text: //*****************************************************************************
  // Copyright 2017-2020 Intel Corporation
  //
  // Licensed under the Apache License, Version 2.0 (the "License");
  // you may not use this file except in compliance with the License.
  // You may obtain a copy of the License at
  //
  //     http://www.apache.org/licenses/LICENSE-2.0
  //
  // Unless required by applicable law or agreed to in writing, software
  // distributed under the License is distributed on an "AS IS" BASIS,
  // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  // See the License for the specific language governing permissions and
  // limitations under the License.
  //*****************************************************************************
  
  #pragma once
  
  #include "ngraph/op/op.hpp"
  #include "ngraph/op/util/attr_types.hpp"
  
  namespace ngraph
  {
      namespace op
      {
          namespace v0
          {
              /// \brief Batched average pooling operation, with optional padding and window stride.
              ///
              class NGRAPH_API AvgPool : public Op
              {
              public:
                  static constexpr NodeTypeInfo type_info{"AvgPool", 0};
                  const NodeTypeInfo& get_type_info() const override { return type_info; }
                  /// \brief Constructs a batched average pooling operation.
                  AvgPool() = default;
  
                  /// \brief Constructs a batched average pooling operation.
                  ///
                  /// \param arg The output producing the input data batch tensor.<br>
                  /// `[d1, dn]`
                  /// \param window_shape The window shape.<br>
                  /// `[n]`
                  /// \param window_movement_strides The window movement strides.<br>
                  /// `[n]`
                  /// \param padding_below The below-padding shape.<br>
                  /// `[n]`
                  /// \param padding_above The above-padding shape.<br>
                  /// `[n]`
                  /// \param include_padding_in_avg_computation If true then averages include padding
                  /// elements, each treated as the number zero.  If false, padding elements are
                  /// entirely ignored when computing averages. \param pad_type Padding type to use
                  /// for additional padded dimensions \param ceil_mode Whether to use ceiling while
                  /// computing output shape.
                  AvgPool(const Output<Node>& arg,
                          const Shape& window_shape,
                          const Strides& window_movement_strides,
                          const Shape& padding_below,
                          const Shape& padding_above,
                          bool include_padding_in_avg_computation,
                          const PadType& pad_type,
                          bool ceil_mode);
  
                  /// \brief Constructs a batched average pooling operation.
                  ///
                  /// \param arg The output producing the input data batch tensor.<br>
                  /// `[d1, dn]`
                  /// \param window_shape The window shape.<br>
                  /// `[n]`
                  /// \param window_movement_strides The window movement strides.<br>
                  /// `[n]`
                  /// \param padding_below The below-padding shape.<br>
                  /// `[n]`
                  /// \param padding_above The above-padding shape.<br>
                  /// `[n]`
                  /// \param include_padding_in_avg_computation If true then averages include padding
                  /// elements, each treated as the number zero.  If false, padding elements are
                  /// entirely ignored when computing averages. \param pad_type Padding type to use
                  /// for additional padded dimensions
                  AvgPool(const Output<Node>& arg,
                          const Shape& window_shape,
                          const Strides& window_movement_strides,
                          const Shape& padding_below,
                          const Shape& padding_above,
                          bool include_padding_in_avg_computation,
                          const PadType& pad_type);
  
                  /// \brief Constructs a batched average pooling operation.
                  ///
                  /// \param arg The output producing the input data batch tensor.<br>
                  /// `[d1, dn]`
                  /// \param window_shape The window shape.<br>
                  /// `[n]`
                  /// \param window_movement_strides The window movement strides.<br>
                  /// `[n]`
                  /// \param padding_below The below-padding shape.<br>
                  /// `[n]`
                  /// \param padding_above The above-padding shape.<br>
                  /// `[n]`
                  /// \param include_padding_in_avg_computation If true then averages include padding
                  /// elements, each treated as the number zero.  If false, padding elements are
                  /// entirely ignored when computing averages.
                  AvgPool(const Output<Node>& arg,
                          const Shape& window_shape,
                          const Strides& window_movement_strides,
                          const Shape& padding_below,
                          const Shape& padding_above,
                          bool include_padding_in_avg_computation = false);
  
                  /// \brief Constructs a batched, unpadded average pooling operation (i.e., all
                  /// padding shapes are set to 0).
                  ///
                  /// \param arg The output producing the input data batch tensor.<br>
                  /// `[d1, ..., dn]`
                  /// \param window_shape The window shape.<br>
                  /// `[n]`
                  /// \param window_movement_strides The window movement strides.<br>
                  /// `[n]`
                  AvgPool(const Output<Node>& arg,
                          const Shape& window_shape,
                          const Strides& window_movement_strides);
  
