diff --git a/python_binding/data/pybind_Tensor.cpp b/python_binding/data/pybind_Tensor.cpp
index 9e58f2a07ca73d65f15290982ae833ac2dc3b4c9..973fc6f9a94d108d8b81c93384ef8468d8247c41 100644
--- a/python_binding/data/pybind_Tensor.cpp
+++ b/python_binding/data/pybind_Tensor.cpp
@@ -40,16 +40,16 @@ using NumpyDType = py::detail::npy_api::constants;
// Map Numpy dtype ids to aidge datatypes.
// If a numpy dtype is not present, np array of this type is rejected.
static const std::map<NumpyDType, DataType> NumpyTypeNameAsNativeType = {
- { NumpyDType::NPY_INT8_, NativeType<std::int8_t>::type },
- { NumpyDType::NPY_INT16_, NativeType<std::int16_t>::type },
- { NumpyDType::NPY_INT32_, NativeType<std::int32_t>::type },
- { NumpyDType::NPY_INT64_, NativeType<std::int64_t>::type },
- { NumpyDType::NPY_UINT8_, NativeType<std::uint8_t>::type },
- { NumpyDType::NPY_UINT16_, NativeType<std::uint16_t>::type },
- { NumpyDType::NPY_UINT32_, NativeType<std::uint32_t>::type },
- { NumpyDType::NPY_UINT64_, NativeType<std::uint64_t>::type },
- { NumpyDType::NPY_FLOAT_, NativeType<float>::type },
- { NumpyDType::NPY_DOUBLE_, NativeType<double>::type },
+ { NumpyDType::NPY_INT8_, NativeType_v<std::int8_t> },
+ { NumpyDType::NPY_INT16_, NativeType_v<std::int16_t> },
+ { NumpyDType::NPY_INT32_, NativeType_v<std::int32_t> },
+ { NumpyDType::NPY_INT64_, NativeType_v<std::int64_t> },
+ { NumpyDType::NPY_UINT8_, NativeType_v<std::uint8_t> },
+ { NumpyDType::NPY_UINT16_, NativeType_v<std::uint16_t> },
+ { NumpyDType::NPY_UINT32_, NativeType_v<std::uint32_t> },
+ { NumpyDType::NPY_UINT64_, NativeType_v<std::uint64_t> },
+ { NumpyDType::NPY_FLOAT_, NativeType_v<float> },
+ { NumpyDType::NPY_DOUBLE_, NativeType_v<double> },
};
// The Numpy API indexes that we need to convert bare numpy scalars
@@ -159,20 +159,20 @@ static bool getScalarNativeVal(const py::object obj, NativeValue* val_ptr, DataT
using caster_i64 = py::detail::type_caster<std::int64_t>;
using caster_f32 = py::detail::type_caster<float>;
if (caster_i32().load(obj, false)) {
- native_dtype = NativeType<std::int32_t>::type;
+ native_dtype = NativeType_v<std::int32_t>;
native_val.i32 = py::cast<std::int32_t>(obj);
} else if (caster_i64().load(obj, false)) {
- native_dtype = NativeType<std::int64_t>::type;
+ native_dtype = NativeType_v<std::int64_t>;
native_val.i64 = py::cast<std::int64_t>(obj);
} else {
- native_dtype = NativeType<float>::type;
+ native_dtype = NativeType_v<float>;
native_val.f32 = py::cast<float>(obj);
}
found = true;
} else if (py::isinstance<py::float_>(obj)) {
// Note that for native python float, we cast to float32 which may loss
// precision as python floats are of type float64.
