fake_quantize.hpp

// Copyright (C) 2018-2021 Intel Corporation
// SPDX-License-Identifier: Apache-2.0
//
 
#pragma once
 
#include "ngraph/node.hpp"
#include "ngraph/op/util/attr_types.hpp"
#include "ngraph/op/util/fused_op.hpp"
 
namespace ngraph
{
    namespace op
    {
        namespace v0
        {
            ///
            /// \brief      Class performing element-wise linear quantization.
            ///
            /// \note       Input floating point values are quantized into a discrete
            ///             set of floating point values.
            ///
            /// \paragraph Implementation This class creates a node which performs the following
            ///            operation:
            ///
            ///            round((data - input_low) / (input_high - input_low) * (levels-1)) /
            ///                 (levels-1) * (output_high - output_low) + output_low
            ///
            ///
            class NGRAPH_API FakeQuantize : public ngraph::op::Op
            {
            public:
                NGRAPH_RTTI_DECLARATION;
 
                FakeQuantize();
                ///
                /// \brief      Constructs a FakeQuantize operation node.
                ///
                /// \param[in]  data            The input data tensor.
                /// \param[in]  input_low       The minimum limit for input values.
                /// \param[in]  input_high      The maximum limit for input values.
                /// \param[in]  output_low      The minimum quantized value.
                /// \param[in]  output_high     The maximum quantized value.
                /// \param[in]  levels          The number of quantization levels.
                /// \param[in]  auto_broadcast  AutoBroadcast mode to be used for broadcasting
                ///                             limit values
                ///
                FakeQuantize(const Output<Node>& data,
                             const Output<Node>& input_low,
                             const Output<Node>& input_high,
                             const Output<Node>& output_low,
                             const Output<Node>& output_high,
                             std::size_t levels,
                             const AutoBroadcastSpec& auto_broadcast =
                                 AutoBroadcastSpec(AutoBroadcastType::NUMPY));
 
                bool visit_attributes(AttributeVisitor& visitor) override;
                virtual void validate_and_infer_types() override;
 
                virtual std::shared_ptr<Node>
                    clone_with_new_inputs(const OutputVector& new_args) const override;
 
                std::size_t get_levels() const { return m_levels; }
                void set_levels(std::size_t levels) { m_levels = levels; }
                const AutoBroadcastSpec& get_auto_broadcast() const { return m_auto_broadcast; }
                void set_auto_broadcast(const AutoBroadcastSpec& auto_broadcast)
                {
                    m_auto_broadcast = auto_broadcast;
                }
 
            private:
                std::size_t m_levels;
                AutoBroadcastSpec m_auto_broadcast = op::AutoBroadcastType::NUMPY;
            };
        } // namespace v0
        using v0::FakeQuantize;
    } // namespace op
} // namespace ngraph