/********************************************************************************
* 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_DATA_TENSOR_H_
#define AIDGE_CORE_DATA_TENSOR_H_
#include <cstring>
#include <set>
#include <memory>
#include <numeric>
#include <string>
#include <vector>
#include "aidge/backend/TensorImpl.hpp"
#include "aidge/data/Data.hpp"
#include "aidge/utils/Registrar.hpp"
#include "aidge/utils/Types.h"
namespace Aidge {
// Helper to create default arrays
template <typename T, std::size_t ... Is>
constexpr std::array<T, sizeof...(Is)>
create_array_impl(T value, std::index_sequence<Is...>)
{
// cast Is to void to remove the warning: unused value
return {{(static_cast<void>(Is), value)...}};
}
template <typename T, std::size_t N>
constexpr std::array<T, N> create_array(const T& value)
{
return create_array_impl(value, std::make_index_sequence<N>());
}
// Helper to convert vector to array
template <typename T, typename Iter, std::size_t... Is>
constexpr auto to_array(Iter &iter, std::index_sequence<Is...>) -> std::array<T, sizeof...(Is)> {
return {{((void)Is, T(*iter++))...}};
}
/**
* @brief Convert an object with an iterator to an std::array.
*/
template <std::size_t N, typename U = void, typename Iter, typename V = typename std::iterator_traits<Iter>::value_type,
typename T = std::conditional_t<std::is_same<U, void>{}, V, U>>
constexpr auto to_array(Iter iter) -> std::array<T, N> {
return to_array<T>(iter, std::make_index_sequence<N>{});
}
namespace detail {
template <class T, std::size_t N, std::size_t... I>
constexpr std::array<std::remove_cv_t<T>, N> to_array_impl(T (&a)[N], std::index_sequence<I...>) {
return {{a[I]...}};
}
} // namespace detail
/**
* @brief Convert a C-stype array into a C++ std::array.
*
* @tparam T Data type.
* @tparam N Number of elements.
* @param a C-style array to convert.
* @return constexpr std::array<std::remove_cv_t<T>, N>
*/
template <class T, std::size_t N>
constexpr std::array<std::remove_cv_t<T>, N> to_array(T (&a)[N]) {
return detail::to_array_impl(a, std::make_index_sequence<N>{});
}
template <typename T, std::size_t N, std::size_t... I>
constexpr std::array<T, N + 1> append(std::array<T, N> a, T t, std::index_sequence<I...>) {
return std::array<T, N + 1>{a[I]..., t};
}
template <typename T, std::size_t N, std::size_t... I>
constexpr std::array<T, N + 1> append(T t, std::array<T, N> a, std::index_sequence<I...>) {
return std::array<T, N + 1>{t, a[I]...};
}
/**
* @brief Create a new array concatenating the initial one with the value to
* add.
* @details append({1,2,7}, 3) -> {1,2,7,3}
*
* @tparam T Data type.
* @tparam N Number of elements in the initilial array.
* @param a Initial array.
* @param t Element to add.
* @return constexpr std::array<T, N + 1>
*/
template <typename T, std::size_t N>
constexpr std::array<T, N + 1> append(std::array<T, N> a, T t) {
return append(a, t, std::make_index_sequence<N>());
}
template <typename T, std::size_t N>
constexpr std::array<T, N + 1> append(T t, std::array<T, N> a) {
return append(t, a, std::make_index_sequence<N>());
}
// Generic helper for initializing a Tensor
template <typename T, std::size_t SIZE_0>
struct Array1D {
T data[SIZE_0];
};
template <typename T, std::size_t SIZE_0, std::size_t SIZE_1>
struct Array2D {
T data[SIZE_0][SIZE_1];
};
template <typename T, std::size_t SIZE_0, std::size_t SIZE_1, std::size_t SIZE_2>
struct Array3D {
T data[SIZE_0][SIZE_1][SIZE_2];
};
template <typename T, std::size_t SIZE_0, std::size_t SIZE_1, std::size_t SIZE_2, std::size_t SIZE_3>
struct Array4D {
T data[SIZE_0][SIZE_1][SIZE_2][SIZE_3];
};
/**
* @brief Description for the tensor data structure.
* @details Sets the properties of the tensor without actually containing any data.
* Contains a pointer to an actual contiguous implementation of data.