- native_dtype = NativeType<float>::type;
+ native_dtype = NativeType_v<float>;
native_val.f32 = py::cast<float>(obj);
found = true;
}
@@ -196,19 +196,19 @@ static void getConservativeNativeVal(const py::object obj, NativeValue *val_ptr,
using caster_i64 = py::detail::type_caster<std::int64_t>;
using caster_u64 = py::detail::type_caster<std::uint64_t>;
if (caster_i64().load(obj, false)) {
- native_dtype = NativeType<std::int64_t>::type;
+ native_dtype = NativeType_v<std::int64_t>;
native_val.i64 = py::cast<std::int64_t>(obj);
} else if (caster_u64().load(obj, false)) {
- native_dtype = NativeType<std::uint64_t>::type;
+ native_dtype = NativeType_v<std::uint64_t>;
native_val.u64 = py::cast<std::uint64_t>(obj);
} else {
- native_dtype = NativeType<double>::type;
+ native_dtype = NativeType_v<double>;
native_val.f64 = py::cast<double>(obj);
}
found = true;
} else if (py::isinstance<py::float_>(obj)) {
// Note that for conservative cast we use double which is our larger float
- native_dtype = NativeType<double>::type;
+ native_dtype = NativeType_v<double>;
native_val.f64 = py::cast<double>(obj);
found = true;
}
@@ -289,7 +289,7 @@ void addArrayCtor(pyTensorClass& mTensor) {
/* Request a buffer descriptor from Python */
py::buffer_info info = b.request();
Tensor* newTensor = new Tensor();
- newTensor->setDataType(NativeType<T>::type);
+ newTensor->setDataType(NativeType_v<T>);
const std::vector<DimSize_t> dims(info.shape.begin(), info.shape.end());
newTensor->resize(dims);
diff --git a/src/filler/ConstantFiller.cpp b/src/filler/ConstantFiller.cpp
index b2118866f92290103d50290085c7675215a4d997..82ac42293bcb6956ba6bc4d2896f5ffc33286fba 100644
--- a/src/filler/ConstantFiller.cpp
+++ b/src/filler/ConstantFiller.cpp
@@ -23,7 +23,7 @@ template<typename T>
void Aidge::constantFiller(std::shared_ptr<Aidge::Tensor> tensor, T constantValue) {
AIDGE_ASSERT(tensor->getImpl(),
"Tensor got no implementation, cannot fill it.");
- AIDGE_ASSERT(NativeType<T>::type == tensor->dataType(), "Wrong data type");
+ AIDGE_ASSERT(NativeType_v<T> == tensor->dataType(), "Wrong data type");
std::shared_ptr<Aidge::Tensor> cpyTensor;
// Create cpy only if tensor not on CPU
diff --git a/src/filler/HeFiller.cpp b/src/filler/HeFiller.cpp
index ff20b76183c03e7ac90b5c225b3da7a8c6ffb2df..866e6581f3245d4324f5796a571c35c9c46a703d 100644
--- a/src/filler/HeFiller.cpp
+++ b/src/filler/HeFiller.cpp
@@ -20,7 +20,7 @@ void Aidge::heFiller(std::shared_ptr<Aidge::Tensor> tensor,
Aidge::VarianceNorm varianceNorm, T meanNorm, T scaling) {
AIDGE_ASSERT(tensor->getImpl(),
"Tensor got no implementation, cannot fill it.");
- AIDGE_ASSERT(NativeType<T>::type == tensor->dataType(), "Wrong data type");
+ AIDGE_ASSERT(NativeType_v<T> == tensor->dataType(), "Wrong data type");
unsigned int fanIn, fanOut = 0;
Aidge::calculateFanInFanOut(tensor, fanIn, fanOut);
diff --git a/src/filler/NormalFiller.cpp b/src/filler/NormalFiller.cpp
index f30b32431cf466b10c1b10df8e0e5ccec9f483b6..334c0c12d3e821bf371e32c818acc5e8a3c85ea0 100644
--- a/src/filler/NormalFiller.cpp
+++ b/src/filler/NormalFiller.cpp
@@ -20,7 +20,7 @@ void Aidge::normalFiller(std::shared_ptr<Aidge::Tensor> tensor, double mean,
double stdDev) {
AIDGE_ASSERT(tensor->getImpl(),
"Tensor got no implementation, cannot fill it.");
- AIDGE_ASSERT(NativeType<T>::type == tensor->dataType(), "Wrong data type");
+ AIDGE_ASSERT(NativeType_v<T> == tensor->dataType(), "Wrong data type");
std::normal_distribution<T> normalDist(mean, stdDev);
