class ngraph::op::v5::NonMaxSuppression

Overview

NonMaxSuppression operation. More…

#include <non_max_suppression.hpp>

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

    enum BoxEncodingType;

    // construction

    NonMaxSuppression();

    NonMaxSuppression(
        const Output<Node>& boxes,
        const Output<Node>& scores,
        const BoxEncodingType box_encoding = BoxEncodingType::CORNER,
        const bool sort_result_descending = true,
        const ngraph::element::Type& output_type = ngraph::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 ngraph::element::Type& output_type = ngraph::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 ngraph::element::Type& output_type = ngraph::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 ngraph::element::Type& output_type = ngraph::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 ngraph::element::Type& output_type = ngraph::element::i64
        );

    // methods

    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, std::shared_ptr<Variant>> RTMap;

    // fields

    NGRAPH_DEPRECATED("The tensor name was deprecated. Use get_input_tensor(i).get_names() instead.") const std std::unordered_set<descriptor::Tensor*> liveness_new_list;
    std::unordered_set<descriptor::Tensor*> liveness_free_list;

    // methods

    virtual void validate_and_infer_types();
    void constructor_validate_and_infer_types();
    virtual bool visit_attributes(AttributeVisitor&);
    virtual const op::AutoBroadcastSpec& get_autob() const;
    virtual bool has_evaluate() const;

    virtual bool evaluate(
        const HostTensorVector& output_values,
        const HostTensorVector& input_values
        ) const;

    virtual bool evaluate(
        const HostTensorVector& output_values,
        const HostTensorVector& input_values,
        const EvaluationContext& evaluationContext
        ) const;

    virtual bool evaluate_lower(const HostTensorVector& output_values) const;
    virtual bool evaluate_upper(const HostTensorVector& output_values) 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;
    NGRAPH_DEPRECATED("The tensor name was deprecated. Use get_output_tensor(i).get_names() instead.") const std 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;
    const std::unordered_set<std::string>& get_provenance_tags() const;
    void add_provenance_tag(const std::string& tag);

    template <typename T>
    void add_provenance_tags(T tag_set);

    void add_provenance_tags_above(
        const OutputVector& base,
        const std::unordered_set<std::string>& tag_set
        );

    void remove_provenance_tag(const std::string& tag);
    void add_provenance_group_member(const std::shared_ptr<Node>& node);
    void remove_provenance_group_member(const std::shared_ptr<Node>& node);

    void replace_provenance_group_member(
        const std::shared_ptr<Node>& current_node,
        const std::shared_ptr<Node>& replacement_node
        );

    const std::set<std::shared_ptr<Node>>& get_provenance_group_members() const;
    std::shared_ptr<Node> add_provenance_group_members_above(const OutputVector& base);
    void merge_provenance_tags_from(const std::shared_ptr<const Node>& source);
    void transfer_provenance_tags(const std::shared_ptr<Node>& replacement);
    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;
    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 bool match_value(
        pattern::Matcher* matcher,
        const Output<Node>& pattern_value,
        const Output<Node>& graph_value
        );

    virtual bool match_node(
        pattern::Matcher* matcher,
        const Output<Node>& graph_value
        );

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 ngraph::element::Type& output_type = ngraph::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 ngraph::element::Type& output_type = ngraph::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 ngraph::element::Type& output_type = ngraph::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 ngraph::element::Type& output_type = ngraph::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 ngraph::element::Type& output_type = ngraph::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.