range.hpp

//*****************************************************************************
// 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/node.hpp"
#include "ngraph/op/op.hpp"
 
namespace ngraph
{
    namespace op
    {
        namespace v4
        {
            /// \brief Range operation, analogous to `arange()` in Numpy.
            class NGRAPH_API Range : public Op
            {
            public:
                NGRAPH_RTTI_DECLARATION;
                /// \brief Constructs an unitialized range operation.
                Range() = default;
 
                /// \brief Constructs a range operation.
                ///
                /// \param start The tensor producing the start value. Must be a scalar of integer
                ///              element type, and same element type as `stop` and `step`.
                /// \param stop The tensor producing the stop value. Must be a scalar of integer
                ///             element type, and same element type as `start` and `step`.
                /// \param step The tensor producing the step value. Must be a scalar of integer
                ///             element type, and same element type as `start` and `stop`.
                /// \param output_type The type of the output.
                Range(const Output<Node>& start,
                      const Output<Node>& stop,
                      const Output<Node>& step,
                      element::Type output_type);
 
                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;
                bool evaluate(const HostTensorVector& outputs,
                              const HostTensorVector& inputs) const override;
 
            private:
                element::Type m_output_type;
            };
        }
        namespace v0
        {
            /// \brief Range operation, analogous to `range()` in Python.
            class NGRAPH_API Range : public Op
            {
            public:
                static constexpr NodeTypeInfo type_info{"Range", 0};
                const NodeTypeInfo& get_type_info() const override { return type_info; }
                /// \brief Constructs an unitialized range operation.
                Range() = default;
 
                /// \brief Constructs a range operation.
                ///
                /// \param start The tensor producing the start value. Must be a scalar of integer
                ///              element type, and same element type as `stop` and `step`.
                /// \param stop The tensor producing the stop value. Must be a scalar of integer
                ///             element type, and same element type as `start` and `step`.
                /// \param step The tensor producing the step value. Must be a scalar of integer
                ///             element type, and same element type as `start` and `stop`.
                Range(const Output<Node>& start,
                      const Output<Node>& stop,
                      const Output<Node>& step);
 
                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;
                bool evaluate(const HostTensorVector& outputs,
                              const HostTensorVector& inputs) const override;
            };
        }
        using v0::Range;
    }
}