*/
class Tensor : public Data,
public Registrable<Tensor, std::tuple<std::string, DataType>, std::unique_ptr<TensorImpl>(const Tensor &)> {
private:
DataType mDataType; /** enum to specify data type. */
std::vector<DimSize_t> mDims; /** Dimensions of the tensor. */
std::unique_ptr<TensorImpl> mImpl; /** Pointer to the actual data implementation. */
std::shared_ptr<Tensor> mGrad; /** Pointer to the associated gradient Tensor instance. */
// Cached data
std::size_t mSize; /** Number of elements in the Tensor. */
std::size_t mSizeM1; /** Number of elements in the N-1 first dimensions */
public:
static constexpr const char *Type = "Tensor";
/**
* @brief Construct a new empty Tensor object.
* @param dataType Sets the type of inserted data.
*/
Tensor(DataType dataType = DataType::Float32)
: Data(Type),
mDataType(dataType),
mDims({}),
mSize(0),
mSizeM1(0)
{
// ctor
}
/**
* @brief Construct a new Tensor object copied from another one.
* @param otherTensor
*/
Tensor(const Tensor& otherTensor)
: Data(Type),
mDataType(otherTensor.mDataType),
mDims(otherTensor.mDims),
mSize(otherTensor.mSize),
mSizeM1(otherTensor.mSizeM1)
{
if (otherTensor.hasImpl()) {
mImpl = Registrar<Tensor>::create({otherTensor.mImpl->backend(), dataType()})(*this);
mImpl->setDevice(otherTensor.mImpl->device().second);
// Same backend, same device => directly use copy()
mImpl->copy(otherTensor.mImpl->rawPtr(), mSize);
}
}
/**
* @brief Construct a new Tensor object from the 1-dimension Array helper.
* @tparam T datatype
* @tparam SIZE_0 first array dimension.
*/
template <typename T, std::size_t SIZE_0>
constexpr Tensor(Array1D<T, SIZE_0> &&arr)
: Data(Type),
mDataType(NativeType<T>::type),
mDims({SIZE_0}),
mImpl(Registrar<Tensor>::create({"cpu", NativeType<T>::type})(*this)),
mSize(SIZE_0),
mSizeM1(SIZE_0) {
mImpl->copyFromHost(&arr.data[0], SIZE_0);
}
template <typename T, std::size_t SIZE_0>
constexpr Tensor &operator=(Array1D<T, SIZE_0> &&arr) {
resize({SIZE_0});
if (!mImpl) {
mImpl = Registrar<Tensor>::create({"cpu", NativeType<T>::type})(*this);
}
mImpl->copyFromHost(&arr.data[0], SIZE_0);
return *this;
}
/**
* @brief Construct a new Tensor object from the 2-dimensions Array helper.
* @tparam T datatype
* @tparam SIZE_0 first array dimension.
* @tparam SIZE_1 second array dimension.
*/
template <typename T, std::size_t SIZE_0, std::size_t SIZE_1>
constexpr Tensor(Array2D<T, SIZE_0, SIZE_1> &&arr)
: Data(Type),
mDataType(NativeType<T>::type),
mDims({SIZE_0, SIZE_1}),
mImpl(Registrar<Tensor>::create({"cpu", NativeType<T>::type})(*this)),
mSize(SIZE_0 * SIZE_1),
mSizeM1(SIZE_1) {
mImpl->copyFromHost(&arr.data[0][0], SIZE_0 * SIZE_1);
}
template <typename T, std::size_t SIZE_0, std::size_t SIZE_1>
constexpr Tensor &operator=(Array2D<T, SIZE_0, SIZE_1> &&arr) {
resize({SIZE_0, SIZE_1});
if (!mImpl) {
mImpl = Registrar<Tensor>::create({"cpu", NativeType<T>::type})(*this);
}
mImpl->copyFromHost(&arr.data[0][0], SIZE_0 * SIZE_1);
return *this;
}
/**
* @brief Construct a new Tensor object from the 3-dimensions Array helper.
* @tparam T datatype
* @tparam SIZE_0 first array dimension.
* @tparam SIZE_1 second array dimension.
* @tparam SIZE_2 third array dimension.
