diff --git a/include/aidge/operator/ReduceMean.hpp b/include/aidge/operator/ReduceMean.hpp
index 686ffd86c3e5e40fae2186b87572a179068b5417..10aa7356e16d569cfe290b74fcdfc7960a4e1140 100644
--- a/include/aidge/operator/ReduceMean.hpp
+++ b/include/aidge/operator/ReduceMean.hpp
@@ -26,7 +26,7 @@
#include "aidge/utils/Types.h"
namespace Aidge {
-enum class ReduceMeanAttr { Axes, KeepDims };
+enum class ReduceMeanAttr { Axes, KeepDims, NoopWithEmptyAxes };
class ReduceMean_Op : public OperatorTensor,
public Registrable<ReduceMean_Op, std::string, std::shared_ptr<OperatorImpl>(const ReduceMean_Op &)> {
@@ -37,6 +37,7 @@ public:
private:
using Attributes_ = StaticAttributes<ReduceMeanAttr,
std::vector<std::int32_t>,
+ DimSize_t,
DimSize_t>;
template <ReduceMeanAttr e>
using attr = typename Attributes_::template attr<e>;
@@ -45,11 +46,12 @@ private:
public:
ReduceMean_Op() = delete;
- ReduceMean_Op(const std::vector<std::int32_t>& axes, DimSize_t keep_dims)
+ ReduceMean_Op(const std::vector<std::int32_t>& axes, DimSize_t keep_dims, DimSize_t noop_with_empty_axes)
: OperatorTensor(Type, {InputCategory::Data}, 1),
mAttributes(std::make_shared<Attributes_>(
attr<ReduceMeanAttr::Axes>(axes),
- attr<ReduceMeanAttr::KeepDims>(keep_dims)))
+ attr<ReduceMeanAttr::KeepDims>(keep_dims),
+ attr<ReduceMeanAttr::NoopWithEmptyAxes>(noop_with_empty_axes)))
{}
/**
@@ -82,6 +84,7 @@ public:
inline std::shared_ptr<Attributes> attributes() const override { return mAttributes; }
inline std::vector<std::int32_t>& axes() const noexcept { return mAttributes -> getAttr<ReduceMeanAttr::Axes>(); }
inline DimSize_t& keepDims() const noexcept { return mAttributes -> getAttr<ReduceMeanAttr::KeepDims>(); }
+ inline DimSize_t& noopWithEmptyAxes() const noexcept { return mAttributes -> getAttr<ReduceMeanAttr::NoopWithEmptyAxes>(); }
static const std::vector<std::string> getInputsName() {
@@ -103,10 +106,11 @@ public:
*/
inline std::shared_ptr<Node> ReduceMean(const std::vector<std::int32_t> &axes={},
DimSize_t keep_dims=1,
+ DimSize_t noop_with_empty_axes=0,
const std::string& name = "") {
// FIXME: properly handle default w&b initialization in every cases
AIDGE_ASSERT(axes.size()<=MaxDim, "Too many kernel dimensions required by ReduceMean, not supported");
- return std::make_shared<Node>(std::make_shared<ReduceMean_Op>(axes, keep_dims), name);
+ return std::make_shared<Node>(std::make_shared<ReduceMean_Op>(axes, keep_dims, noop_with_empty_axes), name);
}
@@ -127,7 +131,7 @@ inline std::shared_ptr<Node> ReduceMean(const std::vector<std::int32_t> &axes={}
namespace {
template <>
-const char *const EnumStrings<Aidge::ReduceMeanAttr>::data[] = {"axes", "keep_dims"};
+const char *const EnumStrings<Aidge::ReduceMeanAttr>::data[] = {"axes", "keep_dims", "noop_with_empty_axes"};
}
#endif /* AIDGE_CORE_OPERATOR_REDUCEMEAN_H_ */
diff --git a/include/aidge/operator/ReduceSum.hpp b/include/aidge/operator/ReduceSum.hpp
index 22aa01d11612ffcec4fa3ea48f791286f105373a..c179cfd1e6719667ff7de62837772f0828fc5f5b 100644
--- a/include/aidge/operator/ReduceSum.hpp
+++ b/include/aidge/operator/ReduceSum.hpp
@@ -26,7 +26,7 @@
#include "aidge/utils/Types.h"
namespace Aidge {
-enum class ReduceSumAttr { Axes, KeepDims };
+enum class ReduceSumAttr { Axes, KeepDims, NoopWithEmptyAxes };
class ReduceSum_Op : public OperatorTensor,
public Registrable<ReduceSum_Op, std::string, std::shared_ptr<OperatorImpl>(const ReduceSum_Op &)> {
