for matcher passes execution on Function. To register MatcherPass use
See also
add_matcher<T>(args) method where T is a MatcherPass class. As a default algorithm graph rewrite pass traverse Function in topological order and applies registered matcher passes for each node. But if all registered matcher passes have type based root node in Matcher pattern then efficient mechanism is used to execute them. Matcher pattern root is type based if it’s operation from opset or pattern::op::WrapType. Note: when implementing pattern for Matcher make sure that root node is an operation from opset or has ov::pattern::op::WrapType. That will help GraphRewrite to execute matcher passes more efficient.
Subclassed by ConvertBitwiseToLogical, ov::pass::BackwardGraphRewrite, ov::pass::BidirectionalSequenceDecomposition, ov::pass::CompressFloatConstants, ov::pass::CompressQuantizeWeights, ov::pass::ConcatReduceFusion, ov::pass::ConvertNmsGatherPathToUnsigned, ov::pass::ConvertReduceToPooling, ov::pass::ConvertReduceToReshape, ov::pass::ConvertSequenceToTensorIterator, ov::pass::ConvertTensorIteratorToSequence, ov::pass::FuseFilteringBoxesBySize, ov::pass::GeluFusion, ov::pass::HSigmoidFusion, ov::pass::HSwishFusion, ov::pass::InitMasks, ov::pass::LinOpSequenceFusion, ov::pass::MVNFusion, ov::pass::NopElimination, ov::pass::PReluFusion, ov::pass::PadFusion, ov::pass::PropagateMasks, ov::pass::PullThroughReduce, ov::pass::SwishFusion, ov::pass::TransposeSinking, ov::pass::low_precision::TypeRelaxedReplacer, ov::pass::transpose_sinking::TSGeneralBackward, ov::pass::transpose_sinking::TSGeneralForward
Public Functions
Register given transformation class type to GraphRewrite execution list All registered transformations will be executed in a single graph traversal. Example below show the basic usage of pass::GraphRewrite.
For some purposes transformation can be registered and disabled by default.pass::Manager manager; auto anchor = manager.register_pass<GraphRewrite>(); anchor->add_matcher<MatcherPassA>(); anchor->add_matcher<MatcherPassB>(); anchor->set_name("CommonMatchers"); manager.run_passes(f);
anchor->add_matcher<MatcherPassB, false>();
- Returns:
shared_ptr to the transformation instance
-
template<typename T, class ...Args, typename std::enable_if<std::is_base_of<pass::GraphRewrite, T>::value, bool>::type = true>
inline void add_matcher(Args&&... args) Register passes from GraphRewrite class that contains sequence of matcher passes registered in its ctor. For example:
class ov::pass::LinFusions: public ov::pass::GraphRewrite { public: OPENVINO_RTTI(“LinFusion”); Fusions() { add_matcher<ov::pass::AddFusion>(); add_matcher<ov::pass::MulFusion>(); } };
pass::Manager manager; auto anchor = manager.register_pass<GraphRewrite>(); anchor->add_matcher<LinFusions>(); anchor->add_matcher<OtherFusions>(); anchor->set_name(“CommonFusions”); manager.run_passes(f);
In this case all matcher passes from LinFusions pass will be united with other registered matchers.
Set PassConfig for particular transformation instance.
- Parameters:
pass_config – is a PassConfig shared_ptr
-
class GroupConvolutionBackpropDataMultiplyFusion : public ov::pass::MatcherPass
- #include <conv_mul_fusion.hpp>
-
class GroupConvolutionMultiplyFusion : public ov::pass::MatcherPass
- #include <conv_mul_fusion.hpp>
-
class GroupedGatherElimination : public ov::pass::MatcherPass
- #include <simplify_shape_of_sub_graph.hpp>
GroupedGatherElimination transformation replaces group of Gather operations with the first Gather in this group and updated indices input in case all Gathers in the group are consumed by the same Concat in incremental order.
-
class GroupedSliceToVSplitOptimization : public ov::pass::ModelPass
- #include <optimize_strided_slice.hpp>
GroupedSliceToVSplitOptimization transformation replaces group of Slice operations with VariadicSplit. All Slice operations must slice data with the same axis and step = 1.
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class GroupedStridedSliceOptimizer : public ov::pass::ModelPass
- #include <optimize_strided_slice.hpp>
GroupedStridedSliceOptimizer transformation replaces group of StridedSlice operations with VariadicSplit. All StridedSlice operations must slice data with the same axis and stride = 1.
-
class GroupNormalizationDecomposition : public ov::pass::MatcherPass
- #include <group_normalization_decomposition.hpp>
-
class GRUCellDecomposition : public ov::pass::MatcherPass
- #include <gru_cell_decomposition.hpp>
GRUCellDecomposition transformation decomposes GRUCell layer with inputs X, H, W, R, B to Add, Split, MatMul, Multiply and Subtract ops according to the formula: (.) - Denotes element-wise multiplication.
Denotes dot product. f, g - are activation functions
zt = f(Xt*(Wz^T) + Ht-1*(Rz^T) + Wbz + Rbz) rt = f(Xt*(Wr^T) + Ht-1*(Rr^T) + Wbr + Rbr) ht = g(Xt*(Wh^T) + (rt (.) Ht-1)*(Rh^T) + Rbh + Wbh) # when linear_before_reset := false # (default) ht = g(Xt*(Wh^T) + (rt (.) (Ht-1*(Rh^T) + Rbh)) + Wbh) # when linear_before_reset:= true Ht = (1 - zt) (.) ht + zt (.) Ht-1
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class GRUCellFusion : public ov::pass::MatcherPass
- #include <gru_cell_fusion.hpp>
GRUCellFusion transformation replaces a sequence of operations with GRUCell op.
If BiasAdds are not present in the pattern, then Constants with zero values will be created to match the specification.
Supported activations: Relu, Sigmoid, Tanh Clip attribute is not supported. Linear_before_reset attribute is not supported. Supported weights formats: zr, rz
-
class Hash : public ov::pass::ModelPass
- #include <hash.hpp>
Hash transformation calculates hash value for ov::Model.
Public Functions
-
Hash(uint64_t &output_hash_value)
Hash pass constructor.
- Parameters:
output_hash_value – Reference to output value. By applying hash pass on function, resulting hash value will be set to this variable
-
Hash(uint64_t &output_hash_value)
-
class HSigmoidDecomposition : public ov::pass::MatcherPass
- #include <hsigmoid_decomposition.hpp>
HSigmoidDecomposition transformation into sub-graph (min(Relu(x + 3), 6) * const(1/6).
-
class HSigmoidFusion : public ov::pass::GraphRewrite
- #include <hsigmoid_fusion.hpp>
HSigmoidFusion transformation replaces various sub-graphs with a HSigmoid op.
-
class HSigmoidFusionWithClampDiv : public ov::pass::MatcherPass
- #include <hsigmoid_fusion.hpp>
HSigmoidFusion transformation replaces a sub-graph (Clamp(x + 3, 0, 6) * / 6) with a HSigmoid op.
-
class HSigmoidFusionWithClampMul : public ov::pass::MatcherPass
- #include <hsigmoid_fusion.hpp>
HSigmoidFusion transformation replaces a sub-graph (Clamp(x + 3, 0, 6) * const(1/6)) with a HSigmoid op.
-
class HSigmoidFusionWithoutRelu : public ov::pass::MatcherPass
- #include <hsigmoid_fusion.hpp>
HSigmoidFusion transformation replaces a sub-graph (min(max(x + 3, 0), 6) / 6) with a HSigmoid op.
-
class HSigmoidFusionWithReluDiv : public ov::pass::MatcherPass
- #include <hsigmoid_fusion.hpp>
HSigmoidFusion transformation replaces a sub-graph ((min(Relu(x + 3), 6)) / 6) with a HSigmoid op.
-
class HSigmoidFusionWithReluMul : public ov::pass::MatcherPass
- #include <hsigmoid_fusion.hpp>
HSigmoidFusion transformation replaces a sub-graph ((min(Relu(x + 3), 6)) * const(1/6)) with a HSigmoid op.
-
class HSwishDecomposition : public ov::pass::MatcherPass
- #include <hswish_decomposition.hpp>
HSwishDecomposition transformation into sub-graph x * (min(Relu(x + 3), 6) * const(1/6).
-
class HSwishFusion : public ov::pass::GraphRewrite
- #include <hswish_fusion.hpp>
HSwishFusion transformation replaces various sub-graphs with a HSwish op.
-
class HSwishFusionWithClamp : public ov::pass::MatcherPass
- #include <hswish_fusion.hpp>
HSwishFusion transformation replaces a sub-graph (Clamp(x + 3, 0, 6) * x) with a HSwish * 6.
-
class HSwishFusionWithHSigmoid : public ov::pass::MatcherPass
- #include <hswish_fusion.hpp>
HSwishFusion transformation replaces a sub-graph x * HSigmoid(x) with a HSwish op.
-
class HSwishFusionWithReluDiv : public ov::pass::MatcherPass
- #include <hswish_fusion.hpp>
HSwishFusion transformation replaces a sub-graph (x * (min(Relu(x + 3), 6))) / 6 with a HSwish op.
-
class HSwishFusionWithReluMul : public ov::pass::MatcherPass
- #include <hswish_fusion.hpp>
HSwishFusion transformation replaces a sub-graph (x * (min(Relu(x + 3), 6)) * const(1/6) with a HSwish op.
-
class InitConstMask : public ov::pass::MatcherPass
- #include <pruning.hpp>
Check Constant operation values by given dimensions and set masks according to results that are bases on
conditionlambda function. Works for Constant with floating point type (f16, f32, f64).
-
class InitMasks : public ov::pass::GraphRewrite
- #include <pruning.hpp>
Initialising masks for pruned operations.
-
class InitNodeInfo : public ov::pass::ModelPass
- #include <init_node_info.hpp>
InitNodeInfo transformation helps to set runtime info attributes in a single place.
Every runtime info attribute that needs to be initialized should be registered in run_on_function method. Also do not forget to override init methods for registered attribute. This transformations should be called first in transformation pipeline. If attribute was already set initialization will be skipped for this node.
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class InterpolateSequenceFusion : public ov::pass::MatcherPass
- #include <interpolate_sequence_fusion.hpp>
InterpolateSequenceFusion transformation replaces a sequence of operations to Interpolate op.
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class KeepConstAndDecompression : public ov::pass::MatcherPass
- #include <mark_decompression_convert_constant_folding.hpp>
Disables ConstantFolding for Convert operation and prevents conversion of f16 Consts to f32.
-
class KeepConstantsPrecisionAndAddConverts : public ov::pass::MatcherPass
- #include <mark_decompression_convert_constant_folding.hpp>
Prevents Consts precision conversion and adds Convert with disabled ConstantFolding.
-
class LabelResolvingThroughSelect : public ov::pass::MatcherPass
- #include <symbolic_optimizations.hpp>
Transformation requires equal labels on one input of Add and output of last Reshape in the pattern: -> Add -> Reshape -[then or else input]-> Select -> Softmax -> Reshape ->
If shape labels onn mentioned tensors are equal we proved that no broadcasting of this input was done for Add and for Select. Therefore, we can put the same labels on the output of Add and Select. This transformation helps propagate labels and will not be needed if we would use information on equality of products of input and output dimensions of Reshape operations
-
class LeakyReluFusion : public ov::pass::MatcherPass
- #include <leaky_relu_fusion.hpp>
LeakyReluFusion transformation replaces following graph: Multiply->Maximum to LeakyRelu.
-
class LinOpSequenceFusion : public ov::pass::GraphRewrite
- #include <lin_op_sequence_fusion.hpp>
LinOpSequenceFusion transformation fuses linear operation sequence.
-
class LogSoftmaxDecomposition : public ov::pass::MatcherPass
- #include <log_softmax_decomposition.hpp>
LogSoftmaxDecomposition transformation into sub-graph x - log(reduce_sum(exp(x), axis)).
-
class LowLatency2 : public ov::pass::ModelPass
- #include <low_latency.hpp>
The transformation finds all TensorIterator/Loop layers in the network, processes all back edges that describe a connection between Result and Parameter of the TensorIterator/Loop bodies,and inserts ReadValue and Assign layers at the input and output corresponding to this back edge. Supported platform: CPU.
The example below describes the changes made by the transformation [] - TensorIterator body () - new layer BE - back-edge
before applying the transformation: -> input1[BE_1 -> Parameter -> Layers … -> Result -> BE_1 ]output1->
after applying the transformation: ->(ReadValue)-> input1[BE_1 ->Parameter->Layers …->Result->BE_1]output1 ->(Assign) \ ->… After applying the transformation, the resulting network can be inferred step by step, the states will store between inferences.
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class LSTMCellDecomposition : public ov::pass::MatcherPass
- #include <lstm_cell_decomposition.hpp>
LSTMCellDecomposition transformation decomposes LSTMCell layer with inputs X, H, C, W, R, B to Add, Split, MatMul, Multiply ops according to the formula: (.) - Denotes element-wise multiplication.
Denotes dot product. f, g, h - are activation functions.
it = f(Xt*(Wi^T) + Ht-1*(Ri^T) + Wbi + Rbi) ft = f(Xt*(Wf^T) + Ht-1*(Rf^T) + Wbf + Rbf) ct = g(Xt*(Wc^T) + Ht-1*(Rc^T) + Wbc + Rbc) ot = f(Xt*(Wo^T) + Ht-1*(Ro^T) + Wbo + Rbo) Ct = ft (.) Ct-1 + it (.) ct Ht = ot (.) h(Ct)
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class LSTMCellFusion : public ov::pass::MatcherPass
- #include <lstm_cell_fusion.hpp>
LSTMCellFusion transformation replaces a sequence of operations with LSTMCell op.
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class LSTMStatesBroadcast : public ov::pass::ModelPass
- #include <lstm_states_broadcast.hpp>
In case LSTMCell has constant initial hidden and cell state with single batch size we make them broadcast-able by batch.
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class MakeStateful : public ov::pass::ModelPass
- #include <make_stateful.hpp>
The transformation replaces the provided pairs Parameter and Result with Memory layers ReadValue and Assign.
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class Manager
- #include <manager.hpp>
Manager class allows to manage transformation passes.
Public Functions
Register given transformation class type to execution list Example below show the basic usage of pass::Manager.
For some purposes transformation can be registered and disabled by default.pass::Manager manager; manager.register_pass<MyTransformation>(/*transformation constructor ars*‍/); manager.run_passes(f);
manager.register_pass<MyTransformation, false>();
- Returns:
shared_ptr to the transformation instance
Runs registered transformations on a given model.
- Parameters:
model – Input model
- Returns:
Returns true if the model was changed by transformations, false otherwise.
-
void set_per_pass_validation(bool new_state)
Set flag to enable/disable running Validate pass after executing each registered pass.
- Parameters:
new_state – Value “true” enables Validate pass run; “false”, otherwise
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inline std::shared_ptr<PassConfig> get_pass_config()
- Returns:
PassConfig shared object. This object is used for transformations pipeline configuration. This object allows to disable/enable transformations execution, set callback to particular transformation. For mo details see PassConfig class.
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class MarkCompressedFloatConstants : public ov::pass::MatcherPass
- #include <mark_decompression_convert_constant_folding.hpp>
Prevents ConstantFolding for f16/bf16 Const + Convert_To_FP32 to keep original FW float Constants. Original precision should be kept as long as possible, this prevents redundant conversions and saves memory. E.g. if original FW model was already compressed no need to upcast during CF, store intermediate f32 consts and then again compress them to f16 during save_model.
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class MarkDequantizationSubgraph : public ov::pass::MatcherPass
- #include <mark_dequantization_subgraph.hpp>
MarkDequantizationSubgraph marks dequantization subgraph, that is: Convert->Subtract(optional)->Multiply in two ways:
first Convert is marked with DisableConstantFolding attribute, also if Subtract is present and its second input is a Convert - that Convert is marked with DisableConstantFolding as well,
Subtract and Multiply are marked with ‘DequantizationNode’ attribute
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class MarkDividesInShapeSubgraphs : public ov::pass::MarkPrecisionSensitiveShapeOfSubgraphs
- #include <mark_precision_sensitive_shapeof_subgraphs.hpp>
MarkDividesInShapeSubgraphs marks the Divide layers inside of all shape subgraphs starting from precision-sensitive input and ending at the ShapeOf node as disabled for ConvertDivide transformation.
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class MarkPrecisionSensitiveConstants : public ov::pass::MarkPrecisionSensitiveShapeOfSubgraphs
- #include <mark_precision_sensitive_shapeof_subgraphs.hpp>
MarkPrecisionSensitiveConstants marks the constants inside of all shape subgraphs starting from precision-sensitive inputs and ending at the ShapeOf node as disabled for FP16 compression.
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class MarkPrecisionSensitiveShapeOfSubgraphs : public ov::pass::ModelPass
- #include <mark_precision_sensitive_shapeof_subgraphs.hpp>
MarkPrecisionSensitiveShapeOfSubgraphs marks entirely all shape subgraphs starting from precision-sensitive inputs and ending at the ShapeOf node as disabled for FP16 compression.
Subclassed by ov::pass::MarkDividesInShapeSubgraphs, ov::pass::MarkPrecisionSensitiveConstants, ov::pass::MarkShapeOfSubgraphs
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class MarkShapeOfSubgraphs : public ov::pass::MarkPrecisionSensitiveShapeOfSubgraphs
- #include <mark_precision_sensitive_shapeof_subgraphs.hpp>
MarkShapeOfSubgraphs marks shape subgraphs. Information whether the node belongs to the shape path or to the data path is needed during evaluate and CF.
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class MarkSugraphsToKeepInMixedPrecision : public ov::pass::ModelPass
- #include <mark_subgraphs_to_keep_in_mixed_precision.hpp>
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class MatcherPass : public ov::pass::PassBase
- #include <graph_rewrite.hpp>
MatcherPass is a basic block for pattern based transformations. It describes pattern and action that is applied if pattern is matched.
MatcherPass consists of Matcher and matcher_pass_callback that needs to be implemented and finally registered by using
See also
register_matcher. MatcherPass can be executed on node within
See also
apply method. To run matcher pass on Function use GraphRewrite. In addition MatcherPass provides a way for adding new operations into GraphRewrite execution queue. That means that operations that were created inside transformation callback can be added for matching. To register node use
See also
register_new_node method. GraphRewrite automatically takes registered nodes and put them to execution queue. If multiple nodes were register make sure that they were registered in topological order. Note: when implementing pattern for Matcher make sure that root node is an operation from opset or has ov::pass::pattern::op::WrapType. That will help GraphRewrite to execute matcher passes more efficient.
Subclassed by ConvertReduceBase, CvtReduceBase, ov::pass::AUGRUCellFusion, ov::pass::AdaptivePoolToReduce, ov::pass::AddAddFusion, ov::pass::AddFakeQuantizeFusion, ov::pass::AddMultiplyFusion, ov::pass::AlignEltwiseInputRanks, ov::pass::BatchNormDecomposition, ov::pass::BatchToSpaceFusion, ov::pass::BidirectionalGRUSequenceDecomposition, ov::pass::BidirectionalLSTMSequenceDecomposition, ov::pass::BidirectionalRNNSequenceDecomposition, ov::pass::BinarizeWeights, ov::pass::BroadcastConstRangeReplacement, ov::pass::BroadcastElementwiseFusion, ov::pass::BroadcastTransition, ov::pass::ChainedMaximumOptimization, ov::pass::ClampFusion, ov::pass::CompressFloatConstantsImpl, ov::pass::CompressWeightsWithFakeConvert, ov::pass::CompressWeightsWithFakeQuantize, ov::pass::ConcatFusion, ov::pass::ConvStridesPropagation, ov::pass::ConvToBinaryConv, ov::pass::ConvertBatchToSpace, ov::pass::ConvertBitwiseAndToLogicalAnd, ov::pass::ConvertBitwiseNotToLogicalNot, ov::pass::ConvertBitwiseOrToLogicalOr, ov::pass::ConvertBitwiseXorToLogicalXor, ov::pass::ConvertBroadcast3, ov::pass::ConvertBroadcastToTiles, ov::pass::ConvertConvertLike, ov::pass::ConvertConvertPromoteTypes, ov::pass::ConvertDeformableConv8To1, ov::pass::ConvertDepthToSpace, ov::pass::ConvertDetectionOutput1ToDetectionOutput8, ov::pass::ConvertDetectionOutput8ToDetectionOutput1, ov::pass::ConvertDivide, ov::pass::ConvertDivideWithConstant, ov::pass::ConvertGELU, ov::pass::ConvertGP9ToGPIEInternal, ov::pass::ConvertGRUSequenceToTensorIterator, ov::pass::ConvertGather0D, ov::pass::ConvertGather1ToGather7, ov::pass::ConvertGather7ToGather1, ov::pass::ConvertGather7ToGather8, ov::pass::ConvertGather8ToGather7, ov::pass::ConvertGatherToGatherCompressed, ov::pass::ConvertInterpolate11ToInterpolate4, ov::pass::ConvertInterpolate1ToInterpolate4, ov::pass::ConvertLSTMSequenceToTensorIterator, ov::pass::ConvertLoopToLSTMSequence, ov::pass::ConvertMVN1ToMVN6, ov::pass::ConvertMatrixNmsToMatrixNmsIE, ov::pass::ConvertMaxPool1ToMaxPool8, ov::pass::ConvertMaxPool8ToMaxPool1, ov::pass::ConvertMinimum, ov::pass::ConvertMod, ov::pass::ConvertMulticlassNms8ToMulticlassNms9, ov::pass::ConvertMulticlassNmsToMulticlassNmsIE, ov::pass::ConvertNMS1ToNMS5, ov::pass::ConvertNMS1ToNMS9, ov::pass::ConvertNMS3ToNMS5, ov::pass::ConvertNMS3ToNMS9, ov::pass::ConvertNMS4ToNMS5, ov::pass::ConvertNMS4ToNMS9, ov::pass::ConvertNMS5ToNMS9, ov::pass::ConvertNMS9ToNMSIEInternal, ov::pass::ConvertNMSRotatedToNMSIEInternal, ov::pass::ConvertNMSToNMSIEInternal, ov::pass::ConvertNegative, ov::pass::ConvertPad12ToPad1, ov::pass::ConvertPadToGroupConvolution, ov::pass::ConvertPriorBox8To0, ov::pass::ConvertQuantizeDequantize, ov::pass::ConvertRNNSequenceToTensorIterator, ov::pass::ConvertROIAlign3To9, ov::pass::ConvertROIAlign9To3, ov::pass::ConvertScatterElementsToScatter, ov::pass::ConvertScatterElementsUpdate12ToScatterElementsUpdate3, ov::pass::ConvertShapeOf3, ov::pass::ConvertShuffleChannels3, ov::pass::ConvertSoftMax1ToSoftMax8, ov::pass::ConvertSoftMax8ToSoftMax1, ov::pass::ConvertSpaceToBatch, ov::pass::ConvertSpaceToDepth, ov::pass::ConvertSubtract, ov::pass::ConvertSubtractWithConstant, ov::pass::ConvertTensorIteratorToGRUSequence, ov::pass::ConvertTensorIteratorToLSTMSequence, ov::pass::ConvertTensorIteratorToRNNSequence, ov::pass::ConvertTopK11ToTopK3, ov::pass::ConvertTopK3, ov::pass::ConvertU4WeightsZeroPointToScalar, ov::pass::ConvertXorToLogicalXor, ov::pass::ConvolutionBackpropDataMultiplyFusion, ov::pass::ConvolutionMultiplyFusion, ov::pass::ConvolutionToGroupConvolutionFusion, ov::pass::DeReshapeFullyConnected, ov::pass::DeReshapeMatMul, ov::pass::DepthToSpaceFusion, ov::pass::DilatedConvolutionConverter, ov::pass::DisableDecompressionConvertConstantFolding, ov::pass::DisableRandomUniformConstantFolding, ov::pass::DisableShapeOfConstantFolding, ov::pass::DivideFusion, ov::pass::DropoutWithRandomUniformReplacer, ov::pass::EinsumDecomposition, ov::pass::EliminateConcat, ov::pass::EliminateConvert, ov::pass::EliminateConvertNonZero, ov::pass::EliminateDuplicateTIInputs, ov::pass::EliminateEltwise, ov::pass::EliminateGatherUnsqueeze, ov::pass::EliminateNopBroadcast, ov::pass::EliminatePad, ov::pass::EliminateScatterUpdate, ov::pass::EliminateSplit, ov::pass::EliminateSplitConcat, ov::pass::EliminateSqueeze, ov::pass::EliminateTranspose, ov::pass::EliminateUnsqueezeGather, ov::pass::EnableDecompressionConvertConstantFolding, ov::pass::EnableShapeOfConstantFolding, ov::pass::EyeDecomposition, ov::pass::FakeQuantizeDecomposition, ov::pass::FakeQuantizeMulFusion, ov::pass::FakeQuantizeReshapeFusion, ov::pass::FlushFP32SubnormalsToZero, ov::pass::FoldSubgraphEmptyInputs, ov::pass::FuseLSTMSequencesToBidirectionalLSTMSequence, ov::pass::FuseReverseLSTMSequence, ov::pass::GRUCellDecomposition, ov::pass::GRUCellFusion, ov::pass::GatherNopElimination, ov::pass::Gelu7Downgrade, ov::pass::GeluFusionWithErfFour, ov::pass::GeluFusionWithErfOne, ov::pass::GeluFusionWithErfThree, ov::pass::GeluFusionWithErfTwo, ov::pass::GeluFusionWithTanh, ov::pass::GeluFusionWithTanhNoPower, ov::pass::GroupConvolutionBackpropDataMultiplyFusion, ov::pass::GroupConvolutionMultiplyFusion, ov::pass::GroupNormalizationDecomposition, ov::pass::GroupedGatherElimination, ov::pass::HSigmoidDecomposition, ov::pass::HSigmoidFusionWithClampDiv, ov::pass::HSigmoidFusionWithClampMul, ov::pass::HSigmoidFusionWithReluDiv, ov::pass::HSigmoidFusionWithReluMul, ov::pass::HSigmoidFusionWithoutRelu, ov::pass::HSwishDecomposition, ov::pass::HSwishFusionWithClamp, ov::pass::HSwishFusionWithHSigmoid, ov::pass::HSwishFusionWithReluDiv, ov::pass::HSwishFusionWithReluMul, ov::pass::InitConstMask, ov::pass::InterpolateSequenceFusion, ov::pass::KeepConstAndDecompression, ov::pass::KeepConstantsPrecisionAndAddConverts, ov::pass::LSTMCellDecomposition, ov::pass::LSTMCellFusion, ov::pass::LabelResolvingThroughSelect, ov::pass::LeakyReluFusion, ov::pass::LogSoftmaxDecomposition, ov::pass::MVN6Decomposition, ov::pass::MVNFusionWithConstantsInside, ov::pass::MVNFusionWithoutConstants, ov::pass::MarkCompressedFloatConstants, ov::pass::MarkDequantizationSubgraph, ov::pass::MatMulConstTransposesExtraction, ov::pass::MatMulMultiplyFusion, ov::pass::MishFusion, ov::pass::MulFakeQuantizeFusion, ov::pass::MultiplyConvolutionBackpropDataFusion, ov::pass::MultiplyConvolutionFusion, ov::pass::MultiplyGroupConvolutionBackpropDataFusion, ov::pass::MultiplyGroupConvolutionFusion, ov::pass::MultiplyMultiplyFusion, ov::pass::NearestNeighborUpsamplingFusion, ov::pass::NonZeroHorizontalFusion, ov::pass::NopBroadcast, ov::pass::NopSliceBeforeGatherElements, ov::pass::NopStridedSlice, ov::pass::NopStridedSliceByShape, ov::pass::NormalizeL2Decomposition, ov::pass::NormalizeL2Fusion, ov::pass::PReluFusionAbsSubMulMulAdd, ov::pass::PReluFusionMultiplyAdd, ov::pass::PReluFusionMultiplySub, ov::pass::PReluFusionNegReluMulAdd, ov::pass::PReluFusionNegativeAdd, ov::pass::PReluFusionNegativeSub, ov::pass::PadFusionAvgPool, ov::pass::PadFusionConvolution, ov::pass::PadFusionConvolutionBackpropData, ov::pass::PadFusionGroupConvolution, ov::pass::PadFusionGroupConvolutionBackpropData, ov::pass::PrepareShapeOpsForEliminationAroundBE, ov::pass::Proposal1Scales, ov::pass::Proposal4Scales, ov::pass::PullReshapeThroughReduce, ov::pass::PullSqueezeThroughEltwise, ov::pass::PullTransposeThroughFQUp, ov::pass::PullUnsqueezeThroughReduce, ov::pass::RNNCellDecomposition, ov::pass::RPE_Fusion, ov::pass::RandomUniformFusion, ov::pass::ReduceL1Decomposition, ov::pass::ReduceL2Decomposition, ov::pass::ReduceMerge, ov::pass::ReduceReshapeFusion, ov::pass::ReluFakeQuantizeFusion, ov::pass::RemoveConcatZeroDimInput, ov::pass::RemoveFilteringBoxesBySize, ov::pass::ReplaceConcatReduceByMinOrMax, ov::pass::ReshapeAMatMul, ov::pass::ReshapeBMatMul, ov::pass::ReshapeOptimizations, ov::pass::ReshapePRelu, ov::pass::ReshapeSequenceFusion, ov::pass::ReshapeSinkingMatMul, ov::pass::ReshapeTo1D, ov::pass::ScaledDotProductAttentionDecomposition, ov::pass::SelectWithOneValueCondition, ov::pass::SequenceFusion, ov::pass::ShapeOfConstFolding, ov::pass::ShuffleChannelsFusion, ov::pass::SimplifyCTCGreedyDecoderSeqLen, ov::pass::SimplifyGatherShapeOf, ov::pass::SimplifySecondInputOfReshape, ov::pass::SkipGatherBeforeTransposeAndReshape, ov::pass::SliceToStridedSlice, ov::pass::SoftPlusDecomposition, ov::pass::SoftPlusFusion, ov::pass::SoftPlusToMishFusion, ov::pass::SoftSignDecomposition, ov::pass::SoftmaxDecomposition, ov::pass::SoftmaxFusion, ov::pass::SpaceToBatchFusion, ov::pass::SplitConcatPairToInterpolateFusion, ov::pass::SplitSqueezeConcatFusion, ov::pass::SqueezeStridedSlice, ov::pass::StridedSliceSqueeze, ov::pass::SubtractFusion, ov::pass::SupportedNodesStridesPropagation, ov::pass::SwishFusionWithBeta, ov::pass::SwishFusionWithSigmoid, ov::pass::SwishFusionWithSigmoidWithBeta, ov::pass::SwishFusionWithoutBeta, ov::pass::TransposeConvert, ov::pass::TransposeEltwise, ov::pass::TransposeFQReduction, ov::pass::TransposeFuse, ov::pass::TransposeMatMul, ov::pass::TransposeReduction, ov::pass::TransposeReshapeEliminationForMatmul, ov::pass::TransposeToReshape, ov::pass::UniqueDecomposition, ov::pass::UnsupportedNodesStridesPropagation, ov::pass::WeightsDequantizeToFakeQuantize, ov::pass::WrapInterpolateIntoTransposes, ov::pass::low_precision::ConvertSubtractConstant, ov::pass::low_precision::CreatePrecisionsDependentAttribute< AttributeType, OperationType >, ov::pass::low_precision::LayerTransformation, ov::pass::low_precision::MarkupBias, ov::pass::low_precision::PropagateThroughPrecisionPreserved< AttributeType >, ov::pass::low_precision::PropagateToInput< AttributeType >, ov::pass::low_precision::PullReshapeThroughDequantization, ov::pass::low_precision::PullTransposeThroughDequantization, ov::pass::low_precision::UpdateSharedPrecisionPreserved< AttributeType, ExpectedAttributeType >, ov::pass::transpose_sinking::TSBinaryBackward, ov::pass::transpose_sinking::TSConcatBackward, ov::pass::transpose_sinking::TSCumSumBackward, ov::pass::transpose_sinking::TSDataMovementBackward, ov::pass::transpose_sinking::TSForwardBase, ov::pass::transpose_sinking::TSFuse, ov::pass::transpose_sinking::TSGatherBackward, ov::pass::transpose_sinking::TSInterpolateBackward, ov::pass::transpose_sinking::TSReductionBackward, ov::pass::transpose_sinking::TSResetNoSinkingAttribute, ov::pass::transpose_sinking::TSSliceBackward, ov::pass::transpose_sinking::TSSplitBackward, ov::pass::transpose_sinking::TSSqueezeBackward, ov::pass::transpose_sinking::TSTileBackward, ov::pass::transpose_sinking::TSUnaryBackward, ov::pass::transpose_sinking::TSUnsqueezeBackward
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class MatMulConstTransposesExtraction : public ov::pass::MatcherPass
- #include <matmul_const_transposes_extraction.hpp>
-
class MatMulMultiplyFusion : public ov::pass::MatcherPass
- #include <matmul_multiply_fusion.hpp>
MatMulMultiplyFusion transformation matches following graph:
+----------+ +----------+ | A | | B | +----------+ +----------+ | | ----------- ---------- | | v v +--------+ | MatMul | +--------+ | v +----------+ +----------+ | Multiply |<----| Constant | +----------+ +----------+
and replaces with:
+-------+ +----------+ | B | | Constant | +-------+ +----------+ | | ------ ------ | | v v +----------+ +----------+ | A | | Multiply | +----------+ +----------+ | | ----------- ---------- | | v v +--------+ | MatMul | +--------+
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class MishFusion : public ov::pass::MatcherPass
- #include <mish_fusion.hpp>
MishFusion transformation replaces group of operations: x * tanh(log(exp(x) + 1)) to Mish op.
