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Commit 49f9606c authored by Maxence Naud's avatar Maxence Naud
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Change required dependencies includes

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2 merge requests!318[Upd] release verision 0.5.0,!306ENHANCE: split DataType, DataFormat, EnumString from other files
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with 30 additions and 21 deletions
include/aidge/backend/OperatorImpl.hpp
+ 5
− 2
View file @ 49f9606c
......@@ -12,13 +12,16 @@
#ifndef AIDGE_BACKEND_OPERATORIMPL_H_
#define AIDGE_BACKEND_OPERATORIMPL_H_
#include <functional> // std::function
#include <memory>
#include <string>
#include <vector>
#include <functional>
#include <utility> // std::pair
#include "aidge/utils/Types.h"
#include "aidge/utils/DynamicAttributes.hpp"
#include "aidge/data/Data.hpp"
#include "aidge/data/DataFormat.hpp"
#include "aidge/data/DataType.hpp"
#include "aidge/data/Elts.hpp"
#include "aidge/scheduler/ProdConso.hpp"
......
include/aidge/backend/TensorImpl.hpp
+ 1
− 1
View file @ 49f9606c
......@@ -19,7 +19,7 @@
#include <vector>
#include <string>
#include "aidge/data/Data.hpp"
#include "aidge/data/DataType.hpp"
#include "aidge/utils/ErrorHandling.hpp"
#include "aidge/utils/Types.h"
......
include/aidge/backend/cpu/data/TensorImpl.hpp
+ 1
− 0
View file @ 49f9606c
......@@ -13,6 +13,7 @@
#define AIDGE_CPU_DATA_TENSORIMPL_H_
#include "aidge/backend/TensorImpl.hpp"
#include "aidge/data/DataType.hpp"
#include "aidge/data/Tensor.hpp"
#include "aidge/utils/Registrar.hpp"
#include "aidge/utils/Types.h"
......
include/aidge/data/Elts.hpp
+ 1
− 0
View file @ 49f9606c
......@@ -14,6 +14,7 @@
#include "aidge/utils/ErrorHandling.hpp"
#include "aidge/utils/Types.h"
#include "aidge/utils/logger/EnumString.hpp"
namespace Aidge {
/**
......
include/aidge/data/Tensor.hpp
+ 16
− 14
View file @ 49f9606c
......@@ -27,6 +27,8 @@
#include "aidge/backend/TensorImpl.hpp"
#include "aidge/data/Data.hpp"
#include "aidge/data/DataType.hpp"
#include "aidge/data/DataFormat.hpp"
#include "aidge/utils/ArrayHelpers.hpp"
#include "aidge/utils/ErrorHandling.hpp"
#include "aidge/utils/Registrar.hpp"
......@@ -85,11 +87,11 @@ class Tensor : public Data,
typename VT = std::enable_if_t<std::is_arithmetic<T>::value, std::decay_t<T>>>
Tensor(T val)
: Data(Type),
mDataType(NativeType<VT>::type),
mDataType(NativeType_v<VT>),
mDataFormat(DataFormat::Default),
mDims({}),
mStrides({1}),
mImpl(Registrar<Tensor>::create({"cpu", NativeType<VT>::type})(0, std::vector<std::size_t>())),
mImpl(Registrar<Tensor>::create({"cpu", NativeType_v<VT>})(0, std::vector<std::size_t>())),
mSize(1)
{
*static_cast<VT*>(mImpl->rawPtr()) = static_cast<VT>(val);
......@@ -114,10 +116,10 @@ class Tensor : public Data,
template <typename T>
Tensor(Vector<T> &&arr)
: Data(Type),
mDataType(NativeType<T>::type),
mDataType(NativeType_v<T>),
mDims({arr.data.size()}),
mStrides({1}),
mImpl(Registrar<Tensor>::create({"cpu", NativeType<T>::type})(0, {arr.data.size()})),
mImpl(Registrar<Tensor>::create({"cpu", NativeType_v<T>})(0, {arr.data.size()})),
mSize(arr.data.size())
{
mImpl->copyFromHost(&arr.data[0], arr.data.size());
......@@ -131,11 +133,11 @@ class Tensor : public Data,
template <typename T, std::size_t SIZE_0>
constexpr Tensor(Array1D<T, SIZE_0> &&arr)
: Data(Type),
mDataType(NativeType<T>::type),
mDataType(NativeType_v<T>),
mDataFormat(DataFormat::Default),
mDims({SIZE_0}),
mStrides({1}),
mImpl(Registrar<Tensor>::create({"cpu", NativeType<T>::type})(0, {SIZE_0})),
mImpl(Registrar<Tensor>::create({"cpu", NativeType_v<T>})(0, {SIZE_0})),
mSize(SIZE_0)
{
mImpl->copyFromHost(&arr.data[0], SIZE_0);
......@@ -150,11 +152,11 @@ class Tensor : public Data,
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),
mDataType(NativeType_v<T>),
mDataFormat(DataFormat::Default),
mDims({SIZE_0, SIZE_1}),
mStrides({SIZE_1, 1}),
mImpl(Registrar<Tensor>::create({"cpu", NativeType<T>::type})(0, {SIZE_0, SIZE_1})),
mImpl(Registrar<Tensor>::create({"cpu", NativeType_v<T>})(0, {SIZE_0, SIZE_1})),
mSize(SIZE_0 * SIZE_1) {
mImpl->copyFromHost(&arr.data[0][0], SIZE_0 * SIZE_1);
}