*/
template <typename T, std::size_t SIZE_0, std::size_t SIZE_1, std::size_t SIZE_2>
constexpr Tensor(Array3D<T, SIZE_0, SIZE_1, SIZE_2> &&arr)
: Data(Type),
mDataType(NativeType<T>::type),
mDims({SIZE_0, SIZE_1, SIZE_2}),
mImpl(Registrar<Tensor>::create({"cpu", NativeType<T>::type})(*this)),
mSize(SIZE_0 * SIZE_1 * SIZE_2),
mSizeM1(SIZE_1 * SIZE_2) {
mImpl->copyFromHost(&arr.data[0][0][0], SIZE_0 * SIZE_1 * SIZE_2);
}
template <typename T, std::size_t SIZE_0, std::size_t SIZE_1, std::size_t SIZE_2>
constexpr Tensor &operator=(Array3D<T, SIZE_0, SIZE_1, SIZE_2> &&arr) {
resize({SIZE_0, SIZE_1, SIZE_2});
if (!mImpl) {
mImpl = Registrar<Tensor>::create({"cpu", NativeType<T>::type})(*this);
}
mImpl->copyFromHost(&arr.data[0][0][0], SIZE_0 * SIZE_1 * SIZE_2);
return *this;
}
/**
* @brief Construct a new Tensor object from the 4-dimensions Array helper.
* @tparam T datatype
* @tparam SIZE_0 first array dimension.
* @tparam SIZE_1 second array dimension.
* @tparam SIZE_2 third array dimension.
* @tparam SIZE_3 fourth array dimension.
*/
template <typename T, std::size_t SIZE_0, std::size_t SIZE_1, std::size_t SIZE_2, std::size_t SIZE_3>
constexpr Tensor(Array4D<T, SIZE_0, SIZE_1, SIZE_2, SIZE_3> &&arr)
: Data(Type),
mDataType(NativeType<T>::type),
mDims({SIZE_0, SIZE_1, SIZE_2, SIZE_3}),
mImpl(Registrar<Tensor>::create({"cpu", NativeType<T>::type})(*this)),
mSize(SIZE_0 * SIZE_1 * SIZE_2 * SIZE_3),
mSizeM1(SIZE_1 * SIZE_2 * SIZE_3) {
mImpl->copyFromHost(&arr.data[0][0][0][0], SIZE_0 * SIZE_1 * SIZE_2 * SIZE_3);
}
template <typename T, std::size_t SIZE_0, std::size_t SIZE_1, std::size_t SIZE_2, std::size_t SIZE_3>
constexpr Tensor &operator=(Array4D<T, SIZE_0, SIZE_1, SIZE_2, SIZE_3> &&arr) {
resize({SIZE_0, SIZE_1, SIZE_2, SIZE_3});
if (!mImpl) {
mImpl = Registrar<Tensor>::create({"cpu", NativeType<T>::type})(*this);
}
mImpl->copyFromHost(&arr.data[0][0][0][0], SIZE_0 * SIZE_1 * SIZE_2 * SIZE_3);
return *this;
}
/**
* @brief Copy dimensions, datatype and data of another Tensor.
* @param t other Tensor object.
* @return Tensor&
*/
Tensor &operator=(const Tensor &t) {
resize(t.dims());
setDataType(t.dataType());
if (t.hasImpl()) {
if (hasImpl()) {
copyCastFrom(t);
}
else {
mImpl = Registrar<Tensor>::create({t.mImpl->backend(), dataType()})(*this);
mImpl->setDevice(t.mImpl->device().second);
// Same backend, same device => directly use copy()
mImpl->copy(t.mImpl->rawPtr(), mSize);
}
}
else {
mImpl = nullptr;
}
return *this;
}
/**
* @brief Assess data type, dimensions, backend and data are the same.
* @param otherTensor
*/
bool operator==(const Tensor &otherTensor) const {
if ((!mImpl && !otherTensor.mImpl) || (dataType() != otherTensor.dataType()) ||
(dims() != otherTensor.dims()) || (mImpl->backend() != otherTensor.mImpl->backend())) {
return false;
}
return *mImpl == *(otherTensor.mImpl);
}
/**
* @brief Set the backend of the Tensor associated implementation
* @details Create and initialized an implementation if non was associated.