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class MOCLegacyTransformations : public ov::pass::ModelPass
- #include <moc_legacy_transformations.hpp>
This transformation is an entry point for OpenVINO transformations that will be applied inside MOC. This transformations container is filled with legacy transformations to reach parity between legacy front-ends and new frontends calling from the Model Optimizer. It contains transformations to avoid limitations of OpenVINO 1.X API such as unsupported INT64 for inputs, usage of NCHW layout that is critical for TensorFlow models.
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class MOCTransformations : public ov::pass::ModelPass
- #include <moc_transformations.hpp>
This transformation is an entry point for OpenVINO transformations that will be applied inside MOC. And in future this transformations container will be filled with transformations pipeline but now it remains empty.
Public Functions
-
inline explicit MOCTransformations(bool use_shapes, bool low_precision_enabled = true)
use_shapes = True enables transformations which are depends on shapes and also it enables ConstantFolding for all ShapeOf operations.
low_precision_enabled = True enables preserving mechanisms that helps to keep low_precision sub-graphs as is.
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inline explicit MOCTransformations(bool use_shapes, bool low_precision_enabled = true)
-
class ModelPass : public ov::pass::PassBase
- #include <pass.hpp>
Base class for Model passes.
Subclassed by ov::pass::AlignMixedFP32FP16Types, ov::pass::ApplySymbolEquivalence, ov::pass::ChangePlaceholderTypes, ov::pass::CommonOptimizations, ov::pass::ConstantFolding, ov::pass::ConvertCompressedOnlyToLegacy, ov::pass::ConvertFP32ToFP16, ov::pass::ConvertOpSet2ToOpSet1, ov::pass::ConvertOpSet3ToOpSet2, ov::pass::ConvertPrecision, ov::pass::FindBatch, ov::pass::FusedNamesCleanup, ov::pass::GraphRewrite, ov::pass::GroupedSliceToVSplitOptimization, ov::pass::GroupedStridedSliceOptimizer, ov::pass::Hash, ov::pass::InitNodeInfo, ov::pass::LSTMStatesBroadcast, ov::pass::LowLatency2, ov::pass::MOCLegacyTransformations, ov::pass::MOCTransformations, ov::pass::MakeStateful, ov::pass::MarkPrecisionSensitiveShapeOfSubgraphs, ov::pass::MarkSugraphsToKeepInMixedPrecision, ov::pass::OptimizeSymbolsUsedAsValues, ov::pass::Pruning, ov::pass::PushConstantToSubgraph, ov::pass::RemoveMultiSubGraphOpDanglingParamsResults, ov::pass::ResolveNameCollisions, ov::pass::ReverseInputChannelsFusion, ov::pass::ReverseShapeAndTypeInfer, ov::pass::Serialize, ov::pass::SharedOpOptimization, ov::pass::ShrinkWeights, ov::pass::SimplifyShapeOfSubGraph, ov::pass::SmartReshape, ov::pass::StreamSerialize, ov::pass::StridedSliceOptimization, ov::pass::SymbolicOptimizations, ov::pass::SymbolicPropagation, ov::pass::UnrollIf, ov::pass::UnrollTensorIterator, ov::pass::UselessSliceEraser, ov::pass::Validate, ov::pass::VisualizeTree, ov::pass::low_precision::AlignQuantizationIntervals, ov::pass::low_precision::AlignQuantizationParameters, ov::pass::low_precision::MarkupAvgPoolPrecisionPreserved, ov::pass::low_precision::MarkupCanBeQuantized, ov::pass::low_precision::MarkupOptimizations, ov::pass::low_precision::MarkupPrecisions, ov::pass::low_precision::MarkupQuantizationGranularity, ov::pass::low_precision::PropagatePrecisions, ov::pass::low_precision::PropagateSharedValue< AttributeType >, ov::pass::transpose_sinking::TSGeneral
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class MulFakeQuantizeFusion : public ov::pass::MatcherPass
- #include <mul_fake_quantize_fusion.hpp>
MulFakeQuantizeFusion transformation replaces following graph: Mul->FakeQuantize to a single FakeQuantize Restrictions:
second input to Mul is a Constant
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class MultiplyConvolutionBackpropDataFusion : public ov::pass::MatcherPass
- #include <mul_conv_fusion.hpp>
-
class MultiplyConvolutionFusion : public ov::pass::MatcherPass
- #include <mul_conv_fusion.hpp>
Multiply->Convolution fusion replaces following graph:
+—-—+ +——-—+ | Input
| | Constant | +—-—+ +——-—+ | |
| | v v +——-
—+ +———+ | Multiply | | Weights | +——-—+ +———+ | |
| | v v +————-
—+ | Convolution Op | +————-—+to following:
where ‘Convolution Op’ is one of:+---------+ +----------+ | Weights | | Constant | +---------+ +----------+ | | ------ ------ | | v v +-------+ +----------+ | Input | | Multiply | +-------+ +----------+ | | ----------- ---------- | | v v +----------------+ | Convolution Op | +----------------+
Convolution
ConvolutionBackpropData
GroupConvolution
GroupConvolutionBackpropData
Restrictions:
weights’ shape is static
if the constant input to Multiply has the same rank as ‘input’, the constant first dimension has to be 1
constant input to Multiply has to be broadcastable to weights when ‘Convolution Op’ is either Convolution or GroupConvolution
shape of a constant input to Multiply has to be in one of following forms: (1), (1, 1, …, 1), (C, 1, …, 1), (1, C, 1, …, 1) when ‘Convolution Op’ is either ConvolutionBackpropData or GroupConvolutionBackpropData
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class MultiplyGroupConvolutionBackpropDataFusion : public ov::pass::MatcherPass
- #include <mul_conv_fusion.hpp>
-
class MultiplyGroupConvolutionFusion : public ov::pass::MatcherPass
- #include <mul_conv_fusion.hpp>
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class MultiplyMultiplyFusion : public ov::pass::MatcherPass
- #include <lin_op_sequence_fusion.hpp>
-
class MVN6Decomposition : public ov::pass::MatcherPass
- #include <mvn6_decomposition.hpp>
MVN6Decomposition transformation into sub-graph x - ReduceMean(x, axes) if normalize_variance is false and into sub-graph (x - ReduceMean(x, axes)) / Sqrt(ReduceMean((x - ReduceMean(x, axes)) ^ 2)) if normalize_variance is true.
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class MVNFusion : public ov::pass::GraphRewrite
- #include <mvn_fusion.hpp>
MVNFusion transformation replaces various sub-graphs with a MVN op.
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class MVNFusionWithConstantsInside : public ov::pass::MatcherPass
- #include <mvn_fusion.hpp>
MVNFusion transformation replaces group of operations: gamma * (x - ReduceMean(x, axes)) / (Sqrt(ReduceMean((x - ReduceMean(x, axes)) ^ 2)) + eps) + beta to MVN op.
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class MVNFusionWithoutConstants : public ov::pass::MatcherPass
- #include <mvn_fusion.hpp>
MVNFusion transformation replaces group of operations: (x - ReduceMean(x, axes)) / (Sqrt(ReduceMean((x - ReduceMean(x, axes)) ^ 2)) + eps) to MVN op.
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class NearestNeighborUpsamplingFusion : public ov::pass::MatcherPass
- #include <nearest_neighbor_upsampling_fusion.hpp>
NearestNeighborUpsamplingFusion transformation fuses subgraph that uses the simpler operations, as ShapeOf, StridedSlice, Concat, Reshape, Mul to calculate Interpolate with mode=’nearest’.
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class NodeRegistry
- #include <graph_rewrite.hpp>
Register openvino node pointers into container. Can create and/or add existing node pointers into register.
Public Functions
Make new node and add it to register.
Add node to register.
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inline const std::vector<std::shared_ptr<Node>> &get() const
Get nodes container.
- Returns:
Const reference to nodes container.
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inline void clear()
Clear register.
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class NonZeroHorizontalFusion : public ov::pass::MatcherPass
- #include <nonzero_horizontal_fusion.hpp>
NonZeroHorizontalFusion transformation makes horizontal fusion for equal NonZero layers.
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class NopBroadcast : public ov::pass::MatcherPass
- #include <nop_broadcast.hpp>
Optimizes out Broadcast(data, Maximum(shape, ones)) if labels on data and shape are equal Use case with data being empty should not be considered here since original graph has Maximum with ones.
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class NopElimination : public ov::pass::GraphRewrite
- #include <nop_elimination.hpp>
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class NopSliceBeforeGatherElements : public ov::pass::MatcherPass
- #include <nop_elimination.hpp>
NopSliceBeforeGatherElements eliminates slice before GElements if slicing from 0 It is valid since GatherElements doesn’t support negative indices and Slice won’t affect indexing of elements in the original tensor that GatherElements would like to take.
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class NopStridedSlice : public ov::pass::MatcherPass
- #include <nop_elimination.hpp>
NopStridedSlice eliminates Strided Slice in case tensors were not changed.
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class NopStridedSliceByShape : public ov::pass::MatcherPass
- #include <nop_elimination.hpp>
NopStridedSlice eliminates Strided Slice in case tensors were not changed.
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class NormalizeL2Decomposition : public ov::pass::MatcherPass
- #include <normalize_l2_decomposition.hpp>
Decomposes NormalizeL2 into subgraph.
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class NormalizeL2Fusion : public ov::pass::MatcherPass
- #include <normalize_l2_fusion.hpp>
NormalizeL2Fusion transformation replaces sub-graphs: x/(sqrt(max(reduce_sum(x[j0, …, jN]**2, axes), eps)) x/(sqrt(add(reduce_sum(x[j0, …, jN]**2, axes), eps)) x/(pow(max(reduce_sum(x[j0, …, jN]**2, axes), eps), 0.5) x/(pow(add(reduce_sum(x[j0, …, jN]**2, axes), eps), 0.5) x*(pow(max(reduce_sum(x[j0, …, jN]**2, axes), eps), -0.5) x*(pow(add(reduce_sum(x[j0, …, jN]**2, axes), eps), -0.5) with a NormalizeL2(x, axes, eps, eps_mode[MAX|ADD]) op.
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class OptimizeSymbolsUsedAsValues : public ov::pass::ModelPass
- #include <symbol_optimization.hpp>
Collects sources where each symbol initially appeared (on shape or shape sub-graph) and attaches all value usages of this label to this initial source.
-
class PadFusion : public ov::pass::GraphRewrite
- #include <pad_fusion.hpp>
-
class PadFusionAvgPool : public ov::pass::MatcherPass
- #include <pad_fusion.hpp>
PadFusion transformation replaces following graph: Pad -> AvgPool to AvgPool, under following conditions.
pad mode is op::PadMode::CONSTANT
pad value is 0
exclude_pad in AvgPool is set to false or pads_begin, pads_end are set to zero
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class PadFusionConvolution : public ov::pass::MatcherPass
- #include <pad_fusion.hpp>
PadFusion transformation replaces following graph: Pad -> Convolution to Convolution, under following conditions.
pad mode is op::PadMode::CONSTANT
pad value is 0
-
class PadFusionConvolutionBackpropData : public ov::pass::MatcherPass
- #include <pad_fusion.hpp>
PadFusion transformation replaces following graph: Pad -> ConvolutionBackpropData to ConvolutionBackpropData, under following conditions.
pad mode is op::PadMode::CONSTANT
pad value is 0
pads in ConvolutionBackpropData are greater than pads in Pad node
-
class PadFusionGroupConvolution : public ov::pass::MatcherPass
- #include <pad_fusion.hpp>
PadFusion transformation replaces following graph: Pad -> GroupConvolution to GroupConvolution, under following conditions.
pad mode is op::PadMode::CONSTANT
pad value is 0
-
class PadFusionGroupConvolutionBackpropData : public ov::pass::MatcherPass
- #include <pad_fusion.hpp>
PadFusion transformation replaces following graph: Pad -> GroupConvolutionBackpropData to GroupConvolutionBackpropData, under following conditions.
pad mode is op::PadMode::CONSTANT
pad value is 0
pads in GroupConvolutionBackpropData are greater than pads in Pad node
-
class PassBase
- #include <pass.hpp>
Base class for transformation passes.
Subclassed by ov::pass::MatcherPass, ov::pass::ModelPass
Public Functions
-
bool get_property(const PassPropertyMask &prop_mask) const
Check if this pass has all the pass properties.
-
void set_callback(const param_callback &callback)
Set callback for particular transformation type. This method set global callback. For more details see PassConfig class documentation.
- Parameters:
callback – lambda function that takes node and returns bool
Set PassConfig for particular transformation instance.
- Parameters:
pass_config – is a PassConfig shared_ptr
-
inline std::shared_ptr<PassConfig> get_pass_config()
Allows to access PassConfig shared instance.
- Returns:
Shared instance of PassConfig class
Applies callback for given node. By default callback returns false.
- Parameters:
node – which will be used inside callback
- Returns:
result of callback execution for given node
-
bool get_property(const PassPropertyMask &prop_mask) const
-
class PassConfig
- #include <pass_config.hpp>
Class representing a transformations config that is used for disabling/enabling transformations registered inside pass::Manager and also allows to set callback for all transformations or for particular transformation.
When pass::Manager is created all passes registered inside this manager including nested passes will share the same instance of PassConfig class. To work with this class first you need to get shared instance of this class by calling manager.get_pass_config() method. Then you will be able to disable/enable passes based on transformations type_info. For example:
Sometimes it is needed to call transformation inside other transformation manually. And for that case before running transformation you need manually check that this pass is not disabled and then you need to set current PassConfig instance to this transformation. For example:pass::Manager manager; manager.register_pass<CommonOptimizations>(); auto pass_config = manager.get_pass_config(); pass_config->disable<ConvertGELU>(); // this will disable nested pass inside // CommonOptimizations pipeline manager.run_passes(f);
Following this logic inside your transformations you will guaranty that transformations will be executed in a right way.// Inside MatcherPass callback or inside FunctionPass run_on_function() method // you need to call get_pass_config() method to get shared instance of PassConfig auto pass_config = get_pass_config(); // Before running nested transformation you need to check is it disabled or not if (!pass_config->is_disabled<ConvertGELU>()) { auto pass = ConvertGELU(); pass->set_pass_config(pass_config); pass.apply(node); }Public Functions
-
PassConfig()
Default constructor.
-
void disable(const DiscreteTypeInfo &type_info)
Disable transformation by its type_info.
- Parameters:
type_info – Transformation type_info
-
template<class T>
inline void disable() Disable transformation by its class type (based on type_info)
-
void enable(const DiscreteTypeInfo &type_info)
Enable transformation by its type_info.
- Parameters:
type_info – Transformation type_info
-
template<class T>
inline void enable() Enable transformation by its class type (based on type_info)
-
inline void set_callback(const param_callback &callback)
Set callback for all kind of transformations.
-
template<typename T, class ...Args>
inline void set_callback(const param_callback &callback) Set callback for particular transformation class types.
Example below show how to set callback for one or multiple passes using this method.
Note that inside transformations you must provide code that work with this callback. See example below:pass_config->set_callback<ov::pass::ConvertBatchToSpace, ov::pass::ConvertSpaceToBatch>( [](const_node_ptr &node) -> bool { // Disable transformations for cases when input shape rank is not equal to 4 const auto input_shape_rank = node->get_output_partial_shape(0).rank().get_length(); if (input_shape_rank != 4) { return false; } return true; });
if (transformation_callback(node)) { return false; // exit from transformation }
-
param_callback get_callback(const DiscreteTypeInfo &type_info) const
Get callback for given transformation type_info.
In case if callback wasn’t set for given transformation type then global callback will be returned. But if even global callback wasn’t set then default callback will be returned.
- Parameters:
type_info – Transformation type_info
-
template<class T>
inline param_callback get_callback() const Get callback for given transformation class type.
- Returns:
callback lambda function
-
inline bool is_disabled(const DiscreteTypeInfo &type_info) const
Check either transformation type is disabled or not.
- Parameters:
type_info – Transformation type_info
- Returns:
true if transformation type was disabled and false otherwise
-
template<class T>
inline bool is_disabled() const Check either transformation class type is disabled or not.
- Returns:
true if transformation type was disabled and false otherwise
-
inline bool is_enabled(const DiscreteTypeInfo &type_info) const
Check either transformation type is force enabled or not.
- Parameters:
type_info – Transformation type_info
- Returns:
true if transformation type was force enabled and false otherwise
-
template<class T>
inline bool is_enabled() const Check either transformation class type is force enabled or not.
- Returns:
true if transformation type was force enabled and false otherwise
-
PassConfig()
-
class PReluFusion : public ov::pass::GraphRewrite
- #include <prelu_fusion.hpp>
PReluFusion transformation replaces various sub-graphs with a PRelu op.
-
class PReluFusionAbsSubMulMulAdd : public ov::pass::MatcherPass
- #include <prelu_fusion.hpp>
PReluFusionAbsSubMulMulAdd transformation replaces a sub-graph Op / | \ Relu | Abs | \ | | Subtract | | | Multiply | | | Multiply (0.5) \ / Add.
-
class PReluFusionMultiplyAdd : public ov::pass::MatcherPass
- #include <prelu_fusion.hpp>
PReluFusionMultiplyAdd transformation replaces a sub-graph Op / \ Relu Multiply (-1) | | | Relu | | | Multiply \ / Add.
-
class PReluFusionMultiplySub : public ov::pass::MatcherPass
- #include <prelu_fusion.hpp>
PReluFusionMultiplySub transformation replaces a sub-graph Op / \ Relu Multiply (-1) | | | Relu | | | Multiply \ / Sub.
-
class PReluFusionNegativeAdd : public ov::pass::MatcherPass
- #include <prelu_fusion.hpp>
PReluFusionNegativeAdd transformation replaces a sub-graph Op / \ Relu Negative | | | Relu | | | Negative | | | Multiply \ / Add.
-
class PReluFusionNegativeSub : public ov::pass::MatcherPass
- #include <prelu_fusion.hpp>
PReluFusionNegativeSub transformation replaces a sub-graph Op / \ Relu Negative | | | Relu | | | Multiply \ / Sub.
-
class PReluFusionNegReluMulAdd : public ov::pass::MatcherPass
- #include <prelu_fusion.hpp>
PReluFusionNegReluMulAdd transformation replaces a sub-graph Op / \ Relu Negative | | | Relu | | | Multiply \ / Add.
-
class PrepareShapeOpsForEliminationAroundBE : public ov::pass::MatcherPass
- #include <nop_elimination.hpp>
PrepareShapeOpsForEliminationAroundBE works on the subgraph like Reshape/Squeeze/Unsqueeze -> BinaryElementwiseOperation -> Reshape/Squeeze/Unsqueeze and prepares it for the following optimizations by moving bottom op up through Binary op.
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class PropagateMasks : public ov::pass::GraphRewrite
- #include <pruning.hpp>
Contains several MatcherPasses that initialize and propagate masks from Constant operation to the network output.
-
class Proposal1Scales : public ov::pass::MatcherPass
- #include <proposal_scales_stridedslice.hpp>
ProposalScales transformation helps to silently avoid reshape issues on the scale-input of Proposal layer.
Expected sub-graph looks like: Parameter [batch, 3 or 4] -> Reshape [-1] -(in: 3)-> PriorBox
PriorBox operation accepts 3 or 4 values as scales from specification standpoint PriorBox uses first set (batch) of scale values to proceed in the plugins According to this we explicitly take first batch of scales with StridedSlice operation
Resulting sub-graph: Parameter [batch, 3 or 4] -> Reshape [-1] -> StridedSlice[0: 3 or 4] -(in: 3)-> PriorBox
-
class Proposal4Scales : public ov::pass::MatcherPass
- #include <proposal_scales_stridedslice.hpp>
-
class Pruning : public ov::pass::ModelPass
- #include <pruning.hpp>
This is just a sequence of passes that performs pruning transformations pipeline.
-
class PullReshapeThroughReduce : public ov::pass::MatcherPass
- #include <pull_through_reduce.hpp>
PullReshapeThroughReduce transformation The transformation pulls Reshape operator though Reduce ops if possible. In the further processing such Reshape can be often skipped as nop.
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class PullSqueezeThroughEltwise : public ov::pass::MatcherPass
- #include <concat_reduce_fusion.hpp>
PullSqueezeThroughEltwise transformation propagates Squeeze up through binary elementwise operations:
-
class PullThroughReduce : public ov::pass::GraphRewrite
- #include <pull_through_reduce.hpp>
PullThroughReduce transformation The transformation pulls Reshape or Unsqueeze operators though Reduce ops if possible. In the further processing such Reshape/Unsqueeze can be often skipped as nop.
-
class PullTransposeThroughFQUp : public ov::pass::MatcherPass
- #include <pull_transpose_through_fq.hpp>
-
class PullUnsqueezeThroughReduce : public ov::pass::MatcherPass
- #include <pull_through_reduce.hpp>
PullUnsqueezeThroughReduce transformation The transformation pulls Unsqueeze operator though Reduce ops if possible. In the further processing such Unsqueeze can be often skipped as nop.
-
class PushConstantToSubgraph : public ov::pass::ModelPass
- #include <push_constant_to_subgraph.hpp>
PushConstantToSubgraph transformation detects MultiSubGraphOp inputs that can be constfoldable pushes that inputs to subgraphs.
-
class RandomUniformFusion : public ov::pass::MatcherPass
- #include <random_uniform_fusion.hpp>
RandomUniformFusion transformation replaces RandomUniform -> Add or RandomUniform -> Mul subgraph with a RandomUniform and replaces min and max const with corrected values.
-
class ReduceL1Decomposition : public ov::pass::MatcherPass
- #include <reduce_l1_decomposition.hpp>
Decomposes ReduceL1 into ReduceSum(abs(x)).
-
class ReduceL2Decomposition : public ov::pass::MatcherPass
- #include <reduce_l2_decomposition.hpp>
Decomposes ReduceL2 into sqrt(ReduceSum(x * x)).
-
class ReduceMerge : public ov::pass::MatcherPass
- #include <reduce_merge.hpp>
ReduceMerge transformation matches following graph:
+——-—+ +——-—+ | A | | B | +——-—+ +——-—+ | |
| | v v +—–
—+ +—–—+ | Reduce | | C | +—–—+ +—–—+ | | | —-— | | v v +——-—+ | Reduce | +——-—+and replaces with:
+——-—+ +—————-—+ | A | | Concat/Constant | +——-—+ +—————-—+ | | | —–— | | v v +——-—+ | Reduce | +——-—++----------+ +----------+ | B | | C | +----------+ +----------+ | | ------- ------- | | v v
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class ReduceReshapeFusion : public ov::pass::MatcherPass
- #include <reduce_reshape_fusion.hpp>
ReduceReshapeFusion transformation Fuse ReduceOp(keep_dims=false)+Reshape to ReduceOp(keep_dims=true)
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class ReluFakeQuantizeFusion : public ov::pass::MatcherPass
- #include <relu_fake_quantize_fusion.hpp>
ReluFakeQuantizeFusion transformation replaces following graph: Relu -> FakeQuantize to FakeQuantize under following conditions:
‘input_low’ input to FakeQuantize is a Constant
’input_low’ has non negative values
-
class RemoveConcatZeroDimInput : public ov::pass::MatcherPass
- #include <remove_concat_zero_dim_input.hpp>
RemoveConcatZeroDimInput transformation removes input of Concat if the tensor size is equal to 0.
-
class RemoveFilteringBoxesBySize : public ov::pass::MatcherPass
- #include <remove_filtering_boxes_by_size.hpp>
-
class RemoveMultiSubGraphOpDanglingParamsResults : public ov::pass::ModelPass
- #include <remove_multi_subgraph_op_dangling_params.hpp>
-
class ReplaceConcatReduceByMinOrMax : public ov::pass::MatcherPass
- #include <concat_reduce_fusion.hpp>
ReplaceConcatReduceByMinOrMax transformation replaces Concat with 2 inputs and ReduceMin/Max by a single Minimum/Maximum with 2 inputs and inserts squeeze in case when Reduce has keep_dims = false.
-
class ReshapeAMatMul : public ov::pass::MatcherPass
- #include <matmul_sr.hpp>
ReshapeAMatMul and ReshapeBMatMul transformations relax hard-coded Reshape followed by MatMul operation For 2D Reshape search patterns are:
MatMul(Reshape(any_input, any_input), any_input)
MatMul(any_input, Reshape(any_input, any_input))
-
class ReshapeBMatMul : public ov::pass::MatcherPass
- #include <matmul_sr.hpp>
-
class ReshapeOptimizations : public ov::pass::MatcherPass
- #include <reshape_optimizations.hpp>
Searches for Flatten-like Reshape operations and simplifies 2nd input of such Reshape using special zero feature.
-
class ReshapePRelu : public ov::pass::MatcherPass
- #include <reshape_prelu.hpp>
ReshapePRelu reshape second input of PRelu (slope)
-
class ReshapeSequenceFusion : public ov::pass::MatcherPass
- #include <reshape_sequence_fusion.hpp>
ReshapeSequenceFusion fuses sequence of Reshape operation into single Reshape or eliminates full redundant sequence.
-
class ReshapeSinkingMatMul : public ov::pass::MatcherPass
- #include <reshape_sinking.hpp>
ReshapeSinkingMatMul transformation looks for MatMul followed by optional Add surrounded with Reshape operations which are only needed to merge and unmerge dimensions into MatMuls batch. In case of success upscales MatMul to work with multidimensional batch and updates Reshape operators to make batch propagate through freely.
-
class ReshapeTo1D : public ov::pass::MatcherPass
- #include <reshape_to_1D.hpp>
ReshapeTo1D transformation looks for Reshape from nD to 1D tensor and replaces its pattern to [-1].
-
class ResolveNameCollisions : public ov::pass::ModelPass
- #include <resolve_names_collisions.hpp>
ResolveNameCollisions transformation helps to fix names collisions if nodes with autogenerated names have conflicts with other node names.
Every transformation call can change the graph structure and create some additional operations, autogenerated name is used if new operation doesn’t have friendly name. This transformations should be called after the transformation pipeline in order to fix names collisions.
There is also an additional mode “resolve_all_names”, the logic of which is the same, but for all “friendly_names” in the model ov, not only for autogenerated.
-
class ReverseShapeAndTypeInfer : public ov::pass::ModelPass
- #include <reverse_shape_and_type_infer.hpp>
Perform reverse shape and type infer to deduce input rank and type in certain cases.
-
class RNNCellDecomposition : public ov::pass::MatcherPass
- #include <rnn_cell_decomposition.hpp>
RNNCellDecomposition transformation decomposes RNNCell layer with inputs X, H, W, R, B to Add, MatMul ops according to the formula:
Denotes dot product. f - is an activation functions.
Ht = f(Xt*(Wi^T) + Ht-1*(Ri^T) + Wbi + Rbi)
-
class RPE_Fusion : public ov::pass::MatcherPass
- #include <fuse_rotary_positional_embeddings.hpp>
Fuses special sub-graph into an internal Rotary Positional Embedding operation.
-
class ScaledDotProductAttentionDecomposition : public ov::pass::MatcherPass
- #include <scaled_dot_product_attention_decomposition.hpp>
-
class SelectWithOneValueCondition : public ov::pass::MatcherPass
- #include <select_with_one_value_condition.hpp>
SelectWithOneValueCondition transformation eliminates Select operation if the condition is constant and consists of al True or False elements.
-
class SequenceFusion : public ov::pass::MatcherPass
- #include <sequence_fusion.hpp>
SequenceFusion transformation replaces a chain of Cells operations with single Sequence op.
Supported cells: GRUCell, LSTMCell, RNNCell, AUGRUCell Prerequisites: the source of W,R,B inputs must be the same or it can be different Constants with the same type, shape and value.
-
class Serialize : public ov::pass::ModelPass
- #include <serialize.hpp>
Serialize transformation converts ov::Model into IR files.
- Attention
dynamic shapes are not supported
-
class ShapeOfConstFolding : public ov::pass::MatcherPass
- #include <shape_of_const_folding.hpp>
ShapeOfConstFolding constantfolds ShapeOf->Constant subgraph.
- #include <shared_ops_optimization.hpp>
SharedOpOptimization optimizes operations which are sourcing from same Output<Node> and perform the same action on the same data.
-
class ShrinkWeights : public ov::pass::ModelPass
- #include <pruning.hpp>
Based on masks in Constant operation it inserts Gather operations to shrink them. After this pass execution ConstantFolding is required.
-
class ShuffleChannelsFusion : public ov::pass::MatcherPass
- #include <shuffle_channels_fusion.hpp>
ShuffleChannelsFusion transformation detects Reshape-Transpose-Reshape pattern and tries to fuse it into a single ShuffleChannels layer with axis = 1.
x’ = reshape(x, [N, group, C / group, H, W]) or reshape(x, [N, group, C / group, H * W]) x’’ = transpose(x’, [0, 2, 1, 3, 4]) or transpose(x’, [0, 2, 1, 3]) y = reshape(x’’, [N, C, H, W])
- Param reshape_constants_check:
the flag that defines the need for additional checks of reshapes constant Additional checks are required when ShuffleChannelsFusion using inside offline transformations and are not necessary when ShuffleChannelsFusion using inside CommonOptimizations
-
class SkipGatherBeforeTransposeAndReshape : public ov::pass::MatcherPass
- #include <skip_gather_before_transpose_and_reshape.hpp>
SkipGatherBeforeTransposeAndReshape transformation removes Gather from the Gather->Transpose->Reshape sequence in case when input has batch=1 and gather has axis=0 and indices={0}. Also, this transformation corrects a transpose constant to save semantic.
-
class SliceToStridedSlice : public ov::pass::MatcherPass
- #include <convert_slice_to_strided_slice.hpp>
SliceToStridedSlice transformation convert v8::Slice to v1::StridedSlice.
-
class SoftmaxDecomposition : public ov::pass::MatcherPass
- #include <softmax_decomposition.hpp>
SoftmaxDecomposition transformation replaces softmax with following graph:
+---------------+ │ │ │ input │ │ │ +---------------+ │ │ │ v │ +-----------+ │ │ │ │ │ ReduceMax │ │ │ │ │ +-----------+ │ │ │ │ v v +---------------+ │ │ │ Sub │ │ │ +---------------+ | | v +---------------+ │ │ │ Exp │ │ │ +---------------+ │ │ │ v │ +-----------+ │ │ │ │ │ ReduceSum │ │ │ │ │ +-----------+ │ │ │ │ v v +-------------+ | │ | Div │ │ │ +-------------+
-
class SoftmaxFusion : public ov::pass::MatcherPass
- #include <softmax_fusion.hpp>
SoftmaxFusion transformation replaces following graphs:
and +—————+ │ │ │ input │ │ │ +—————++---------------+ │ │ │ input │ │ │ +---------------+ │ │ │ v │ +-----------+ │ │ │ │ │ ReduceMax │ │ │ │ │ +-----------+ │ │ │ │ v v +---------------+ │ │ │ Sub │ │ │ +---------------+ | | v +---------------+ │ │ │ Exp │ │ │ +---------------+ │ │ │ v │ +-----------+ │ │ │ │ │ ReduceSum │ │ │ │ │ +-----------+ │ │ │ │ v v +-------------+ | │ | Div │ │ │ +-------------+
v +—————+ │ │ │ Exp │ │ │ +—————+ │ │ │ v │ +——–—+ │ │ │ │ │ ReduceSum │ │ │ │ │ +——–—+ │ │ │ │ v v +———-—+ | │ | Div │ │ │ +———-—+to a single Softmax node
Restrictions:
ReduceMax and ReduceSum axes must be scalar constants and they have to point to the same axis
-
class SoftPlusDecomposition : public ov::pass::MatcherPass
- #include <softplus_decomposition.hpp>
SoftPlusDecomposition transformation replaces SoftPlus op to group of operations: log(exp(x) + 1).
-
class SoftPlusFusion : public ov::pass::MatcherPass
- #include <softplus_fusion.hpp>
SoftPlusFusion transformation replaces group of operations: log(exp(x) + 1) to SoftPlus op.
