class ov::op::v9::NonMaxSuppression¶

Overview¶

#include <non_max_suppression.hpp>

class NonMaxSuppression: public ov::op::Op
{
public:
    // enums

    enum BoxEncodingType;

    // fields

     BWDCMP_RTTI_DECLARATION;

    // construction

    NonMaxSuppression();

    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
        );

    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
        );

    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
        );

    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
        );

    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
        );

    // methods

    OPENVINO_OP("NonMaxSuppression", "opset9", op::Op, 9);
    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;
    BoxEncodingType get_box_encoding() const;
    void set_box_encoding(const BoxEncodingType box_encoding);
    bool get_sort_result_descending() const;
    void set_sort_result_descending(const bool sort_result_descending);
    element::Type get_output_type() const;
    void set_output_type(const element::Type& output_type);
    int64_t max_boxes_output_from_input() const;
    float iou_threshold_from_input() const;
    float score_threshold_from_input() const;
    float soft_nms_sigma_from_input() const;
    bool is_soft_nms_sigma_constant_and_default() const;

    void set_output_type(
        size_t i,
        const element::Type& element_type,
        const PartialShape& pshape
        );
};

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¶

NonMaxSuppression operation.

Construction¶

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.

Parameters:

boxes	Node producing the box coordinates
scores	Node producing the box scores
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 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

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.