                  /// \brief Constructs an unstrided batched convolution operation (i.e., all window
                  /// movement strides are 1 and all padding shapes are set to 0).
                  ///
                  /// \param arg The output producing the input data batch tensor.<br>
                  /// `[d1, ..., dn]`
                  /// \param window_shape The window shape.<br>
                  /// `[n]`
                  AvgPool(const Output<Node>& arg, const Shape& window_shape);
  
                  bool visit_attributes(AttributeVisitor& visitor) override;
  
                  void validate_and_infer_types() override;
  
                  virtual std::shared_ptr<Node>
                      clone_with_new_inputs(const OutputVector& new_args) const override;
  
                  virtual void generate_adjoints(autodiff::Adjoints& adjoints,
                                                 const OutputVector& deltas) override;
  
                  /// \return The window shape.
                  const Shape& get_window_shape() const;
                  void set_window_shape(const Shape& window_shape);
                  /// \return The window movement strides.
                  const Strides& get_window_movement_strides() const;
                  void set_window_movement_strides(const Strides& window_movement_strides);
                  /// \return The below-padding shape.
                  const Shape& get_padding_below() const;
                  void set_padding_below(const Shape& padding_below);
                  /// \return The above-padding shape.
                  const Shape& get_padding_above() const;
                  void set_padding_above(const Shape& padding_above);
                  bool get_include_padding_in_avg_computation() const;
                  void
                      set_include_padding_in_avg_computation(bool include_padding_in_avg_computation);
                  /// \return The pad type for pooling.
                  const PadType& get_pad_type() const;
                  void set_pad_type(const PadType& pad_type);
                  bool get_ceil_mode() const;
                  void set_ceil_mode(bool ceil_mode);
                  /// \return The default value for AvgPool.
                  virtual std::shared_ptr<Node> get_default_value() const override;
  
              protected:
                  Shape m_window_shape;
                  Strides m_window_movement_strides;
                  Shape m_padding_below;
                  Shape m_padding_above;
                  bool m_include_padding_in_avg_computation{false};
                  PadType m_pad_type{PadType::EXPLICIT};
                  bool m_ceil_mode{false};
              };
  
              class NGRAPH_API AvgPoolBackprop : public Op
              {
              public:
                  static constexpr NodeTypeInfo type_info{"AvgPoolBackprop", 0};
                  const NodeTypeInfo& get_type_info() const override { return type_info; }
                  AvgPoolBackprop() = default;
                  AvgPoolBackprop(const Shape& forward_arg_shape,
                                  const Output<Node>& delta,
                                  const Shape& window_shape,
                                  const Strides& window_movement_strides,
                                  const Shape& padding_below,
                                  const Shape& padding_above,
                                  bool include_padding_in_avg_computation);
  
                  void validate_and_infer_types() override;
                  bool visit_attributes(AttributeVisitor& visitor) override;
  
                  virtual std::shared_ptr<Node>
                      clone_with_new_inputs(const OutputVector& new_args) const override;
  
                  const Shape& get_forward_arg_shape() const;
                  void set_forward_arg_shape(const Shape& forward_arg_shape);
                  const Shape& get_window_shape() const;
                  void set_window_shape(const Shape& window_shape);
                  const Strides& get_window_movement_strides() const;
                  void set_window_movement_strides(const Strides& window_movement_strides);
                  const Shape& get_padding_below() const;
                  void set_padding_below(const Shape& padding_below);
                  const Shape& get_padding_above() const;
                  void set_padding_above(const Shape& padding_abve);
                  bool get_include_padding_in_avg_computation() const;
                  void
                      set_include_padding_in_avg_computation(bool include_padding_in_avg_computation);
  
              protected:
                  Shape m_forward_arg_shape;
                  Shape m_window_shape;
                  Strides m_window_movement_strides;
                  Shape m_padding_below;
                  Shape m_padding_above;
                  bool m_include_padding_in_avg_computation{false};
              };
          }
  
          namespace v1
          {
              /// \brief Batched average pooling operation.
              ///
              class NGRAPH_API AvgPool : public Op
              {
              public:
                  static constexpr NodeTypeInfo type_info{"AvgPool", 1};
                  const NodeTypeInfo& get_type_info() const override { return type_info; }
                  /// \brief Constructs a batched average pooling operation.
                  AvgPool() = default;
  