-
class SoftPlusToMishFusion : public ov::pass::MatcherPass
- #include <softplus_to_mish_fusion.hpp>
SoftPlusToMishFusion transformation replaces group of operations: x * tanh(softplus(x)) to Mish op.
-
class SoftSignDecomposition : public ov::pass::MatcherPass
- #include <softsign_decomposition.hpp>
SoftSignDecomposition transformation replaces SoftSign with the following graph.
Input ---> Abs | | | | | | | V | Add <--- 1 | | | | V | Divide <----| | | | V Output
-
class SpaceToBatchFusion : public ov::pass::MatcherPass
- #include <space_to_batch_fusion.hpp>
SpaceToBatchFusion transformation replaces following graph: Transpose (or Reshape) -> Pad -> SpaceToDepth -> Transpose (or Reshape) to SpaceToBatch Restrictions:
input rank must be 4
Transpose permutation must be [1, 0, 2, 3]
pad value is 0, PadMode is CONSTANT
SpaceToDepthMode must be BLOCKS_FIRST
-
class SplitConcatPairToInterpolateFusion : public ov::pass::MatcherPass
- #include <split_concat_pair_to_interpolate_fusion.hpp>
SplitConcatPairToInterpolateFusion transformation replaces group of operations: Split -> Concat to Interpolate op.
-
class SplitSqueezeConcatFusion : public ov::pass::MatcherPass
- #include <split_squeeze_concat_fusion.hpp>
SplitSqueezeConcatFusion transformation replaces group of operations: Split -> Squeeze (multiple) -> Concat to Transpose -> Reshape ops.
-
class SqueezeStridedSlice : public ov::pass::MatcherPass
- #include <strided_slice_squeeze.hpp>
StridedSliceSqueeze transformation looks for Squeeze -> SSe and corrects SS inputs and attributes for SS output to be squeeze-able.
-
class StreamSerialize : public ov::pass::ModelPass
- #include <serialize.hpp>
StreamSerialize transformation converts ov::Model into single binary stream.
- Attention
dynamic shapes are not supported
-
struct DataHeader
- #include <serialize.hpp>
-
class StridedSliceOptimization : public ov::pass::ModelPass
- #include <optimize_strided_slice.hpp>
StridedSliceOptimization transformation executes all transformations related to StridedSlice optimizations.
-
class StridedSliceSqueeze : public ov::pass::MatcherPass
- #include <strided_slice_squeeze.hpp>
StridedSliceSqueeze transformation looks for SS -> Squeeze and corrects SS inputs and attributes for SS output to be squeeze-able.
-
class StridesOptimization : public ov::pass::BackwardGraphRewrite
- #include <strides_optimization.hpp>
StridesOptimization transformation works backward on function and propagates strides up through the graph if possible.
-
class SubtractFusion : public ov::pass::MatcherPass
- #include <subtract_fusion.hpp>
SubtractFusion transformation replaces a sub-graph Mul(y, -1) + x or x + Mul(y, -1) with Subtract(x,y)
-
class SupportedNodesStridesPropagation : public ov::pass::MatcherPass
- #include <strides_optimization.hpp>
SupportedNodesStridesPropagation either propagates stride (greater than 1) from current node up through the graph or inserts pooling between current node and its consumers if the consumers have different StridesProp attributes.
-
class SwishFusion : public ov::pass::GraphRewrite
- #include <swish_fusion.hpp>
SwishFusion transformation replaces various sub-graphs with a Swish op.
-
class SwishFusionWithBeta : public ov::pass::MatcherPass
- #include <swish_fusion.hpp>
SwishFusionWithSigmoid replaces a sub-graphs x / (1.0 + exp(-x * beta)) with a Swish op.
-
class SwishFusionWithoutBeta : public ov::pass::MatcherPass
- #include <swish_fusion.hpp>
SwishFusionWithSigmoid replaces a sub-graphs x / (1.0 + exp(-x)) with a Swish op.
-
class SwishFusionWithSigmoid : public ov::pass::MatcherPass
- #include <swish_fusion.hpp>
SwishFusionWithSigmoid replaces a sub-graphs x * Sigmoid(x) with a Swish op.
-
class SwishFusionWithSigmoidWithBeta : public ov::pass::MatcherPass
- #include <swish_fusion.hpp>
SwishFusionWithSigmoid replaces a sub-graphs x * Sigmoid(x * beta) with a Swish op.
-
class SymbolicOptimizations : public ov::pass::ModelPass
- #include <symbolic_optimizations.hpp>
Runs optimizations which are based on symbolic shape inference.
-
class SymbolicPropagation : public ov::pass::ModelPass
- #include <symbolic_optimizations.hpp>
Assigns labels / symbols to all tensors on shapes and values. Uses shape inference and other special rules to propagate labels / symbols.
-
class TransposeConvert : public ov::pass::MatcherPass
- #include <transpose_sinking.hpp>
TransposeConvert transformation sinks Transpose through Convert.
-
class TransposeEltwise : public ov::pass::MatcherPass
- #include <transpose_sinking.hpp>
TransposeEltwise transformation sinks Transpose through Eltwise.
-
class TransposeFQReduction : public ov::pass::MatcherPass
- #include <transpose_sinking.hpp>
TransposeFQReduction transformation sinks Transpose through FakeQuantize in case it is followed by reduction or squeeze.
-
class TransposeFuse : public ov::pass::MatcherPass
- #include <transpose_sinking.hpp>
TransposeFuse transformation eliminates 2 consequtive Transposes if they result in no changes to input or fuses them to single Transpose if input gets changed.
-
class TransposeMatMul : public ov::pass::MatcherPass
- #include <matmul_sr.hpp>
-
class TransposeReduction : public ov::pass::MatcherPass
- #include <transpose_sinking.hpp>
TransposeReduction transformation sinks Transpose through Reduce operations.
-
class TransposeReshapeEliminationForMatmul : public ov::pass::MatcherPass
- #include <transpose_reshape_elimination_for_matmul.hpp>
TransposeReshapeEliminationForMatmul transformation eliminates Transpose and Reshape which were created to align input and output dimension ranks before second MatMul input and after MatMul output (for example, after Einsum Decomposition inside TensorFlow 1 and OpenVINO EinsumDecomposition transformation)
-
class TransposeSinking : public ov::pass::GraphRewrite
- #include <transpose_sinking.hpp>
TransposeSinking transformation sinks Transposes through known operations.
-
class TransposeToReshape : public ov::pass::MatcherPass
- #include <transpose_to_reshape.hpp>
TransposeToReshape transformation replaces suitable Transposes with Reshape operation or optimizes them out.
-
class UniqueDecomposition : public ov::pass::MatcherPass
- #include <unique_decomposition.hpp>
-
class UnrollIf : public ov::pass::ModelPass
- #include <unroll_if.hpp>
The transformation replaces ‘If’ operations with one of the internal functions (bodies) if the provided condition is constant. The condition is true: ‘If’ op is replaced with then_body The condition is false ‘If’ op is replaced with else_body.
-
class UnrollTensorIterator : public ov::pass::ModelPass
- #include <unroll_tensor_iterator.hpp>
Unrolls the body of the TensorIterator layer. Multiple body copies, the number of which is determined by the number of iterations of the TensorIterator layer, are created and connected to each other and to the external network. If the number of TensorIterator iterations is greater than 1, then additional Concat and Split layers are added to the network.
-
class UnsupportedNodesStridesPropagation : public ov::pass::MatcherPass
- #include <strides_optimization.hpp>
UnsupportedNodesStridesPropagation inserts pooling between current node and its consumers if the consumers have different StridesProp attributes.
-
class UselessSliceEraser : public ov::pass::ModelPass
- #include <optimize_strided_slice.hpp>
UselessSliceEraser transformation removes Slice/StridedSlice operations with equal input and output shapes.
-
class Validate : public ov::pass::ModelPass
- #include <validate.hpp>
The Validate pass performs sanity checks on attributes and inputs, and computes output shapes and element types for all computation nodes in a given computation graph.
The verification and inference is done via invoking each node’s specific implementation of ov::Node::validate_and_infer_types() function.
By default, the ov::pass::Manager runs this pass after executing every optimization pass. This is to ensure that any update to the graph by an optimization pass does not break the shape and data type requirement on a computation node. This default validation run can be changed via calling the ov::pass::Manager::set_per_pass_validation(bool) function.
-
class VisualizeTree : public ov::pass::ModelPass
- #include <visualize_tree.hpp>
VisualizeTree pass allows to serialize ov::Model to xDot format.
-
class WeightsDequantizeToFakeQuantize : public ov::pass::MatcherPass
- #include <weights_dequantize_to_fake_quantize.hpp>
WeightsDequantizeToFakeQuantize transformation replaces Constant (i8) -> Convert (to fp) -> Subtract (zp) -> Multiply (scale) -> with Constant (i8) -> Convert (to fp) -> FakeQuantize -> deducing levels and FakeQuantize limits according to actual values in the weights Constant.
-
class WrapInterpolateIntoTransposes : public ov::pass::MatcherPass
- #include <wrap_interpolate_into_transposes.hpp>
WrapInterpolateIntoTransposes transformation replaces Interpolate with Transpose -> Interpolate -> Transpose when 1) the source Interpolate has the static input rank; 2) ‘axes’ input is a Constant; 3) number of axes is equal to input rank minus 2; 4) axes contain 0 or 1. The reason of this transformation is that now CPU plugin supports interpolation only with respect to spatial dimensions, but TensorFlow frontend gives Interpolate with axes {1, 2} for 4D tensors.
-
namespace low_precision#
Typedefs
-
typedef std::tuple<std::shared_ptr<ov::Node>, std::shared_ptr<ov::Node>> FakeQuantizeDequantizationValues#
-
typedef std::shared_ptr<LayerTransformation> LayerTransformationPtr#
Enums
Functions
-
inline bool operator==(const DataPrecision &value1, const DataPrecision &value2)#
-
inline bool operator!=(const DataPrecision &value1, const DataPrecision &value2)#
-
inline std::ostream &operator<<(std::ostream &os, const DataPrecision &value)#
-
inline std::ostream &operator<<(std::ostream &os, const QuantizationDetails &value)#
Variables
-
class LP_TRANSFORMATIONS_API AlignQuantizationIntervals
-
class LP_TRANSFORMATIONS_API AlignQuantizationParameters
-
class LP_TRANSFORMATIONS_API BaseMatcherPass#
-
class LP_TRANSFORMATIONS_API ConvertSubtractConstant
-
class LP_TRANSFORMATIONS_API TypeRelaxedReplacer
-
class LP_TRANSFORMATIONS_API MarkupOptimizations
-
class LP_TRANSFORMATIONS_API LowPrecision#
-
class LP_TRANSFORMATIONS_API MarkupAvgPoolPrecisionPreserved
-
class LP_TRANSFORMATIONS_API MarkupCanBeQuantized
-
class LP_TRANSFORMATIONS_API MarkupPrecisions
-
class LP_TRANSFORMATIONS_API MarkupQuantizationGranularity
-
class LP_TRANSFORMATIONS_API PropagatePrecisions
-
class LP_TRANSFORMATIONS_API PullReshapeThroughDequantization
-
class LP_TRANSFORMATIONS_API PullTransposeThroughDequantization
-
static std::set<levels> all_levels = {levels::int4, levels::int4_narrow_range, levels::int8, levels::int8_narrow_range, levels::int16, levels::int16_narrow_range, levels::int32, levels::int32_narrow_range}#
-
class AddTransformation : public ov::pass::low_precision::EltwiseBaseTransformation
- #include <add.hpp>
AddTransformation propagates dequantization subtraction from one input branch to another and propagates dequantization multiplication from the same branch through Add operation.
For more details about the transformation, refer to AddTransformation page in the OpenVINO Developer Guide.
-
class AlignQuantizationIntervals : public ov::pass::ModelPass
- #include <align_quantization_intervals.hpp>
AlignQuantizationIntervals transformation marks precision preserved operations subgraph by
IntervalsAlignmentAttributeafter FakeQuantize operations.For more details about the transformation, refer to AlignQuantizationIntervals page in the OpenVINO Developer Guide.
-
class AlignQuantizationParameters : public ov::pass::ModelPass
- #include <align_quantization_parameters.hpp>
AlignQuantizationParameters transformation marks precision preserved operations subgraph by
QuantizationAlignmentAttributeattribute after FakeQuantize operations.For more details about the transformation, refer to AlignQuantizationParameters page in the OpenVINO Developer Guide.
-
class AssignAndReadValueTransformation : public ov::pass::low_precision::LayerTransformation
- #include <assign_and_read_value.hpp>
-
class AvgPoolTransformation : public ov::pass::low_precision::LayerTransformation
- #include <avg_pool.hpp>
AvgPoolTransformation propagates dequantization operations through AvgPool operation.
For more details about the transformation, refer to AvgPoolTransformation page in the OpenVINO Developer Guide.
-
class BatchToSpaceTransformation : public ov::pass::low_precision::LayerTransformation
- #include <batch_to_space.hpp>
BatchToSpaceTransformation propagates dequantization operations through BatchToSpace operation.
For more details about the transformation, refer to BatchToSpaceTransformation page in the OpenVINO Developer Guide.
-
class ClampTransformation : public ov::pass::low_precision::LayerTransformation
- #include <clamp.hpp>
ClampTransformation propagates dequantization operations through Clamp operation.
For more details about the transformation, refer to ClampTransformation page in the OpenVINO Developer Guide.
-
class CleanupTransformation : public ov::pass::low_precision::LayerTransformation
- #include <cleanup_transformation.hpp>
Base class for cleanup low precision transformation.
Subclassed by ov::pass::low_precision::EliminateFakeQuantizeTransformation, ov::pass::low_precision::FoldConvertTransformation, ov::pass::low_precision::FuseConvertTransformation, ov::pass::low_precision::FuseElementwiseToFakeQuantizeTransformation, ov::pass::low_precision::MultiplyToGroupConvolutionTransformation
-
class ConcatTransformation : public ov::pass::low_precision::LayerTransformation
- #include <concat.hpp>
ConcatTransformation propagates dequantization operations through Concat operation.
For more details about the transformation, refer to ConcatTransformation page in the OpenVINO Developer Guide.
-
class ConvertSubtractConstant : public ov::pass::MatcherPass
- #include <convert_subtract_constant.hpp>
ConvertSubtractConstant marks Convert operations on constant subgraph by DISABLED_CONSTANT_FOLDING attribute to prevent constant folding.
For more details about the transformation, refer to ConvertSubtractConstant page in the OpenVINO Developer Guide.
-
class ConvertTransformation : public ov::pass::low_precision::LayerTransformation
- #include <convert.hpp>
-
class ConvolutionBackpropDataTransformation : public ov::pass::low_precision::WeightableLayerTransformation
- #include <convolution_backprop_data.hpp>
ConvolutionBackpropDataTransformation propagates dequantization operations through ConvolutionBackpropData operation.
For more details about the transformation, refer to ConvolutionBackpropDataTransformation page in the OpenVINO Developer Guide.
-
class ConvolutionTransformation : public ov::pass::low_precision::WeightableLayerTransformation
- #include <convolution.hpp>
ConvolutionTransformation propagates dequantization operations through Convolution operation.
For more details about the transformation, refer to ConvolutionTransformation page in the OpenVINO Developer Guide.
Subclassed by ov::pass::low_precision::GroupConvolutionTransformation
-
template<typename AttributeType, typename OperationType = ov::pass::pattern::op::Label>
class CreateAttribute : public ov::pass::low_precision::BaseMatcherPass - #include <create_attribute.hpp>
CreateAttribute transformation marks OperationType operations by AttributeType attribute.
For more details about the transformation, refer to CreateAttribute page in the OpenVINO Developer Guide.
-
template<typename AttributeType, typename OperationType>
class CreatePrecisionsDependentAttribute : public ov::pass::MatcherPass - #include <create_precisions_dependent_attribute.hpp>
CreatePrecisionsDependentAttribute transformation marks OperationType operations by PrecisionPreservedAttribute and AttributeType attributes with the same shared part.
For more details about the transformation, refer to CreatePrecisionsDependentAttribute page in the OpenVINO Developer Guide.
-
class DataPrecision
- #include <layer_transformation.hpp>
-
class DepthToSpaceTransformation : public ov::pass::low_precision::TransparentBaseTransformation
- #include <depth_to_space.hpp>
DepthToSpaceTransformation propagates dequantization operations through DepthToSpace operation.
For more details about the transformation, refer to DepthToSpaceTransformation page in the OpenVINO Developer Guide.
-
class EliminateFakeQuantizeTransformation : public ov::pass::low_precision::CleanupTransformation
- #include <eliminate_fake_quantize.hpp>
EliminateFakeQuantizeTransformation removes FakeQuantize operations.
For more details about the transformation, refer to EliminateFakeQuantizeTransformation page in the OpenVINO Developer Guide.
-
class EltwiseBaseTransformation : public ov::pass::low_precision::LayerTransformation
- #include <eltwise_base_transformation.hpp>
EltwiseBaseTransformation is base class for element-wise LPT transformations.
Subclassed by ov::pass::low_precision::AddTransformation, ov::pass::low_precision::MultiplyPartialTransformation
-
class Exception : public std::exception
- #include <ie_lpt_exception.hpp>
Subclassed by ov::pass::low_precision::InferenceEngineLptException
-
class FakeQuantizeDecompositionTransformation : public ov::pass::low_precision::LayerTransformation
- #include <fake_quantize_decomposition.hpp>
FakeQuantizeDecompositionTransformation decomposes FakeQuantize operations to quantize (FakeQuantize with changes output intervals and low precision output type) and dequantize operations.
For more details about the transformation, refer to FakeQuantizeDecompositionTransformation page in the OpenVINO Developer Guide.
-
class FakeQuantizeDequantization
- #include <fake_quantize_dequantization.hpp>
-
class FakeQuantizeTransformation : public ov::pass::low_precision::LayerTransformation
- #include <fake_quantize.hpp>
FakeQuantizeTransformation fuses dequantization operations into FakeQuantize operation.
For more details about the transformation, refer to FakeQuantizeTransformation page in the OpenVINO Developer Guide.
-
class FoldConvertTransformation : public ov::pass::low_precision::CleanupTransformation
- #include <fold_convert.hpp>
FoldConvertTransformation evaluates Convert operation on Subtract constant subgraph. Important notice: this transformation ignores DisableConstantFolding runtime attribute.
For more details about the transformation, refer to FoldConvertTransformation page in the OpenVINO Developer Guide.
-
class FoldFakeQuantizeTransformation : public ov::pass::low_precision::LayerTransformation
- #include <fold_fake_quantize.hpp>
FoldFakeQuantizeTransformation evaluate FakeQuantize operations.
For more details about the transformation, refer to FoldFakeQuantizeTransformation page in the OpenVINO Developer Guide.
-
class FuseConvertTransformation : public ov::pass::low_precision::CleanupTransformation
- #include <fuse_convert.hpp>
FuseConvertTransformation fuses Convert operation with Multiply, Subtract or Add operations.
For more details about the transformation, refer to FuseConvertTransformation page in the OpenVINO Developer Guide.
-
class FuseElementwiseToFakeQuantizeTransformation : public ov::pass::low_precision::CleanupTransformation
- #include <fuse_elementwise_to_fake_quantize.hpp>
Base class for fuse elementwise to FakeQuantize low precision transformation.
Subclassed by ov::pass::low_precision::FuseMultiplyToFakeQuantizeTransformation, ov::pass::low_precision::FuseSubtractToFakeQuantizeTransformation
-
class FuseMultiplyToFakeQuantizeTransformation : public ov::pass::low_precision::FuseElementwiseToFakeQuantizeTransformation
- #include <fuse_multiply_to_fake_quantize.hpp>
FuseMultiplyToFakeQuantizeTransformation fuses Multiply operation to FakeQuantize.
For more details about the transformation, refer to FuseMultiplyToFakeQuantizeTransformation page in the OpenVINO Developer Guide.
-
class FuseSubtractToFakeQuantizeTransformation : public ov::pass::low_precision::FuseElementwiseToFakeQuantizeTransformation
- #include <fuse_subtract_to_fake_quantize.hpp>
FuseSubtractToFakeQuantizeTransformation fuses Subtract operation to FakeQuantize.
For more details about the transformation, refer to FuseSubtractToFakeQuantizeTransformation page in the OpenVINO Developer Guide.
-
class GatherTransformation : public ov::pass::low_precision::LayerTransformation
- #include <gather.hpp>
-
class GroupConvolutionTransformation : public ov::pass::low_precision::ConvolutionTransformation
- #include <group_convolution.hpp>
GroupConvolutionTransformation propagates dequantization operations through GroupConvolution operation.
For more details about the transformation, refer to GroupConvolutionTransformation page in the OpenVINO Developer Guide.
-
class InferenceEngineLptException : public ov::pass::low_precision::Exception
- #include <ie_lpt_exception.hpp>
-
class InterpolateTransformation : public ov::pass::low_precision::LayerTransformation
- #include <interpolate.hpp>
InterpolateTransformation propagates dequantization operations through Interpolate operation.
For more details about the transformation, refer to InterpolateTransformation page in the OpenVINO Developer Guide.
-
class LayerTransformation : public ov::pass::MatcherPass
- #include <layer_transformation.hpp>
Base class for low precision transformation.
Subclassed by ov::pass::low_precision::AssignAndReadValueTransformation, ov::pass::low_precision::AvgPoolTransformation, ov::pass::low_precision::BatchToSpaceTransformation, ov::pass::low_precision::ClampTransformation, ov::pass::low_precision::CleanupTransformation, ov::pass::low_precision::ConcatTransformation, ov::pass::low_precision::ConvertTransformation, ov::pass::low_precision::EltwiseBaseTransformation, ov::pass::low_precision::FakeQuantizeDecompositionTransformation, ov::pass::low_precision::FakeQuantizeTransformation, ov::pass::low_precision::FoldFakeQuantizeTransformation, ov::pass::low_precision::GatherTransformation, ov::pass::low_precision::InterpolateTransformation, ov::pass::low_precision::MVNTransformation, ov::pass::low_precision::MatMulTransformation, ov::pass::low_precision::MaxPoolTransformation, ov::pass::low_precision::MoveFakeQuantize, ov::pass::low_precision::NormalizeL2Transformation, ov::pass::low_precision::PReluTransformation, ov::pass::low_precision::PadTransformation, ov::pass::low_precision::RecurrentCellTransformation, ov::pass::low_precision::ReduceBaseTransformation, ov::pass::low_precision::ReluTransformation, ov::pass::low_precision::ReshapeTransformation, ov::pass::low_precision::ShuffleChannelsTransformation, ov::pass::low_precision::SpaceToBatchTransformation, ov::pass::low_precision::SplitTransformation, ov::pass::low_precision::SqueezeTransformation, ov::pass::low_precision::StridedSliceTransformation, ov::pass::low_precision::SubtractTransformation, ov::pass::low_precision::TransparentBaseTransformation, ov::pass::low_precision::TransposeTransformation, ov::pass::low_precision::UnsqueezeTransformation, ov::pass::low_precision::WeightableLayerTransformation
-
class Params
- #include <layer_transformation.hpp>
-
class PrecisionDetails
- #include <layer_transformation.hpp>
-
class Params
-
class MarkupAvgPoolPrecisionPreserved : public ov::pass::ModelPass
- #include <markup_avg_pool_precision_preserved.hpp>
MarkupAvgPoolPrecisionPreserved transformation marks AvgPool operations as precision preserved or not.
For more details about the transformation, refer to MarkupAvgPoolPrecisionPreserved page in the OpenVINO Developer Guide.
-
class MarkupBias : public ov::pass::MatcherPass
- #include <markup_bias.hpp>
MarkupBias transformation marks biases after target layers.
For more details about the transformation, refer to MarkupBias page in the OpenVINO Developer Guide.
-
class MarkupCanBeQuantized : public ov::pass::ModelPass
- #include <markup_can_be_quantized.hpp>
MarkupCanBeQuantized transformation marks Convolution, ConvolutionBackpropData, GroupConvolution and Concat operations as able to be quantized or not. If an operation is not quantized, then PrecisionsAttribute attribute instance is created with empty precisions.
For more details about the transformation, refer to MarkupCanBeQuantized page in the OpenVINO Developer Guide.
-
class MarkupPrecisions : public ov::pass::ModelPass
- #include <markup_precisions.hpp>
MarkupPrecisions transformation marks: 1) not supported operations by PrecisionsAttribute attribute with empty precisions, 2) operations with required precisions by PrecisionsAttribute attribute according to the provided restrictions, 3) precision preserved operations by PrecisionPreservedAttribute attribute.
For more details about the transformation, refer to MarkupPrecisions page in the OpenVINO Developer Guide.
-
class Restriction
- #include <markup_precisions.hpp>
-
class RestrictionByVersion
- #include <markup_precisions.hpp>
-
class RestrictionByVersion
-
class Restriction
-
class MarkupQuantizationGranularity : public ov::pass::ModelPass
- #include <markup_quantization_granularity.hpp>
MarkupPerTensorQuantization transformation marks operations as required per-tensor quantization according to the provided restrictions.
For more details about the transformation, refer to MarkupPerTensorQuantization page in the OpenVINO Developer Guide.
-
class PerTensorQuantization
- #include <markup_quantization_granularity.hpp>
-
class PerTensorQuantization
-
class MatMulTransformation : public ov::pass::low_precision::LayerTransformation
- #include <mat_mul.hpp>
MatMulTransformation propagates dequantization operations through MatMul operation.
For more details about the transformation, refer to MatMulTransformation page in the OpenVINO Developer Guide.
-
class MaxPoolTransformation : public ov::pass::low_precision::LayerTransformation
- #include <max_pool.hpp>
MaxPoolTransformation propagates dequantization operations through MaxPool operation.
For more details about the transformation, refer to MaxPoolTransformation page in the OpenVINO Developer Guide.
-
class MoveFakeQuantize : public ov::pass::low_precision::LayerTransformation
- #include <move_fake_quantize.hpp>
-
class MultiplyPartialTransformation : public ov::pass::low_precision::EltwiseBaseTransformation
- #include <multiply_partial.hpp>
MultiplyPartialTransformation propagates dequantization operations through Multiply operation.
For more details about the transformation, refer to MultiplyPartialTransformation page in the OpenVINO Developer Guide.
-
class MultiplyToGroupConvolutionTransformation : public ov::pass::low_precision::CleanupTransformation
- #include <multiply_to_group_convolution.hpp>
MultiplyToGroupConvolutionTransformation replace quantized Multiply operations to GroupConvolution to speed up inference.
For more details about the transformation, refer to MultiplyToGroupConvolutionTransformation page in the OpenVINO Developer Guide.
-
class MultiplyTransformation : public ov::pass::low_precision::WeightableLayerTransformation
- #include <multiply.hpp>
MultiplyTransformation propagates dequantization operations through Multiply operation.
For more details about the transformation, refer to MultiplyTransformation page in the OpenVINO Developer Guide.
-
class MVNTransformation : public ov::pass::low_precision::LayerTransformation
- #include <mvn.hpp>
MVNTransformation propagates dequantization operations through MVN operation.
For more details about the transformation, refer to MVNTransformation page in the OpenVINO Developer Guide.
-
class NetworkHelper
- #include <network_helper.hpp>
NetworkHelper class encapsulates manipulations with ov::Model.
-
class InsertDequantizationResult
- #include <network_helper.hpp>
-
class InsertDequantizationResult
-
class NormalizeL2Transformation : public ov::pass::low_precision::LayerTransformation
- #include <normalize_l2.hpp>
NormalizeL2Transformation propagates dequantization operations through NormalizeL2 operation.
For more details about the transformation, refer to NormalizeL2Transformation page in the OpenVINO Developer Guide.
-
class PadTransformation : public ov::pass::low_precision::LayerTransformation
- #include <pad.hpp>
PadTransformation propagates dequantization operations through Pad operation.
For more details about the transformation, refer to PadTransformation page in the OpenVINO Developer Guide.
-
class PortQuantizationGranularityRestriction
- #include <port_quantization_granularity_restriction.hpp>
-
class PrecisionsRestriction
- #include <precisions_restriction.hpp>
PrecisionsRestriction defines a set of precision restrictions for each input port Common precision restriction can be also set for several ports. In this case, an operation will have the same precision for mentioned.
// One restriction for each port PrecisionsRestriction::create<ov::opset1::Convolution>({ {{0}, {ov::element::u8}}, {{1}, {ov::element::i8}}, }),
// Common precision restriction for several ports: // both inputs will have the same precision PrecisionsRestriction::create<ov::opset5::LSTMSequence>({ {{0, 1}, {ov::element::u8, ov::element::i8}} }),
-
class PReluTransformation : public ov::pass::low_precision::LayerTransformation
- #include <prelu.hpp>
PReluTransformation propagates dequantization operations through PRelu operation.
For more details about the transformation, refer to PReluTransformation page in the OpenVINO Developer Guide.
-
class PropagatePrecisions : public ov::pass::ModelPass
- #include <propagate_precisions.hpp>
PropagatePrecisions transformation propagates PrecisionsAttribute attribute instances precision preserved operations.
For more details about the transformation, refer to PropagatePrecisions page in the OpenVINO Developer Guide.
-
template<class AttributeType>
class PropagateSharedValue : public ov::pass::ModelPass - #include <propagate_shared_value.hpp>
PropagateSharedValue transformation propagates shared value AttributeType attribute instances through precision preserved operations.
For more details about the transformation, refer to PropagateSharedValue page in the OpenVINO Developer Guide.
-
template<typename AttributeType>
class PropagateThroughPrecisionPreserved : public ov::pass::MatcherPass - #include <propagate_through_precision_preserved.hpp>
PropagateThroughPrecisionPreserved transformation propagates AttributeType attribute instances through precision preserved operations.
For more details about the transformation, refer to PropagateThroughPrecisionPreserved page in the OpenVINO Developer Guide.
-
template<typename AttributeType>
class PropagateToInput : public ov::pass::MatcherPass - #include <propagate_to_input.hpp>
PropagateToInput transformation propagates AttributeType shared value attribute instances from parent output ports to consumers input ports.
For more details about the transformation, refer to PropagateToInput page in the OpenVINO Developer Guide.
-
class PullReshapeThroughDequantization : public ov::pass::MatcherPass
- #include <pull_reshape_through_dequantization.hpp>
PullReshapeThroughDequantization propagates dequantization operations through Reshape operations. The transformation is used on constant subgraph weights to prepare a model for the next low precision transformations.
For more details about the transformation, refer to PullReshapeThroughDequantization page in the OpenVINO Developer Guide.
-
class PullTransposeThroughDequantization : public ov::pass::MatcherPass
- #include <pull_transpose_through_dequantization.hpp>
PullTransposeThroughDequantization propagates dequantization operations through Transpose operations. The transformation is used on constant subgraph weights to prepare a model for the next low precision transformations.
For more details about the transformation, refer to PullTransposeThroughDequantization page in the OpenVINO Developer Guide.
-
class QuantizationDetails
- #include <quantization_details.hpp>
-
class QuantizationGranularityRestriction
- #include <quantization_granularity_restriction.hpp>
-
class RecurrentCellTransformation : public ov::pass::low_precision::LayerTransformation
- #include <recurrent_cell.hpp>
-
class ReduceBaseTransformation : public ov::pass::low_precision::LayerTransformation
- #include <reduce_base_transformation.hpp>
ReduceBaseTransformation: base class for Reduce*Transformation, detects dequantization operations in front of the Reduce* operation and propagates them through the Reduce* if possible.
Subclassed by ov::pass::low_precision::ReduceMaxTransformation, ov::pass::low_precision::ReduceMeanTransformation, ov::pass::low_precision::ReduceMinTransformation, ov::pass::low_precision::ReduceSumTransformation
-
class ReduceMaxTransformation : public ov::pass::low_precision::ReduceBaseTransformation
- #include <reduce_max.hpp>
ReduceMaxTransformation propagates dequantization operations through ReduceMax operation.
For more details about the transformation, refer to ReduceMaxTransformation page in the OpenVINO Developer Guide.
-
class ReduceMeanTransformation : public ov::pass::low_precision::ReduceBaseTransformation
- #include <reduce_mean.hpp>
ReduceMeanTransformation propagates dequantization operations through ReduceMean operation.
For more details about the transformation, refer to ReduceMeanTransformation page in the OpenVINO Developer Guide.
-
class ReduceMinTransformation : public ov::pass::low_precision::ReduceBaseTransformation
- #include <reduce_min.hpp>
ReduceMinTransformation propagates dequantization operations through ReduceMin operation.
For more details about the transformation, refer to ReduceMinTransformation page in the OpenVINO Developer Guide.
