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Commit 44740599 authored by Houssem ROUIS's avatar Houssem ROUIS
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add ReduceMean operator

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/********************************************************************************
* 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 <array>
#include <cmath>
#include <numeric>
#include <vector>
#include "aidge/data/Tensor.hpp"
#include "aidge/graph/Node.hpp"
#include "aidge/operator/Operator.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 Operator,
public Registrable<ReduceMean_Op<DIM>, std::string, std::unique_ptr<OperatorImpl>(const ReduceMean_Op<DIM> &)>,
public StaticAttributes<ReduceMeanAttr, std::array<DimSize_t, DIM>, DimSize_t> {
public:
// FIXME: change accessibility
std::shared_ptr<Tensor> mInput = std::make_shared<Tensor>();
const std::shared_ptr<Tensor> mOutput = std::make_shared<Tensor>();
public:
static constexpr const char *Type = "ReduceMean";
ReduceMean_Op() = delete;
using Attributes_ = StaticAttributes<ReduceMeanAttr, std::array<DimSize_t, DIM>, DimSize_t>;
template <ReduceMeanAttr e>
using attr = typename Attributes_::template attr<e>;
constexpr ReduceMean_Op(const std::array<DimSize_t, DIM> &axes, DimSize_t keep_dims)
: Operator(Type),
Attributes_(attr<ReduceMeanAttr::Axes>(axes),
attr<ReduceMeanAttr::KeepDims>(keep_dims)) {
setDatatype(DataType::Float32);
}
/**
* @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)
: Operator(Type),
Attributes_(op),
mOutput(std::make_shared<Tensor>(*op.mOutput))
{
// cpy-ctor
setDatatype(op.mOutput->dataType());
mImpl = op.mImpl ? Registrar<ReduceMean_Op<DIM>>::create(mOutput->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 associateInput(const IOIndex_t inputIdx, std::shared_ptr<Data> data) override final {
assert(inputIdx == 0 && "ReduceMean operator supports only 1 input");
assert(strcmp(data->type(), Tensor::Type) == 0 && "input data must be of Tensor type");
mInput = std::dynamic_pointer_cast<Tensor>(data);
}
void computeOutputDims() override final {
if (!mInput->empty()) {
std::vector<DimSize_t> outDims;
for(std::size_t d=0; d<mInput->dims().size(); ++d)
{
bool reducedDim = false;
for(std::size_t i=0; i<DIM; ++i)
{
if(this->template getAttr<ReduceMeanAttr::Axes>()[i] == d)
{
reducedDim = true;
break;
}
}
if(!reducedDim)
{
if(this->template getAttr<ReduceMeanAttr::KeepDims>())
outDims.push_back(1);
}
else
outDims.push_back(mInput->dims()[d]);
}
mOutput->resize(outDims);
}
}
bool outputDimsForwarded() const override final { return !(mOutput->empty()); }
inline Tensor& input(const IOIndex_t /*inputIdx*/) const override final { return *(mInput.get()); }
inline Tensor& output(const IOIndex_t /*outputIdx*/) const override final { return *(mOutput.get()); }
inline std::shared_ptr<Tensor> getInput(const IOIndex_t inputIdx) const override final {
assert(inputIdx == 0 && "ReduceMean Operators supports only 1 input");
(void) inputIdx; // avoid unused warning
return mInput;
}
inline std::shared_ptr<Tensor> getOutput(const IOIndex_t outputIdx) const override final {
assert((outputIdx == 0) && "ReduceMean Operator has only 1 output");
(void) outputIdx; // avoid unused warning
return mOutput;
}
std::shared_ptr<Data> getRawInput(const IOIndex_t inputIdx) const override final {
assert(inputIdx == 0 && "ReduceMean Operators supports only 1 input");
(void) inputIdx; // avoid unused warning
return std::static_pointer_cast<Data>(mInput);
}
std::shared_ptr<Data> getRawOutput(const IOIndex_t outputIdx) const override final {
assert(outputIdx == 0 && "ReduceMean Operator supports only 1 output");
(void) outputIdx; // avoid unused warning
return std::static_pointer_cast<Data>(mOutput);
}
void setBackend(const std::string &name) override {
mImpl = Registrar<ReduceMean_Op<DIM>>::create(name)(*this);
mOutput->setBackend(name);
// FIXME: temporary workaround
mInput->setBackend(name);
}
void setDatatype(const DataType &datatype) override {
mOutput->setDatatype(datatype);
// FIXME: temporary workaround
mInput->setDatatype(datatype);
}
inline IOIndex_t nbInputs() const noexcept override final { return 1; }
inline IOIndex_t nbDataInputs() const noexcept override final { return 1; }
inline IOIndex_t nbOutputs() const noexcept override final { return 1; }
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<DimSize_t, DIM> &axes,
DimSize_t keep_dims,
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(
DimSize_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);
}
} // namespace Aidge
namespace {
template <>
const char *const EnumStrings<Aidge::ReduceMeanAttr>::data[] = {"Axes", "KeepDims"};
}
#endif /* AIDGE_CORE_OPERATOR_REDUCEMEAN_H_ */
// /********************************************************************************
// * 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
// *
// ********************************************************************************/
// #include <pybind11/pybind11.h>
// #include <pybind11/stl.h>
// #include <iostream>
// #include <string>
// #include <vector>
// #include <array>
// #include "aidge/backend/OperatorImpl.hpp"
// #include "aidge/operator/ReduceMean.hpp"
// #include "aidge/operator/Operator.hpp"
// #include "aidge/utils/Types.h"
// namespace py = pybind11;
// namespace Aidge {
// template <DimIdx_t DIM> void declare_ReduceMeanOp(py::module &m) {
// py::class_<ReduceMean_Op<DIM>, std::shared_ptr<ReduceMean_Op<DIM>>, Operator, Attributes>(
// m, ("ReduceMeanOp" + std::to_string(DIM) + "D").c_str(),
// py::multiple_inheritance())
// .def(py::init<const std::array<DimSize_t, DIM> &, DimSize_t>(),
// py::arg("axes"),
// py::arg("keep_dims"))
// .def("get_inputs_name", &ReduceMean_Op<DIM>::getInputsName)
// .def("get_outputs_name", &ReduceMean_Op<DIM>::getOutputsName)
// ;
// m.def(("ReduceMean" + std::to_string(DIM) + "D").c_str(), [](const std::vector<DimSize_t>& axes,
// DimSize_t keepDims,
// const std::string& name) {
// AIDGE_ASSERT(axes.size() == DIM, "axes size [%ld] does not match DIM [%d]", axes.size(), DIM);
// return ReduceMean<DIM>(to_array<DIM>(axes.begin()), keepDims, name);
// }, py::arg("axes"),
// py::arg("keep_dims") = 1);
// }
// void init_ReduceMean(py::module &m) {
// declare_ReduceMeanOp<1>(m);
// declare_ReduceMeanOp<2>(m);
// declare_ReduceMeanOp<3>(m);
// // FIXME:
// // m.def("ReduceMean1D", static_cast<NodeAPI(*)(const char*, int, int, int const
// // (&)[1])>(&ReduceMean));
// }
// } // namespace Aidge
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