class ov::op::v8::DeformableConvolution

Overview

DeformableConvolution operation. More…

#include <deformable_convolution.hpp>

class DeformableConvolution: public ov::op::util::DeformableConvolutionBase
{
public:
    // construction

    DeformableConvolution();

    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
        );

    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
        );

    // methods

    OPENVINO_OP(
        "DeformableConvolution",
        "opset8",
        op::util::DeformableConvolutionBase
        );

    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;
    bool get_bilinear_interpolation_pad() const;
    void set_bilinear_interpolation_pad(const bool bilinear_interpolation_pad);
};

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;
    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;
    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;
    OPENVINO_OP("DeformableConvolutionBase", "util");
    virtual bool visit_attributes(AttributeVisitor& visitor);
    virtual void validate_and_infer_types();
    const Strides& get_strides() const;
    void set_strides(const Strides& strides);
    const Strides& get_dilations() const;
    void set_dilations(const Strides& dilations);
    const CoordinateDiff& get_pads_begin() const;
    void set_pads_begin(const CoordinateDiff& pads_begin);
    const CoordinateDiff& get_pads_end() const;
    void set_pads_end(const CoordinateDiff& pads_end);
    const PadType& get_auto_pad() const;
    void set_auto_pad(const PadType& auto_pad);
    int64_t get_group() const;
    void set_group(const int64_t group);
    int64_t get_deformable_group() const;
    void set_deformable_group(const int64_t deformable_group);

Detailed Documentation

DeformableConvolution operation.

Construction

DeformableConvolution()

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 true and 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 is false and 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 true and 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 is false and 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.

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.