-
class ReduceSumTransformation : public ov::pass::low_precision::ReduceBaseTransformation
- #include <reduce_sum.hpp>
ReduceSumTransformation propagates dequantization operations through ReduceSum operation.
For more details about the transformation, refer to ReduceSumTransformation page in the OpenVINO Developer Guide.
-
class ReluTransformation : public ov::pass::low_precision::LayerTransformation
- #include <relu.hpp>
ReluTransformation propagates dequantization operations through Relu operation.
For more details about the transformation, refer to ReluTransformation page in the OpenVINO Developer Guide.
-
class ReshapeTransformation : public ov::pass::low_precision::LayerTransformation
- #include <reshape.hpp>
ReshapeTransformation propagates dequantization operations through Reshape operation.
For more details about the transformation, refer to ReshapeTransformation page in the OpenVINO Developer Guide.
-
class ShuffleChannelsTransformation : public ov::pass::low_precision::LayerTransformation
- #include <shuffle_channels.hpp>
ShuffleChannelsTransformation propagates dequantization operations through ShuffleChannels operation.
For more details about the transformation, refer to ShuffleChannelsTransformation page in the OpenVINO Developer Guide.
-
class SpaceToBatchTransformation : public ov::pass::low_precision::LayerTransformation
- #include <space_to_batch.hpp>
SpaceToBatchTransformation propagates dequantization operations through SpaceToBatch operation.
For more details about the transformation, refer to SpaceToBatchTransformation page in the OpenVINO Developer Guide.
-
class SplitTransformation : public ov::pass::low_precision::LayerTransformation
- #include <split.hpp>
SplitTransformation propagates dequantization operations through Split operation.
For more details about the transformation, refer to SplitTransformation page in the OpenVINO Developer Guide.
Subclassed by ov::pass::low_precision::VariadicSplitTransformation
-
class SqueezeTransformation : public ov::pass::low_precision::LayerTransformation
- #include <squeeze.hpp>
SqueezeTransformation propagates dequantization operations through Squeeze operation.
For more details about the transformation, refer to SqueezeTransformation page in the OpenVINO Developer Guide.
-
class StridedSliceTransformation : public ov::pass::low_precision::LayerTransformation
- #include <strided_slice.hpp>
StridedSliceTransformation propagates dequantization operations through StridedSlice operation.
For more details about the transformation, refer to StridedSliceTransformation page in the OpenVINO Developer Guide.
-
class SubtractTransformation : public ov::pass::low_precision::LayerTransformation
- #include <subtract.hpp>
SubtractTransformation propagates dequantization operations through Subtract operation.
For more details about the transformation, refer to SubtractTransformation page in the OpenVINO Developer Guide.
-
class TransformationContext
- #include <transformation_context.hpp>
TransformationContext instance is used to pass model transformation context data between transformations.
-
class TransparentBaseTransformation : public ov::pass::low_precision::LayerTransformation
- #include <transparent_base_transformation.hpp>
TransparentBaseTransformation is base type for precision preserved operation transformation.
Subclassed by ov::pass::low_precision::DepthToSpaceTransformation
-
class TransposeTransformation : public ov::pass::low_precision::LayerTransformation
- #include <transpose.hpp>
TransposeTransformation propagates dequantization operations through Transpose operation.
For more details about the transformation, refer to TransposeTransformation page in the OpenVINO Developer Guide.
-
class TypeRelaxedReplacer : public ov::pass::GraphRewrite
- #include <low_precision.hpp>
-
class UnsqueezeTransformation : public ov::pass::low_precision::LayerTransformation
- #include <unsqueeze.hpp>
UnsqueezeTransformation propagates dequantization operations through Unsqueeze operation.
For more details about the transformation, refer to UnsqueezeTransformation page in the OpenVINO Developer Guide.
- #include <update_shared_precision_preserved.hpp>
UpdateSharedPrecisionPreserved transformation updates shared AttributeType attribute instance value to true for precision preserved operations if ExpectedAttributeType exist.
For more details about the transformation, refer to UpdateSharedPrecisionPreserved page in the OpenVINO Developer Guide.
-
class VariadicSplitTransformation : public ov::pass::low_precision::SplitTransformation
- #include <variadic_split.hpp>
VariadicSplitTransformation propagates dequantization operations through VariadicSplit operation.
For more details about the transformation, refer to VariadicSplitTransformation page in the OpenVINO Developer Guide.
-
class WeightableLayerTransformation : public ov::pass::low_precision::LayerTransformation
- #include <weightable_layer_transformation.hpp>
WeightableLayerTransformation is base type for weightable operation transformation.
Subclassed by ov::pass::low_precision::ConvolutionBackpropDataTransformation, ov::pass::low_precision::ConvolutionTransformation, ov::pass::low_precision::MultiplyTransformation
-
struct CanBeTransformedParams
- #include <weightable_layer_transformation.hpp>
-
struct CanBeTransformedParams
-
namespace precision_set#
Functions
- LP_TRANSFORMATIONS_API const std::vector< element::Type > & get_int8_support ()
- LP_TRANSFORMATIONS_API const std::vector< element::Type > & get_int8_int16_int32_support ()
-
typedef std::tuple<std::shared_ptr<ov::Node>, std::shared_ptr<ov::Node>> FakeQuantizeDequantizationValues#
-
namespace pattern#
Typedefs
-
using PatternValueMaps = std::vector<PatternValueMap>#
Functions
- OPENVINO_API std::shared_ptr< Node > any_input ()
- OPENVINO_API std::shared_ptr< Node > any_input (const pattern::op::ValuePredicate &pred)
-
template<class NodeType>
void collect_type_info(std::vector<DiscreteTypeInfo> &type_info_vec)#
-
template<class NodeType, class ...NodeTypeArgs, typename std::enable_if<sizeof...(NodeTypeArgs) != 0, bool>::type = true>
void collect_type_info(std::vector<DiscreteTypeInfo> &type_info_vec)#
-
PatternMap as_pattern_map(const PatternValueMap &pattern_value_map)#
-
PatternValueMap as_pattern_value_map(const PatternMap &pattern_map)#
- OPENVINO_API std::function< bool(Output< Node >)> consumers_count (size_t n)
- OPENVINO_API std::function< bool(Output< Node >)> consumers_more_than (size_t n)
- OPENVINO_API std::function< bool(Output< Node >)> has_static_dim (size_t pos)
- OPENVINO_API std::function< bool(Output< Node >)> has_static_dims (const std::vector< size_t > &dims)
- OPENVINO_API std::function< bool(Output< Node >)> has_static_shape ()
- OPENVINO_API std::function< bool(Output< Node >)> has_static_rank ()
- OPENVINO_API std::function< bool(Output< Node >)> rank_equals (const Dimension &expected_rank)
- OPENVINO_API std::function< bool(Output< Node >)> type_matches (const element::Type &type)
- OPENVINO_API std::function< bool(Output< Node >)> type_matches_any (const std::vector< element::Type > &types)
- OPENVINO_API std::function< bool(Output< Node >)> all_of (const std::vector< std::function< bool(Output< Node >)> > &predicates)
-
template<class T>
void collect_wrap_info(std::vector<DiscreteTypeInfo> &info)#
-
template<class T, class ...Targs, typename std::enable_if<sizeof...(Targs) != 0, bool>::type = true>
void collect_wrap_info(std::vector<DiscreteTypeInfo> &info)#
-
class Matcher
- #include <matcher.hpp>
Matcher looks for node patterns in a computation graph. The patterns are described by an automaton that is described by an extended computation graph. The matcher executes by attempting to match the start node of the pattern to a computation graph value (output of a Node). In addition to determing if a match occurs, a pattern node may add graph nodes to a list of matched nodes, associate nodes with graph values, and start submatches. Submatches add match state changes to the enclosing match if the submatch succeeds; otherwise the state is reverted.
The default match behavior of a pattern node with a graph nodes is that the computation graph value is added to the end of the matched value list and the match succeeds if the node/pattern types match and the input values match. In the case of a commutative node, the inputs can match in any order. If the matcher is in strict mode, the graph value element type and shape must also match.
Pattern nodes that have different match behavior are in ov::pass::pattern::op and have descriptions of their match behavior.
Public Functions
-
inline Matcher(const Output<Node> &pattern_node, const std::string &name, bool strict_mode)
Constructs a Matcher object.
- Parameters:
pattern_node – is a pattern sub graph that will be matched against input graphs
name – is a string which is used for logging and disabling a matcher
strict_mode – forces a matcher to consider shapes and ET of nodes
-
bool match(const Output<Node> &graph_value)
Matches a pattern to
graph_node.- Parameters:
graph_value – is an input graph to be matched against
-
bool match(const Output<Node> &graph_value, const PatternMap &previous_matches)
Matches a pattern to
graph_node.- Parameters:
graph_value – is an input graph to be matched against
previous_matches – contains previous mappings from labels to nodes to use
-
size_t add_node(Output<Node> node)
Low-level helper to match recurring patterns.
- Parameters:
graph – is a graph to be matched against
pattern – is a recurring pattern
rpattern – specifies a node to recur from next
patterns – a map from labels to matches
-
MatcherState start_match()
Try a match.
-
inline Matcher(const Output<Node> &pattern_node, const std::string &name, bool strict_mode)
-
class MatcherState
- #include <matcher.hpp>
-
namespace op#
Typedefs
Functions
- OPENVINO_API ValuePredicate as_value_predicate (NodePredicate pred)
-
class Any : public ov::pass::pattern::op::Pattern
- #include <any.hpp>
The graph value is to the matched value list. If the predicate is true for the node and the arguments match, the match succeeds.
Public Functions
-
inline Any(const element::Type &type, const PartialShape &s, ValuePredicate pred, const OutputVector &wrapped_values)
creates a Any node containing a sub-pattern described by
See also
type and
See also
shape.
-
inline Any(const Output<Node> &node, ValuePredicate pred, const OutputVector &wrapped_values)
creates a Any node containing a sub-pattern described by the type and shape of
See also
node.
-
inline Any(const element::Type &type, const PartialShape &s, ValuePredicate pred, const OutputVector &wrapped_values)
-
class AnyOf : public ov::pass::pattern::op::Pattern
- #include <any_of.hpp>
The graph value is added to the matched values list. If the predicate is true for the graph node, a submatch is performed on the input of AnyOf and each input of the graph node. The first match that succeeds results in a successful match. Otherwise the match fails.
AnyOf may be given a type and shape for use in strict mode.
Public Functions
-
inline AnyOf(const element::Type &type, const PartialShape &s, ValuePredicate pred, const OutputVector &wrapped_values)
creates a AnyOf node containing a sub-pattern described by
See also
type and
See also
shape.
-
inline AnyOf(const Output<Node> &node, ValuePredicate pred, const OutputVector &wrapped_values)
creates a AnyOf node containing a sub-pattern described by the type and shape of
See also
node.
-
inline AnyOf(const element::Type &type, const PartialShape &s, ValuePredicate pred, const OutputVector &wrapped_values)
-
class AnyOutput : public ov::pass::pattern::op::Pattern
- #include <any_output.hpp>
Matches any output of a node.
Public Functions
creates an AnyOutput node matching any output of a node
- Parameters:
node – The node to match
-
class Branch : public ov::pass::pattern::op::Pattern
- #include <branch.hpp>
A branch adds a loop to the pattern. The branch match is successful if the destination node pattern matches the graph value. The destination node is a node in the pattern graph that will not have been created some time after the Branch node is created; use set_destination to add it.
The branch destination is not stored as a shared pointer to prevent reference cycles. Thus the destination node must be referenced in some other way to prevent it from being deleted.
Public Functions
-
inline Branch()
Creates a Branch pattern.
-
inline Branch()
-
class Capture : public ov::pass::pattern::op::Pattern
- #include <capture.hpp>
Experimental for support of recurrent matches.
Capture adds the pattern value map to a list of pattern value maps and resets matches for pattern nodes not in the static node list. The match always succeeds.
Public Functions
-
inline std::set<Node*> get_static_nodes()
static nodes are retained after a capture. All other nodes are dropped
-
inline std::set<Node*> get_static_nodes()
-
class Label : public ov::pass::pattern::op::Pattern
- #include <label.hpp>
Fails if the predicate returns false on the graph value.
The graph value is added to the matched values list. If the Label is already associated with a value, the match succeeds if the value is the same as the graph value. Otherwise, the label is associated with the graph value and the match succeeds if the pattern input matches the graph value.
DEPRECATED: If no inputs are given to Label, a True node is serves as the input. If more than one inputs are given, an Or pattern of the inputs serves as the input.
Public Functions
-
inline Label(const element::Type &type, const PartialShape &s, const ValuePredicate pred, const OutputVector &wrapped_values)
creates a Label node containing a sub-pattern described by
this Label node can be bound only to the nodes in the input graph that match the pattern specified by
See also
type and
See also
shape.
See also
wrapped_nodes Example:
auto add = a + b; // a and b are op::Parameter in this example auto label = std::make_shared<pattern::op::Label>(element::f32, PartialShape{2,2}, nullptr, OutputVector{add});
-
inline Label(const Output<Node> &value, const ValuePredicate pred, const OutputVector &wrapped_values)
creates a Label node containing a sub-pattern described by the type and shape of
this Label node can be bound only to the nodes in the input graph that match the pattern specified by
See also
node.
See also
wrapped_values Example:
auto add = a + b; // a and b are op::Parameter in this example auto label = std::make_shared<pattern::op::Label>(add, nullptr, OutputVector{add});
-
inline Label(const element::Type &type, const PartialShape &s, const ValuePredicate pred, const OutputVector &wrapped_values)
-
class Optional : public ov::pass::pattern::op::Pattern
- #include <optional.hpp>
A submatch on the graph value which contains optional op types defined in constructor.
Optionalpattern supports multi input operations. In this case the pattern checks inputs with optional node type or 1st input. The match is succeed in case of full graphs matching or extended by one of optional type graph or pattern. Otherwise fails.Public Functions
-
inline Optional(const std::vector<DiscreteTypeInfo> &type_infos, const OutputVector &inputs = {}, const pattern::op::ValuePredicate &pred =
[](const Output< Node > &output) { return true;}) creates an optional node matching one pattern. Add nodes to match list.
- Parameters:
type_infos – Optional operation types to exclude them from the matching in case the following op types do not exist in a pattern to match.
patterns – The pattern to match a graph.
-
inline Optional(const std::vector<DiscreteTypeInfo> &type_infos, const OutputVector &inputs = {}, const pattern::op::ValuePredicate &pred =
-
class Or : public ov::pass::pattern::op::Pattern
- #include <or.hpp>
A submatch on the graph value is performed on each input to the Or; the match succeeds on the first match. Otherwise the match fails.
Public Functions
-
inline Or(const OutputVector &patterns)
creates an Or node matching one of several sub-patterns in order. Does not add node to match list.
- Parameters:
patterns – The patterns to try for matching
-
inline Or(const OutputVector &patterns)
-
class Pattern : public ov::Node
- #include <pattern.hpp>
Subclassed by ov::pass::pattern::op::Any, ov::pass::pattern::op::AnyOf, ov::pass::pattern::op::AnyOutput, ov::pass::pattern::op::Branch, ov::pass::pattern::op::Capture, ov::pass::pattern::op::Label, ov::pass::pattern::op::Optional, ov::pass::pattern::op::Or, ov::pass::pattern::op::Skip, ov::pass::pattern::op::True, ov::pass::pattern::op::WrapType
Public Functions
-
inline Pattern(const OutputVector &patterns, ValuePredicate pred)
abase class forSee also
Skip and
See also
-
inline Pattern(const OutputVector &patterns, ValuePredicate pred)
-
class Skip : public ov::pass::pattern::op::Pattern
- #include <skip.hpp>
The graph value is added to the matched value list. If the predicate is true, the match succeeds if the arguments match; if the predicate is false, the match succeeds if the pattern input matches the graph value.
-
using PatternValueMaps = std::vector<PatternValueMap>#
-
namespace transpose_sinking#
-
class TSBinaryBackward : public ov::pass::MatcherPass
- #include <ts_binary.hpp>
TSBinaryBackward transformation sinks Transpose through BinaryElementwiseArithmetic, BinaryElementwiseComparison, BinaryElementwiseLogical and PRelu operations in the backward direction.
-
class TSBinaryForward : public ov::pass::transpose_sinking::TSForwardBase
- #include <ts_binary.hpp>
TSBinaryForward transformation sinks Transpose through BinaryElementwiseArithmetic, BinaryElementwiseComparison, BinaryElementwiseLogical and PRelu operations in the forward direction.
-
class TSConcatBackward : public ov::pass::MatcherPass
- #include <ts_concat.hpp>
TSConcatBackward transformation sinks Transpose through Concat operation in the backward direction.
-
class TSConcatForward : public ov::pass::transpose_sinking::TSForwardBase
- #include <ts_concat.hpp>
TSConcatForward transformation sinks Transpose through Concat operation in the forward direction.
-
class TSCumSumBackward : public ov::pass::MatcherPass
- #include <ts_cumsum.hpp>
TSCumSumBackward transformation sinks Transpose through CumSum in the backward direction.
-
class TSCumSumForward : public ov::pass::transpose_sinking::TSForwardBase
- #include <ts_cumsum.hpp>
TSCumSumForward transformation sinks Transpose through CumSum in the forward direction.
-
class TSDataMovementBackward : public ov::pass::MatcherPass
- #include <ts_data_movement.hpp>
TSDataMovementBackward transformation sinks Transpose through BatchToSpace, SpaceToBatch, ReverseSequence and Pad operations in the backward direction. These operations are categorized as “DataMovement” and are handled in a similar way in this transformation.
-
class TSDataMovementForward : public ov::pass::transpose_sinking::TSForwardBase
- #include <ts_data_movement.hpp>
TSDataMovementForward transformation sinks Transpose through BatchToSpace, SpaceToBatch, ReverseSequence and Pad operations in the forward direction. These operations are categorized as “DataMovement” and are handled in a similar way in this transformation.
-
class TSForwardBase : public ov::pass::MatcherPass
- #include <ts_base.hpp>
TSForwardBase is a base class for all forward transformations.
Subclassed by ov::pass::transpose_sinking::TSBinaryForward, ov::pass::transpose_sinking::TSConcatForward, ov::pass::transpose_sinking::TSCumSumForward, ov::pass::transpose_sinking::TSDataMovementForward, ov::pass::transpose_sinking::TSGatherForward, ov::pass::transpose_sinking::TSInterpolateForward, ov::pass::transpose_sinking::TSReductionForward, ov::pass::transpose_sinking::TSShapeOfForward, ov::pass::transpose_sinking::TSSliceForward, ov::pass::transpose_sinking::TSSplitForward, ov::pass::transpose_sinking::TSSqueezeForward, ov::pass::transpose_sinking::TSTileForward, ov::pass::transpose_sinking::TSUnaryForward, ov::pass::transpose_sinking::TSUnsqueezeForward
-
class TSFuse : public ov::pass::MatcherPass
- #include <ts_fuse.hpp>
TSFuse transformation eliminates 2 consecutive Transposes if they result in no changes to input or fuses them to single Transpose if input gets changed.
-
class TSGatherBackward : public ov::pass::MatcherPass
- #include <ts_gather.hpp>
TSGatherBackward transformation sinks Transpose through Gather operation in the backward direction.
-
class TSGatherForward : public ov::pass::transpose_sinking::TSForwardBase
- #include <ts_gather.hpp>
TSGatherForward transformation sinks Transpose through Gather operations in the forward direction.
-
class TSGeneral : public ov::pass::ModelPass
- #include <ts_general.hpp>
TSGeneral transformation combines TSGeneralForward and TSGeneralBackward transformations into single ModelPass pass and inserts ConstantFolding pass after them.
-
class TSGeneralBackward : public ov::pass::GraphRewrite
- #include <ts_general.hpp>
TSGeneralBackward transformation combines all TransposeSinkingBackward* transformations into single GraphRewrite pass.
-
class TSGeneralForward : public ov::pass::GraphRewrite
- #include <ts_general.hpp>
TSGeneralForward transformation combines all TransposeSinkingForward* transformations into single GraphRewrite pass.
-
class TSInterpolateBackward : public ov::pass::MatcherPass
- #include <ts_interpolate.hpp>
TSInterpolateBackward transformation sinks Transpose through Interpolate operation in the backward direction.
-
class TSInterpolateForward : public ov::pass::transpose_sinking::TSForwardBase
- #include <ts_interpolate.hpp>
TSInterpolateForward transformation sinks Transpose through Interpolate operation in the forward direction.
-
class TSReductionBackward : public ov::pass::MatcherPass
- #include <ts_reduction.hpp>
TSReductionBackward transformation sinks Transpose through Reduce operations in the backward direction.
-
class TSReductionForward : public ov::pass::transpose_sinking::TSForwardBase
- #include <ts_reduction.hpp>
TSReductionForward transformation sinks Transpose through Reduce operations in the forward direction.
-
class TSResetNoSinkingAttribute : public ov::pass::MatcherPass
- #include <ts_reset_no_sinking_attribute.hpp>
TSResetNoSinkingAttribute transformation resets all NoSinkingAttribute runtime attributes in Transpose operations.
-
class TSShapeOfForward : public ov::pass::transpose_sinking::TSForwardBase
- #include <ts_shape_of.hpp>
TSShapeOfForward transformation sinks Transpose through ShapeOf in the forward direction.
It replaces:
+———+ |Transpose| +———+ | v +———+ | ShapeOf | +———+
with the following:
+———+ | ShapeOf | +———+ | v +——+ |Gather| +——+
-
class TSSliceBackward : public ov::pass::MatcherPass
- #include <ts_slice.hpp>
-
class TSSliceForward : public ov::pass::transpose_sinking::TSForwardBase
- #include <ts_slice.hpp>
-
class TSSplitBackward : public ov::pass::MatcherPass
- #include <ts_split.hpp>
TSSplitBackward transformation sinks Transpose through Split, VariadicSplit operations in the backward direction.
-
class TSSplitForward : public ov::pass::transpose_sinking::TSForwardBase
- #include <ts_split.hpp>
TSSplitForward transformation sinks Transpose through Split, VariadicSplit operations in the forward direction.
-
class TSSqueezeBackward : public ov::pass::MatcherPass
- #include <ts_squeeze.hpp>
TSSqueezeBackward transformation sinks Transpose through Reshape, Squeeze operations in the backward direction.
-
class TSSqueezeForward : public ov::pass::transpose_sinking::TSForwardBase
- #include <ts_squeeze.hpp>
TSSqueezeForward transformation sinks Transpose through Reshape, Squeeze operations in the forward direction.
-
class TSTileBackward : public ov::pass::MatcherPass
- #include <ts_tile.hpp>
TSTileBackward transformation sinks Transpose through Tile in the backward direction.
-
class TSTileForward : public ov::pass::transpose_sinking::TSForwardBase
- #include <ts_tile.hpp>
TSTileForward transformation sinks Transpose through Tile in the forward direction.
-
class TSUnaryBackward : public ov::pass::MatcherPass
- #include <ts_unary.hpp>
TSUnaryBackward transformation sinks Transpose through UnaryElementwiseArithmetic, Clamp, Elu, SoftPlus, LogicalNot, Convert, IsInf, IsNaN, IsFinite in the backward direction.
-
class TSUnaryForward : public ov::pass::transpose_sinking::TSForwardBase
- #include <ts_unary.hpp>
TSUnaryForward transformation sinks Transpose through UnaryElementwiseArithmetic, Clamp, Elu, SoftPlus, LogicalNot, Convert, IsInf, IsNaN, IsFinite operations in the forward direction.
-
class TSUnsqueezeBackward : public ov::pass::MatcherPass
- #include <ts_unsqueeze.hpp>
TSUnsqueezeBackward transformation sinks Transpose through Unsqueeze, Reshape operations in the backward direction.
-
class TSUnsqueezeForward : public ov::pass::transpose_sinking::TSForwardBase
- #include <ts_unsqueeze.hpp>
TSUnsqueezeForward transformation sinks Transpose through Unsqueeze, Reshape operations in the forward direction.
-
namespace utils#
Functions
Finds node first input that is a transpose operation and returns filled TransposeInputsInfo for it.
-
bool IfNodeHasTransposeInputs(const ov::Output<ov::Node>&, const std::vector<size_t> &indices = {})#
Checks if.
has any input node that is a transpose operation
-
ov::AxisVector ReverseTransposeOrder(const ov::AxisVector&)#
Reverses order of transpose operation. Do it in a such way that if we had couple following one after another transposes (one would be reversed version of another) we will have no transpose as a result of that couple of transposes.
Swaps @args friendly names.
-
std::shared_ptr<ov::Node> InsertBroadcastUnsqueeze(const ov::Output<ov::Node> &node, size_t n_dims)#
Inserts Unsqueeze node as a child to
node with axes {0, 1, … N - 1}, where N =
n_dims
-
bool CheckTransposeConsumers(const ov::Output<ov::Node>&)#
Checks if.
has consumers that are all the same Transpose operation and that sinking is enabled for all these Transpose ops. Otherwise returns false. If no consumers at all returns false.
Removes all Transpose consumers for given node.
Inserts Gather operation which changes the order of values in.
input according to
transpose_axis_order along
axis.
Inserts Gather operation which changes the order of values in.
input according to
transpose_axis_order along
axis.
Inserts Gather operation which changes the order of values in.
input according to
transpose_axis_order along
axis.
-
std::vector<size_t> GetOrderAfterReduction(const std::vector<size_t> &axes_values, const std::vector<size_t> &order_values)#
Returns the updated axes order for case when the initial axes order has more elements than after TransposeSinking, e.g.:
before: Transpose(the initial axes order) -> ReduceMax after : ReduceMax -> Transpose (the updated axes order)
before: Unsqueeze -> Transpose (the initial axes order) after : Transpose (the updated axes order) -> Unsqueeze
-
std::vector<size_t> GetOrderBeforeReduction(const std::vector<size_t> &axes_values, const std::vector<size_t> &order_values)#
Returns the updated axes order for case when the initial axes order has less elements than after TransposeSinking, e.g.:
before : ReduceMax -> Transpose (the updated axes order) after: Transpose(the initial axes order) -> ReduceMax
before: Transpose (the updated axes order) -> Unsqueeze after : Unsqueeze -> Transpose (the initial axes order)
-
struct TransposeInputsInfo#
- #include <ts_utils.hpp>
-
namespace sink_backward#
Functions
Inserts transposes on inputs of.
main_node specified by
input_indexes with the order specified in
transpose_const. If
input_indexes is empty, then it inserts transposes for all inputs.
-
namespace sink_forward#
Functions
Inserts reversed transposed on @args main_node inputs. Removes input transpose specified in.
transpose_input_info
Removes.
input node
Inserts transposes on each main_node output with the order specified in.
transpose_input_info
-
class TSBinaryBackward : public ov::pass::MatcherPass
-
using TransposeSinkingGeneral = ov::pass::transpose_sinking::TSGeneral#
-
namespace preprocess#
-
Enums
-
enum class ColorFormat#
Color format enumeration for conversion.
Values:
-
enumerator UNDEFINED#
Undefined color format.
-
enumerator NV12_SINGLE_PLANE#
Image in NV12 format represented as separate tensors for Y and UV planes.
-
enumerator NV12_TWO_PLANES#
Image in NV12 format represented as separate tensors for Y and UV planes.
-
enumerator I420_SINGLE_PLANE#
Image in I420 (YUV) format as single tensor.
-
enumerator I420_THREE_PLANES#
Image in I420 format represented as separate tensors for Y, U and V planes.
-
enumerator RGB#
Image in RGB interleaved format (3 channels)
-
enumerator BGR#
Image in BGR interleaved format (3 channels)
-
enumerator GRAY#
Image in GRAY format (1 channel)
-
enumerator RGBX#
Image in RGBX interleaved format (4 channels)
-
enumerator BGRX#
Image in BGRX interleaved format (4 channels)
-
enumerator UNDEFINED#
-
enum class ResizeAlgorithm#
An enum containing all supported resize(interpolation) algorithms available in preprocessing.
Values:
-
enumerator RESIZE_LINEAR#
Linear interpolation matching the TensorFlow behavior.
-
enumerator RESIZE_CUBIC#
Cubic interpolation.
-
enumerator RESIZE_NEAREST#
Nearest interpolation.
-
enumerator RESIZE_BILINEAR_PILLOW#
Bilinear interpolation matching the Pillow behavior.
-
enumerator RESIZE_BICUBIC_PILLOW#
Bicubic interpolation matching the Pillow behavior.
-
enumerator RESIZE_LINEAR#
Functions
- OPENVINO_API std::ostream & operator<< (std::ostream &str, const PrePostProcessor &prePostProcessor)
Inserts a human-readable representation of a PrePostProcessors into an output stream. The output to the stream is in “informal” notation and can be used for debugging purposes.
- Parameters:
str – The output stream targeted for insertion.
prePostProcessor – The shape to be inserted into output stream.
- Returns:
A reference to same output stream after insertion.
-
class InputInfo
- #include <input_info.hpp>
Class holding preprocessing information for one input From preprocessing pipeline perspective, each input can be represented as:
User’s input parameter info (InputInfo::tensor)
Preprocessing steps applied to user’s input (InputInfo::preprocess)
Model’s input info, which is a final input’s info after preprocessing (InputInfo::model)
Public Functions
-
InputInfo(InputInfo &&other) noexcept
Move constructor.
-
~InputInfo()
Default destructor.
-
InputTensorInfo &tensor()
Get current input tensor information with ability to change specific data.
- Returns:
Reference to current input tensor structure
-
PreProcessSteps &preprocess()
Get current input preprocess information with ability to add more preprocessing steps.
- Returns:
Reference to current preprocess steps structure
-
InputModelInfo &model()
Get current input model information with ability to change original model’s input data.
- Returns:
Reference to current model’s input information structure
-
class InputModelInfo
- #include <input_model_info.hpp>
Information about model’s input tensor. If all information is already included to loaded model, this info may not be needed. However it can be set to specify additional information about model, like ‘layout’.
Example of usage of model ‘layout’: Support model has input parameter with shape {1, 3, 224, 224} and user needs to resize input image to model’s dimensions. It can be done like this
<model has input parameter with shape {1, 3, 224, 224}> auto proc = PrePostProcessor(function); proc.input().preprocess().resize(ResizeAlgorithm::RESIZE_LINEAR); proc.input().model().set_layout("NCHW");
Public Functions
-
~InputModelInfo()
Default destructor.
-
InputModelInfo &set_layout(const ov::Layout &layout)
Set layout for model’s input tensor This version allows chaining for Lvalue objects.
- Parameters:
layout – Layout for model’s input tensor.
- Returns:
Reference to ‘this’ to allow chaining with other calls in a builder-like manner
-
~InputModelInfo()
-
class InputTensorInfo
- #include <input_tensor_info.hpp>
Information about user’s input tensor. By default, it will be initialized to same data (type/shape/etc) as model’s input parameter. User application can override particular parameters (like ‘element_type’) according to application’s data and specify appropriate conversions in pre-processing steps.
auto proc = PrePostProcessor(function); proc.input().tensor().set_element_type(ov::element::u8);
Public Functions
-
~InputTensorInfo()
Default destructor.
-
InputTensorInfo &set_element_type(const ov::element::Type &type)
Set element type for user’s input tensor.
- Parameters:
type – Element type for user’s input tensor.
- Returns:
Reference to ‘this’ to allow chaining with other calls in a builder-like manner
-
InputTensorInfo &set_layout(const ov::Layout &layout)
Set layout for user’s input tensor.
- Parameters:
layout – Layout for user’s input tensor.
- Returns:
Reference to ‘this’ to allow chaining with other calls in a builder-like manner
-
InputTensorInfo &set_spatial_dynamic_shape()
By default, input image shape is inherited from model input shape. This method specifies that user’s input image has dynamic spatial dimensions (width & height). This can be useful for adding resize preprocessing from any input image to model’s expected dimensions.
- Returns:
Reference to ‘this’ to allow chaining with other calls in a builder-like manner.
-
InputTensorInfo &set_spatial_static_shape(size_t height, size_t width)
By default, input image shape is inherited from model input shape. Use this method to specify different width and height of user’s input image. In case if input image size is not known, use
set_spatial_dynamic_shapemethod.- Parameters:
height – Set fixed user’s input image height.
width – Set fixed user’s input image width.
- Returns:
Reference to ‘this’ to allow chaining with other calls in a builder-like manner.
-
InputTensorInfo &set_color_format(const ov::preprocess::ColorFormat &format, const std::vector<std::string> &sub_names = {})
Set color format for user’s input tensor.
In general way, some formats support multi-plane input, e.g. NV12 image can be represented as 2 separate tensors (planes): Y plane and UV plane. set_color_format API also allows to set sub_names for such parameters for convenient usage of plane parameters. During build stage, new parameters for each plane will be inserted to the place of original parameter. This means that all parameters located after will shift their positions accordingly (e.g. {param1, param2} will become {param1/Y, param1/UV, param2})
- Parameters:
format – Color format of input image.
sub_names – Optional list of sub-names assigned for each plane (e.g. {“Y”, “UV”}). If specified, number of sub-names shall match with number of planes. If not specified, friendly name and tensor name for plane parameters will be empty. It is not allowed to specify sub-names for single-plane inputs.