* @param name
*/
inline void setBackend(const std::string &name, int device = 0) {
if (mImpl) {
if (mImpl->device() != std::make_pair(name, device)) {
// Backend change: create new impl, copy from old to new and replace
// impl
std::unique_ptr<TensorImpl> newImpl = Registrar<Tensor>::create({name, mDataType})(*this);
newImpl->setDevice(device);
newImpl->copyFrom(*mImpl, size());
mImpl = std::move(newImpl);
}
}
else {
mImpl = Registrar<Tensor>::create({name, mDataType})(*this);
mImpl->setDevice(device);
}
}
/**
* @brief Get a list of available backends.
* @return std::set<std::string>
*/
static std::set<std::string> getAvailableBackends(){
std::set<std::string> backendsList;
for(std::tuple<std::string, DataType> tupleKey : Registrar<Tensor>::getKeys())
backendsList.insert(std::get<0>(tupleKey));
return backendsList;
}
/**
* @brief Get the data type enum.
* @return constexpr DataType
*/
constexpr DataType dataType() const { return mDataType; }
/**
* @brief Set the DataType of the Tensor and converts data
* if the Tensor has already been initialized.
* @param dt DataType.
*/
void setDataType(const DataType dt) {
if (mImpl && (dataType() != dt)) {
std::unique_ptr<TensorImpl> newImpl = Registrar<Tensor>::create({mImpl->backend(), dt})(*this);
newImpl->copyCast(mImpl->rawPtr(), size(), mDataType);
mImpl = std::move(newImpl);
}
mDataType = dt;
}
/**
* @brief Get the Impl object
* @return constexpr const std::unique_ptr<TensorImpl>&
*/
constexpr const std::unique_ptr<TensorImpl> &getImpl() { return mImpl; }
constexpr const std::unique_ptr<TensorImpl> &getImpl() const { return mImpl; }
/**
* @brief Return if an implementaiton has been associated.
* @return true
* @return false
*/
bool hasImpl() const { return (mImpl) ? true : false; }
/**
* @brief Get number of dimensions of the Tensor.
* @return std::size_t
*/
inline std::size_t nbDims() const { return mDims.size(); }
/**
* @brief Get dimensions of the Tensor object.
* @tparam DIM number of dimensions.
* @return constexpr std::array<DimSize_t, DIM>
*/
template <DimIdx_t DIM>
constexpr std::array<DimSize_t, DIM> dims() const {
assert(DIM == mDims.size() && "wrong number of dimensions");
return to_array<DIM>(mDims.cbegin());
}
/**
* @brief Get dimensions of the Tensor object.
* @return constexpr const std::vector<DimSize_t>&
*/
constexpr const std::vector<DimSize_t> &dims() const { return mDims; }
/**
* @brief Get the number of elements in the Tensor object.
* @return constexpr std::size_t
*/
constexpr std::size_t size() const { return mSize; }
/**
* @brief Get the number of elements in the N-1 dimensions of the Tensor object.
* @return constexpr std::size_t
*/
constexpr std::size_t sizeM1() const { return mSizeM1; }
/**
* @brief Change the shape of the Tensor object according to the given argument.
* @tparam DIM new dimensions.
* @param dims
*/
template <std::array<DimSize_t, 1>::size_type DIM> // deducing std::array size_type and declaring DIM accordingly
void resize(const std::array<DimSize_t, DIM> &dims) {
static_assert(DIM<=MaxDim,"Too many tensor dimensions required by resize, not supported");
mDims.assign(dims.begin(), dims.end());
computeSize();
}
void resize(const std::vector<DimSize_t> &dims) {
mDims = dims;
computeSize();
}
/**
* @brief Return if the Tensor object has at leastone element.
* @return true
* @return false
*/
bool empty() const { return mDims.empty(); }
template <typename expectedType>
expectedType& get(std::size_t idx){
// TODO : add assert expected Type compatible with datatype
// TODO : add assert idx < Size
return *reinterpret_cast<expectedType *>(mImpl->getRaw(idx));
}
template <typename expectedType>
expectedType& get(std::vector<std::size_t> coordIdx){
return get<expectedType>(getIdx(coordIdx));
}
template <typename expectedType>
void set(std::size_t idx, expectedType value){
// TODO : add assert expected Type compatible with datatype
// TODO : add assert idx < Size
void* dataPtr = mImpl->getRaw(idx);
std::memcpy(dataPtr, &value, sizeof(expectedType));
}
template <typename expectedType>
void set(std::vector<std::size_t> coordIdx, expectedType value){
set<expectedType>(getIdx(coordIdx), value);
}
std::string toString() const {
// TODO: move lambda elsewhere?
