class ov::op::v3::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::util::TopKBase
{
public:
// 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 TopKMode mode,
const TopKSortType sort,
const element::Type& index_element_type = element::i32
);
// methods
OPENVINO_OP("TopK", "opset3", op::util::TopKBase);
virtual std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) 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;
};Inherited Members¶
public:
// typedefs
typedef DiscreteTypeInfo type_info_t;
typedef std::map<std::string, Any> RTMap;
typedef TopKMode Mode;
typedef TopKSortType SortType;
// 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(
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(const std::shared_ptr<const Node>& source_node);
void add_node_control_dependents(const std::shared_ptr<const 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;
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;
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;
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;
virtual 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;
OPENVINO_OP("TopKBase", "util");
virtual void validate_and_infer_types();
virtual bool visit_attributes(AttributeVisitor& visitor);
uint64_t get_axis() const;
int64_t get_provided_axis() const;
void set_axis(const int64_t axis);
void set_axis(const Rank& input_rank, const int64_t axis);
TopKMode get_mode() const;
void set_mode(const TopKMode mode);
TopKSortType get_sort_type() const;
void set_sort_type(const TopKSortType 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;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 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.