binary_elementwise_logical.hpp

// Copyright (C) 2018-2021 Intel Corporation
// SPDX-License-Identifier: Apache-2.0
//
 
#pragma once
 
#include "ngraph/op/op.hpp"
 
namespace ngraph
{
    namespace op
    {
        namespace util
        {
            // clang-format off
            /// \brief Abstract base class for elementwise binary logical operations, i.e.,
            ///        operations where the same scalar binary logical operation is applied to
            ///        each corresponding pair of elements in two boolean input tensors. Implicit
            ///        broadcast of input tensors is supported through one of the AutoBroadcast
            ///        modes.
            ///
            /// For example, if the underlying operation (determined by the subclass) is
            /// \f$\mathit{op}(x,y)\f$, the input tensors \f$[[x_0,y_0],[z_0,w_0]]\f$ and
            /// \f$[[x_1,y_1],[z_1,w_1]]\f$ will be mapped to
            /// \f$[[\mathit{op}(x_0,x_1),\mathit{op}(y_0,y_1)],[\mathit{op}(z_0,z_1),\mathit{op}(w_0,w_1)]]\f$.
            ///
            /// ## Inputs
            ///
            /// |        | Type                                          | Description                                            |
            /// | ------ | --------------------------------------------- | ------------------------------------------------------ |
            /// | `arg0` | \f$\texttt{bool}[d_1,\dots,d_n]~(n \geq 0)\f$ | A tensor of any shape, with element type `bool`.       |
            /// | `arg1` | \f$\texttt{bool}[d_1,\dots,d_n]~(n \geq 0)\f$ | A tensor of the same shape and element type as `arg0`. |
            /// | `autob`| AutoBroadcastSpec                             | Auto broadcast specification.                          |
            ///
            /// ## Output
            ///
            /// | Type                               | Description                                                                                                                                                                                                        |
            /// | ---------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
            /// | \f$\texttt{bool}[d_1,\dots,d_n]\f$ | The tensor \f$T\f$, where \f$T[i_1,\dots,i_n] = \mathit{op}(\texttt{arg0}[i_1,\dots,i_n],\texttt{arg1}[i_1,\dots,i_n])\f$. This will always have the same shape as the input tensors, and the element type `bool`. |
            // clang-format on
            class NGRAPH_API BinaryElementwiseLogical : public Op
            {
            protected:
                NGRAPH_RTTI_DECLARATION;
 
                BinaryElementwiseLogical();
 
                /// \brief Constructs a binary elementwise logical operation.
                ///
                /// \param arg0 Output that produces the first input tensor.
                /// \param arg1 Output that produces the second input tensor.
                BinaryElementwiseLogical(const Output<Node>& arg0,
                                         const Output<Node>& arg1,
                                         const AutoBroadcastSpec& autob = AutoBroadcastSpec());
 
            public:
                void validate_and_infer_types() override;
 
                const AutoBroadcastSpec& get_autob() const override { return m_autob; }
                void set_autob(const AutoBroadcastSpec& autob) { m_autob = autob; }
                bool visit_attributes(AttributeVisitor& visitor) override;
 
            private:
                void validate_and_infer_elementwise_logical(const op::AutoBroadcastSpec& autob);
                AutoBroadcastSpec m_autob = AutoBroadcastSpec::NUMPY;
            };
        } // namespace util
    }     // namespace op
} // namespace ngraph