diff --git a/src/filler/UniformFiller.cpp b/src/filler/UniformFiller.cpp
index 1951fcc623612bd688048fcc5fb71526032b2a4a..22dc479b63045d35db8f7319b06a464f191f920e 100644
--- a/src/filler/UniformFiller.cpp
+++ b/src/filler/UniformFiller.cpp
@@ -20,7 +20,7 @@ template <typename T>
void Aidge::uniformFiller(std::shared_ptr<Aidge::Tensor> tensor, T min, T max) {
AIDGE_ASSERT(tensor->getImpl(),
"Tensor got no implementation, cannot fill it.");
- AIDGE_ASSERT(NativeType<T>::type == tensor->dataType(), "Wrong data type {} and {}",NativeType<T>::type, tensor->dataType());
+ AIDGE_ASSERT(NativeType_v<T> == tensor->dataType(), "Wrong data type {} and {}",NativeType_v<T>, tensor->dataType());
using DistType = typename std::conditional<
diff --git a/src/filler/XavierFiller.cpp b/src/filler/XavierFiller.cpp
index 734874d449c83087ca0e93df7eeb620e178ee7ba..6856ab218f7d78e9b0ed3331a1424734bf0e573a 100644
--- a/src/filler/XavierFiller.cpp
+++ b/src/filler/XavierFiller.cpp
@@ -20,7 +20,7 @@ void Aidge::xavierUniformFiller(std::shared_ptr<Aidge::Tensor> tensor,
T scaling, Aidge::VarianceNorm varianceNorm) {
AIDGE_ASSERT(tensor->getImpl(),
"Tensor got no implementation, cannot fill it.");
- AIDGE_ASSERT(NativeType<T>::type == tensor->dataType(), "Wrong data type");
+ AIDGE_ASSERT(NativeType_v<T> == tensor->dataType(), "Wrong data type");
unsigned int fanIn, fanOut = 0;
Aidge::calculateFanInFanOut(tensor, fanIn, fanOut);
@@ -54,7 +54,7 @@ void Aidge::xavierNormalFiller(std::shared_ptr<Aidge::Tensor> tensor, T scaling,
Aidge::VarianceNorm varianceNorm) {
AIDGE_ASSERT(tensor->getImpl(),
"Tensor got no implementation, cannot fill it.");
- AIDGE_ASSERT(NativeType<T>::type == tensor->dataType(), "Wrong data type");
+ AIDGE_ASSERT(NativeType_v<T> == tensor->dataType(), "Wrong data type");
unsigned int fanIn, fanOut = 0;
Aidge::calculateFanInFanOut(tensor, fanIn, fanOut);
diff --git a/src/operator/Clip.cpp b/src/operator/Clip.cpp
index 10b864b54594c86ed1486611fdd91fd916f2291b..62787ebcf4e0633292cf54a3161248b5dcd30fac 100644
--- a/src/operator/Clip.cpp
+++ b/src/operator/Clip.cpp
@@ -32,10 +32,10 @@ bool Aidge::Clip_Op::dimsForwarded() const {
}
-bool Aidge::Clip_Op::forwardDims(bool allowDataDependency)
+bool Aidge::Clip_Op::forwardDims(bool allowDataDependency)
{
- if (getInput(1) )
- {
+ if (getInput(1) )
+ {
if( this->min() != std::numeric_limits<float>::lowest())
{
Log::notice("{} : ignoring non-empty min attribute because input#1 "
@@ -49,11 +49,11 @@ bool Aidge::Clip_Op::forwardDims(bool allowDataDependency)
return false;
}
std::shared_ptr<Tensor> fallback;
- const auto& minV = mInputs[1]->refCastFrom(fallback, NativeType<float>::type, "cpu");
+ const auto& minV = mInputs[1]->refCastFrom(fallback, NativeType_v<float>, "cpu");
this->min() = *(static_cast<float*>(minV.getImpl()->hostPtr()));
}
- if (getInput(2))
- {
+ if (getInput(2))
+ {
if( this->max() != std::numeric_limits<float>::max())
{
Log::notice("{} : ignoring non-empty max attribute because input#2 "
@@ -67,7 +67,7 @@ bool Aidge::Clip_Op::forwardDims(bool allowDataDependency)
return false;
}
std::shared_ptr<Tensor> fallback;
- const auto& maxV = mInputs[2]->refCastFrom(fallback, NativeType<float>::type, "cpu");
+ const auto& maxV = mInputs[2]->refCastFrom(fallback, NativeType_v<float>, "cpu");
this->max() = *(static_cast<float*>(maxV.getImpl()->hostPtr()));
}
if (!inputsAssociated(false)) {
diff --git a/src/operator/Gather.cpp b/src/operator/Gather.cpp
