reshape.hpp

//*****************************************************************************
// Copyright 2017-2021 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/axis_vector.hpp"
#include "ngraph/node.hpp"
#include "ngraph/op/op.hpp"
#include "ngraph/runtime/host_tensor.hpp"
 
namespace ngraph
{
    namespace op
    {
        namespace v1
        {
            /// \brief Tensor dynamic reshape operation.
            ///
            /// "Converts" an input tensor into a new shape with the same number of elements.
            /// This op does not touch the actual data. If needed, use Transpose for that purpose.
            ///
            class NGRAPH_API Reshape : public Op
            {
            public:
                NGRAPH_RTTI_DECLARATION;
                Reshape() = default;
                /// \brief Constructs a dynamic reshape operation. This operation does not perform
                ///        transpose.
                ///
                /// \param arg The tensor to be reshaped.
                /// \param shape_pattern The node that defines output shape shape_pattern.
                ///        If the input shape is \f$(a_0,\dots,a_{k-1})\f$ then the output shape
                ///        must
                ///        be of the form \f$(b_0,\dots,b_{j-1})\f$ where \f$\Pi(a_i) = \Pi(b_i)\f$.
                ///        A value of -1 is allowed for at most one dimension, in which case the
                ///        dimension size is inferred based on element count of input tensor.
                /// \param special_zero Treats zeros in `shape_pattern` as wildcard flags indicating
                /// a
                ///        copy from input shape at the same index.
                ///
                Reshape(const Output<Node>& arg,
                        const Output<Node>& shape_pattern,
                        bool special_zero);
 
                bool visit_attributes(AttributeVisitor& visitor) override;
                void validate_and_infer_types() override;
 
                size_t get_version() const override { return 1; }
                virtual std::shared_ptr<Node>
                    clone_with_new_inputs(const OutputVector& new_args) const override;
 
                bool get_special_zero() const { return m_special_zero; }
                void set_special_zero(bool special_zero) { m_special_zero = special_zero; }
                bool evaluate(const HostTensorVector& outputs,
                              const HostTensorVector& inputs) const override;
                bool evaluate_lower(const HostTensorVector& outputs) const override;
                bool evaluate_upper(const HostTensorVector& outputs) const override;
                bool constant_fold(OutputVector& output_values,
                                   const OutputVector& inputs_values) override;
 
            protected:
                bool m_special_zero;
                bool evaluate_reshape(const HostTensorVector& outputs,
                                      const HostTensorVector& inputs) const;
            };
        }
    }
}