- Returns:
Reference to ‘this’ to allow chaining with other calls in a builder-like manner.
-
InputTensorInfo &set_memory_type(const std::string &memory_type)
Set memory type runtime information for user’s input tensor.
- Parameters:
memory_type – Memory type. Refer to specific plugin’s documentation for exact string format
- Returns:
Reference to ‘this’ to allow chaining with other calls in a builder-like manner
-
InputTensorInfo &set_shape(const ov::PartialShape &shape)
By default, input shape is inherited from model’s input shape. Use this method to specify different input data shape. If it is needed to change only input height & width of input image, consider define layout and use set_spatial_static_shape or ‘set_spatial_dynamic_shape’ instead. This method allows defining any custom input shape and can be useful for custom preprocessing operations.
Note
Methods ‘set_spatial_dynamic_shape’, ‘set_spatial_static_shape’ are also intended to modify input shape, using those methods together will throw ov::AssertFailure exception
- Parameters:
shape – New shape for input tensor.
- Returns:
Reference to ‘this’ to allow chaining with other calls in a builder-like manner.
-
InputTensorInfo &set_from(const ov::Tensor &runtime_tensor)
Helper function to reuse element type and shape from user’s created tensor. Use this only in case if input tensor is already known and available before. Overwrites previously set element type & shape via
set_element_typeandset_shape. Tensor’s memory type is not reused, so ifruntime_tensorrepresents remote tensor with particular memory type - you should still specify appropriate memory type manually usingset_memory_typeNote
As for
InputTensorInfo::set_shape, this method shall not be used together with methods ‘set_spatial_dynamic_shape’ and ‘set_spatial_static_shape’, otherwise ov::AssertFailure exception will be thrown- Parameters:
runtime_tensor – User’s created tensor.
- Returns:
Reference to ‘this’ to allow chaining with other calls in a builder-like manner.
-
~InputTensorInfo()
-
class OutputInfo
- #include <output_info.hpp>
Class holding postprocessing information for one output From postprocessing pipeline perspective, each output can be represented as:
Model’s output info, (OutputInfo::model)
Postprocessing steps applied to user’s input (OutputInfo::postprocess)
User’s desired output parameter information, which is a final one after preprocessing (OutputInfo::tensor)
Public Functions
-
OutputInfo(OutputInfo &&other) noexcept
Move constructor.
-
OutputInfo &operator=(OutputInfo &&other) noexcept
Move assignment operator.
-
~OutputInfo()
Default destructor.
-
OutputModelInfo &model()
Get current output model information with ability to change original model’s output data.
- Returns:
Reference to current model’s output information structure
-
PostProcessSteps &postprocess()
Get current output post-process information with ability to add more post-processing steps.
- Returns:
Reference to current preprocess steps structure
-
OutputTensorInfo &tensor()
Get current output tensor information with ability to change specific data.
- Returns:
Reference to current output tensor structure
-
class OutputModelInfo
- #include <output_model_info.hpp>
Information about model’s output tensor. If all information is already included to loaded model, this info may not be needed. However it can be set to specify additional information about model’s output, like ‘layout’.
Example of usage of model’s ‘layout’: Suppose model has output result with shape {1, 3, 224, 224} and
NHWClayout. User may need to transpose output picture to interleaved format {1, 224, 224, 3}. This can be done with the following code<model has output result with shape {1, 3, 224, 224}> auto proc = PrePostProcessor(function); proc.output().model().set_layout("NCHW"); proc.output().postprocess().convert_layout("NHWC"); function = proc.build();
Public Functions
-
~OutputModelInfo()
Default destructor.
-
OutputModelInfo &set_layout(const ov::Layout &layout)
Set layout for model’s output tensor.
- Parameters:
layout – Layout for model’s output tensor.
- Returns:
Reference to ‘this’ to allow chaining with other calls in a builder-like manner
-
OutputModelInfo &set_color_format(const ov::preprocess::ColorFormat &format, const std::vector<std::string> &sub_names = {})
Set color format for model’s output tensor.
- Parameters:
format – Color format for model’s output tensor.
sub_names – Optional list of sub-names, not used, placeholder for future.
- Returns:
Reference to ‘this’ to allow chaining with other calls in a builder-like manner
-
~OutputModelInfo()
-
class OutputTensorInfo
- #include <output_tensor_info.hpp>
Information about user’s desired output tensor. By default, it will be initialized to same data (type/shape/etc) as model’s output parameter. User application can override particular parameters (like ‘element_type’) according to application’s data and specify appropriate conversions in post-processing steps.
auto proc = PrePostProcessor(function); auto& output = proc.output(); output.postprocess().<add steps + conversion to user's output element type>; output.tensor().set_element_type(ov::element::u8); function = proc.build();
Public Functions
-
~OutputTensorInfo()
Default destructor.
-
OutputTensorInfo &set_element_type(const ov::element::Type &type)
Set element type for user’s desired output tensor.
- Parameters:
type – Element type for user’s output tensor.
- Returns:
Reference to ‘this’ to allow chaining with other calls in a builder-like manner.
-
OutputTensorInfo &set_layout(const ov::Layout &layout)
Set layout for user’s output tensor.
- Parameters:
layout – Layout for user’s output tensor.
- Returns:
Reference to ‘this’ to allow chaining with other calls in a builder-like manner
-
~OutputTensorInfo()
-
class PostProcessSteps
- #include <postprocess_steps.hpp>
Postprocessing steps. Each step typically intends adding of some operation to output parameter User application can specify sequence of postprocessing steps in a builder-like manner.
auto proc = PrePostProcessor(function); proc.output().postprocess().convert_element_type(element::u8); function = proc.build();
Public Types
-
using CustomPostprocessOp = std::function<ov::Output<ov::Node>(const ov::Output<ov::Node> &node)>
Signature for custom postprocessing operation. Custom postprocessing operation takes one output node and produces one output node. For more advanced cases, client’s code can use transformation passes over ov::Model directly.
- Param node:
Output node for custom post-processing operation
- Return:
New node after applying custom post-processing operation
Public Functions
-
~PostProcessSteps()
Default destructor.
-
PostProcessSteps &convert_element_type(const ov::element::Type &type = {})
Add convert element type post-process operation.
- Parameters:
type – Desired type of output. If not specified, type will be obtained from ‘tensor’ output information
- Returns:
Reference to ‘this’ to allow chaining with other calls in a builder-like manner
-
PostProcessSteps &convert_layout(const Layout &dst_layout = {})
Add ‘convert layout’ operation to specified layout.
Adds appropriate ‘transpose’ operation between model layout and user’s desired layout. Current implementation requires source and destination layout to have same number of dimensions
Example: when model data has output in ‘NCHW’ layout ([1, 3, 224, 224]) but user needs interleaved output image (‘NHWC’, [1, 224, 224, 3]). Post-processing may look like this:
auto proc = PrePostProcessor(function); proc.output().model(OutputTensorInfo().set_layout("NCHW"); // model output is NCHW proc.output().postprocess().convert_layout("NHWC"); // User needs output as NHWC
- Parameters:
dst_layout – New layout after conversion. If not specified - destination layout is obtained from appropriate tensor output properties.
- Returns:
Reference to ‘this’ to allow chaining with other calls in a builder-like manner.
-
PostProcessSteps &convert_layout(const std::vector<uint64_t> &dims)
Add convert layout operation by direct specification of transposed dimensions.
Example: model produces output with shape [1, 3, 480, 640] and user’s needs interleaved output image [1, 480, 640, 3]. Post-processing may look like this:
auto proc = PrePostProcessor(function); proc.output().postprocess().convert_layout({0, 2, 3, 1}); function = proc.build();
- Parameters:
dims – Dimensions array specifying places for new axis. If not empty, array size (N) must match to input shape rank. Array values shall contain all values from 0 to N-1. If empty, no actual conversion will be added.
- Returns:
Reference to ‘this’ to allow chaining with other calls in a builder-like manner.
-
PostProcessSteps &custom(const CustomPostprocessOp &postprocess_cb)
Add custom post-process operation. Client application can specify callback function for custom action.
- Parameters:
postprocess_cb – Client’s custom postprocess operation.
- Returns:
Reference to ‘this’ to allow chaining with other calls in a builder-like manner
-
PostProcessSteps &convert_color(const ov::preprocess::ColorFormat &dst_format)
Converts color format for user’s output tensor. Requires destinantion color format to be specified by OutputTensorInfo::set_color_format.
- Parameters:
dst_format – Destination color format of input image
- Returns:
Reference to ‘this’ to allow chaining with other calls in a builder-like manner
-
using CustomPostprocessOp = std::function<ov::Output<ov::Node>(const ov::Output<ov::Node> &node)>
-
class PrePostProcessor
- #include <pre_post_process.hpp>
Main class for adding pre- and post- processing steps to existing ov::Model.
This is a helper class for writing easy pre- and post- processing operations on ov::Model object assuming that any preprocess operation takes one input and produces one output.
For advanced preprocessing scenarios, like combining several functions with multiple inputs/outputs into one, client’s code can use transformation passes over ov::Model
Public Functions
Default constructor.
- Parameters:
function – Existing function representing loaded model
-
PrePostProcessor(PrePostProcessor&&) noexcept
Default move constructor.
-
PrePostProcessor &operator=(PrePostProcessor&&) noexcept
Default move assignment operator.
-
~PrePostProcessor()
Default destructor.
-
InputInfo &input()
Gets input pre-processing data structure. Should be used only if model/function has only one input Using returned structure application’s code is able to set user’s tensor data (e.g layout), preprocess steps, target model’s data.
- Returns:
Reference to model’s input information structure
-
InputInfo &input(const std::string &tensor_name)
Gets input pre-processing data structure for input identified by it’s tensor name.
- Parameters:
tensor_name – Tensor name of specific input. Throws if tensor name is not associated with any input in a model
- Returns:
Reference to model’s input information structure
-
InputInfo &input(size_t input_index)
Gets input pre-processing data structure for input identified by it’s order in a model.
- Parameters:
input_index – Input index of specific input. Throws if input index is out of range for associated function
- Returns:
Reference to model’s input information structure
-
OutputInfo &output()
Gets output post-processing data structure. Should be used only if model/function has only one output Using returned structure application’s code is able to set model’s output data, post-process steps, user’s tensor data (e.g layout)
- Returns:
Reference to model’s output information structure
-
OutputInfo &output(const std::string &tensor_name)
Gets output post-processing data structure for output identified by it’s tensor name.
- Parameters:
tensor_name – Tensor name of specific output. Throws if tensor name is not associated with any input in a model
- Returns:
Reference to model’s output information structure
-
OutputInfo &output(size_t output_index)
Gets output post-processing data structure for output identified by it’s order in a model.
- Parameters:
output_index – Output index of specific output. Throws if output index is out of range for associated function
- Returns:
Reference to model’s output information structure
-
std::shared_ptr<Model> build()
Adds pre/post-processing operations to function passed in constructor.
- Returns:
Function with added pre/post-processing operations
-
class PreProcessSteps
- #include <preprocess_steps.hpp>
Preprocessing steps. Each step typically intends adding of some operation to input parameter User application can specify sequence of preprocessing steps in a builder-like manner.
auto proc = PrePostProcessor(function); proc.input().preprocess() .mean(0.2f) // Subtract 0.2 from each element .scale(2.3f)); // then divide each element to 2.3
Public Types
-
using CustomPreprocessOp = std::function<Output<Node>(const Output<Node> &node)>
Signature for custom preprocessing operation. Custom preprocessing operation takes one input node and produces one output node. For more advanced cases, client’s code can use transformation passes over ov::Model directly.
- Param node:
Input node for custom preprocessing operation (output of previous preprocessing operation)
- Return:
New node after applying custom preprocessing operation
Public Functions
-
~PreProcessSteps()
Default destructor.
-
PreProcessSteps &convert_element_type(const ov::element::Type &type = {})
Add convert element type preprocess operation.
- Parameters:
type – Desired type of input.
- Returns:
Reference to ‘this’ to allow chaining with other calls in a builder-like manner
-
PreProcessSteps &convert_color(const ov::preprocess::ColorFormat &dst_format)
Converts color format for user’s input tensor. Requires source color format to be specified by InputTensorInfo::set_color_format.
- Parameters:
dst_format – Destination color format of input image
- Returns:
Reference to ‘this’ to allow chaining with other calls in a builder-like manner
-
PreProcessSteps &scale(float value)
Add scale preprocess operation Divide each element of input by specified value.
- Parameters:
value – Scaling value.
- Returns:
Reference to ‘this’ to allow chaining with other calls in a builder-like manner
-
PreProcessSteps &scale(const std::vector<float> &values)
Add scale preprocess operation by specified array of scale values for each channel.
- Parameters:
values – Scaling values. Layout runtime info with channels dimension must be specified for input tensor
- Returns:
Reference to ‘this’ to allow chaining with other calls in a builder-like manner
-
PreProcessSteps &mean(float value)
Add mean preprocess operation Subtract specified value from each element of input.
- Parameters:
value – Value to subtract from each element.
- Returns:
Reference to ‘this’ to allow chaining with other calls in a builder-like manner
-
PreProcessSteps &mean(const std::vector<float> &values)
Add mean preprocess operation by specified array of mean values for each channel.
- Parameters:
values – Mean values. Layout runtime info with channels dimension must be specified for input tensor
- Returns:
Reference to ‘this’ to allow chaining with other calls in a builder-like manner
-
PreProcessSteps &pad(const std::vector<int> &pads_begin, const std::vector<int> &pads_end, float value, PaddingMode mode)
Add pad preprocess operation Extends an input tensor on edges with constants.
- Parameters:
pads_begin – Number of padding elements to add at the beginning of each axis.
pads_end – Number of padding elements to add at the end of each axis.
value – Value to be populated in the padded area
- Returns:
Reference to ‘this’ to allow chaining with other calls in a builder-like manner
-
PreProcessSteps &pad(const std::vector<int> &pads_begin, const std::vector<int> &pads_end, const std::vector<float> &values, PaddingMode mode)
Add pad preprocess operation Extends an input tensor on edges with constants.
- Parameters:
pads_begin – Number of padding elements to add at the beginning of each axis.
pads_end – Number of padding elements to add at the end of each axis.
values – Values to be populated in the padded area
- Returns:
Reference to ‘this’ to allow chaining with other calls in a builder-like manner
-
PreProcessSteps &custom(const CustomPreprocessOp &preprocess_cb)
Add custom preprocess operation Client application can specify callback function for custom action.
- Parameters:
preprocess_cb – Client’s custom preprocess operation.
- Returns:
Reference to ‘this’ to allow chaining with other calls in a builder-like manner
-
PreProcessSteps &resize(ResizeAlgorithm alg, size_t dst_height, size_t dst_width)
Add resize operation to known dimensions - Lvalue version.
- Parameters:
alg – Resize algorithm.
dst_height – Desired height of resized image.
dst_width – Desired width of resized image.
- Returns:
Reference to ‘this’ to allow chaining with other calls in a builder-like manner.
-
PreProcessSteps &resize(ResizeAlgorithm alg)
Add resize operation to model’s dimensions.
- Parameters:
alg – Resize algorithm.
- Returns:
Reference to ‘this’ to allow chaining with other calls in a builder-like manner.
-
PreProcessSteps &crop(const std::vector<int> &begin, const std::vector<int> &end)
Crop input tensor between begin and end coordinates. Under the hood, inserts
opset8::Sliceoperation to execution graph. It is recommended to use to together withov::preprocess::InputTensorInfo::set_shapeto set original input shape before cropping.- Parameters:
begin – Begin indexes for input tensor cropping. Negative values represent counting elements from the end of input tensor
end – End indexes for input tensor cropping. End indexes are exclusive, which means values including end edge are not included in the output slice. Negative values represent counting elements from the end of input tensor
- Returns:
Reference to ‘this’ to allow chaining with other calls in a builder-like manner.
-
PreProcessSteps &convert_layout(const Layout &dst_layout = {})
Add ‘convert layout’ operation to specified layout.
Adds appropriate ‘transpose’ operation between user layout and target layout. Current implementation requires source and destination layout to have same number of dimensions
Example: when user data has ‘NHWC’ layout (example is RGB image, [1, 224, 224, 3]) but model expects planar input image (‘NCHW’, [1, 3, 224, 224]). Preprocessing may look like this:
auto proc = PrePostProcessor(model); proc.input().tensor().set_layout("NHWC"); // User data is NHWC proc.input().preprocess().convert_layout("NCHW")) // model expects input as NCHW
- Parameters:
dst_layout – New layout after conversion. If not specified - destination layout is obtained from appropriate model input properties.
- Returns:
Reference to ‘this’ to allow chaining with other calls in a builder-like manner.
-
PreProcessSteps &convert_layout(const std::vector<uint64_t> &dims)
Add convert layout operation by direct specification of transposed dimensions.
Example: when user data has input RGB image {1x480x640x3} but model expects planar input image (‘NCHW’, [1, 3, 480, 640]). Preprocessing may look like this:
auto proc = PrePostProcessor(function); proc.input().preprocess().convert_layout({0, 3, 1, 2});
- Parameters:
dims – Dimensions array specifying places for new axis. If not empty, array size (N) must match to input shape rank. Array values shall contain all values from 0 to N-1. If empty, no actual conversion will be added.
- Returns:
Reference to ‘this’ to allow chaining with other calls in a builder-like manner.
-
PreProcessSteps &reverse_channels()
Reverse channels operation.
Adds appropriate operation which reverses channels layout. Operation requires layout having ‘C’ dimension Operation convert_color (RGB<->BGR) does reversing of channels also, but only for NHWC layout
Example: when user data has ‘NCHW’ layout (example is [1, 3, 224, 224] RGB order) but model expects BGR planes order. Preprocessing may look like this:
auto proc = PrePostProcessor(function); proc.input().tensor().set_layout("NCHW"); // User data is NCHW proc.input().preprocess().reverse_channels();
- Returns:
Reference to ‘this’ to allow chaining with other calls in a builder-like manner.
-
using CustomPreprocessOp = std::function<Output<Node>(const Output<Node> &node)>
-
class TensorInfoMemoryType : public ov::RuntimeAttribute
- #include <input_tensor_info.hpp>
-
enum class ColorFormat#
-
namespace proxy#
-
namespace reference#
Typedefs
-
using Nearest_mode = op::v4::Interpolate::NearestMode#
-
using Transform_mode = op::v4::Interpolate::CoordinateTransformMode#
-
using InterpolateMode = op::v4::Interpolate::InterpolateMode#
Enums
-
enum [anonymous]#
Values:
-
enumerator idxLocation#
-
enumerator idxConfidence#
-
enumerator idxPriors#
-
enumerator idxArmConfidence#
-
enumerator idxArmLocation#
-
enumerator numInputs#
-
enumerator idxLocation#
Functions
-
template<class T>
void abs(const T *in, T *out, const size_t count)# Reference implementation of Abs operator.
-
template<class T>
void acos(const T *arg, T *out, const size_t count)# Reference implementation of Acos operator.
-
template<class T>
void acosh(const T *arg, T *out, const size_t count)# Reference implementation of Acosh operator.
-
template<typename T>
void adaptive_avg_pool(const T *arg, T *out, const Shape &arg_shape, const Shape &out_shape)#
-
template<typename T, typename IT>
void adaptive_max_pool_1d(const T *arg, T *out, IT *indices, size_t h_in, size_t h_out)#
-
template<typename T, typename IT>
void adaptive_max_pool_2d(const T *arg, T *out, IT *indices, size_t h_in, size_t h_out, size_t w_in, size_t w_out)#
-
template<typename T, typename IT>
void adaptive_max_pool_3d(const T *arg, T *out, IT *indices, size_t d_in, size_t d_out, size_t h_in, size_t h_out, size_t w_in, size_t w_out)#
-
template<typename T, typename IT>
void adaptive_max_pool(const T *arg, T *out, IT *selected_indices, const Shape &arg_shape, const Shape &out_shape)#
-
template<class T>
void add(const T *arg0, const T *arg1, T *out, const Shape &arg0_shape, const Shape &arg1_shape, const op::AutoBroadcastSpec &broadcast_spec)# Reference implementation of binary elementwise Add operator.
-
template<class T>
void logical_and(const T *arg0, const T *arg1, T *out, const Shape &arg0_shape, const Shape &arg1_shape, const op::AutoBroadcastSpec &broadcast_spec)# Reference implementation of binary elementwise LogicalAnd operator.
-
template<typename T>
void asin(const T *arg, T *out, const size_t count)# Reference implementation of Asin operator.
-
template<class T>
void asinh(const T *arg, T *out, const size_t count)# Reference implementation of Asinh operator.
-
template<class T>
void atan(const T *arg, T *out, const size_t count)# Reference implementation of Atan operator.
-
template<typename X, typename Y, typename Z>
void atan2(const X *py, const Y *px, Z *pout, size_t count)#
-
template<class T>
void atanh(const T *arg, T *out, const size_t count)# Reference implementation of Atanh operator.
-
template<typename T, typename U, class Functor>
void no_broadcast_binop(const T *arg0, const T *arg1, U *out, const size_t count, Functor f)# Apply elementwise function for 2 inputs of same size.
- Parameters:
arg0 – Pointer to input 0 data.
arg1 – Pointer to input 1 data.
out – Pointer to output data.
count – Number of elements in inputs
f – Binary elementwise functions.
-
template<typename T, typename U, class Functor>
void numpy_broadcast_binop(const T *arg0, const T *arg1, U *out, const Shape &arg0_shape, const Shape &arg1_shape, Functor f)# Apply elementwise function for 2 inputs and apply NUMPY broadcasting.
-
template<typename T, typename U, class Functor>
void pdpd_broadcast_binop(const T *arg0, const T *arg1, U *out, const Shape &arg0_shape, const Shape &arg1_shape, int64_t axis, Functor f)# Apply elementwise function for 2 inputs and apply PDPP broadcasting.
-
template<typename T, typename U, typename Functor>
void autobroadcast_binop(const T *arg0, const T *arg1, U *out, const Shape &arg0_shape, const Shape &arg1_shape, const op::AutoBroadcastSpec &broadcast_spec, Functor elementwise_functor)# Helper function to implement auto broadcasting elementwise binop references.
- Template Parameters:
T – Element type of the input tensors.
U – Element type of the output tensor.
Functor – Type of the functor for the elementwise operation. Must support operator()(T,T), and operator()(T,T) must return a value of type U.
- Parameters:
arg0 – Pointer to the buffer for left operand input tensor.
arg1 – Pointer to the buffer for right operand input tensor.
out – Pointer to the buffer for output tensor. This must be pre-allocated by the caller, and must be large enough to hold a tensor of the correct shape.
broadcast_spec – Specification of the auto-broadcasting scheme.
elementwise_functor – Functor implementing the elementwise operation to be applied across the input tensors. Must accept two arguments of type T, and return a value of type U.
-
template<typename T, typename U, typename Functor>
void autobroadcast_select(const U *arg0, const T *arg1, const T *arg2, T *out, const Shape &arg0_shape, const Shape &arg1_shape, const Shape &arg2_shape, const op::AutoBroadcastSpec &broadcast_spec, Functor elementwise_functor)# Helper function to implement auto broadcasting elementwise ternary op references.
- Template Parameters:
U – Element type of the selector tensor.
T – Element type of the input tensors.
Functor – Type of the functor for the elementwise operation. Must support operator()(U,T,T), and operator()(U,T,T) must return a value of type T.
- Parameters:
arg0 – Pointer to the buffer for selector tensor.
arg1 – Pointer to the buffer for left operand input tensor.
arg2 – Pointer to the buffer for right operand input tensor.
out – Pointer to the buffer for output tensor. This must be pre-allocated by the caller, and must be large enough to hold a tensor of the correct shape.
broadcast_spec – Specification of the auto-broadcasting scheme.
elementwise_functor – Functor implementing the elementwise operation to be applied across the input tensors Must accept an argument of type U and two of type T, and return a value of type T.
-
template<typename T>
void avg_pool(const T *const arg, T *const out, const Shape &arg_shape, const Shape &out_shape, const Shape &window_shape, const Strides &window_movement_strides, const Shape &padding_below, const Shape &padding_above, const bool include_padding_in_avg_computation)#
-
template<typename T>
void batch_norm_inference(float eps, const T *in, const T *gamma, const T *beta, const T *mean, const T *variance, T *out, const Shape &in_shape)#
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template<typename T_IN, typename T_F>
void binary_convolution(const T_IN *in, const T_F *f, T_IN *out, const Shape &in_shape, const Shape &f_shape, const Shape &out_shape, const Strides &strides, const Strides &dilations, const CoordinateDiff &pads_begin, const CoordinateDiff &pads_end, const float pad_value)#
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template<class T, typename std::enable_if<std::is_same<typename std::decay<T>::type, char>::value>::type* = nullptr>
void bitwise_and(const T *arg0, const T *arg1, T *out, const Shape &arg0_shape, const Shape &arg1_shape, const op::AutoBroadcastSpec &broadcast_spec)# Reference implementation of binary elementwise bitwise AND operator.
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template<class T>
void bitwise_not(const T *in, T *out, size_t count)# Reference implementation of BitwiseNot operator.
-
template<class T, typename std::enable_if<std::is_same<typename std::decay<T>::type, char>::value>::type* = nullptr>
void bitwise_or(const T *arg0, const T *arg1, T *out, const Shape &arg0_shape, const Shape &arg1_shape, const op::AutoBroadcastSpec &broadcast_spec)# Reference implementation of binary elementwise bitwise OR operator.
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template<class T, typename std::enable_if<std::is_same<typename std::decay<T>::type, char>::value>::type* = nullptr>
void bitwise_xor(const T *arg0, const T *arg1, T *out, const Shape &arg0_shape, const Shape &arg1_shape, const op::AutoBroadcastSpec &broadcast_spec)# Reference implementation of binary elementwise bitwise XOR operator.
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void broadcast(const char *arg, char *out, const Shape &in_shape, const Shape &out_shape, const AxisSet &broadcast_axes, size_t elem_size)#
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template<typename T, typename B, typename P>
void bucketize(const T *data, const B *buckets, P *out, const Shape &data_shape, const Shape &buckets_shape, bool with_right_bound)#
-
template<class T, typename std::enable_if<std::is_integral<T>::value>::type* = nullptr>
void ceiling(const T *arg, T *out, const size_t count)# Reference implementation of Ceiling operator (integral types).
Reference implementation of Ceiling operator (floating point types).
-
template<typename T>
void clamp(const T *arg, T *out, const T min, const T max, const size_t count)# Reference implementation of Clamp operator.
- Parameters:
arg – Pointer to input data.
out – Pointer to output data.
min – Minimum value used to clamp input data.
max – Maximum value used to clamp input data.
count – Number of elements in input buffer.
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void concat(const std::vector<const char*> &args, char *out, const std::vector<Shape> &in_shapes, const Shape &out_shape, int64_t concatenation_axis, size_t elem_size, const ov::element::Type &elem_type = ov::element::Type_t::undefined)#
-
template<typename InputIt, typename OutputIt>
void convert(InputIt arg, OutputIt out, const size_t count)#
-
size_t count_out_of_f16_range(const float *arg, size_t count)#
-
template<typename T>
void color_convert_nv12(const T *arg_y, const T *arg_uv, T *out_ptr, size_t batch_size, size_t image_h, size_t image_w, size_t stride_y, size_t stride_uv, ov::op::util::ConvertColorNV12Base::ColorConversion color_format)#
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template<typename T>
void color_convert_i420(const T *arg_y, const T *arg_u, const T *arg_v, T *out_ptr, size_t batch_size, size_t image_h, size_t image_w, size_t stride_y, size_t stride_uv, ov::op::util::ConvertColorI420Base::ColorConversion color_format)#
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template<typename T>
void convolution(const T *in, const T *f, T *out, const Shape &in_shape, const Shape &f_shape, const Shape &out_shape, const Strides &strides, const Strides &dilations, const CoordinateDiff &pads_begin, const CoordinateDiff &pads_end)#
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template<typename T>
void convolution_backprop_in(const T *delta_in, const T *filter, T *delta_out, const Shape &in_shape, const Shape &filter_shape, const Shape &out_shape, const Strides &in_dilation, const Strides &filter_dilation, const CoordinateDiff &forward_in_pad_bellow, const CoordinateDiff &forward_in_pad_above, const Strides &stride, const CoordinateDiff &output_padding)#
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template<class T>
void cos(const T *arg, T *out, const size_t count)# Reference implementation of Cos operator.
-
template<class T>
void cosh(const T *arg, T *out, size_t count)# Reference implementation of Cosh operator.
-
template<typename T>
void ctc_greedy_decoder(const T *data, const T *sequence_masks, T *out, const Shape &data_shape, const Shape &sequence_masks_shape, const Shape &out_shape, const bool ctc_merge_repeated)#
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template<typename TF, typename TI, typename TCI, typename TSL>
void ctc_greedy_decoder_seq_len(const TF *data, const TI *sequence_length, const TI *blank_index, TCI *out1, TSL *out2, const Shape &data_shape, const Shape &out_shape, const bool ctc_merge_repeated)#
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template<typename T, typename U>
void CTCLoss(const T *logits, const Shape &logitsShape, const U *logitsLength, const U *labels, const U *labelsLength, const U *blankIndexP, const bool preprocessCollapseRepeated, const bool ctcMergeRepeated, const bool unique, T *output)#
-
template<typename T, typename P>
void cumsum(const T *arg, const P axis, T *out, const Shape &tensor_shape, const bool exclusive, const bool reverse)#
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template<typename T>
void deformable_convolution(const T *in, const T *offsets, const T *filters, const T *mask, T *out, const Shape &in_shape, const Shape &o_shape, const Shape &f_shape, const Shape &m_shape, const Shape &out_shape, const Strides &strides, const Strides &dilation, const CoordinateDiff &pads_begin, const CoordinateDiff &pads_end, const int64_t groups, const int64_t deformable_groups, const bool bilinear_interpolation_pad)#
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template<typename T>
void deformable_convolution(const T *in, const T *offsets, const T *filters, T *out, const Shape &in_shape, const Shape &o_shape, const Shape &f_shape, const Shape &out_shape, const Strides &strides, const Strides &dilation, const CoordinateDiff &pads_begin, const CoordinateDiff &pads_end, const int64_t groups, const int64_t deformable_groups, const bool bilinear_interpolation_pad = false)#
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template<typename T>
void deformable_psroi_pooling(const T *data_input, const Shape &data_input_shape, const T *rois_input, const Shape &rois_input_shape, const T *offsets_input, const Shape &offsets_input_shape, T *output, const Shape &output_shape, const std::string &mode_str, const float spatial_scale, const int64_t spatial_bins_x, const int64_t spatial_bins_y, const float trans_std, const int64_t part_size)#
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void depth_to_space(const char *const in, const Shape &in_shape, char *const out, const Shape &out_shape, const size_t block_size, const op::v0::DepthToSpace::DepthToSpaceMode mode, const size_t elem_size)#
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template<typename T>
std::enable_if<std::is_integral<T>::value>::type divide(const T *arg0, const T *arg1, T *out, size_t count, bool pythondiv)#
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template<typename T>
std::enable_if<std::is_integral<T>::value>::type divide(const T *arg0, const T *arg1, T *out, const Shape &arg0_shape, const Shape &arg1_shape, const op::AutoBroadcastSpec &broadcast_spec, bool pythondiv)#
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template<typename T>
std::enable_if<std::is_floating_point<T>::value || std::is_same<T, bfloat16>::value || std::is_same<T, float16>::value>::type divide(const T *arg0, const T *arg1, T *out, size_t count, bool pythondiv)#
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template<typename T>
std::enable_if<std::is_floating_point<T>::value || std::is_same<T, bfloat16>::value || std::is_same<T, float16>::value>::type divide(const T *arg0, const T *arg1, T *out, const Shape &arg0_shape, const Shape &arg1_shape, const op::AutoBroadcastSpec &broadcast_spec, bool pythondiv)#
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void einsum(ov::TensorVector &outputs, const ov::TensorVector &inputs, const std::string &equation)#
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template<typename T, typename U>
void embeddingBagOffsetsSum(const T *emb_table, const U *indices, const U *offsets, const U *default_index, const T *weights, T *out, const size_t indices_count, const Shape &outShape)#
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template<typename T, typename U>
void embeddingBagPackedSum(const T *emb_table, const U *indices, const T *weights, T *out, const Shape &indicesShape, const Shape &outShape)#
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template<typename T, typename U>
void embeddingSegmentsSum(const T *embTable, const U *indices, const U *segmentIds, const U *defaultIndex, const T *weights, T *out, const Shape &embTableShape, const Shape &indicesShape, const Shape &outShape)#
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template<typename T, typename U, typename std::enable_if<std::is_integral<T>::value>::type* = nullptr>
void equal(const T *arg0, const T *arg1, U *out, const Shape &arg0_shape, const Shape &arg1_shape, const op::AutoBroadcastSpec &broadcast_spec)# Reference implementation of binary elementwise Equal operator.
