ReduceMean.hpp

/********************************************************************************
 * Copyright (c) 2023 CEA-List
 *
 * This program and the accompanying materials are made available under the
 * terms of the Eclipse Public License 2.0 which is available at
 * http://www.eclipse.org/legal/epl-2.0.
 *
 * SPDX-License-Identifier: EPL-2.0
 *
 ********************************************************************************/

#ifndef AIDGE_CORE_OPERATOR_REDUCEMEAN_H_
#define AIDGE_CORE_OPERATOR_REDUCEMEAN_H_

#include <algorithm>  // std::for_each
#include <array>
#include <cmath>
#include <cstdint>    // std::int32_t
#include <numeric>
#include <vector>

#include "aidge/data/Tensor.hpp"
#include "aidge/graph/Node.hpp"
#include "aidge/operator/OperatorTensor.hpp"
#include "aidge/operator/Producer.hpp"
#include "aidge/utils/StaticAttributes.hpp"
#include "aidge/utils/Registrar.hpp"
#include "aidge/utils/Types.h"

namespace Aidge {
enum class ReduceMeanAttr { Axes, KeepDims };

template <DimIdx_t DIM>
class ReduceMean_Op : public OperatorTensor,
                public Registrable<ReduceMean_Op<DIM>, std::string, std::unique_ptr<OperatorImpl>(const ReduceMean_Op<DIM> &)>,
                public StaticAttributes<ReduceMeanAttr, std::array<std::int32_t, DIM>, DimSize_t> {

   public:
    static const std::string Type;

    ReduceMean_Op() = delete;

    using Attributes_ = StaticAttributes<ReduceMeanAttr, std::array<std::int32_t, DIM>, DimSize_t>;
    template <ReduceMeanAttr e>
    using attr = typename Attributes_::template attr<e>;

    constexpr ReduceMean_Op(const std::array<std::int32_t, DIM> &axes, DimSize_t keep_dims)
        : OperatorTensor(Type, 1, 0, 1),
          Attributes_(attr<ReduceMeanAttr::Axes>(axes),
                      attr<ReduceMeanAttr::KeepDims>(keep_dims)) {}

    /**
     * @brief Copy-constructor. Copy the operator attributes and its output tensor(s), but not its input tensors (the new operator has no input associated).
     * @param op Operator to copy.
     */
    ReduceMean_Op(const ReduceMean_Op<DIM>& op)
        : OperatorTensor(op),
          Attributes_(op)
    {
        mImpl = op.mImpl ? Registrar<ReduceMean_Op<DIM>>::create(mOutputs[0]->getImpl()->backend())(*this) : nullptr;
    }

    /**
     * @brief Clone the operator using its copy-constructor.
     * @see Operator::ReduceMean_Op
     */
    std::shared_ptr<Operator> clone() const override {
        return std::make_shared<ReduceMean_Op<DIM>>(*this);
    }

    void computeOutputDims() override final {
        if (!getInput(0)) {
            AIDGE_THROW_OR_ABORT(std::runtime_error, "Every input should be associated with a Tensor");
        }
        if (!getInput(0)->empty()) {
            // make Axes attribute positive
            std::array<std::int32_t, DIM>& axes = this->template getAttr<ReduceMeanAttr::Axes>();
            std::for_each(axes.begin(), axes.end(), [&] (std::int32_t& val) {
                if (val < 0)
                    val+=static_cast<std::int32_t>(getInput(0)->nbDims());
            });
            std::sort(axes.begin(), axes.end());

            // build output dimensions
            std::vector<DimSize_t> outDims = getInput(0)->dims();
            if (this->template getAttr<ReduceMeanAttr::KeepDims>()) {
                std::for_each(axes.begin(), axes.end(), [&outDims] (const std::int32_t& val) { outDims[val] = 1; });
            }
            else {
                for (auto it = axes.crbegin(); it != axes.crend(); ++it)
                    outDims.erase(outDims.begin() + static_cast<std::size_t>(*it));
            }

            if(outDims.size()>0)
                mOutputs[0]->resize(outDims);
            else
                mOutputs[0]->resize({1});
        }
    }

    void setBackend(const std::string &name, DeviceIdx_t device = 0) override {
        mImpl = Registrar<ReduceMean_Op<DIM>>::create(name)(*this);
        mOutputs[0]->setBackend(name, device);
    }

    static const std::vector<std::string> getInputsName(){
        return {"data_input"};
    }
    static const std::vector<std::string> getOutputsName(){
        return {"data_output"};
    }
};

template <std::array<DimSize_t, 1>::size_type DIM>
inline std::shared_ptr<Node> ReduceMean(const std::array<std::int32_t, DIM> &axes,
                                        DimSize_t keep_dims=1,
                                        const std::string& name = "") {
    // FIXME: properly handle default w&b initialization in every cases
    static_assert(DIM<=MaxDim,"Too many kernel dimensions required by ReduceMean, not supported");
    return std::make_shared<Node>(std::make_shared<ReduceMean_Op<static_cast<DimIdx_t>(DIM)>>(axes, keep_dims), name);

}

// helper with C-style array instead of std::array for kernel_dims to allow automatic template DIM deduction
template <DimSize_t DIM>
inline std::shared_ptr<Node> ReduceMean(
    std::int32_t const (&axes)[DIM],
    DimSize_t keep_dims = 1,
    const std::string& name = "") {
    static_assert(DIM<=MaxDim,"Too many kernel dimensions required by ReduceMean, not supported");
    return ReduceMean(to_array(axes), keep_dims, name);
}

template <DimIdx_t DIM>
const std::string ReduceMean_Op<DIM>::Type = "ReduceMean";

}  // namespace Aidge

namespace {
template <>
const char *const EnumStrings<Aidge::ReduceMeanAttr>::data[] = {"Axes", "KeepDims"};
}

#endif /* AIDGE_CORE_OPERATOR_REDUCEMEAN_H_ */