class ov::op::v1::TopK¶
Overview¶
Computes indices and values of the k maximum/minimum values for each slice along specified axis. More…
#include <topk.hpp>
class TopK: public ov::op::Op
{
public:
// typedefs
typedef TopKSortType SortType;
typedef TopKMode Mode;
// fields
BWDCMP_RTTI_DECLARATION;
// construction
TopK();
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
);
TopK(
const Output<Node>& data,
const Output<Node>& k,
const int64_t axis,
const Mode mode,
const SortType sort,
const element::Type& index_element_type = element::i32
);
// methods
OPENVINO_OP("TopK", "opset1", op::Op, 1);
virtual bool visit_attributes(AttributeVisitor& visitor);
virtual void validate_and_infer_types();
virtual std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const;
uint64_t get_axis() const;
int64_t get_provided_axis() const;
void set_axis(const int64_t axis);
Mode get_mode() const;
void set_mode(const Mode mode);
SortType get_sort_type() const;
void set_sort_type(const SortType sort);
element::Type get_index_element_type() const;
void set_index_element_type(const element::Type& index_element_type);
size_t get_k() const;
void set_k(size_t k);
virtual size_t get_default_output_index() const;
virtual OPENVINO_SUPPRESS_DEPRECATED_START bool evaluate(
const HostTensorVector& output_values,
const HostTensorVector& input_values
) const;
virtual OPENVINO_SUPPRESS_DEPRECATED_END bool has_evaluate() const;
};
// direct descendants
class TopK;Inherited Members¶
public:
// typedefs
typedef DiscreteTypeInfo type_info_t;
typedef std::map<std::string, Any> RTMap;
// methods
virtual void validate_and_infer_types();
void constructor_validate_and_infer_types();
virtual bool visit_attributes(AttributeVisitor&);
virtual const ov::op::AutoBroadcastSpec& get_autob() const;
virtual bool has_evaluate() const;
virtual bool evaluate(
const ov::HostTensorVector& output_values,
const ov::HostTensorVector& input_values
) const;
virtual bool evaluate(
const ov::HostTensorVector& output_values,
const ov::HostTensorVector& input_values,
const EvaluationContext& evaluationContext
) const;
virtual bool evaluate_lower(const ov::HostTensorVector& output_values) const;
virtual bool evaluate_upper(const ov::HostTensorVector& output_values) const;
virtual bool evaluate(
ov::TensorVector& output_values,
const ov::TensorVector& input_values
) const;
virtual bool evaluate(
ov::TensorVector& output_values,
const ov::TensorVector& input_values,
const ov::EvaluationContext& evaluationContext
) const;
virtual bool evaluate_lower(ov::TensorVector& output_values) const;
virtual bool evaluate_upper(ov::TensorVector& output_values) const;
virtual bool evaluate_label(TensorLabelVector& output_labels) const;
virtual bool constant_fold(
OutputVector& output_values,
const OutputVector& inputs_values
);
virtual OutputVector decompose_op() const;
virtual const type_info_t& get_type_info() const = 0;
const char \* get_type_name() const;
void set_arguments(const NodeVector& arguments);
void set_arguments(const OutputVector& arguments);
void set_argument(size_t position, const Output<Node>& argument);
void set_output_type(
size_t i,
const element::Type& element_type,
const PartialShape& pshape
);
void set_output_size(size_t output_size);
void invalidate_values();
virtual void revalidate_and_infer_types();
virtual std::string description() const;
const std::string& get_name() const;
void set_friendly_name(const std::string& name);
const std::string& get_friendly_name() const;
virtual bool is_dynamic() const;
size_t get_instance_id() const;
virtual std::ostream& write_description(std::ostream& os, uint32_t depth = 0) const;
const std::vector<std::shared_ptr<Node>>& get_control_dependencies() const;
const std::vector<Node \*>& get_control_dependents() const;
void add_control_dependency(std::shared_ptr<Node> node);
void remove_control_dependency(std::shared_ptr<Node> node);
void clear_control_dependencies();
void clear_control_dependents();
void add_node_control_dependencies(std::shared_ptr<Node> source_node);
void add_node_control_dependents(std::shared_ptr<Node> source_node);