Used for integral types with custom
equalfunction (reduce binary size).Used for floating-point types to (avoid warning compare floating point with
==).- Parameters:
arg0 – Pointer to input 0 data.
arg1 – Pointer to input 1 data.
out – Pointer to output data.
arg_shape0 – Input 0 shape.
arg_shape1 – Input 1 shape.
broadcast_spec – Broadcast specification mode.
arg0 – Pointer to input 0 data.
arg1 – Pointer to input 1 data.
out – Pointer to output data.
arg_shape0 – Input 0 shape.
arg_shape1 – Input 1 shape.
broadcast_spec – Broadcast specification mode.
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template<class T>
void erf(const T *arg, T *out, const size_t count)# Reference implementation of Erf operator.
- Parameters:
arg – Pointer to input data.
out – Pointer to output data.
count – Number of elements in input buffer.
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template<class T>
void exp(const T *arg, T *out, size_t count)# Reference implementation of Exp operator.
- Parameters:
arg – Pointer to input data.
out – Pointer to output data.
count – Number of elements in input buffer.
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void experimental_detectron_detection_output(const float *input_rois, const float *input_deltas, const float *input_scores, const float *input_im_info, const op::v6::ExperimentalDetectronDetectionOutput::Attributes &attrs, float *output_boxes, float *output_scores, int32_t *output_classes)#
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void experimental_detectron_detection_output_postprocessing(void *pboxes, void *pclasses, void *pscores, const element::Type output_type, const std::vector<float> &output_boxes, const std::vector<int32_t> &output_classes, const std::vector<float> &output_scores, const Shape &output_boxes_shape, const Shape &output_classes_shape, const Shape &output_scores_shape)#
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template<typename T>
void experimental_detectron_prior_grid_generator(const T *priors, const Shape &priors_shape, const Shape &feature_map_shape, const Shape &im_data_shape, T *output_rois, int64_t grid_h, int64_t grid_w, float stride_h, float stride_w)#
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void experimental_detectron_proposals_single_image(const float *im_info, const float *anchors, const float *deltas, const float *scores, const op::v6::ExperimentalDetectronGenerateProposalsSingleImage::Attributes &attrs, const Shape &im_info_shape, const Shape &anchors_shape, const Shape &deltas_shape, const Shape &scores_shape, float *output_rois, float *output_scores)#
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void experimental_detectron_proposals_single_image_postprocessing(void *prois, void *pscores, const element::Type output_type, const std::vector<float> &output_rois, const std::vector<float> &output_scores, const Shape &output_rois_shape, const Shape &output_scores_shape)#
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void experimental_detectron_roi_feature_extractor(const std::vector<std::vector<float>> &inputs, const std::vector<Shape> &input_shapes, const op::v6::ExperimentalDetectronROIFeatureExtractor::Attributes &attrs, float *output_rois_features, float *output_rois)#
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void experimental_detectron_roi_feature_extractor_postprocessing(void *prois_features, void *prois, const element::Type output_type, const std::vector<float> &output_roi_features, const std::vector<float> &output_rois, const Shape &output_roi_features_shape, const Shape &output_rois_shape)#
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template<typename T>
void experimental_detectron_topk_rois(const T *input_rois, const T *input_probs, const Shape &input_rois_shape, const Shape &input_probs_shape, size_t max_rois, T *output_rois)#
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template<typename T>
void eye(T *data, const Shape &out_shape, const int64_t diagonal_index)# Reference implementation of Eye operator.
- Parameters:
data – Pointer to output data.
out_shape – Output data size.
diagonal_index – Eye diagonal index to populate matrix with ones
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template<typename T, typename std::enable_if<std::is_same<T, float16>::value, bool>::type = true>
void fake_convert(const T *data, const T *scale, const T *shift, T *out, const Shape &data_shape, const Shape &scale_shape, const Shape &shift_shape, const element::Type &destination_type)# Reference implementation of the FakeConvert operation specialized for float16 type. It emulates format specified by “destination_type” on the input type. FakeConvert performs element-wise quantization of floating-point input values into a set of values corresponding to a target low-precision floating-point type.
Reference implementation of the FakeConvert operation for floating-point types, with middle conversion to float16. It emulates format specified by “destination_type” on the input type. FakeConvert performs element-wise quantization of floating-point input values into a set of values corresponding to a target low-precision floating-point type.
- Parameters:
data – Pointer to the input data.
scale – Pointer to the input scale.
shift – Pointer to the input shift.
out – Pointer to the output data.
data_shape – Shape of the input data.
scale_shape – Shape of the input scale.
shift_shape – Shape of the input shift.
destination_type – Name of the destination type.
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template<typename T>
void fake_convert(const T *data, const T *scale, T *out, const Shape &data_shape, const Shape &scale_shape, const element::Type &destination_type)# Reference implementation of the FakeConvert operation, with default
shiftinput.
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template<typename T>
void fake_quantize(const T *arg, const T *in_low, const T *in_high, const T *out_low, const T *out_high, T *out, const Shape &arg_shape, const Shape &in_low_shape, const Shape &in_high_shape, const Shape &out_low_shape, const Shape &out_high_shape, size_t levels, const op::AutoBroadcastSpec &broadcast)#
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void fft(const float *input_data, const Shape &input_data_shape, const int64_t *axes_data, const Shape &axes_data_shape, float *fft_result, const Shape &output_shape, FFTKind fft_kind)#
-
void fft_postprocessing(ov::TensorVector &outputs, const ov::element::Type output_type, const std::vector<float> &fft_result)#
-
std::vector<int64_t> canonicalize_axes(const int64_t *axes_data, const Shape &axes_data_shape, int64_t complex_data_rank)#
-
template<class T, typename std::enable_if<std::is_integral<T>::value>::type* = nullptr>
void floor(const T *arg, T *out, const size_t count)# Reference implementation of Floor operator (integral types).
Reference implementation of Floor operator (floating point types).
-
template<typename T>
void floor_mod(const T *arg0, const T *arg1, T *out, const Shape &arg0_shape, const Shape &arg1_shape, const op::AutoBroadcastSpec &broadcast_spec)#
-
template<typename T, typename U>
void gather(const T *const data, const U *const indices, T *out, const Shape &data_shape, const Shape &indices_shape, const Shape &out_shape, size_t axis, size_t batch_dims = 0)#
-
template<typename T, typename U>
void gather_elements(const T *data, const U *indices, T *out, const Shape &data_shape, const Shape &indices_shape, const Shape &out_shape, int64_t axis)#
-
template<typename T, typename U>
void gather_nd(const T *const params, const U *const indices, T *const out, const Shape ¶ms_shape, const Shape &indices_shape, const Shape &out_shape, const int batch_dims = 0)# Implementation find maximum length of slice of input params which might be copied to out index by index. +—-—+———–—+—-—+ | batch | indices[:-1] | slice | | shape | shape | shape | +—-—+———–—+—-—+
-
void gather_tree(const char *step_ids, const char *parent_ids, const char *max_seq_len, const char *end_token, char *out, const Shape &step_ids_shape, const Shape &parent_ids_shape, const Shape &max_seq_len_shape, const Shape &end_token_shape, const element::Type &type)#
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template<typename T>
void gelu(const T *arg, T *out, op::GeluApproximationMode mode, size_t count)#
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void generate_proposals(const std::vector<float> &im_info, const std::vector<float> &anchors, const std::vector<float> &deltas, const std::vector<float> &scores, const op::v9::GenerateProposals::Attributes &attrs, const Shape &im_info_shape, const Shape &anchors_shape, const Shape &deltas_shape, const Shape &scores_shape, std::vector<float> &output_rois, std::vector<float> &output_scores, std::vector<int64_t> &num_rois)#
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void generate_proposals_postprocessing(void *prois, void *pscores, void *proi_num, const element::Type &output_type, const element::Type &roi_num_type, const std::vector<float> &output_rois, const std::vector<float> &output_scores, const std::vector<int64_t> &num_rois, const Shape &output_rois_shape, const Shape &output_scores_shape)#
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template<typename T, typename U>
void greater(const T *arg0, const T *arg1, U *out, const Shape &arg0_shape, const Shape &arg1_shape, const op::AutoBroadcastSpec &broadcast_spec)# Reference implementation of binary elementwise Greater operator.
-
template<typename T, typename U>
void greater_eq(const T *arg0, const T *arg1, U *out, const Shape &arg0_shape, const Shape &arg1_shape, const op::AutoBroadcastSpec &broadcast_spec)#
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template<typename DATA_ET, typename GRID_ET>
void grid_sample(DATA_ET *output, const DATA_ET *data, const GRID_ET *grid, const Shape &data_shape, const Shape &grid_shape, const bool align_corners, const ov::op::v9::GridSample::InterpolationMode interpolation_mode, const ov::op::v9::GridSample::PaddingMode padding_mode)#
-
void validate_group_convolution_parameters(const Shape &in_shape, const Shape &f_shape, const Shape &out_shape, const Strides &strides, const Strides &dilations, const CoordinateDiff &pads_begin, const CoordinateDiff &pads_end)#
-
template<typename INPUT, typename FILTER, typename OUTPUT, typename ACCU = typename widen<OUTPUT>::type>
void group_convolution(const INPUT *in, const FILTER *f, OUTPUT *out, const Shape &in_shape, const Shape &filter_shape, const Shape &out_shape, const Strides &strides, const Strides &dilation, const CoordinateDiff &pads_begin, const CoordinateDiff &pads_end)#
-
void infer_backward_conv_output_shape(const Shape &in_spatial_shape, const Shape &f_spatial_shape, Shape &out_spatial_shape, const Strides &strides, const Strides &dilations, const CoordinateDiff &pads_begin, const CoordinateDiff &pads_end)#
-
void validate_convolution_backprop_data_parameters(const Shape &in_shape, const Shape &f_shape, const Shape &out_shape, const Strides &strides, const Strides &dilations, const CoordinateDiff &pads_begin, const CoordinateDiff &pads_end)#
-
void validate_group_convolution_backprop_data_parameters(const Shape &in_shape, const Shape &f_shape, const Shape &out_shape, const Strides &strides, const Strides &dilations, const CoordinateDiff &pads_begin, const CoordinateDiff &pads_end)#
-
template<typename T>
void group_convolution_backprop_data(const T *in, const T *f, T *out, const Shape &in_shape, const Shape &filter_shape, const Shape &out_shape, const Strides &strides, const Strides &dilation, const CoordinateDiff &pads_begin, const CoordinateDiff &pads_end, const CoordinateDiff &output_padding)#
-
template<typename T>
void group_normalization(const T *const data, const T *const scale, const T *const bias, T *const out, const Shape &data_shape, const size_t num_groups, const double epsilon)#
-
template<typename T>
void gru_cell(const T *X, const Shape &X_shape, const T *H, const Shape &H_shape, const T *W, const Shape &W_shape, const T *R, const Shape &R_shape, const T *B, const Shape &B_shape, T *dst_data, const std::string &activation_f, const std::string &activation_g, float clip, bool linear_before_reset, const T *A = nullptr)#
-
template<typename T>
void hard_sigmoid(const T *arg, const T alpha, const T beta, T *out, size_t count)#
-
inline void pad_input_data(const uint8_t *data_ptr, uint8_t *padded_data_ptr, size_t type_size, const ov::Shape &input_shape, const ov::Shape &padded_input_shape, const std::vector<size_t> &pads_begin)#
-
inline PartialShape get_padded_input_shape(const PartialShape &input_shape, const op::v0::Interpolate::Attributes &attrs)#
-
inline std::vector<float> get_scales(const PartialShape &input_data_partial_shape, const Shape &out_shape, const op::v0::Interpolate::Attributes &attrs)#
-
inline op::v4::Interpolate::InterpolateAttrs transform_v0_to_v4(const PartialShape &input_partial_shape, const op::v0::Interpolate::Attributes &attrs_v0)#
-
template<typename T>
void interpolate(const T *input_data, const Shape &input_data_shape, const std::vector<float> &scales, const std::vector<int64_t> &axes, T *out, const Shape &out_shape, const op::v4::Interpolate::InterpolateAttrs &attrs)#
-
template<typename T>
void interpolate(T *input_data, const PartialShape &input_data_shape, T *out, const Shape &out_shape, const op::v0::Interpolate::Attributes &attrs)#
-
template<typename T>
void inverse(const T *input, T *output, const Shape &shape, const bool adjoint)# Inverse operation computes the inverse of the input tensor.
-
void irdft(const std::vector<float> &input_data, const Shape &input_data_shape, const std::vector<int64_t> &axes_data, float *irdft_result, const Shape &fft_output_shape, const Shape &irdft_output_shape, const int64_t last_signal_size)#
-
template<typename T, typename U>
std::enable_if<std::is_floating_point<T>::value, void>::type is_finite(const T *input, U *output, size_t count)#
-
template<typename T, typename U>
std::enable_if<std::is_class<T>::value, void>::type is_finite(const T *input, U *output, size_t count)#
-
template<typename T, typename U>
std::enable_if<std::is_floating_point<T>::value, void>::type is_inf(const T *input, U *output, size_t count, const ov::op::v10::IsInf::Attributes &attributes)#
-
template<typename T, typename U>
std::enable_if<std::is_class<T>::value, void>::type is_inf(const T *input, U *output, size_t count, const ov::op::v10::IsInf::Attributes &attributes)#
-
template<typename T, typename U>
std::enable_if<std::is_floating_point<T>::value, void>::type is_nan(const T *input, U *output, size_t count)#
-
template<typename T, typename U>
std::enable_if<std::is_class<T>::value, void>::type is_nan(const T *input, U *output, size_t count)#
-
template<typename T, typename U>
void less(const T *arg0, const T *arg1, U *out, const Shape &arg0_shape, const Shape &arg1_shape, const op::AutoBroadcastSpec &broadcast_spec)# Reference implementation of binary elementwise Less operator.
-
template<typename T, typename U>
void less_eq(const T *arg0, const T *arg1, U *out, const Shape &arg0_shape, const Shape &arg1_shape, const op::AutoBroadcastSpec &broadcast_spec)# Reference implementation of binary elementwise LessEqual operator.
-
template<typename T>
void log_softmax(const T *arg, T *out, const Shape &shape, const AxisSet &axes)#
-
template<class T>
void logical_not(const T *arg, T *out, const size_t count)# Reference implementation of LogicalNot operator.
- Parameters:
arg – Pointer to input data.
out – Pointer to output data.
count – Number of elements in input buffer.
-
static inline void reduce_logical_and(const char *arg, char *out, const Shape &in_shape, const AxisSet &reduction_axes)#
Reference implementation of ReduceLogicalAnd operator.
-
static inline void reduce_logical_or(const char *arg, char *out, const Shape &in_shape, const AxisSet &reduction_axes)#
Reference implementation of ReduceLogicalOr operator.
-
static std::vector<size_t> slice_indices(const Shape &full_shape, const std::vector<size_t> &begin, const Shape &slice_shape)#
-
template<typename T>
static T sum_region_across_axes(const T *arg, const std::vector<size_t> &indices)#
-
template<typename T>
void lrn(const T *arg, const AxisSet &axes, T *out, const Shape &arg_shape, double dalpha, double dbeta, double dbias, size_t size)#
-
template<typename T>
void lstm_cell(const T *X, const Shape &X_shape, const T *H, const Shape &H_shape, const T *C, const Shape &C_shape, const T *W, const Shape &W_shape, const T *R, const Shape &R_shape, const T *B, const Shape &B_shape, T *out_Ht, T *out_Ct, const std::string &activation_f, const std::string &activation_g, const std::string &activation_h, float clip)#
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template<typename T>
void lstm_cell_v1(const T *X, const Shape &X_shape, const T *H, const Shape &H_shape, const T *C, const Shape &C_shape, const T *W, const Shape &W_shape, const T *R, const Shape &R_shape, const T *B, const Shape &B_shape, const T *P, const Shape &P_shape, T *out_Ht, T *out_Ct, const std::string &activation_f, const std::string &activation_g, const std::string &activation_h, float clip, const ov::op::LSTMWeightsFormat weight_format, bool input_forget)#
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template<typename T>
void matmul(const T *arg0, const T *arg1, T *out, const Shape &arg0_shape, const Shape &arg1_shape, const Shape &out_shape, bool transpose_arg0, bool transpose_arg1)# Reference kernel for matmul computation.
- Template Parameters:
T – Type of input and output tensors.
- Parameters:
arg0 – Pointer to the buffer for left operand input tensor.
arg1 – Pointer to the buffer for right operand input tensor.
out – Pointer to the buffer for output tensor. This must be pre-allocated by the caller, and must be large enough to hold a tensor of the correct shape.
arg0_shape – Shape of arg0.
arg1_shape – Shape of arg1.
out_shape – Shape of out.
transpose_arg0 – Flag to indicate if transpose on arg0.
transpose_arg1 – Flag to indicate if transpose on arg1.
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void matrix_nms(const float *boxes_data, const Shape &boxes_data_shape, const float *scores_data, const Shape &scores_data_shape, const op::v8::MatrixNms::Attributes &attrs, float *selected_outputs, const Shape &selected_outputs_shape, int64_t *selected_indices, const Shape &selected_indices_shape, int64_t *valid_outputs)#
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template<typename Values_t, typename Indices_t>
void max_pool(const Values_t *data, Values_t *values, Indices_t *indices, const Shape &data_shape, const Shape &out_shape, const Shape &kernel, const Strides &strides, const Strides &dilations, const Shape &pads_begin, const Shape &pads_end, const int64_t axis = 0)#
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template<typename Value_t>
void max_pool(const Value_t *data, Value_t *values, const Shape &data_shape, const Shape &out_shape, const Shape &kernel, const Strides &strides, const Shape &pads_begin, const Shape &pads_end)#
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template<typename T>
void maximum(const T *arg0, const T *arg1, T *out, const Shape &arg0_shape, const Shape &arg1_shape, const op::AutoBroadcastSpec &broadcast_spec)# Reference implementation of binary elementwise Maximum operator.
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template<typename T>
void minimum(const T *arg0, const T *arg1, T *out, const Shape &arg0_shape, const Shape &arg1_shape, const op::AutoBroadcastSpec &broadcast_spec)#
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template<typename T>
void mish(const T *arg, T *out, const size_t count)# Reference implementation of Mish operator.
- Parameters:
arg – Pointer to input data.
out – Pointer to output data.
count – Number of elements in input buffer.
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template<class InputIt, class OutputIt>
void mod(InputIt arg0, InputIt arg1, OutputIt out, const Shape &arg_shape0, const Shape &arg_shape1, const op::AutoBroadcastSpec &broadcast_spec)# Reference implementation of binary elementwise Mod operator.
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void multiclass_nms(const float *boxes_data, const Shape &boxes_data_shape, const float *scores_data, const Shape &scores_data_shape, const int64_t *roisnum_data, const Shape &roisnum_data_shape, const op::util::MulticlassNmsBase::Attributes &attrs, float *selected_outputs, const Shape &selected_outputs_shape, int64_t *selected_indices, const Shape &selected_indices_shape, int64_t *valid_outputs)#
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template<typename T>
void multiply(const T *arg0, const T *arg1, T *out, const Shape &arg0_shape, const Shape &arg1_shape, const op::AutoBroadcastSpec &broadcast_spec)# Reference implementation of binary elementwise Multiply operator.
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template<typename T>
void mvn(const T *arg, T *out, const Shape &in_shape, const bool normalize_variance, const AxisSet &reduction_axes, const double eps)#
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template<typename T>
void mvn_6(const T *arg, T *out, const Shape &in_shape, AxisSet reduction_axes, bool normalize_variance, double eps, op::MVNEpsMode eps_mode)#
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template<typename T>
void negate(const T *arg, T *out, const size_t count)# Reference implementation of Negative operator.
- Parameters:
arg – Pointer to input data.
out – Pointer to output data.
count – Number of elements in input buffer.
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void nms_rotated(const float *boxes_data, const Shape &boxes_data_shape, const float *scores_data, const Shape &scores_data_shape, int64_t max_output_boxes_per_class, float iou_threshold, float score_threshold, float soft_nms_sigma, int64_t *selected_indices, const Shape &selected_indices_shape, float *selected_scores, const Shape &selected_scores_shape, int64_t *valid_outputs, bool sort_result_descending, bool clockwise = true)#
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void non_max_suppression5(const float *boxes_data, const Shape &boxes_data_shape, const float *scores_data, const Shape &scores_data_shape, int64_t max_output_boxes_per_class, float iou_threshold, float score_threshold, float soft_nms_sigma, int64_t *selected_indices, const Shape &selected_indices_shape, float *selected_scores, const Shape &selected_scores_shape, int64_t *valid_outputs, const bool sort_result_descending)#
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void nms5_postprocessing(ov::TensorVector &outputs, const element::Type output_type, const std::vector<int64_t> &selected_indices, const std::vector<float> &selected_scores, int64_t valid_outputs, const element::Type selected_scores_type)#
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void non_max_suppression(const float *boxes_data, const Shape &boxes_data_shape, const float *scores_data, const Shape &scores_data_shape, int64_t max_output_boxes_per_class, float iou_threshold, float score_threshold, float soft_nms_sigma, int64_t *selected_indices, const Shape &selected_indices_shape, float *selected_scores, const Shape &selected_scores_shape, int64_t *valid_outputs, const bool sort_result_descending)#
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void nms_postprocessing(ov::TensorVector &outputs, const element::Type output_type, const std::vector<int64_t> &selected_indices, const std::vector<float> &selected_scores, int64_t valid_outputs, const element::Type selected_scores_type)#
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template<typename T>
size_t non_zero_get_count(const T *arg, const Shape &arg_shape)# Return number of non-zero entries in the input argument.
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template<typename T, typename U>
void non_zero(const T *arg, U *out, const Shape &arg_shape)# Return indices of non-zero entries in input argument.
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template<typename T>
void normalize_l2(const T *data, T *out, const Shape &data_shape, const AxisSet &reduction_axes, float eps, op::EpsMode eps_mode)#
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template<typename T, typename U>
void not_equal(const T *arg0, const T *arg1, U *out, const Shape &arg0_shape, const Shape &arg1_shape, const op::AutoBroadcastSpec &broadcast_spec)#
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template<typename INPUT_TYPE>
void one_hot(const INPUT_TYPE *indices, const Shape &indices_shape, char *out, const size_t out_elem_size, const size_t depth, const int64_t one_hot_axis, const char *on_value, const char *off_value)#
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template<class T>
void logical_or(const T *arg0, const T *arg1, T *out, const Shape &arg0_shape, const Shape &arg1_shape, const op::AutoBroadcastSpec &broadcast_spec)# Reference implementation of binary elementwise LogicalOr operator.
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void pad(const char *data, const char *pad_value, char *out, const size_t elem_size, const Shape &data_shape, const Shape &out_shape, const CoordinateDiff &padding_below, const CoordinateDiff &padding_above, const op::PadMode pad_mode)#
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template<typename T>
void power(const T *arg0, const T *arg1, T *out, const Shape &arg0_shape, const Shape &arg1_shape, const op::AutoBroadcastSpec &broadcast_spec)# Reference implementation of binary elementwise Power operator.
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template<typename T>
void prelu(const T *arg, const T *slope, T *out, const Shape &arg_shape, const Shape &slope_shape)#
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static inline float clip_great(float x, float threshold)#
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static inline float clip_less(float x, float threshold)#
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template<typename T>
void prior_box(const T *data, const T *img, float *dst_data, const Shape &out_shape, const op::v8::PriorBox::Attributes &attrs)#
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template<typename T>
void prior_box_clustered(const T *data, const T *img, float *dst_data, const Shape &out_shape, const op::v0::PriorBoxClustered::Attributes &attrs)#
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template<typename T>
void proposal_v0(const T *class_probs, const T *bbox_deltas, const T *image_shape, T *output, const Shape &class_probs_shape, const Shape &bbox_deltas_shape, const Shape &image_shape_shape, const Shape &output_shape, const op::v0::Proposal::Attributes &attrs)#
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template<typename T>
void proposal_v4(const T *class_probs, const T *bbox_deltas, const T *image_shape, T *output, T *out_probs, const Shape &class_probs_shape, const Shape &bbox_deltas_shape, const Shape &image_shape_shape, const Shape &output_shape, const Shape &out_probs_shape, const op::v0::Proposal::Attributes &attrs)#
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template<typename T>
void psroi_pooling(const T *input, const Shape &input_shape, const T *rois, const Shape &rois_shape, T *output, const Shape &output_shape, const std::string &mode_str, float spatial_scale, int spatial_bins_x, int spatial_bins_y)#
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std::pair<uint64_t, uint64_t> random_uniform(const uint64_t *out_shape, const char *min_val, const char *max_val, char *out, const Shape &out_shape_shape, const element::Type &elem_type, uint64_t seed, uint64_t seed2, std::pair<uint64_t, uint64_t> prev_state)#
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template<typename T>
std::enable_if<ov::is_floating_point<T>()>::type range(const T start, const T step, const size_t num_elem, T *out)# Reference implementation for Range operator (floating-point types).
- Parameters:
start – Start value.
step – Step is difference value for consecutive values.
num_elem – Number of elements to generate
out – Pointer to output data.
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template<typename T>
std::enable_if<std::is_integral<T>::value>::type range(const T start, const T step, const size_t num_elem, T *out)# Reference implementation for Range operator (integral types).
- Parameters:
start – Start value.
step – Step is difference value for consecutive values.
num_elem – Number of elements to generate
out – Pointer to output data.
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void rdft(const std::vector<float> &input_data, const Shape &input_data_shape, const std::vector<int64_t> &axes_data, const Shape &output_fft_shape, float *rdft_result)#
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template<class InputIt, class OutputIt>
void reduce_l1(InputIt in, OutputIt out, const Shape &in_shape, const AxisSet &reduction_axes)# Reference implementation of ReduceL1 operator.
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template<class InputIt, class OutputIt>
void reduce_l2(InputIt in, OutputIt out, const Shape &in_shape, const AxisSet &reduction_axes)# Reference implementation of ReduceL2 operator.
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template<class T>
void reduce_max(const T *in, T *out, const Shape &in_shape, const AxisSet &reduction_axes)# Reference implementation of ReduceMax operator.
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template<class T>
void reduce_mean(const T *in, T *out, const Shape &in_shape, const AxisSet &reduction_axes)# Reference implementation of ReduceMean operator.
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template<class T>
void reduce_min(const T *in, T *out, const Shape &in_shape, const AxisSet &reduction_axes)# Reference implementation of ReduceMin operator.
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template<typename T>
void reduce_prod(const T *arg, T *out, const Shape &in_shape, const AxisSet &reduction_axes)# Reference implementation of ReduceProduct operator.
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template<typename T>
void reduce_sum(const T *in, T *out, const Shape &in_shape, const AxisSet &reduction_axes)# Reference implementation of ReduceSum operator.
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static inline int entry_index(int width, int height, int coords, int classes, int outputs, int batch, int location, int entry)#
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template<typename T>
static inline void softmax_generic(const T *src_data, T *dst_data, int batches, int channels, int height, int width)#
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template<typename T>
void region_yolo(const T *input, T *output, const Shape &input_shape, const int coords, const int classes, const int regions, const bool do_softmax, const std::vector<int64_t> &mask)#
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template<class T, typename std::enable_if<ov::is_floating_point<T>() || std::is_signed<T>::value>::type* = nullptr>
void relu(const T *arg, T *out, const size_t count)# Reference implementation of ReLU operator (signed values).
Reference implementation of ReLU operator (unsigned).
- Parameters:
arg – Pointer to input data.
out – Pointer to output data.
count – Number of elements in input buffer.
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void reorg_yolo(const char *arg, char *out, const Shape &in_shape, int64_t stride, const size_t elem_size)#
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inline void reshape(const char *in, char *out, const Shape &in_shape, size_t elem_size)#
Basic reshape operation, without axes reorder.
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inline void reshape(const std::string *in, std::string *out, const Shape &in_shape)#
Basic reshape operation for string element type. Input data are simply copied to the output. Target shape should be set on the Tensor.
- Parameters:
in – Pointer to input data.
out – Pointer to output data.
in_shape – Input data shape.
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void reshape(const char *in, char *out, const Shape &in_shape, const AxisVector &axes_order, const Shape &out_shape, size_t elem_size)#
Permutes data shape and axes.
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void reverse(const char *arg, char *out, const Shape &arg_shape, const Shape &out_shape, const AxisSet &reversed_axes, size_t elem_size)#
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template<typename T, typename U>
void reverse_sequence(const T *arg, T *out, const Shape &arg_shape, size_t batch_axis, size_t sequence_axis, const U *sequence_lengths)#
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template<typename T>
void rnn_cell(const T *X, const Shape &X_shape, const T *H, const Shape &H_shape, const T *W, const Shape &W_shape, const T *R, const Shape &R_shape, const T *B, const Shape &B_shape, T *dst_data, const std::string &activation_f, float clip)#
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template<typename T, template<typename> class TROIPolicy = roi_policy::ROIAlignOpDefPolicy>
void roi_align(const T *feature_maps, const T *rois, const int64_t *batch_indices, T *out, const Shape &feature_maps_shape, const Shape &rois_shape, const Shape &batch_indices_shape, const Shape &out_shape, const int pooled_height, const int pooled_width, const int sampling_ratio, const float spatial_scale, const ROIPoolingMode &pooling_mode, const AlignedMode &aligned_mode = AlignedMode::ASYMMETRIC, bool clockwise = true)#
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template<typename T>
void roi_pooling(const T *feature_maps, const T *rois, T *output, const Shape &feature_maps_shape, const Shape &rois_shape, const Shape &output_shape, const float spatial_scale, const std::string &pooling_method)#
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size_t shift_pos(size_t pos_in_spanned_data, size_t dim_shift, size_t spanned_shape_size, size_t dim_size)#
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void roll(const char *arg, const int64_t *shift, const int64_t *axes, char *out, const Shape &arg_shape, const Shape &shift_shape, const Shape &axes_shape, size_t elem_size)#
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template<typename T>
T round_to_nearest_even(T value)# Rounding algorithm for ov::op::v5::Round::RoundMode::HALF_TO_EVEN.
- Template Parameters:
T – Value type.
- Parameters:
value – Value for rounding.
- Returns:
Rounded value.
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template<typename T>
T round_half_away_zero(T value)# Rounding algorithm for ov::op::v5::Round::RoundMode::HALF_AWAY_FROM_ZERO.
- Template Parameters:
T – Value type.
- Parameters:
value – Value for rounding.
- Returns:
Rounded value.
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template<typename T, typename std::enable_if<ov::is_floating_point<T>()>::type* = nullptr>
void round(const T *arg, T *out, const size_t count, const op::v5::Round::RoundMode mode)# Reference implementation of Round operator.
Used when T is OpenVINO floating type.
Used when T is OpenVINO integral type.
- Parameters:
arg – Input buffer pointer with data to round.
out – Output buffer pointer with rounded results.
count – Number of elements in input tensor.
mode – Rounding mode.
arg – Input buffer pointer with data to round.
out – Output buffer pointer with rounded results.
count – Number of elements in input tensor.
mode – Rounding mode.