auto ptrToString = [](DataType dt, void* ptr, size_t idx) {
switch (dt) {
case DataType::Float64:
return std::to_string(static_cast<double*>(ptr)[idx]);
case DataType::Float32:
return std::to_string(static_cast<float*>(ptr)[idx]);
case DataType::Float16:
return std::to_string(static_cast<half_float::half*>(ptr)[idx]);
case DataType::Int8:
return std::to_string(static_cast<int8_t*>(ptr)[idx]);
case DataType::Int16:
return std::to_string(static_cast<int16_t*>(ptr)[idx]);
case DataType::Int32:
return std::to_string(static_cast<int32_t*>(ptr)[idx]);
case DataType::Int64:
return std::to_string(static_cast<int64_t*>(ptr)[idx]);
case DataType::UInt8:
return std::to_string(static_cast<uint8_t*>(ptr)[idx]);
case DataType::UInt16:
return std::to_string(static_cast<uint16_t*>(ptr)[idx]);
case DataType::UInt32:
return std::to_string(static_cast<uint32_t*>(ptr)[idx]);
case DataType::UInt64:
return std::to_string(static_cast<uint64_t*>(ptr)[idx]);
default:
AIDGE_ASSERT(true, "unsupported type to convert to string");
}
};
if (dims().empty()) { return "{}"; }
std::string res;
std::size_t dim = 0;
std::size_t counter = 0;
if (nbDims()>=2) {
std::vector<std::size_t> dimVals(nbDims(), 0);
res += "{\n";
while (counter < mSize) {
std::string spaceString = std::string((dim+1)<<1,' ');
if (dim < nbDims()-2) {
if (dimVals[dim] == 0) {
res += spaceString + "{\n";
++dim;
} else if (dimVals[dim] < static_cast<std::size_t>(dims()[dim])) {
res += spaceString + "},\n" + spaceString + "{\n";
++dim;
} else {
res += spaceString + "}\n";
dimVals[dim--] = 0;
dimVals[dim]++;
}
} else {
for (; dimVals[dim] < static_cast<std::size_t>(dims()[dim]); ++dimVals[dim]) {
res += spaceString + "{";
for (DimSize_t j = 0; j < dims()[dim + 1] - 1; ++j) {
res += " " + ptrToString(mDataType, mImpl->rawPtr(), counter++) + ",";
}
res += " " + ptrToString(mDataType, mImpl->rawPtr(), counter++) + "}";
if (dimVals[dim] < static_cast<std::size_t>(dims()[dim] - 1)) {
res += ",";
}
res += "\n";
}
if (dim == 0) {
break;
}
dimVals[dim--] = 0;
dimVals[dim]++;
}
}
for(int i = static_cast<int>(dim); i > 0; --i) {
res += std::string((dim+1)<<1,' ') + "}\n";
}
} else {
res += "{";
for (DimSize_t j = 0; j < dims()[0]; ++j) {
res += " " + ptrToString(mDataType, mImpl->rawPtr(), j) + ((j < dims()[0]-1) ? "," : "");
}
}
res += "}";
return res;
}
inline void print() const { printf("%s\n", toString().c_str()); }
std::shared_ptr<Tensor> grad() {
if (!mGrad) {
mGrad = std::make_shared<Tensor>(mDataType);
mGrad->resize(mDims);
if (mImpl) mGrad->setBackend(mImpl->backend());
}
return mGrad;
}
/**
* @brief From the the 1D index, return the coordinate of an element in the tensor.
*
* @param flatIdx 1D index of the value considering a flatten tensor.
* @return std::vector<DimSize_t>
*/
std::vector<std::size_t> getCoord(std::size_t flatIdx) const {
std::vector<std::size_t> coordIdx = std::vector<std::size_t>(mDims.size());
std::size_t idx = flatIdx;
for (std::size_t i = mDims.size() - 1; i > 0; --i){
coordIdx[i] = (idx % mDims[i]);
idx/=mDims[i];
}
coordIdx[0] = idx % mDims[0];
return coordIdx;
}
/**
* @brief From the coordinate returns the 1D index of an element in the tensor.