......@@ -169,11 +171,11 @@ class Tensor : public Data,
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),
mDataType(NativeType_v<T>),
mDataFormat(DataFormat::Default),
mDims({SIZE_0, SIZE_1, SIZE_2}),
mStrides({SIZE_1 * SIZE_2, SIZE_2, 1}),
mImpl(Registrar<Tensor>::create({"cpu", NativeType<T>::type})(0, {SIZE_0, SIZE_1, SIZE_2})),
mImpl(Registrar<Tensor>::create({"cpu", NativeType_v<T>})(0, {SIZE_0, SIZE_1, SIZE_2})),
mSize(SIZE_0 * SIZE_1 * SIZE_2) {
mImpl->copyFromHost(&arr.data[0][0][0], SIZE_0 * SIZE_1 * SIZE_2);
}
......@@ -189,11 +191,11 @@ class Tensor : public Data,
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),
mDataType(NativeType_v<T>),
mDataFormat(DataFormat::Default),
mDims({SIZE_0, SIZE_1, SIZE_2, SIZE_3}),
mStrides({SIZE_1 * SIZE_2 * SIZE_3, SIZE_2 * SIZE_3, SIZE_3, 1}),
mImpl(Registrar<Tensor>::create({"cpu", NativeType<T>::type})(0, {SIZE_0, SIZE_1, SIZE_2, SIZE_3})),
mImpl(Registrar<Tensor>::create({"cpu", NativeType_v<T>})(0, {SIZE_0, SIZE_1, SIZE_2, SIZE_3})),
mSize(SIZE_0 * SIZE_1 * SIZE_2 * SIZE_3) {
mImpl->copyFromHost(&arr.data[0][0][0][0], SIZE_0 * SIZE_1 * SIZE_2 * SIZE_3);
}
......@@ -615,7 +617,7 @@ public:
template <typename expectedType>
const expectedType& get(std::size_t idx) const {
AIDGE_ASSERT(NativeType<expectedType>::type == mDataType, "Tensor::get<>({}): wrong data type, expected {}, got {}", idx, mDataType, NativeType<expectedType>::type);
AIDGE_ASSERT(NativeType_v<expectedType> == mDataType, "Tensor::get<>({}): wrong data type, expected {}, got {}", idx, mDataType, NativeType_v<expectedType>);
AIDGE_ASSERT(mImpl->hostPtr() != nullptr, "Tensor::get<>({}): can only be used for backends providing a valid host pointer.", idx);
AIDGE_ASSERT(idx < mSize, "Tensor::get<>({}): idx {} out of range, tensor size {}", idx, mSize);
return *reinterpret_cast<expectedType *>(mImpl->hostPtr(mImplOffset + idx));
......@@ -628,7 +630,7 @@ public:
template <typename expectedType>
void set(std::size_t idx, expectedType value){
AIDGE_ASSERT(NativeType<expectedType>::type == mDataType, "wrong data type");
AIDGE_ASSERT(NativeType_v<expectedType> == mDataType, "wrong data type");
AIDGE_ASSERT(mImpl->hostPtr() != nullptr, "get() can only be used for backends providing a valid host pointer");
AIDGE_ASSERT(idx < mSize, "idx out of range");
expectedType* dataPtr = static_cast<expectedType*>(mImpl->hostPtr(mImplOffset + idx));
......
include/aidge/operator/GridSample.hpp
+ 1
− 1
View file @ 49f9606c
......@@ -19,9 +19,9 @@
#include "aidge/graph/Node.hpp"
#include "aidge/operator/OperatorTensor.hpp"
#include "aidge/utils/Attributes.hpp"
#include "aidge/utils/Registrar.hpp"
#include "aidge/utils/StaticAttributes.hpp"
#include "aidge/utils/logger/EnumString.hpp"
namespace Aidge {
......
python_binding/data/pybind_Data.cpp
+ 3
− 1
View file @ 49f9606c
......@@ -13,6 +13,8 @@
#include <pybind11/stl.h>
#include "aidge/data/Data.hpp"
#include "aidge/data/DataType.hpp"
#include "aidge/data/DataFormat.hpp"
namespace py = pybind11;
namespace Aidge {
......@@ -66,7 +68,7 @@ void init_Data(py::module& m){
m.def("format_as", (const char* (*)(DataType)) &format_as, py::arg("dt"));
m.def("format_as", (const char* (*)(DataFormat)) &format_as, py::arg("df"));
m.def("get_data_format_transpose", &getDataFormatTranspose, py::arg("src"), py::arg("dst"));
m.def("get_data_format_transpose", &getDataFormatTranspose, py::arg("src"), py::arg("dst"));
}
}
src/graph/StaticAnalysis.cpp
+ 2
− 2
View file @ 49f9606c
......@@ -20,7 +20,7 @@
#include <fmt/format.h>
#include <fmt/ranges.h>
#include "aidge/data/Data.hpp" // Aidge::isDataTypeFloatingPoint
#include "aidge/data/DataType.hpp" // Aidge::isFloatingPoint
#include "aidge/data/Tensor.hpp"
#include "aidge/graph/GraphView.hpp"
#include "aidge/graph/Node.hpp"
......@@ -38,7 +38,7 @@ Aidge::OperatorStats::~OperatorStats() = default;
std::size_t Aidge::OperatorStats::getNbArithmIntOps() const {
const auto opTensor = dynamic_cast<const OperatorTensor*>(&mOp);
if (opTensor) {
if (!isDataTypeFloatingPoint(opTensor->getOutput(0)->dataType())) {
if (!isFloatingPoint(opTensor->getOutput(0)->dataType())) {
return getNbArithmOps();
}
}
......
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