class ov::op::v8::MaxPool

Overview

MaxPooling operation with values and indices calculated as individual outputs. More…

#include <max_pool.hpp>

class MaxPool: public ov::op::util::MaxPoolBase
{
public:
    // construction

    MaxPool();

    MaxPool(
        const Output<Node>& arg,
        const Strides& strides,
        const Strides& dilations,
        const Shape& pads_begin,
        const Shape& pads_end,
        const Shape& kernel,
        const op::RoundingType rounding_type = op::RoundingType::FLOOR,
        const PadType auto_pad = op::PadType::EXPLICIT,
        const element::Type index_element_type = element::i64,
        const int64_t axis = 0
        );

    // methods

    OPENVINO_OP("MaxPool", "opset8", op::util::MaxPoolBase);
    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;
    const Strides& get_dilations() const;
    void set_dilations(const Strides& dilations);
    element::Type get_index_element_type() const;
    void set_index_element_type(const element::Type index_element_type);
    int64_t get_axis() const;
    void set_axis(const int64_t axis);
    virtual bool has_evaluate() const;

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

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(
        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("MaxPoolBase", "util");
    virtual void validate_and_infer_types();
    const Shape& get_kernel() const;
    void set_kernel(const Shape& kernel);
    const Strides& get_strides() const;
    void set_strides(const Strides& strides);
    const Shape& get_pads_begin() const;
    void set_pads_begin(const Shape& pads_begin);
    const Shape& get_pads_end() const;
    void set_adding_above(const Shape& pads_end);
    PadType get_auto_pad() const;
    void set_auto_pad(const PadType auto_pad);
    op::RoundingType get_rounding_type() const;
    void set_rounding_type(op::RoundingType rounding_type);

Detailed Documentation

MaxPooling operation with values and indices calculated as individual outputs.

Construction

MaxPool()

Constructs an empty MaxPool operation.

MaxPool(
    const Output<Node>& arg,
    const Strides& strides,
    const Strides& dilations,
    const Shape& pads_begin,
    const Shape& pads_end,
    const Shape& kernel,
    const op::RoundingType rounding_type = op::RoundingType::FLOOR,
    const PadType auto_pad = op::PadType::EXPLICIT,
    const element::Type index_element_type = element::i64,
    const int64_t axis = 0
    )

Constructs a parametrized MaxPool operation.

Parameters:

arg

Output of a node producing the feature tensor to be pooled.

strides

The strides of the pooling filter.

dilations

The dilations of the pooling filter.

pads_begin

Paddings at the beginning of each spatial axis.

pads_end

Paddings at the end of each spatial axis.

kernel

The kernel shape.

rounding_type

Whether to use ceiling or floor rounding type while computing the output shape.

auto_pad

The pad type for automatic calculation of the padding sizes.

index_element_type

The data type used by the second output tensor containing the selected indices.

axis

Indicates a dimension in the input data shape which should be used as a starting point for calculation of the upper bound of allowed values of the indices output.

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.

const Strides& get_dilations() const

Returns:

The pooling filter’s dilations.

element::Type get_index_element_type() const

Returns:

The data type of the second output tensor (indices).

virtual bool has_evaluate() const

Allows to get information about availability of evaluate method for the current operation.

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

Evaluates 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