index 0ebc3e3bc81b15d9414d01f12a2768be6a7ddc42..e0990437a06d5b9fb72cf1909d78f6094120bf80 100644
--- a/src/operator/Gather.cpp
+++ b/src/operator/Gather.cpp
@@ -104,7 +104,7 @@ bool Aidge::Gather_Op::forwardDims(bool allowDataDependency) {
this->gatheredShape() = getInput(1)->dims();
this->indices().clear(); // If both are provided input would override attrs
this->indices().reserve(getInput(1)->size());
- const auto& indices = mInputs[1]->refCastFrom(fallback, NativeType<int64_t>::type, "cpu");
+ const auto& indices = mInputs[1]->refCastFrom(fallback, NativeType_v<int64_t>, "cpu");
std::copy_n(static_cast<int64_t*>(indices.getImpl()->hostPtr()),
indices.size(),
std::back_inserter(this->indices()));
diff --git a/src/operator/Reshape.cpp b/src/operator/Reshape.cpp
index 429bbe17dffd879d700efb6110d01a11e2bb140f..8b42cb51440cbc61bf8d4dbf69524adb15dbeb44 100644
--- a/src/operator/Reshape.cpp
+++ b/src/operator/Reshape.cpp
@@ -91,7 +91,7 @@ bool Aidge::Reshape_Op::forwardDims(bool allowDataDependency) {
std::shared_ptr<Tensor> fallback;
this->shape().clear(); // If both are provided input would override attrs
this->shape().reserve(getInput(1)->size());
- const auto& shape = mInputs[1]->refCastFrom(fallback, NativeType<int64_t>::type, "cpu");
+ const auto& shape = mInputs[1]->refCastFrom(fallback, NativeType_v<int64_t>, "cpu");
std::copy_n(static_cast<int64_t*>(shape.getImpl()->hostPtr()),
shape.size(),
std::back_inserter(this->shape()));
diff --git a/src/operator/Resize.cpp b/src/operator/Resize.cpp
index 252f55a6abdea13cc43cf21d3e8c7ab33ddbb86e..b2ef56572a5f972cd0f5be6a276780e5f27536de 100644
--- a/src/operator/Resize.cpp
+++ b/src/operator/Resize.cpp
@@ -125,7 +125,7 @@ bool Resize_Op::forwardDims(bool allowDataDependency) {
std::shared_ptr<Tensor> fallback;
const auto &sizes = resizeParam
->refCastFrom(fallback,
- NativeType<DimSize_t>::type,
+ NativeType_v<DimSize_t>,
resizeParam->backend());
for (std::size_t dim = 0; dim < getInput(inSizesIdx)->size(); ++dim) {
diff --git a/src/operator/Slice.cpp b/src/operator/Slice.cpp
index 31c7c09c9ff41c163f5d505bd4ce6b3aeaf42872..7945200aabbae23abce7d1698b5ddbe8f7ec0882 100644
--- a/src/operator/Slice.cpp
+++ b/src/operator/Slice.cpp
@@ -172,7 +172,7 @@ bool Aidge::Slice_Op::forwardDims(bool allowDataDependency) {
this->starts().clear(); // If both are provided input would override attrs
this->starts().reserve(getInput(1)->size());
- const auto& starts = getInput(1)->refCastFrom(fallback, NativeType<int64_t>::type, "cpu");
+ const auto& starts = getInput(1)->refCastFrom(fallback, NativeType_v<int64_t>, "cpu");
std::copy_n(static_cast<int64_t*>(starts.getImpl()->hostPtr()),
starts.size(),
std::back_inserter(this->starts()));
@@ -193,7 +193,7 @@ bool Aidge::Slice_Op::forwardDims(bool allowDataDependency) {
this->ends().clear(); // If both are provided input would override attrs
this->ends().reserve(getInput(2)->size());
- const auto& ends = getInput(2)->refCastFrom(fallback, NativeType<int64_t>::type, "cpu");
+ const auto& ends = getInput(2)->refCastFrom(fallback, NativeType_v<int64_t>, "cpu");
std::copy_n(static_cast<int64_t*>(ends.getImpl()->hostPtr()),
ends.size(),
std::back_inserter(this->ends()));
@@ -214,7 +214,7 @@ bool Aidge::Slice_Op::forwardDims(bool allowDataDependency) {
this->axes().clear(); // If both are provided input would override attrs
this->axes().reserve(getInput(3)->size());
- const auto& axes = getInput(3)->refCastFrom(fallback, NativeType<int8_t>::type, "cpu");
