180 lines
7.1 KiB
C++
180 lines
7.1 KiB
C++
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// Copyright (C) 2018-2025 Intel Corporation
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// SPDX-License-Identifier: Apache-2.0
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//
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#pragma once
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#include <limits>
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#include "openvino/op/util/max_pool_base.hpp"
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namespace ov {
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namespace op {
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namespace v1 {
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/// \brief Batched max pooling operation.
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/// \ingroup ov_ops_cpp_api
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class OPENVINO_API MaxPool : public op::util::MaxPoolBase {
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public:
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OPENVINO_OP("MaxPool", "opset1", op::util::MaxPoolBase);
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/// \brief Constructs a batched max pooling operation.
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MaxPool() = default;
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/// \brief Constructs a batched max pooling operation.
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///
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/// \param arg The node producing the input data batch tensor.
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/// \param strides The strides.
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/// \param pads_begin The beginning of padding shape.
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/// \param pads_end The end of padding shape.
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/// \param kernel The kernel shape.
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/// \param rounding_type Whether to use ceiling or floor rounding type while
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/// computing output shape.
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/// \param auto_pad The pad type for automatically computing padding sizes.
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MaxPool(const Output<Node>& arg,
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const Strides& strides,
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const Shape& pads_begin,
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const Shape& pads_end,
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const Shape& kernel,
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const op::RoundingType rounding_type = op::RoundingType::FLOOR,
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const PadType auto_pad = op::PadType::EXPLICIT);
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bool visit_attributes(AttributeVisitor& visitor) override;
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void validate_and_infer_types() override;
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std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
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bool evaluate(TensorVector& outputs, const TensorVector& inputs) const override;
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bool has_evaluate() const override;
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};
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} // namespace v1
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namespace v8 {
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/// \brief MaxPooling operation with values and indices calculated as individual outputs
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/// \ingroup ov_ops_cpp_api
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class OPENVINO_API MaxPool : public op::util::MaxPoolBase {
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public:
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OPENVINO_OP("MaxPool", "opset8", op::util::MaxPoolBase);
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/// \brief Constructs an empty MaxPool operation.
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MaxPool() = default;
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/// \brief Constructs a parametrized MaxPool operation.
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///
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/// \param arg Output of a node producing the feature tensor to be pooled.
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/// \param strides The strides of the pooling filter.
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/// \param dilations The dilations of the pooling filter.
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/// \param pads_begin Paddings at the beginning of each spatial axis.
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/// \param pads_end Paddings at the end of each spatial axis.
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/// \param kernel The kernel shape.
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/// \param rounding_type Whether to use ceiling or floor rounding type while
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/// computing the output shape.
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/// \param auto_pad The pad type for automatic calculation of the padding sizes.
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/// \param index_element_type The data type used by the second output tensor
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/// containing the selected indices.
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/// \param axis Indicates a dimension in the input data shape which should be used
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/// as a starting point for calculation of the upper bound of allowed
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/// values of the indices output.
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MaxPool(const Output<Node>& arg,
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const Strides& strides,
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const Strides& dilations,
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const Shape& pads_begin,
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const Shape& pads_end,
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const Shape& kernel,
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const op::RoundingType rounding_type = op::RoundingType::FLOOR,
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const PadType auto_pad = op::PadType::EXPLICIT,
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const element::Type index_element_type = element::i64,
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const int64_t axis = 0);
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bool visit_attributes(AttributeVisitor& visitor) override;
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void validate_and_infer_types() override;
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std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
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/// \return The pooling filter's dilations.
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const Strides& get_dilations() const noexcept;
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void set_dilations(const Strides& dilations);
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/// \return The data type of the second output tensor (indices).
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element::Type get_index_element_type() const noexcept;
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void set_index_element_type(const element::Type index_element_type);
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// \return The 'axis' attribute value.
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int64_t get_axis() const;
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void set_axis(const int64_t axis);
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bool evaluate(TensorVector& outputs, const TensorVector& inputs) const override;
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bool has_evaluate() const override;
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private:
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Strides m_dilations;
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element::Type m_index_element_type{element::i64};
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int64_t m_axis{0};
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};
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} // namespace v8
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namespace v14 {
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/// \brief MaxPooling operation with values and indices calculated as individual outputs
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/// \ingroup ov_ops_cpp_api
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class OPENVINO_API MaxPool : public op::util::MaxPoolBase {
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public:
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OPENVINO_OP("MaxPool", "opset14", op::util::MaxPoolBase);
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/// \brief Constructs an empty MaxPool operation.
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MaxPool() = default;
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/// \brief Constructs a parametrized MaxPool operation.
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///
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/// \param arg Output of a node producing the feature tensor to be pooled.
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/// \param strides The strides of the pooling filter.
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/// \param dilations The dilations of the pooling filter.
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/// \param pads_begin Paddings at the beginning of each spatial axis.
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/// \param pads_end Paddings at the end of each spatial axis.
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/// \param kernel The kernel shape.
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/// \param rounding_type Whether to use ceiling or floor rounding type while
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/// computing the output shape.
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/// \param auto_pad The pad type for automatic calculation of the padding sizes.
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/// \param index_element_type The data type used by the second output tensor
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/// containing the selected indices.
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/// \param axis Indicates a dimension in the input data shape which should be used
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/// as a starting point for calculation of the upper bound of allowed
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/// values of the indices output.
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MaxPool(const Output<Node>& arg,
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const Strides& strides,
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const Strides& dilations,
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const Shape& pads_begin,
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const Shape& pads_end,
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const Shape& kernel,
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const op::RoundingType rounding_type = op::RoundingType::FLOOR,
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const PadType auto_pad = op::PadType::EXPLICIT,
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const element::Type index_element_type = element::i64,
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const int64_t axis = 0);
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bool visit_attributes(AttributeVisitor& visitor) override;
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void validate_and_infer_types() override;
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std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
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/// \return The pooling filter's dilations.
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const Strides& get_dilations() const noexcept;
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void set_dilations(const Strides& dilations);
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/// \return The data type of the second output tensor (indices).
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element::Type get_index_element_type() const noexcept;
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void set_index_element_type(const element::Type index_element_type);
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// \return The 'axis' attribute value.
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int64_t get_axis() const;
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void set_axis(const int64_t axis);
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bool evaluate(TensorVector& outputs, const TensorVector& inputs) const override;
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bool has_evaluate() const override;
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private:
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Strides m_dilations;
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element::Type m_index_element_type{element::i64};
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int64_t m_axis{0};
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};
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} // namespace v14
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} // namespace op
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} // namespace ov
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