*
* @param coordIdx Coordinate to an element in the tensor
* @return DimSize_t
*/
std::size_t getIdx(std::vector<std::size_t> coordIdx) const {
// std::size_t flatIdx = 0;
// std::size_t stride = 1;
std::size_t flatIdx = 0;
assert(coordIdx.size() == mDims.size() && "Coordinates does not match number of dimensions");
std::size_t i = 0;
for(; i < mDims.size() - 1; ++i){
assert(coordIdx[i] < mDims[i] && "Coordinates dimensions does not fit the dimensions of the tensor");
flatIdx = (flatIdx + coordIdx[i]) * mDims[i + 1];
}
return flatIdx + coordIdx[i];
}
/**
* Copy-cast data from a Tensor.
* @param src Source tensor to copy-cast from.
* @param movedSrc shared_ptr to an indermediate Tensor that will
* contain the moved data if a device change should occur AND a type
* conversion is necessary (otherwise it remains unused).
* Any data already present will be overwritten. No new memory allocation
* will occur if movedSrc has already been allocated with the right
* type/size/device.
* If required, memory is always allocated on current (destination)
* Tensor's device.
*/
void copyCastFrom(const Tensor& src, std::shared_ptr<Tensor>& movedSrc);
/**
* Copy-cast data from a Tensor.
* In case of both a device change AND a data type conversion, an
* intermediate buffer on will be allocated and deallocated each time.
* If required, buffer's memory is always allocated on current (destination)
* Tensor's device.
* @param src Source tensor to copy-cast from.
*/
void copyCastFrom(const Tensor& src) {
// Internal buffer will be allocated and deallocated at each call
// (only if needed)
std::shared_ptr<Tensor> movedSrc;
copyCastFrom(src, movedSrc);
}
/**
* Return a reference to a Tensor casted to the desired data type:
* - itself, if already at the right data type;
* - the provided Tensor, overwritten with the copy-casted data.
* The backend stays the same.
* @param fallback A shared_ptr to Tensor ready to be overwritten if necessary.
* The shared_ptr does not need to be initialized. No new memory allocation
* will occur if fallback has already been allocated with the right
* type/size/device.
* @param dt The desired data type.
* @return Reference to either itself or to fallback.
*/
Tensor& refCast(std::shared_ptr<Tensor>& fallback, const Aidge::DataType& dt);
const Tensor& refCast(std::shared_ptr<Tensor>& fallback, const Aidge::DataType& dt) const;
/**
* Return a reference to a Tensor on the desired backend/device:
* - itself, if already on the right device;
* - the provided Tensor, overwritten with the copied data.
* The data type stays the same.
* @param fallback A shared_ptr to Tensor ready to be overwritten if necessary.
* The shared_ptr does not need to be initialized. No new memory allocation
* will occur if fallback has already been allocated with the right
* type/size/device.
* @param backend The desired backend.
* @param device The desired device.
* @return Reference to either itself or to fallback.
*/
Tensor& ref(std::shared_ptr<Tensor>& fallback, const std::string &backend, int device = 0);
const Tensor& ref(std::shared_ptr<Tensor>& fallback, const std::string &backend, int device = 0) const;
/**
* Return a reference to a Tensor with same characteristics
* (data type, backend/device) as target Tensor:
* - itself, if already with the right characteristics;
* - the provided Tensor, overwritten with the copy-casted data.
* @param fallback A shared_ptr to Tensor ready to be overwritten if necessary.
* The shared_ptr does not need to be initialized. No new memory allocation
* will occur if fallback has already been allocated with the right
* type/size/device.
* @param target Tensor with the desired target characteristics.
* @return Reference to either itself or to fallback.
*/
Tensor& refCast(std::shared_ptr<Tensor>& fallback, const Tensor& target) {
const auto& device = target.getImpl()->device();
return refCast(fallback, target.dataType()).ref(fallback, device.first, device.second);
}
private:
///\bug not protected against overflow
std::size_t computeSize() {
if (mDims.empty()) {
mSizeM1 = DimSize_t(0);
mSize = DimSize_t(0);
}
else if (mDims.size() == 1)
{
mSizeM1 = mDims[0];
mSize = mDims[0];
}
else {
mSizeM1 = std::accumulate(++mDims.begin(),mDims.end(), DimSize_t(1), std::multiplies<DimSize_t>());
mSize = static_cast<std::size_t>(mSizeM1 * mDims[0]);
}
return mSize;
}
};
} // namespace Aidge
#endif /* AIDGE_CORE_DATA_TENSOR_H_ */