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template<typename T>
T reduction_neutral_value(const ov::op::v12::ScatterElementsUpdate::Reduction reduction_type)#
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template<typename T>
std::function<T(const T, const T)> reduction_functor_for(const ov::op::v12::ScatterElementsUpdate::Reduction reduction_type)#
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template<>
std::function<char(const char, const char)> reduction_functor_for<char>(const ov::op::v12::ScatterElementsUpdate::Reduction reduction_type)#
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template<typename T>
std::enable_if<std::is_floating_point<T>::value || std::is_class<T>::value, T>::type arithmetic_mean(const T accumulator, const int32_t N)#
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template<typename T>
std::enable_if<std::is_integral<T>::value, T>::type arithmetic_mean(const T accumulator, const int32_t N)#
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template<typename DataType>
void scatter_elem_update_with_reduction(const int64_t *indices, const DataType *updates, const int64_t axis, DataType *out_buf, const Shape &data_shape, const Shape &indices_shape, const ov::op::v12::ScatterElementsUpdate::Reduction reduction_type, const bool use_init_val)#
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template<typename InType, typename OutType>
const OutType *convert_indices(const InType *indices, const size_t indices_count, std::vector<OutType> &buffer)#
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template<typename DataType, typename IndicesType>
void scatter_elem_update(const DataType *input_data, const IndicesType *indices, const DataType *updates, const int64_t axis, DataType *out_buf, const Shape &data_shape, const Shape &indices_shape, const ov::op::v12::ScatterElementsUpdate::Reduction reduction_type = ov::op::v12::ScatterElementsUpdate::Reduction::NONE, const bool use_init_val = true)#
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template<typename dataType, typename indicesType>
void scatterNdUpdate(const dataType *const inputData, const indicesType *const indices, const dataType *const updates, dataType *const outBuf, const Shape &dataShape, const Shape &indicesShape, const Shape &updatesShape, const ov::op::v15::ScatterNDUpdate::Reduction reduction_type = ov::op::v15::ScatterNDUpdate::Reduction::NONE)#
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static const CoordinateTransformBasic get_target_shape(const Shape &data_shape, const Coordinate &start_corner, const Coordinate &end_corner)#
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static void scatter_update(const char *input_data, const int64_t *indices, const char *updates, const int64_t axis, char *out_buf, const size_t elem_size, const Shape &data_shape, const Shape &indices_shape, const Shape &updates_shape)#
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template<typename T>
void select(const char *arg0, const T *arg1, const T *arg2, T *out, size_t arg0_count, size_t arg1_count, size_t arg2_count, size_t out_count)#
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template<typename T>
void select(const char *arg0, const T *arg1, const T *arg2, T *out, const Shape &arg0_shape, const Shape &arg1_shape, const Shape &arg2_shape, const op::AutoBroadcastSpec &broadcast_spec)#
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template<typename T>
void selu(const T *arg, const T *alpha, const T *lambda, T *out, size_t size_arg, size_t size_alpha, size_t size_lambda)#
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template<typename T, typename U>
void cell_pass(CellType type, const std::vector<const char*> &inputs, const std::vector<Shape> &shapes, const std::vector<char*> &outputs, const CellArgs &args, bool is_reverse)#
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template<typename T, typename U>
void lstm_sequence(const char *X, const Shape &X_shape, const char *H, const Shape &H_shape, const char *C, const Shape &C_shape, const char *seq_lengths, const Shape &seq_lengths_shape, const char *W, const Shape &W_shape, const char *R, const Shape &R_shape, const char *B, const Shape &B_shape, char *Y, char *Ho, char *Co, const std::string &activation_f, const std::string &activation_g, const std::string &activation_h, float clip, op::RecurrentSequenceDirection direction)#
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template<typename T, typename U>
void lstm_sequence_v1(const char *X, const Shape &X_shape, const char *H, const Shape &H_shape, const char *C, const Shape &C_shape, const char *seq_lengths, const Shape &seq_lengths_shape, const char *W, const Shape &W_shape, const char *R, const Shape &R_shape, const char *B, const Shape &B_shape, const char *P, const Shape &P_shape, char *Y, char *Ho, char *Co, const std::string &activation_f, const std::string &activation_g, const std::string &activation_h, float clip, const ov::op::LSTMWeightsFormat weight_format, bool input_forget, op::RecurrentSequenceDirection direction)#
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template<typename T, typename U>
void gru_sequence(const char *X, const Shape &X_shape, const char *H, const Shape &H_shape, const char *seq_lengths, const Shape &seq_lengths_shape, const char *W, const Shape &W_shape, const char *R, const Shape &R_shape, const char *B, const Shape &B_shape, char *Y, char *Ho, const std::string &activation_f, const std::string &activation_g, const float clip, const op::RecurrentSequenceDirection direction, const bool linear_before_reset, const char *A = nullptr)#
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template<typename T, typename U>
void rnn_sequence(const char *X, const Shape &X_shape, const char *H, const Shape &H_shape, const char *seq_lengths, const Shape &seq_lengths_shape, const char *W, const Shape &W_shape, const char *R, const Shape &R_shape, const char *B, const Shape &B_shape, char *Y, char *Ho, const std::string &activation_f, float clip, const op::RecurrentSequenceDirection direction)#
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void shuffle_channels(const char *arg, char *out, const Shape &data_shape, size_t elem_size, const int64_t axis, const int64_t group)#
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template<typename T>
void sign(const T *arg, T *out, size_t count)# Reference implementation of Sign operator.
- Parameters:
arg – Pointer to input data.
out – Pointer to output data.
count – Number of elements in input buffer.
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template<typename T>
void sin(const T *arg, T *out, const size_t count)# Reference implementation of Sin operator.
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template<class T>
void sinh(const T *arg, T *out, size_t count)# Reference implementation of Sinh operator.
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void slice(const char *data, const Shape &data_shape, char *out, const Shape &out_shape, size_t elem_size, const std::vector<int64_t> &starts, const std::vector<int64_t> &steps, const std::vector<int64_t> &axes)#
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void slice(const char *arg, char *out, const Shape &arg_shape, const Coordinate &lower_bounds, const Coordinate &upper_bounds, const Strides &strides, const Shape &out_shape, size_t elem_size)#
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void space_to_depth(const char *const in, const Shape &in_shape, char *const out, const Shape &out_shape, const size_t block_size, const op::v0::SpaceToDepth::SpaceToDepthMode mode, const size_t elem_size)#
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void split(const char *data, const Shape &data_shape, size_t elem_size, int64_t axis, size_t num_splits, char **out_data)#
Reference implementation of Split operator.
- Parameters:
data – Pointer to input data.
data_shape – Input data shape.
elem_size – Size of single element type.
axis – Axis used for split input data.
num_splits – Number of splits
out_data – Pointer to output data pointers (must have size of num_splits)
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template<class T>
void sqrt(const T *arg, T *out, const size_t count)# Reference implementation of Sqrt operator.
- Parameters:
arg – Pointer to input data.
out – Pointer to output data.
count – Number of elements in input buffer.
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template<typename T>
void squared_difference(const T *arg0, const T *arg1, T *out, const Shape &arg0_shape, const Shape &arg1_shape, const op::AutoBroadcastSpec &broadcast_spec)#
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void strided_slice(const char *arg, char *out, const Shape &arg_shape, const op::util::SlicePlan &sp, size_t elem_type)#
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template<class T>
void subtract(const T *arg0, const T *arg1, T *out, const Shape &arg0_shape, const Shape &arg1_shape, const op::AutoBroadcastSpec &broadcast_spec)# Reference implementation of binary elementwise Subtract operator.
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template<class T>
void swish(const T *arg, const T beta, T *out, const size_t count)# Reference implementation of Swish operator.
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template<class T>
void tan(const T *arg, T *out, const size_t count)# Reference implementation of Tan operator.
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template<class T>
void tanh(const T *arg, T *out, const size_t count)# Reference implementation of Tanh operator.
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void tile(const char *arg, char *out, const Shape &in_shape, const Shape &out_shape, const size_t elem_size, const std::vector<int64_t> &repeats)#
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template<bool D, typename T, typename U>
inline bool compare_max(const std::tuple<T, U> &a, const std::tuple<T, U> &b)#
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template<typename T, typename U>
inline bool compare_indices_ascending(const std::tuple<T, U> &a, const std::tuple<T, U> &b)#
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template<typename T, typename U, typename std::enable_if<std::is_same<typename std::decay<U>::type, int64_t>::value>::type* = nullptr>
void topk(const T *arg, U *out_indices, T *out_values, const Shape &in_shape, const Shape &out_shape, const size_t axis, const size_t k, const bool compute_max, const op::TopKSortType sort = op::TopKSortType::NONE)# Reference implementation for TopK operator.
- Parameters:
arg – Pointer to input data.
out_indices – Pointer to output indicies.
out_values – Pointer to output values.
in_shape – Input data shape.
out_shape – Output data (values, indicies) shape.
axis – Axis for search of top K elements.
k – Number to find of top elements.
compute_max – Select mode of find max or min.
sort – Sorting type.
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void transpose(const char *data, char *out, const Shape &data_shape, size_t element_size, const std::vector<int64_t> &axes_order, const Shape &out_shape)#
Reference implementation of Transpose operator.
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template<typename Data_t, typename Index_t, typename Count_t = int64_t>
UniqueElements<Index_t, Count_t> find_unique_elements(const Data_t *data, const Shape &data_shape, std::unique_ptr<int64_t> axis, const bool sorted)#
-
template<typename Index_t, typename Count_t = int64_t>
std::tuple<Shape, Shape, Shape> make_tensor_shapes(const UniqueElements<Index_t, Count_t> &unique_elements, const Shape &data_shape, std::unique_ptr<int64_t> axis)#
-
template<typename Data_t, typename Index_t, typename Count_t = int64_t>
void unique(Data_t *out_unique_elements, Index_t *out_indices, Index_t *out_rev_indices, Count_t *out_counts, const Data_t *data, const Shape &data_shape, const Shape &out_shape, const UniqueElements<Index_t, Count_t> &descriptors)#
-
template<typename Iterator, typename value = typename details::from_iterator<Iterator>::stored_value, details::Required<details::IsRandomAccessIt<Iterator>::value> = true>
constexpr auto span(Iterator first, Iterator second) -> Span<value>#
-
template<typename Container, typename = details::void_t<decltype(std::declval<Container>().data()), decltype(std::declval<Container>().size())>>
constexpr auto span(const Container &c) -> Span<const typename Container::value_type>#
-
template<typename Container, typename = details::void_t<decltype(std::declval<Container>().data()), decltype(std::declval<Container>().size())>>
constexpr auto span(Container &c) -> Span<typename Container::value_type>#
-
template<typename Element>
constexpr auto span(const Element *data, std::size_t size) -> Span<const Element>#
Variables
-
constexpr auto nms_rotated_postprocessing = ov::reference::nms_postprocessing#
-
const uint32_t crush_resistance_const_lower_value = 0x9E3779B9#
-
const uint32_t crush_resistance_const_upper_value = 0xBB67AE85#
-
const uint64_t statistic_maximizing_multiplier_n = 0xD2511F53#
-
const uint64_t statistic_maximizing_multiplier_counter = 0xCD9E8D57#
-
const size_t rounds_number = 10#
-
const uint64_t skip_const = 256#
-
struct CellArgs#
- #include <sequences.hpp>
-
struct convert_types#
- #include <random_uniform.hpp>
-
class GetNearestPixel
- #include <interpolate.hpp>
Calculation of nearest pixel.
Public Functions
-
inline GetNearestPixel()
Constructs calculation of a nearest pixel in the default mode.
-
inline GetNearestPixel(Nearest_mode mode)
Constructs calculation of nearest pixel for the specified mode.
- Parameters:
mode – the mode of the calculation of the nearest pixel
-
inline int64_t operator()(float original, bool is_downsample) const
Performing the nearest pixel calculation.
- Parameters:
original – original coordinate
is_downsample – true if it has downsample and false otherwise
- Returns:
the nearest pixel
-
inline GetNearestPixel()
-
class GetOriginalCoordinate
- #include <interpolate.hpp>
Calculation of the source coordinate using the resized coordinate.
Public Functions
-
inline GetOriginalCoordinate()
Constructs calculation of a nearest pixel in the default mode.
-
inline GetOriginalCoordinate(Transform_mode mode)
Constructs calculation of the source coordinate.
- Parameters:
mode – the mode of the calculation of the source coordinate.
-
inline float operator()(float x_resized, float x_scale, float length_resized, float length_original) const
Performing the source coordinate calculation.
- Parameters:
x_resized – resized coordinate
x_scale – scale for the considered axis
length_resized – length of the resized axis
length_original – original length of the axis
- Returns:
the source coordinate
-
inline GetOriginalCoordinate()
-
template<typename T>
class InterpolateEval - #include <interpolate.hpp>
Class to perform interpolation calculation.
Public Functions
-
inline InterpolateEval(const op::v4::Interpolate::InterpolateAttrs &attrs)
Constructs interpolation calculation using Interpolate attributes.
- Parameters:
attrs – Interpolate-4 attributes.
-
inline void operator()(const T *input_data, const Shape &input_data_shape, const std::vector<float> &scales, const std::vector<int64_t> &axes, T *out, const Shape &out_shape)
Performing interpolation calculation.
- Parameters:
input_data – pointer to input data
input_data_shape – shape of the input data
scales – scale factors for each interpolated axis
axes – axes to interpolate
out – pointer to memory block for output data
out_shape – shape of output data
-
inline InterpolateEval(const op::v4::Interpolate::InterpolateAttrs &attrs)
-
class InterpolateEvalHelper
- #include <interpolate.hpp>
Helper class to implent non-template parts of the interpolation calculation.
-
struct ICoords
- #include <interpolate.hpp>
-
struct InfoForGenericLinearONNXMode
- #include <interpolate.hpp>
-
struct InfoForLinearMode
- #include <interpolate.hpp>
-
struct LinearModeInnerIterationResult
- #include <interpolate.hpp>
-
struct ICoords
-
template<typename dataType>
class referenceDetectionOutput - #include <detection_output.hpp>
-
template<typename T>
struct RoundingDirectionGuard# - #include <scatter_elements_update.hpp>
-
template<typename Element>
class Span - #include <span.hpp>
Span should mimic std::span.
Public Functions
-
inline Span subspan(std::size_t offset, std::size_t size = std::numeric_limits<std::size_t>::max()) const
return sub part of span starting from offset and not greater than size
-
inline Span &drop_front(std::size_t number_of_elements)
drop number of elements from front
-
inline Span &drop_back(std::size_t number_of_elements)
drop number of elements from back
-
inline Span subspan(std::size_t offset, std::size_t size = std::numeric_limits<std::size_t>::max()) const
-
template<typename Index_t, typename Count_t>
struct TensorSlice# - #include <unique.hpp>
Public Members
-
Index_t idx = 0#
The index of the current element in the original input tensor. It never changes even if the elements get sorted. This value is used as a mapping between a unique element in the first output tensor and the position of this element in the original input tensor.
-
Index_t rev_idx = -1#
The rev_idx is a mapping between every element in the original input and the location of a unique element in the first output tensor. More than one Element can have the same rev_idx.
-
Count_t count = 1#
The number of occurrences of a given element in the input tensor. This value is different than one only for duplicates found in the input tensor.
-
DescriptorType descriptor_type = DescriptorType::SINGLE_VALUE#
Indicates if this object points to a single value in the input tensor (rather than a slice of the tensor)
-
Index_t idx = 0#
-
template<typename Index_t, typename Count_t>
struct UniqueElements# - #include <unique.hpp>
Public Members
-
std::vector<TensorSlice<Index_t, Count_t>> all_tensor_elements#
Contains descriptors of all elements in the input tensor. Possibly sorted by value.
-
std::vector<TensorSlice<Index_t, Count_t>> unique_tensor_elements#
Subset of all tensor elements. First occurrences of the unique values.
-
int64_t axis = 0#
Axis (optional). Used to gather unique elements over a given dimension.
-
std::vector<TensorSlice<Index_t, Count_t>> all_tensor_elements#
-
template<typename T>
struct widen# - #include <helpers.hpp>
-
template<>
struct widen<double># - #include <helpers.hpp>
-
template<>
struct widen<float># - #include <helpers.hpp>
-
namespace adaptive_pool#
-
namespace detail#
-
namespace details#
Typedefs
-
template<typename ...Args>
using void_t = void#
Functions
-
inline uint8_t extract_bit(uint8_t val, uint8_t bit)#
-
template<typename T_IN, typename T_F>
void binary_convolve_3D_channels(const ConvolutionParams &p, const T_IN *batch, const Shape &batch_shape, const T_F *filter, const Shape &filter_shape, T_IN *&out, const float pad_value)#
-
template<typename Iterator>
std::vector<size_t> get_indices_offsets(const Iterator beg, const Iterator end, size_t last_slice_size)#
-
template<typename T>
void dot(const T *arg0, const T *arg1, T *out, const Shape &arg0_shape, const Shape &arg1_shape, const Shape &out_shape)#
-
inline std::vector<float> generate_anchors(const op::v0::Proposal::Attributes &attrs, const unsigned int anchor_count)#
-
template<typename T>
static void enumerate_proposals(const T *bottom4d, const T *d_anchor4d, const float *anchors, std::vector<ProposalBox<T>> &proposals, const unsigned int num_anchors, const unsigned int bottom_H, const unsigned int bottom_W, const float img_H, const float img_W, const float min_box_H, const float min_box_W, const int feat_stride, const float box_coordinate_scale, const float box_size_scale, float coordinates_offset, bool initial_clip, bool swap_xy, bool clip_before_nms)#
-
template<typename T>
static void nms(const int num_boxes, const std::vector<ProposalBox<T>> &proposals, std::vector<unsigned int> &index_out, int &num_out, const int base_index, const float nms_thresh, const int max_num_out, T coordinates_offset)#
-
template<typename T>
static void retrieve_rois(const int num_rois, const int item_index, const int num_proposals, const std::vector<ProposalBox<T>> &proposals, const std::vector<unsigned int> &roi_indices, T *rois, int post_nms_topn_, bool normalize, float img_h, float img_w, bool clip_after_nms, T *probs = nullptr)#
-
template<typename T>
static void proposal_exec(const T *class_probs, const T *bbox_deltas, const T *image_shape, T *output, T *out_probs, const Shape &class_probs_shape, const Shape &bbox_deltas_shape, const Shape &image_shape_shape, const Shape &output_shape, const Shape &out_probs_shape, const op::v0::Proposal::Attributes &attrs)#
-
template<typename T, typename std::enable_if<std::is_floating_point<T>::value, bool>::type = true>
bool isfinite(T x)#
-
template<class T, typename std::enable_if<std::is_integral<T>::value>::type* = nullptr>
constexpr T kahan_summation(const T in, const T prev_sum, T&)# Performs one element summation based on Kahan algorithm to significantly reduce (integral types).
- Parameters:
in – Value to add with previous value of summation.
prev_sum – Previous value of summation (accumulator).
- Returns:
Compensate sum.
-
template<class T, typename std::enable_if<ov::is_floating_point<T>()>::type* = nullptr>
T kahan_summation(const T in, const T prev_sum, T &compensation)# Performs one element summation based on Kahan algorithm to significantly reduce (floating point types).
- Parameters:
in – Value to add with previous value of summation.
prev_sum – Previous value of summation (accumulator).
compensation – Accumulates the summation error.
- Returns:
Compensate sum.
-
template<typename It>
struct from_iterator# - #include <span.hpp>
-
template<typename, typename = size_t>
struct is_complete : public std::false_type# - #include <span.hpp>
-
template<typename T>
struct is_complete<T, decltype(sizeof(T))> : public std::true_type# - #include <span.hpp>
-
template<typename It>
struct IsRandomAccessIt# - #include <span.hpp>
-
template<typename T>
struct ProposalBox# - #include <proposal.hpp>
-
template<typename ...Args>
-
namespace fake_convert_details#
Functions
-
template<typename T>
void apply_scale_shift(T *out, const T *data, const T *scale, const T *shift, const Shape &data_shape, const Shape &scale_shape, const Shape &shift_shape, const bool invert = false)# Apply scale and shift for the data input. The scale_shape and shift_shape should be equal and numpy-broadcastable to the data_shape.
- Parameters:
data – Pointer to the input data.
scale – Pointer to the input scale.
shift – Pointer to the input shift.
data_shape – Shape of the input data.
scale_shape – Shape of the input scale.
shift_shape – Shape of the input shift.
invert – Flag denoting applying scale before (if false) or after conversion (if true).
-
void apply_conversion(const float16 *data, float16 *out, size_t element_count, const element::Type &destination_type)#
Call conversion of fp16 value to the desired destination type.
- Parameters:
arg – Pointer to the input data.
out – Pointer to the otuput data.
count – Number of elements in the data input.
destination_type – Name of the destination type.
-
template<typename T>
-
namespace fake_quantize_details#
Functions
-
template<typename T>
static inline T quantize(const T arg, const T in_low, const T in_high, const T out_low, const T out_high, const T levels_minus_one)#
-
static std::tuple<size_t, size_t> get_inner_stride(size_t num_output_elements, const ov::Shape &output_shape, const ov::Shape &shape, size_t current_output_inner_stride)#
-
template<typename T, typename F>
static void fake_quantize_non_unit_inner_stride(const T *arg, const T *in_low, const T *in_high, const T *out_low, const T *out_high, T *out, const Shape &arg_shape, T levels_minus_one, size_t input_inner_stride, const F &get_outer_strides)#
-
template<typename T, typename F>
static void fake_quantize_unit_inner_stride(const T *arg, const T *in_low, const T *in_high, const T *out_low, const T *out_high, T *out, const Shape &arg_shape, T levels_minus_one, size_t input_inner_stride, const F &get_outer_strides)#
-
template<typename T, typename F>
static void fake_quantize_unit_output_intervals_inner_stride(const T *arg, const T *in_low, const T *in_high, const T *out_low, const T *out_high, T *out, const Shape &arg_shape, T levels_minus_one, size_t input_inner_stride, const F &get_outer_strides)#
-
template<typename T, typename F>
static void fake_quantize_unit_input_intervals_inner_stride(const T *arg, const T *in_low, const T *in_high, const T *out_low, const T *out_high, T *out, const Shape &arg_shape, T levels_minus_one, size_t input_inner_stride, const F &get_outer_strides)#
-
template<typename T, typename F>
static void transform(const T *first1, const T *const last1, const T *first2, const T *first3, T *out, const F &f)#
-
template<typename T, typename F>
static void transform(const T *first1, const T *const last1, const T *first2, const T *first3, const T *first4, const T *first5, T *out, const F &f)#
-
template<typename T, typename F1, typename F2>
static void fake_quantize_loop(const Shape &arg_shape, const T *arg, const T *in_low, const T *in_high, const T *out_low, const T *out_high, T *out, size_t input_inner_stride, const F1 &get_outer_strides, const F2 &quantize_loop)#
-
template<typename T, typename F>
void fake_quantize_non_unit_inner_stride(const T *arg, const T *in_low, const T *in_high, const T *out_low, const T *out_high, T *out, const Shape &arg_shape, T levels_minus_one, size_t input_inner_stride, const F &get_outer_strides)
-
template<typename T, typename F>
void fake_quantize_unit_inner_stride(const T *arg, const T *in_low, const T *in_high, const T *out_low, const T *out_high, T *out, const Shape &arg_shape, T levels_minus_one, size_t input_inner_stride, const F &get_outer_strides)
-
template<typename T>
-
namespace fft_common#
Functions
-
std::vector<int64_t> compute_strides(const std::vector<int64_t> &v)#
-
std::vector<int64_t> coords_from_index(int64_t index, const std::vector<int64_t> &strides)#
-
int64_t offset_from_coords_and_strides(const std::vector<int64_t> &coords, const std::vector<int64_t> &strides)#
-
std::vector<int64_t> reverse_fft_axes(const std::vector<int64_t> &axes, int64_t complex_data_rank)#
-
std::vector<int64_t> compute_strides(const std::vector<int64_t> &v)#
-
namespace func#
Functions
-
template<class T, typename std::enable_if<std::is_unsigned<T>::value>::type* = nullptr>
constexpr T abs(const T num)#
-
template<class T, typename std::enable_if<std::is_signed<T>::value || ov::is_floating_point<T>()>::type* = nullptr>
T abs(const T num)#
-
template<class T, typename std::enable_if<ov::is_floating_point<T>()>::type* = nullptr>
T acos(const T in)#
-
template<class T, typename std::enable_if<ov::is_floating_point<T>()>::type* = nullptr>
T acosh(const T in)#
-
template<class T, typename std::enable_if<ov::is_floating_point<T>()>::type* = nullptr>
T asinh(const T in)#
-
template<class T, typename std::enable_if<ov::is_floating_point<T>()>::type* = nullptr>
T atan(const T in)#
-
template<class T, typename std::enable_if<ov::is_floating_point<T>()>::type* = nullptr>
T atanh(const T in)#
-
template<class T, typename std::enable_if<std::is_same<typename std::decay<T>::type, char>::value>::type* = nullptr>
T bitwise_not(const T in)#
-
template<class T, typename std::enable_if<ov::is_floating_point<T>()>::type* = nullptr>
T cos(const T in)#
-
template<class T, typename std::enable_if<ov::is_floating_point<T>()>::type* = nullptr>
T cosh(const T in)#
-
template<class T, typename std::enable_if<std::is_integral<T>::value>::type* = nullptr>
T erf(const T v)#
-
void emulate_f8e5m2_on_fp16(const float16 *const arg_f, float16 *out_f, size_t count)#
Emulation of conversion fp16 value to f8e5m2 format.
- Parameters:
arg_f – Pointer to the input data.
out_f – Pointer to the otuput data.
count – Number of elements in the data input.
-
void emulate_f8e4m3_on_fp16(const float16 *arg_f, float16 *out_f, size_t count)#
Emulation of conversion fp16 value to f8e4m3 format.
Exponent denormal values 0 -7 Exponent normal values 1..15 -6..8 (7 - exponent) Exponent NaN values 15 8
- Parameters:
arg_f – Pointer to the input data.
out_f – Pointer to the otuput data.
count – Number of elements in the data input.
-
template<class T, typename std::enable_if<std::is_unsigned<T>::value>::type* = nullptr>
constexpr T floor_mod(const T x, const T y)#
-
template<class T, typename std::enable_if<ov::is_floating_point<T>() || std::is_signed<T>::value>::type* = nullptr>
T floor_mod(const T x, const T y)#
-
template<class T, typename std::enable_if<std::is_integral<T>::value>::type* = nullptr>
constexpr T mod(const T x, const T y)#
-
template<class T, typename std::enable_if<ov::is_floating_point<T>()>::type* = nullptr>
T mod(const T x, const T y)#
-
template<class T, typename std::enable_if<std::is_integral<T>::value>::type* = nullptr>
std::pair<T, T> mod_interval_value(const T v1, const T v2, const T m)# Estimates division remainder
[v1, v2] % m = [r0, r1]as interval.Assumes that
0 <= v1 <= v2 and m != 0, in other cases result is undefined behaviour. The result interval estimate minimum and maximum but is not true that value can be any value between min and max. e.g.[4,6] % 5 = [0, 4], but in fact accurate result is set of [0,1,4]
- Parameters:
v1 – Minimum of value interval.
v2 – Maximum of value interval.
m – Modulo divisor.
- Returns:
Remainder of division as interval range.
-
template<class T, typename std::enable_if<std::is_integral<T>::value>::type* = nullptr>
std::pair<T, T> mod_interval(const T v1, const T v2, const T m1, const T m2)# Estimates division reminder of
[v1, v2] & [m1, m2] = [r0, r1]as interval.Assumes that
0 <= v1 <= v2 and 0 < m1 <= m2, in other cases result is undefined behaviour.
- Parameters:
v1 – Minimum of value interval.
v2 – Maximum of value interval.
m1 – Minimum of modulo divisor.
m2 – Maximum of modulo divisor.
- Returns:
Remainder of division as interval range.
-
template<class T, typename std::enable_if<std::is_integral<T>::value>::type* = nullptr>
T sigmoid(const T value)#
-
template<class T, typename std::enable_if<std::is_unsigned<T>::value>::type* = nullptr>
constexpr T sign(const T v)#
-
template<class T, typename std::enable_if<std::is_same<float16, typename std::decay<T>::type>::value || std::is_same<bfloat16, typename std::decay<T>::type>::value>::type* = nullptr>
T sign(const T v)#
-
template<class T, typename std::enable_if<ov::is_floating_point<T>()>::type* = nullptr>
T sinh(const T in)#
-
template<class T, typename std::enable_if<ov::is_floating_point<T>()>::type* = nullptr>
T sqrt(const T in)#
-
template<class T, typename std::enable_if<ov::is_floating_point<T>()>::type* = nullptr>
T tan(const T in)#
-
template<class T, typename std::enable_if<std::is_unsigned<T>::value>::type* = nullptr>
-
namespace internal#
Functions
-
template<typename C, typename T>
inline T value_with_padding_or(const C &arr, size_t padding, size_t idx, T &&default_value)#
-
template<int A0, int A1, typename T, typename U, typename Functor>
inline void numpy_autobroadcast_binop(const T *arg0, const T *arg1, U *out, const Shape &shape0, const Shape &shape1, const size_t *strides0, const size_t *strides1, const size_t padding0, const size_t padding1, const Shape &output_shape, const size_t axis, const size_t stride, Functor &&elementwise_functor)#
-
inline size_t calculate_fixed_axis(size_t axis, const size_t *strides)#
-
void lu_decomposition(std::vector<float> &input, std::vector<float> &L, std::vector<float> &U, std::vector<size_t> &P, bool &sign, size_t b, size_t n, size_t n_squared)#
-
void lu_solve(std::vector<float> &output, std::vector<float> &L, std::vector<float> &U, std::vector<size_t> &P, size_t b, size_t n, size_t n_squared)#
-
void to_adjoint(std::vector<float> &output, std::vector<float> &U, bool sign, size_t b, size_t n, size_t n_squared)#
-
template<typename C, typename T>
-
namespace interpolate_pil#
Functions
-
template<typename T_out, typename T_in>
T_out clip(const T_in &x, const T_out &min = std::numeric_limits<T_out>::min(), const T_out &max = std::numeric_limits<T_out>::max())#
-
static inline double bilinear_filter(double x, double)#
-
static inline double bicubic_filter(double x, double a)#
-
static inline int precompute_coeffs(int in_size, float in0, float in1, int out_size, const filter &filterp, std::vector<int> &bounds, std::vector<double> &kk)#
-
template<typename T>
void imaging_resample_horizontal(T *im_out, Shape im_out_shape, const T *im_in, Shape im_in_shape, int offset, int ksize, std::vector<int> &bounds, std::vector<double> &kk)#
-
struct filter#
- #include <interpolate_pil.hpp>
-
template<typename T_out, typename T_in>
-
namespace iou_rotated#
Functions
-
static inline void get_rotated_vertices(const RotatedBox &box, Point2D (&pts)[4])#
-
static inline int get_intersection_points(const Point2D (&pts1)[4], const Point2D (&pts2)[4], Point2D (&intersections)[24])#
-
static inline int convex_hull_graham(const Point2D (&p)[24], const int num_in, Point2D (&q)[24], bool shift_to_zero = false)#
-
static inline float rotated_boxes_intersection(const RotatedBox &box1, const RotatedBox &box2)#
-
struct Point2D#
- #include <nms_rotated_util.hpp>
-
struct RotatedBox#
- #include <nms_rotated_util.hpp>
-
static inline void get_rotated_vertices(const RotatedBox &box, Point2D (&pts)[4])#
-
namespace kernel#
-
namespace multinomial#
Functions
-
template<typename T, typename U, typename V>
void multinomial(const T *probs, const Shape &probs_shape, const U *num_samples, const Shape &num_samples_shape, V *output, const Shape &output_shape, const bool with_replacement, const bool log_probs, const uint64_t global_seed, const uint64_t op_seed)# Multinomial operation creates a sequence of indices of classes sampled from the multinomial distribution.
- Template Parameters:
T – Data type of the probs’ values.
U – Data type of num_samples’ values.
V – Data type of output’s values.
- Parameters:
probs – Input tensor containing at each index poisition probability/log probability of sampling a given class.
probs_shape – Shape of the ‘probs’ tensor.
num_samples – Scalar or 1D tensor with a single value that determines the number of samples to generate per batch.
num_samples_shape – Shape of the ‘num_samples’ tensor.
output – Output tensor for the generated class indices.
output_shape – Shape of the ‘output’ tensor.
with_replacement – Boolean that determines whether a sampled class can appear more than once in the output.
log_probs – Boolean that determines whether to treat input probabilities as log probabilities.
global_seed – First seed value (key) of Phillox random number generation algorithm. (See RandomUniform for details)
op_seed – Second seed value (counter) of Phillox random number generation algorithm. (See RandomUniform for details)
-
template<typename T, typename U, typename V>
-
namespace nms_common#
Functions
-
struct BoxInfo#
- #include <nms_common.hpp>
-
struct Rectangle#
- #include <nms_common.hpp>
-
struct BoxInfo#
-
namespace roi_policy#
-
template<typename T>
struct Point# - #include <roi_align.hpp>
-
template<typename T>
class ROIAlignOpDefPolicy - #include <roi_align.hpp>
-
class ROIAlignSamplingSpace : public ov::reference::roi_policy::SamplingSpaceBase<T>
- #include <roi_align.hpp>
-
class ROIAlignSamplingSpace : public ov::reference::roi_policy::SamplingSpaceBase<T>
-
template<typename T>
class ROIAlignRotatedOpDefPolicy - #include <roi_align.hpp>
-
class ROIAlignRotatedSamplingSpace : public ov::reference::roi_policy::SamplingSpaceBase<T>
- #include <roi_align.hpp>
-
class ROIAlignRotatedSamplingSpace : public ov::reference::roi_policy::SamplingSpaceBase<T>
-
template<typename T>
class SamplingSpaceBase - #include <roi_align.hpp>
Subclassed by ov::reference::roi_policy::ROIAlignOpDefPolicy< T >::ROIAlignSamplingSpace, ov::reference::roi_policy::ROIAlignRotatedOpDefPolicy< T >::ROIAlignRotatedSamplingSpace
-
template<typename T>
-
namespace scatter_nd_update#
-
Functions
-
template<typename T, typename std::enable_if<!std::is_same<typename std::decay<T>::type, char>::value>::type* = nullptr>
reduction_function<T> reduction_functor_for(const ov::op::v15::ScatterNDUpdate::Reduction reduction_type)#
-
template<typename T, typename std::enable_if<!std::is_same<typename std::decay<T>::type, char>::value>::type* = nullptr>
-
using Nearest_mode = op::v4::Interpolate::NearestMode#
-
namespace streams#
Namespace for streams in streams executor.