+ const auto& axes = getInput(3)->refCastFrom(fallback, NativeType_v<int8_t>, "cpu");
std::copy_n(static_cast<int8_t*>(axes.getImpl()->hostPtr()),
axes.size(),
std::back_inserter(this->axes()));
@@ -235,7 +235,7 @@ bool Aidge::Slice_Op::forwardDims(bool allowDataDependency) {
this->steps().clear(); // If both are provided input would override attrs
this->steps().reserve(getInput(4)->size());
- const auto& steps = getInput(4)->refCastFrom(fallback, NativeType<int64_t>::type, "cpu");
+ const auto& steps = getInput(4)->refCastFrom(fallback, NativeType_v<int64_t>, "cpu");
std::copy_n(static_cast<int64_t*>(steps.getImpl()->hostPtr()),
steps.size(),
std::back_inserter(this->steps()));
diff --git a/src/operator/Split.cpp b/src/operator/Split.cpp
index 2191f14a150088dfa1d369d2ef31051e5ab16326..09aad0674bc424f50483c064cb7201bc20499faa 100644
--- a/src/operator/Split.cpp
+++ b/src/operator/Split.cpp
@@ -109,7 +109,7 @@ bool Aidge::Split_Op::forwardDims(bool allowDataDependency) {
std::shared_ptr<Tensor> fallback;
this->split().clear(); // If both are provided input would override attrs
this->split().reserve(getInput(1)->size());
- const auto& splits = getInput(1)->refCastFrom(fallback, NativeType<DimSize_t>::type, "cpu");
+ const auto& splits = getInput(1)->refCastFrom(fallback, NativeType_v<DimSize_t>, "cpu");
std::copy_n(static_cast<DimSize_t*>(splits.getImpl()->hostPtr()),
splits.size(),
std::back_inserter(this->split()));
diff --git a/src/operator/Squeeze.cpp b/src/operator/Squeeze.cpp
index b51b4f346c92f778fe0a044df187cd8d0d0f7304..a44146366d5466768d937261729325697ce24f6e 100644
--- a/src/operator/Squeeze.cpp
+++ b/src/operator/Squeeze.cpp
@@ -67,7 +67,7 @@ bool Squeeze_Op::forwardDims(bool allowDataDependency) {
this->axes().clear(); // If both are provided input would override attrs
this->axes().reserve(getInput(1)->size());
const auto &axes =
- getInput(1)->refCastFrom(fallback, NativeType<int8_t>::type, "cpu");
+ getInput(1)->refCastFrom(fallback, NativeType_v<int8_t>, "cpu");
if (axes.nbDims() == 0) {
this->axes().clear();
} else {
diff --git a/src/operator/Stack.cpp b/src/operator/Stack.cpp
index ab9ddc4f705cb00cebbe5b9ee68fb1433586a043..a938f470ded47d65a9b15b93ca66dfe186d61e9f 100644
--- a/src/operator/Stack.cpp
+++ b/src/operator/Stack.cpp
@@ -102,7 +102,7 @@ bool Aidge::StackOp::forwardDims(bool allowDataDependency) {
}
std::shared_ptr<Tensor> fallback;
- const auto& maxElements = getInput(1)->refCastFrom(fallback, NativeType<std::uint32_t>::type, "cpu");
+ const auto& maxElements = getInput(1)->refCastFrom(fallback, NativeType_v<std::uint32_t>, "cpu");
AIDGE_ASSERT(maxElements.size() > 0, "Input#1 size should be > 0");
this->maxElements() = static_cast<std::uint32_t*>(maxElements.getImpl()->hostPtr())[0];
}
diff --git a/src/operator/Unsqueeze.cpp b/src/operator/Unsqueeze.cpp
index f3353b45cd6a732fa456ea0585ec5d040d53ef31..414afc10f5ea091ba9f30c327ccfbcfe6b3fd558 100644
--- a/src/operator/Unsqueeze.cpp
+++ b/src/operator/Unsqueeze.cpp
@@ -61,7 +61,7 @@ bool Unsqueeze_Op::forwardDims(bool allowDataDependency) {
this->axes().clear(); // If both are provided input would override attrs
this->axes().reserve(getInput(1)->size());
const auto &axes =
- getInput(1)->refCastFrom(fallback, NativeType<int8_t>::type, "cpu");
+ getInput(1)->refCastFrom(fallback, NativeType_v<int8_t>, "cpu");
std::copy_n(static_cast<int8_t *>(axes.getImpl()->hostPtr()),
axes.size(), std::back_inserter(this->axes()));
}