void transfer_control_dependents(std::shared_ptr<Node> replacement);
size_t get_output_size() const;
const element::Type& get_output_element_type(size_t i) const;
const element::Type& get_element_type() const;
const Shape& get_output_shape(size_t i) const;
const PartialShape& get_output_partial_shape(size_t i) const;
Output<const Node> get_default_output() const;
Output<Node> get_default_output();
virtual size_t get_default_output_index() const;
size_t no_default_index() const;
const Shape& get_shape() const;
descriptor::Tensor& get_output_tensor(size_t i) const;
descriptor::Tensor& get_input_tensor(size_t i) const;
const std::string& get_output_tensor_name(size_t i) const;
std::set<Input<Node>> get_output_target_inputs(size_t i) const;
size_t get_input_size() const;
const element::Type& get_input_element_type(size_t i) const;
const Shape& get_input_shape(size_t i) const;
const PartialShape& get_input_partial_shape(size_t i) const;
const std::string& get_input_tensor_name(size_t i) const;
Node \* get_input_node_ptr(size_t index) const;
std::shared_ptr<Node> get_input_node_shared_ptr(size_t index) const;
Output<Node> get_input_source_output(size_t i) const;
virtual std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& inputs) const = 0;
std::shared_ptr<Node> copy_with_new_inputs(const OutputVector& new_args) const;
std::shared_ptr<Node> copy_with_new_inputs(
const OutputVector& inputs,
const std::vector<std::shared_ptr<Node>>& control_dependencies
) const;
bool has_same_type(std::shared_ptr<const Node> node) const;
RTMap& get_rt_info();
const RTMap& get_rt_info() const;
NodeVector get_users(bool check_is_used = false) const;
virtual size_t get_version() const;
virtual std::shared_ptr<Node> get_default_value() const;
bool operator < (const Node& other) const;
std::vector<Input<Node>> inputs();
std::vector<Input<const Node>> inputs() const;
std::vector<Output<Node>> input_values() const;
std::vector<Output<Node>> outputs();
std::vector<Output<const Node>> outputs() const;
Input<Node> input(size_t input_index);
Input<const Node> input(size_t input_index) const;
Output<Node> input_value(size_t input_index) const;
Output<Node> output(size_t output_index);
Output<const Node> output(size_t output_index) const;
OPENVINO_SUPPRESS_DEPRECATED_START void set_op_annotations(std::shared_ptr<ngraph::op::util::OpAnnotations> op_annotations);
std::shared_ptr<ngraph::op::util::OpAnnotations> get_op_annotations() const;
virtual OPENVINO_SUPPRESS_DEPRECATED_END bool match_value(
ov::pass::pattern::Matcher \* matcher,
const Output<Node>& pattern_value,
const Output<Node>& graph_value
);
virtual bool match_node(
ov::pass::pattern::Matcher \* matcher,
const Output<Node>& graph_value
);
static _OPENVINO_HIDDEN_METHODconst ::ov::Node::type_info_t& get_type_info_static();
virtual const ::ov::Node::type_info_t& get_type_info() const;Detailed Documentation¶
Computes indices and values of the k maximum/minimum values for each slice along specified axis.
Construction¶
TopK()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 |
Specyfies type of produced indices |
Methods¶
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.
uint64_t get_axis() constReturns axis value after normalization.
If input rank required to normalization is dynamic, the exception is thrown
int64_t get_provided_axis() constReturns axis value before normalization.
size_t get_k() constReturns the value of K, if available.
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
virtual size_t get_default_output_index() constReturns the output of the default output, or throws if there is none.
virtual OPENVINO_SUPPRESS_DEPRECATED_START bool evaluate(
const HostTensorVector& output_values,
const HostTensorVector& input_values
) constEvaluates the op on input_values putting results in output_values.
Deprecated Use evaluate with ov::Tensor instead
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 OPENVINO_SUPPRESS_DEPRECATED_END bool has_evaluate() constAllows to get information about availability of evaluate method for the current operation.