                  ///
                  /// \brief      Constructs a batched average pooling operation.
                  ///
                  /// \param      arg            The output producing the input data batch tensor.<br>
                  ///                            `[d1, dn]`
                  /// \param      strides        The strides.<br> `[n]`
                  /// \param      pads_begin     The beginning of padding shape.<br> `[n]`
                  /// \param      pads_end       The end of padding shape.<br> `[n]`
                  /// \param      kernel         The kernel shape.<br> `[n]`
                  /// \param      exclude_pad    If false then averages include padding elements, each
                  ///                            treated as the number zero.  If true, padding
                  ///                            elements
                  ///                            are entirely ignored when computing averages.
                  /// \param      rounding_type  Whether to use ceiling or floor rounding type while
                  ///                            computing output shape.
                  /// \param      auto_pad       Padding type to use for additional padded dimensions
                  ///
                  AvgPool(const Output<Node>& arg,
                          const Strides& strides,
                          const Shape& pads_begin,
                          const Shape& pads_end,
                          const Shape& kernel,
                          bool exclude_pad,
                          op::RoundingType rounding_type,
                          const PadType& auto_pad);
  
                  ///
                  /// \brief      Constructs a batched average pooling operation.
                  ///
                  /// \param      arg            The output producing the input data batch tensor.<br>
                  ///                            `[d1, dn]`
                  /// \param      strides        The strides.<br> `[n]`
                  /// \param      pads_begin     The beginning of padding shape.<br> `[n]`
                  /// \param      pads_end       The end of padding shape.<br> `[n]`
                  /// \param      kernel         The kernel shape.<br> `[n]`
                  /// \param      exclude_pad    If false then averages include padding elements, each
                  ///                            treated as the number zero.  If true, padding
                  ///                            elements
                  ///                            are entirely ignored when computing averages.
                  /// \param      rounding_type  Whether to use ceiling or floor rounding type while
                  ///                            computing output shape.
                  ///
                  AvgPool(const Output<Node>& arg,
                          const Strides& strides,
                          const Shape& pads_begin,
                          const Shape& pads_end,
                          const Shape& kernel,
                          bool exclude_pad,
                          op::RoundingType rounding_type);
  
                  void validate_and_infer_types() override;
                  bool visit_attributes(AttributeVisitor& visitor) override;
  
                  virtual std::shared_ptr<Node>
                      clone_with_new_inputs(const OutputVector& new_args) const override;
  
                  virtual void generate_adjoints(autodiff::Adjoints& adjoints,
                                                 const OutputVector& deltas) override;
  
                  /// \return The kernel shape.
                  const Shape& get_kernel() const;
                  void set_kernel(const Shape& kernel);
                  /// \return The strides.
                  const Strides& get_strides() const;
                  void set_strides(const Strides& strides);
                  /// \return The beginning of padding shape.
                  const Shape& get_pads_begin() const;
                  void set_pads_begin(const Shape& pads_begin);
                  /// \return The end of padding shape.
                  const Shape& get_pads_end() const;
                  void set_pads_end(const Shape& pads_end);
                  bool get_exclude_pad() const;
                  void set_exclude_pad(bool exclude_pad);
                  /// \return The pad type for pooling.
                  const 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);
                  /// \return The default value for AvgPool.
                  virtual std::shared_ptr<Node> get_default_value() const override;
  
              protected:
                  Shape m_kernel;
                  Strides m_strides;
                  Shape m_pads_begin;
                  Shape m_pads_end;
                  bool m_exclude_pad{true};
                  PadType m_auto_pad{PadType::EXPLICIT};
                  op::RoundingType m_rounding_type{op::RoundingType::FLOOR};
              };
  
              class NGRAPH_API AvgPoolBackprop : public Op
              {
              public:
                  static constexpr NodeTypeInfo type_info{"AvgPoolBackprop", 1};
                  const NodeTypeInfo& get_type_info() const override { return type_info; }
                  AvgPoolBackprop() = default;
                  AvgPoolBackprop(const Output<Node>& delta,
                                  const Output<Node>& forward_arg_shape,
                                  const Strides& strides,
                                  const Shape& pads_begin,
                                  const Shape& pads_end,
                                  const Shape& kernel,
                                  bool exclude_pad);
  
                  void validate_and_infer_types() override;
                  bool visit_attributes(AttributeVisitor& visitor) override;
  
                  virtual std::shared_ptr<Node>
                      clone_with_new_inputs(const OutputVector& new_args) const override;
  
                  const Shape get_forward_arg_shape() const;
                  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_pads_end(const Shape& padding_abve);
                  bool get_exclude_pad() const;
                  void set_exclude_pad(bool exclude_pad);
  
              protected:
                  Shape m_kernel;
                  Strides m_strides;
                  Shape m_pads_begin;
                  Shape m_pads_end;
                  bool m_exclude_pad{false};
              };
          }
      }
  }
  