-
namespace symbol#
Functions
- void OPENVINO_API set_equal (const std::shared_ptr< Symbol > &lhs, const std::shared_ptr< Symbol > &rhs)
If both symbols are valid, sets them as equal.
- bool OPENVINO_API are_equal (const std::shared_ptr< Symbol > &lhs, const std::shared_ptr< Symbol > &rhs)
Returns true if both symbols are valid and are equal otherwise returns false.
- std::shared_ptr< Symbol > OPENVINO_API ancestor_of (const std::shared_ptr< Symbol > &x)
Returns a representative (the most distant parent) of an equality group of this symbol.
-
namespace util#
Functions
-
bool get_symbols(const ov::PartialShape &shape, ov::TensorSymbol &symbols)#
Collects symbols from shape. Symbols of static dimensions are guaranteed to be nullptr.
- Parameters:
shape – Shape object to collect symbols from
symbols – TensorSymbol object to collect symbols to
- Returns:
Status of collecting the symbols (false if rank is static else true)
-
bool get_symbols(const ov::Output<ov::Node> &output, ov::TensorSymbol &symbols)#
Collects symbols from tensor of Output object.
- Parameters:
output – Output object to collect symbols from
symbols – TensorSymbol object to collect symbols to
- Returns:
Status of collecting the symbols (false if tensor has no symbols else true)
-
bool are_unique_and_equal_symbols(const ov::TensorSymbol &lhs, const ov::TensorSymbol &rhs)#
Compares.
- Parameters:
lhs – TensorSymbol object to compare
rhs – TensorSymbol object to compare
- Returns:
true if symbols are unique and equal between lhs and rhs else false
-
bool get_symbols(const ov::PartialShape &shape, ov::TensorSymbol &symbols)#
-
namespace threading#
Typedefs
-
using Task = std::function<void()>#
OpenVINO Task Executor can use any copyable callable without parameters and output as a task. It would be wrapped into std::function object.
Enums
Functions
-
void get_cur_stream_info(const int stream_id, const bool cpu_pinning, const std::vector<std::vector<int>> org_proc_type_table, const std::vector<std::vector<int>> streams_info_table, StreamCreateType &stream_type, int &concurrency, int &core_type, int &numa_node_id, int &max_threads_per_core)#
Get current stream information.
- Parameters:
stream_id – [in] stream id
cpu_pinning – [in] whether to bind threads to cpus
org_proc_type_table – [in] available processors in the platform
streams_info_table – [in] streams information table
stream_type – [out] stream create type
concurrency – [out] the number of threads created at the same time
core_type – [out] core type
numa_node_id – [out] numa node id
max_threads_per_core – [out] the max number of threads per cpu core
-
void reserve_cpu_by_streams_info(const std::vector<std::vector<int>> _streams_info_table, const int _numa_nodes, std::vector<std::vector<int>> &_cpu_mapping_table, std::vector<std::vector<int>> &_proc_type_table, std::vector<std::vector<int>> &_stream_processors, const int _cpu_status)#
Reserve cpu resource by streams info.
- Parameters:
_streams_info_table – [in] streams info table
_numa_nodes – [in] number of numa nodes
_cpu_mapping_table – [out] CPU mapping table for each processor
_proc_type_table – [out] summary table of number of processors per type
_stream_processors – [out] processors grouped in stream which is used in core binding in cpu streams executor
_cpu_status – [in] set cpu status
- Returns:
-
void update_proc_type_table(const std::vector<std::vector<int>> _cpu_mapping_table, const int _numa_nodes, std::vector<std::vector<int>> &_proc_type_table)#
Update proc_type_table.
- Parameters:
_cpu_mapping_table – [in] CPU mapping table for each processor
_numa_nodes – [in] total number for nodes in system
_proc_type_table – [out] summary table of number of processors per type
- Returns:
-
std::shared_ptr<ExecutorManager> executor_manager()#
-
class CPUStreamsExecutor : public ov::threading::IStreamsExecutor
- #include <cpu_streams_executor.hpp>
CPU Streams executor implementation. The executor splits the CPU into groups of threads, that can be pinned to cores or NUMA nodes. It uses custom threads to pull tasks from single queue.
Public Types
-
using Ptr = std::shared_ptr<CPUStreamsExecutor>
A shared pointer to a CPUStreamsExecutor object.
Public Functions
-
explicit CPUStreamsExecutor(const Config &config)
Constructor.
- Parameters:
config – Stream executor parameters
-
~CPUStreamsExecutor() override
A class destructor.
-
virtual void run(Task task) override
Execute ov::Task inside task executor context.
- Parameters:
task – A task to start
-
virtual void execute(Task task) override
Execute the task in the current thread using streams executor configuration and constraints.
- Parameters:
task – A task to start
-
virtual int get_stream_id() override
Return the index of current stream.
- Returns:
An index of current stream. Or throw exceptions if called not from stream thread
-
virtual int get_numa_node_id() override
Return the id of current NUMA Node Return 0 when current stream cross some NUMA Nodes.
- Returns:
IDof current NUMA Node, or throws exceptions if called not from stream thread
-
virtual int get_socket_id() override
Return the id of current socket Return 0 when current stream cross some sockets.
- Returns:
IDof current socket, or throws exceptions if called not from stream thread
-
virtual void run_sub_stream(Task task, int id) override
Execute ov::Task inside sub stream of task executor context.
- Parameters:
task – A task to start
id – Sub stream id
-
using Ptr = std::shared_ptr<CPUStreamsExecutor>
-
interface ExecutorManager
- #include <executor_manager.hpp>
Interface for tasks execution manager. This is global point for getting task executor objects by string id. It’s necessary in multiple asynchronous requests for having unique executors to avoid oversubscription. E.g. There 2 task executors for CPU device: one - in FPGA, another - in OneDNN. Parallel execution both of them leads to not optimal CPU usage. More efficient to run the corresponding tasks one by one via single executor.
Public Functions
-
virtual std::shared_ptr<ov::threading::ITaskExecutor> get_executor(const std::string &id) = 0
Returns executor by unique identificator.
- Parameters:
id – An unique identificator of device (Usually string representation of TargetDevice)
- Returns:
A shared pointer to existing or newly ITaskExecutor
-
virtual std::shared_ptr<ov::threading::IStreamsExecutor> get_idle_cpu_streams_executor(const ov::threading::IStreamsExecutor::Config &config) = 0
Returns idle cpu streams executor.
- Parameters:
config – Streams executor config
- Returns:
pointer to streams executor config
-
virtual void set_property(const ov::AnyMap &properties) = 0
Allows to configure executor manager.
- Parameters:
properties – map with configuration
-
virtual std::shared_ptr<ov::threading::ITaskExecutor> get_executor(const std::string &id) = 0
-
class ImmediateExecutor : public ov::threading::ITaskExecutor
- #include <immediate_executor.hpp>
Task executor implementation that just run tasks in current thread during calling of run() method.
Public Types
-
using Ptr = std::shared_ptr<ImmediateExecutor>
A shared pointer to a ImmediateExecutor object.
Public Functions
-
~ImmediateExecutor() override = default
Destroys the object.
-
inline virtual void run(Task task) override
Execute ov::Task inside task executor context.
- Parameters:
task – A task to start
-
using Ptr = std::shared_ptr<ImmediateExecutor>
-
interface IStreamsExecutor : public virtual ov::threading::ITaskExecutor
- #include <istreams_executor.hpp>
Interface for Streams Task Executor. This executor groups worker threads into so-called
streams.- CPU
The executor executes all parallel tasks using threads from one stream. With proper pinning settings it should reduce cache misses for memory bound workloads.
- NUMA
On NUMA hosts GetNumaNodeId() method can be used to define the NUMA node of current stream
Subclassed by ov::threading::CPUStreamsExecutor
Public Types
-
enum ThreadBindingType
Defines inference thread binding type.
Values:
-
enumerator NONE
Don’t bind the inference threads.
-
enumerator CORES
Bind inference threads to the CPU cores (round-robin)
-
enumerator NUMA
Bind to the NUMA nodes (default mode for the non-hybrid CPUs on the Win/MacOS, where the ‘CORES’ is not implemeneted)
-
enumerator HYBRID_AWARE
Let the runtime bind the inference threads depending on the cores type (default mode for the hybrid CPUs)
-
enumerator NONE
-
using Ptr = std::shared_ptr<IStreamsExecutor>
A shared pointer to IStreamsExecutor interface
Public Functions
-
~IStreamsExecutor() override
A virtual destructor.
-
virtual int get_stream_id() = 0
Return the index of current stream.
- Returns:
An index of current stream. Or throw exceptions if called not from stream thread
-
virtual int get_numa_node_id() = 0
Return the id of current NUMA Node Return 0 when current stream cross some NUMA Nodes.
- Returns:
IDof current NUMA Node, or throws exceptions if called not from stream thread
-
virtual int get_socket_id() = 0
Return the id of current socket Return 0 when current stream cross some sockets.
- Returns:
IDof current socket, or throws exceptions if called not from stream thread
-
virtual void execute(Task task) = 0
Execute the task in the current thread using streams executor configuration and constraints.
- Parameters:
task – A task to start
-
virtual void run_sub_stream(Task task, int id) = 0
Execute ov::Task inside sub stream of task executor context.
- Parameters:
task – A task to start
id – Sub stream id
-
void run_sub_stream_and_wait(const std::vector<Task> &tasks)
Execute all of the tasks and waits for its completion. Default run_sub_stream_and_wait() method implementation uses run_sub_stream() pure virtual method and higher level synchronization primitives from STL. The task is wrapped into std::packaged_task which returns std::future. std::packaged_task will call the task and signal to std::future that the task is finished or the exception is thrown from task Than std::future is used to wait for task execution completion and task exception extraction.
Note
run_sub_stream_and_wait() does not copy or capture tasks!
- Parameters:
tasks – A vector of tasks to execute
-
struct Config
- #include <istreams_executor.hpp>
Defines IStreamsExecutor configuration.
Public Types
-
enum class StreamsMode
This enum contains definition of each sub streams mode, indicating the main stream situation.
Values:
-
enumerator SUB_STREAMS_NULL
Do not create sub streams.
-
enumerator SUB_STREAMS_FOR_SOCKET
Create sub streams for multiple sockets in main stream.
-
enumerator LATENCY
latency mode
-
enumerator THROUGHPUT
throughput mode
-
enumerator SUB_STREAMS_NULL
Public Functions
-
inline Config(std::string name = "StreamsExecutor", int streams = 1, int threads_per_stream = 0, ov::hint::SchedulingCoreType thread_preferred_core_type = ov::hint::SchedulingCoreType::ANY_CORE, bool cpu_reservation = false, bool cpu_pinning = false, std::vector<std::vector<int>> streams_info_table = {})
A constructor with arguments.
- Parameters:
name – [in] The executor name
streams – [in]
threads_per_stream – [in]
thread_preferred_core_type – [in]
cpu_reservation – [in]
cpu_pinning – [in]
streams_info_table – [in]
-
void set_property(const ov::AnyMap &properties)
Sets configuration.
- Parameters:
properties – map of properties
-
enum class StreamsMode
-
interface ITaskExecutor
- #include <itask_executor.hpp>
Interface for Task Executor. OpenVINO uses
ov::ITaskExecutorinterface to run all asynchronous internal tasks. Different implementations of task executors can be used for different purposes:To improve cache locality of memory bound CPU tasks some executors can limit task’s affinity and maximum concurrency.
The executor with one worker thread can be used to serialize access to acceleration device.
Immediate task executor can be used to satisfy
ov::ITaskExecutorinterface restrictions but run tasks in current thread.
Synchronization#
It is
ov::ITaskExecutoruser responsibility to wait for task execution completion. Thec++11standard way to wait task completion is to usestd::packaged_taskorstd::promisewithstd::future. Here is an example of how to usestd::promiseto wait task completion and process task’s exceptions:// std::promise is move only object so to satisfy copy callable constraint we use std::shared_ptr auto promise = std::make_shared<std::promise<void>>(); // When the promise is created we can get std::future to wait the result auto future = promise->get_future(); // Rather simple task ov::threading::Task task = [] { std::cout << "Some Output" << std::endl; }; // Create an executor ov::threading::ITaskExecutor::Ptr taskExecutor = std::make_shared<ov::threading::CPUStreamsExecutor>(ov::threading::IStreamsExecutor::Config{}); if (taskExecutor == nullptr) { // ProcessError(e); return; } // We capture the task and the promise. When the task is executed in the task executor context // we munually call std::promise::set_value() method taskExecutor->run([task, promise] { std::exception_ptr currentException; try { task(); } catch(...) { // If there is some exceptions store the pointer to current exception currentException = std::current_exception(); } if (nullptr == currentException) { promise->set_value(); // <-- If there is no problems just call std::promise::set_value() } else { promise->set_exception(currentException); // <-- If there is an exception forward it to std::future object } }); // To wait the task completion we call std::future::wait method future.wait(); // The current thread will be blocked here and wait when std::promise::set_value() // or std::promise::set_exception() method will be called. // If the future store the exception it will be rethrown in std::future::get method try { future.get(); } catch(std::exception& /*e*/) { // ProcessError(e); }
Note
Implementation should guaranty thread safety of all methods
Subclassed by ov::threading::IStreamsExecutor, ov::threading::ImmediateExecutor
Public Types
-
using Ptr = std::shared_ptr<ITaskExecutor>
A shared pointer to ITaskExecutor interface
Public Functions
-
virtual ~ITaskExecutor() = default
Destroys the object.
-
virtual void run(Task task) = 0
Execute ov::Task inside task executor context.
- Parameters:
task – A task to start
-
virtual void run_and_wait(const std::vector<Task> &tasks)
Execute all of the tasks and waits for its completion. Default run_and_wait() method implementation uses run() pure virtual method and higher level synchronization primitives from STL. The task is wrapped into std::packaged_task which returns std::future. std::packaged_task will call the task and signal to std::future that the task is finished or the exception is thrown from task Than std::future is used to wait for task execution completion and task exception extraction.
Note
run_and_wait() does not copy or capture tasks!
- Parameters:
tasks – A vector of tasks to execute
-
template<typename T>
class ThreadSafeBoundedPriorityQueue - #include <thread_safe_containers.hpp>
-
template<typename T>
class ThreadSafeQueueWithSize - #include <thread_safe_containers.hpp>
-
using Task = std::function<void()>#
-
namespace util#
Typedefs
-
using FilePath = std::string#
Enums
Functions
-
std::string codec_xor(const std::string &source_str)#
-
std::string to_lower(const std::string &s)#
-
std::string to_upper(const std::string &s)#
-
size_t hash_combine(const std::vector<size_t> &list)#
-
inline std::string ltrim(const std::string &s)#
trim from start (in place)
- Parameters:
s – - string to trim
-
inline std::string rtrim(const std::string &s)#
trim from end (in place)
- Parameters:
s – - string to trim
-
inline std::string trim(const std::string &s)#
Trims std::string from both ends (in place)
- Parameters:
s – A reference to a std::tring to trim
- Returns:
A reference to a trimmed std::string
-
inline bool ends_with(const std::string &src, const char *with)#
check string end with given substring
- Parameters:
src – - string to check
with – - given substring
- Returns:
true if string end with given substring
-
template<typename T>
inline bool ends_with(const std::basic_string<T> &str, const std::basic_string<T> &suffix)# check string/wstring end with given substring
- Parameters:
src – - string/wstring to check
with – - given substring
- Returns:
true if string end with given substring
-
std::vector<std::string> split(const std::string &s, char delimiter, bool trim = false)#
-
template<typename T, typename A, typename V>
bool contains(const std::vector<T, A> &vec, const V &v)#
-
template<typename T, typename A>
T product(std::vector<T, A> const &vec)# multiply vector’s values
- Parameters:
vec – - vector with values
- Returns:
result of multiplication
-
template<typename Container, typename PredicateT>
inline void erase_if(Container &data, const PredicateT &predicate)# Associative containers doesnt work with remove_if algorithm.
- Template Parameters:
ContainerT –
PredicateT –
- Parameters:
data – An associative container
predicate – A predicate to remove values conditionally
-
std::string filter_lines_by_prefix(const std::string &str, const std::string &prefix)#
-
constexpr const char *find_last(ConstString s, size_t offset, char ch)#
-
constexpr const char *find_last(ConstString s, char ch)#
-
constexpr const char *get_file_name(ConstString s)#
-
std::string getenv_string(const char *env_var)#
Get the names environment variable as a string.
- Parameters:
env_var – The string name of the environment variable to get.
- Returns:
Returns string by value or an empty string if the environment variable is not set.
-
int32_t getenv_int(const char *env_var, int32_t default_value = -1)#
Get the names environment variable as an integer. If the value is not a valid integer then an exception is thrown.
- Parameters:
env_var – The string name of the environment variable to get.
default_value – The value to return if the environment variable is not set.
- Returns:
Returns value or default_value if the environment variable is not set.
-
bool getenv_bool(const char *env_var, bool default_value = false)#
Get the names environment variable as a boolean. If the value is not a valid boolean then an exception is thrown. Valid booleans are one of 1, 0, on, off, true, false All values are case insensitive. If the environment variable is not set the default_value is returned.
- Parameters:
env_var – The string name of the environment variable to get.
default_value – The value to return if the environment variable is not set.
- Returns:
Returns the boolean value of the environment variable.
-
std::string sanitize_path(const std::string &path)#
Remove path components which would allow traversing up a directory tree.
- Parameters:
path – A path to file
- Returns:
A sanitized path
-
std::string get_file_name(const std::string &path)#
Returns the name with extension for a given path.
- Parameters:
path – The path to the output file
-
std::string get_absolute_file_path(const std::string &path)#
Interface function to get absolute path of file.
- Parameters:
path – - path to file, can be relative to current working directory
- Throws:
runtime_error – if absolute path can’t be resolved
- Returns:
Absolute path of file
-
bool is_absolute_file_path(const std::string &path)#
Interface function to check path to file is absolute or not.
- Parameters:
path – - path to file, can be relative to current working directory
- Throws:
runtime_error – if any error occurred
- Returns:
True if path is absolute and False otherwise
-
void create_directory_recursive(const std::string &path)#
Interface function to create directorty recursively by given path.
- Parameters:
path – - path to file, can be relative to current working directory
- Throws:
runtime_error – if any error occurred
-
bool directory_exists(const std::string &path)#
Interface function to check if directory exists for given path.
- Parameters:
path – - path to directory
- Returns:
true if directory exists, false otherwise
-
inline int64_t file_size(const char *path)#
Returns file size for file.
- Parameters:
path – [in] The file name
- Returns:
file size
-
inline bool file_exists(const char *path)#
Returns file size for file.
- Parameters:
path – [in] The file name
- Returns:
file size
-
inline int64_t file_size(const std::string &path)#
Returns file size for file.
- Parameters:
path – [in] The file name
- Returns:
file size
-
inline bool file_exists(const std::string &path)#
Returns true if file exists.
- Parameters:
path – [in] The file name
- Returns:
true if file exists
-
std::string get_file_ext(const std::string &path)#
-
std::string get_directory(const std::string &path)#
-
std::string path_join(const std::vector<std::string> &paths)#
-
void iterate_files(const std::string &path, const std::function<void(const std::string &file, bool is_dir)> &func, bool recurse = false, bool include_links = false)#
-
void convert_path_win_style(std::string &path)#
-
std::string get_ov_lib_path()#
-
inline std::string get_ov_library_path()#
-
template<typename C, typename = typename std::enable_if<(std::is_same<C, char>::value || std::is_same<C, wchar_t>::value)>::type>
inline std::basic_string<C> make_plugin_library_name(const std::basic_string<C> &path, const std::basic_string<C> &input)#
-
FilePath get_plugin_path(const std::string &plugin)#
Format plugin path (canonicalize, complete to absolute or complete to file name) for further dynamic loading by OS.
- Parameters:
plugin – - Path (absolute or relative) or name of a plugin. Depending on platform,
pluginis wrapped with shared library suffix and prefix to identify library full name- Returns:
absolute path or file name with extension (to be found in ENV)
-
FilePath get_compiled_plugin_path(const std::string &plugin)#
Find the plugins which are located together with OV library.
- Parameters:
plugin – - Path (absolute or relative) or name of a plugin. Depending on platform,
pluginis wrapped with shared library suffix and prefix to identify library full name- Returns:
absolute path or file name with extension (to be found in ENV)
-
FilePath get_plugin_path(const std::string &plugin, const std::string &xml_path, bool as_abs_only = false)#
Format plugin path (canonicalize, complete to absolute or complete to file name) for further dynamic loading by OS.
- Parameters:
plugin – - Path (absolute or relative) or name of a plugin. Depending on platform,
pluginis wrapped with shared library suffix and prefix to identify library full namexml_path – - Path (absolute or relative) to XML configuration file
as_abs_only – - Bool value, allows return file names or not
- Returns:
absolute path or file name with extension (to be found in ENV)
-
std::vector<uint8_t> load_binary(const std::string &path)#
load binary data from file
- Parameters:
path – - binary file path to load
- Returns:
binary vector
-
void save_binary(const std::string &path, std::vector<uint8_t> binary)#
save binary data to file
- Parameters:
path – - binary file path to store
-
void save_binary(const std::string &path, const char *binary, size_t bin_size)#
-
const char *trim_file_name(const char *const fname)#
Trim OpenVINO project file name path if OpenVINO project directory found.
Function use
OV_NATIVE_PARENT_PROJECT_ROOT_DIRdefinition with project directory name defines ‘openvino_dir_name’. The input file name is scanned for OV_NATIVE_PARENT_PROJECT_ROOT_DIR, if found returns pointer to trimmed name otherwise returns input pointer.e.g: OV_NATIVE_PARENT_PROJECT_ROOT_DIR = openvino
/home/user/openvino/src/example.cpp -> src/example.cpp
../../../../openvino/src/example.cpp -> src/example.cpp
- Parameters:
fname – Pointer to OpenVINO file name path.
- Returns:
Pointer to trimmed file name path.
-
template<typename C, typename = enableIfSupportedChar<C>>
inline std::basic_string<C> make_path(const std::basic_string<C> &folder, const std::basic_string<C> &file)#
-
void default_logger_handler_func(const std::string &s)#
Loads a library with the name specified.
- Parameters:
path – Full or relative path to the plugin library
- Returns:
Reference to shared object
Searches for a function symbol in the loaded module.
- Parameters:
shared_object – shared object reference
symbolName – Name of the function to find
- Throws:
Exception – if the function is not found
- Returns:
A pointer to the function if found
-
template<class T, class U = T>
constexpr bool is_max(const T &value)# Check if value of type T has got maximum value of type U.
-
template<typename T>
struct AsTypePtr#
- template<typename In> shared_ptr< In > >
- #include <type.hpp>
Casts a std::shared_ptr<Value> to a std::shared_ptr<Type> if it is of type Type, nullptr otherwise
-
template<class T>
struct Cast# - #include <shape_infer_type_utils.hpp>
Transform tensor data by cast them to type T.
- Template Parameters:
T – Type of returned value.
-
class ConstString
- #include <const_string.hpp>
-
template<class C>
struct FileTraits# OS specific file traits.
-
template<>
struct FileTraits<char># - #include <file_util.hpp>
-
template<>
struct FileTraits<wchar_t># - #include <file_util.hpp>
-
template<class T>
struct InTypeRange# - #include <shape_infer_type_utils.hpp>
Check if input data is in [T::min(), T::max()] and then cast it to T.
- Template Parameters:
T – Type of returned value and used to specified min, max of valid value range.
- Throws ov::AssertFailure:
if input value not in type range.
-
class Logger
- #include <log.hpp>
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class LogHelper
- #include <log.hpp>
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namespace dim#
Functions
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template<class TDim, typename std::enable_if<std::is_arithmetic<TDim>::value>::type* = nullptr>
constexpr bool is_static(const TDim)#
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template<class TDim, typename std::enable_if<!std::is_arithmetic<TDim>::value>::type* = nullptr>
constexpr bool is_static(const TDim &d)#
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template<class TDim>
constexpr std::enable_if<std::is_arithmetic<TDim>::value, TDim>::type get_length(const TDim &d)#
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template<class TDim>
constexpr std::enable_if<!std::is_arithmetic<TDim>::value, typenameTDim::value_type>::type get_length(const TDim &d)#
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template<class T>
constexpr bool is_inf_bound(const T dim)# Checks if dimension length is infinite bound (undefined).
- Template Parameters:
T – Type of dimension length.
- Parameters:
dim – Dimension length value.
- Returns:
True if dimension length has infinite bound, otherwise false.
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template<class T, class U = typename Dimension::value_type>
constexpr std::enable_if<std::is_same<T, size_t>::value, U>::type value_convert(const T dim)# Convert static dimension length to ov::Dimension::value_type.
As static dimension length type is size_t (bit-length depends on architecture) the maximum value (undefined) is convert to ov::Dimension infinite bound.
- Template Parameters:
T – Static dimension type (size_t)
U – ov::Dimension::value_type
- Parameters:
dim – Dimension length to convert.
- Returns:
Converted input value to ov::Dimension::value_type.
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template<class T, class U = typename Dimension::value_type>
constexpr std::enable_if<std::is_same<T, U>::value, U>::type value_convert(const T dim)# Conversion of dimension when input type is same as ov::Dimension::value_type.
Return value as it is.
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template<class T>
constexpr auto dilated(const T dim, const T dilation) -> T# Calculate dilated dimension value.
- Parameters:
dim – Dimension size value.
dilation – Dilation value
- Returns:
Dilated dimension value.
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template<class TDim>
constexpr auto dilated(const TDim &dim, const typename TDim::value_type dilation) -> TDim# Calculate dilated dimension.
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template<class TDim>
constexpr std::enable_if<std::is_arithmetic<TDim>::value, TDim>::type padded(const TDim dim, const int64_t pad_num)# Calculate padded dimension size as dim size + padding size.
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template<class TDim>
std::enable_if<std::is_class<TDim>::value, TDim>::type padded(const TDim &dim, const int64_t pad_num)# Calculate padded dimension size as dim + padding size.
Note
the Dimension + operator cannot be used if padding is ‘-1’ which result add dynamic dimension.
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template<class TDim, class T = typename std::conditional<std::is_arithmetic<TDim>::value, size_t, typename Dimension::value_type>::type>
inline std::pair<T, T> padding(const TDim &dim, const int64_t kernel_size, const int64_t dilation, int64_t stride)# Calculate dimension padding required by filter/kernel properties.
Provides pair of padding values as left padding is total value of required padding divided by 2 and right as total required padding minus left padding.
- Parameters:
dim – input dimension to calculate its padding.
filter_size – Kernel size for input dimension.
dilation – Kernel dilation.
stride – Kernel stride.
- Returns:
Pair of left, right padding values for input dimension.
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template<class TDim>
auto ceil_div(const TDim &dim, const typename TDim::value_type divisor) -> TDim# Divide dimension using ceil rounding.
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template<class TDim>
auto floor_div(const TDim &dim, const typename TDim::value_type divisor) -> TDim# Divide dimension using floor rounding.
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template<class TDim, typename std::enable_if<!std::is_same<Dimension, typename std::decay<TDim>::type>::value>::type* = nullptr>
bool is_empty(TDim &&d)# Check if dimension is empty.
For static dimension the empty dimension is equal to zero dimension.
For iv::Dimension the empty means that has no dimension at all.
Variables
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constexpr int64_t inf_bound = -1#
Infinite bound value for dimension.
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template<class TDim, typename std::enable_if<std::is_arithmetic<TDim>::value>::type* = nullptr>
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namespace pugixml#
XML helpers function to extract values from
pugi::xml_nodeFunctions
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int get_int_attr(const pugi::xml_node &node, const char *str)#
Gets the integer attribute from
pugi::xml_node- Parameters:
node – [in] The node
str – [in] The string
- Returns:
An integer value
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int get_int_attr(const pugi::xml_node &node, const char *str, int defVal)#
Gets the integer attribute from
pugi::xml_node- Parameters:
node – [in] The node
str – [in] The string identifying value name
defVal – [in] The default value
- Returns:
An integer value
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int64_t get_int64_attr(const pugi::xml_node &node, const char *str)#
Gets the
int64_tattribute frompugi::xml_node- Parameters:
node – [in] The node
str – [in] The string identifying value name
- Returns:
An
int64_tvalue
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int64_t get_int64_attr(const pugi::xml_node &node, const char *str, int64_t defVal)#
Gets the
int64_tattribute frompugi::xml_node- Parameters:
node – [in] The node
str – [in] The string identifying value name
defVal – [in] The default value
- Returns:
An
int64_tvalue
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uint64_t get_uint64_attr(const pugi::xml_node &node, const char *str)#
Gets the
uint64_tattribute frompugi::xml_node- Parameters:
node – [in] The node
str – [in] The string identifying value name
- Returns:
An
uint64_tvalue
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uint64_t get_uint64_attr(const pugi::xml_node &node, const char *str, uint64_t defVal)#
Gets the
uint64_tattribute frompugi::xml_node- Parameters:
node – [in] The node
str – [in] The string identifying value name
defVal – [in] The default value
- Returns:
An
uint64_tvalue
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unsigned int get_uint_attr(const pugi::xml_node &node, const char *str)#
Gets the unsigned integer attribute from
pugi::xml_node- Parameters:
node – [in] The node
str – [in] The string identifying value name
- Returns:
An unsigned integer value
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unsigned int get_uint_attr(const pugi::xml_node &node, const char *str, unsigned int defVal)#
Gets the unsigned integer attribute from
pugi::xml_node- Parameters:
node – [in] The node
str – [in] The string identifying value name
defVal – [in] The default value
- Returns:
An unsigned integer value
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std::string get_str_attr(const pugi::xml_node &node, const char *str)#
Gets the string attribute from
pugi::xml_node- Parameters:
node – [in] The node
str – [in] The string identifying value name
- Returns:
A string value
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std::string get_str_attr(const pugi::xml_node &node, const char *str, const char *def)#
Gets the string attribute from
pugi::xml_node- Parameters:
node – [in] The node
str – [in] The string identifying value name
def – [in] The default value
- Returns:
A string value
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bool get_bool_attr(const pugi::xml_node &node, const char *str)#
Gets the bool attribute from
pugi::xml_node- Parameters:
node – [in] The node
str – [in] The string identifying value name
- Returns:
A boolean value
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bool get_bool_attr(const pugi::xml_node &node, const char *str, const bool def)#
Gets the bool attribute from
pugi::xml_node- Parameters:
node – [in] The node
str – [in] The string identifying value name
def – [in] The default value
- Returns:
A boolean value
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float get_float_attr(const pugi::xml_node &node, const char *str)#
Gets the float attribute from
pugi::xml_node- Parameters:
node – [in] The node
str – [in] The string identifying value name
- Returns:
A single-precision floating point value
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float get_float_attr(const pugi::xml_node &node, const char *str, float defVal)#
Gets the float attribute from
pugi::xml_node- Parameters:
node – [in] The node
str – [in] The string identifying value name
defVal – [in] The default value
- Returns:
A single-precision floating point value
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int get_int_child(const pugi::xml_node &node, const char *str, int defVal)#
Gets the integer value located in a child node.
- Parameters:
node – [in] The node
str – [in] The string value identifying a child node
defVal – [in] The default value
- Returns:
An ingeter value located in a child node,
devValotherwise.
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inline ParseResult parse_xml(const char *file_path)#
Parses a file and returns ParseResult.
- Parameters:
file_path – [in] The file path
- Returns:
The ParseResult.
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struct ParseResult#
- #include <xml_parse_utils.hpp>
A XML parse result structure with an error message and the
pugi::xml_documentdocument.Public Functions
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inline ParseResult(std::unique_ptr<pugi::xml_document> &&xml, std::string error_msg)#
Constructs ParseResult with
pugi::xml_documentand an error message.- Parameters:
xml – The
pugi::xml_documenterror_msg – [in] The error message
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inline ParseResult(std::unique_ptr<pugi::xml_document> &&xml, std::string error_msg)#
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int get_int_attr(const pugi::xml_node &node, const char *str)#
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using FilePath = std::string#
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static const char ORIGINAL_NAMES[] = "originalLayersNames"#