@@ -37,6 +37,7 @@ public:
private:
using Attributes_ = StaticAttributes<ReduceSumAttr,
std::vector<std::int32_t>,
+ DimSize_t,
DimSize_t>;
template <ReduceSumAttr e>
using attr = typename Attributes_::template attr<e>;
@@ -45,11 +46,12 @@ private:
public:
ReduceSum_Op() = delete;
- ReduceSum_Op(const std::vector<std::int32_t>& axes, DimSize_t keep_dims)
+ ReduceSum_Op(const std::vector<std::int32_t>& axes, DimSize_t keep_dims, DimSize_t noop_with_empty_axes)
: OperatorTensor(Type, {InputCategory::Data}, 1),
mAttributes(std::make_shared<Attributes_>(
attr<ReduceSumAttr::Axes>(axes),
- attr<ReduceSumAttr::KeepDims>(keep_dims)))
+ attr<ReduceSumAttr::KeepDims>(keep_dims),
+ attr<ReduceSumAttr::NoopWithEmptyAxes>(noop_with_empty_axes)))
{}
/**
@@ -82,6 +84,7 @@ public:
inline std::shared_ptr<Attributes> attributes() const override { return mAttributes; }
inline std::vector<std::int32_t>& axes() const noexcept { return mAttributes -> getAttr<ReduceSumAttr::Axes>(); }
inline DimSize_t& keepDims() const noexcept { return mAttributes -> getAttr<ReduceSumAttr::KeepDims>(); }
+ inline DimSize_t& noopWithEmptyAxes() const noexcept { return mAttributes -> getAttr<ReduceSumAttr::NoopWithEmptyAxes>(); }
static const std::vector<std::string> getInputsName() {
@@ -103,17 +106,18 @@ public:
*/
inline std::shared_ptr<Node> ReduceSum(const std::vector<std::int32_t> &axes={},
DimSize_t keep_dims=1,
+ DimSize_t noop_with_empty_axes=0,
const std::string& name = "") {
// FIXME: properly handle default w&b initialization in every cases
AIDGE_ASSERT(axes.size()<=MaxDim, "Too many kernel dimensions required by ReduceSum, not supported");
- return std::make_shared<Node>(std::make_shared<ReduceSum_Op>(axes, keep_dims), name);
+ return std::make_shared<Node>(std::make_shared<ReduceSum_Op>(axes, keep_dims, noop_with_empty_axes), name);
}
} // namespace Aidge
namespace {
template <>
-const char *const EnumStrings<Aidge::ReduceSumAttr>::data[] = {"axes", "keep_dims"};
+const char *const EnumStrings<Aidge::ReduceSumAttr>::data[] = {"axes", "keep_dims", "noop_with_empty_axes"};
}
#endif /* AIDGE_CORE_OPERATOR_REDUCESUM_H_ */
diff --git a/python_binding/operator/pybind_ReduceMean.cpp b/python_binding/operator/pybind_ReduceMean.cpp
index dae50b9af7512c5ec78f8fdf5352f527d2596bfd..e8940bad91db6e9fdb87723c60afb974497b1ca1 100644
--- a/python_binding/operator/pybind_ReduceMean.cpp
+++ b/python_binding/operator/pybind_ReduceMean.cpp
@@ -28,20 +28,22 @@ void declare_ReduceMeanOp(py::module &m) {
const std::string pyClassName("ReduceMeanOp");
py::class_<ReduceMean_Op, std::shared_ptr<ReduceMean_Op>, OperatorTensor>(
m, pyClassName.c_str(), py::multiple_inheritance())
- .def(py::init<std::vector<std::int32_t>, DimSize_t>(), py::arg("axes"), py::arg("keep_dims"))
+ .def(py::init<std::vector<std::int32_t>, DimSize_t, DimSize_t>(), py::arg("axes"), py::arg("keep_dims"), py::arg("noop_with_empty_axes"))
.def_static("get_inputs_name", &ReduceMean_Op::getInputsName)
.def_static("get_outputs_name", &ReduceMean_Op::getOutputsName)
;
declare_registrable<ReduceMean_Op>(m, pyClassName);
m.def("ReduceMean", [](const std::vector<int>& axes,
- DimSize_t keepDims,
- const std::string& name) {
+ DimSize_t keepDims,
+ DimSize_t noopWithEmptyAxes,
+ const std::string& name) {
// AIDGE_ASSERT(axes.size() == DIM, "axes size [{}] does not match DIM [{}]", axes.size(), DIM);
- return ReduceMean(axes, keepDims, name);
+ return ReduceMean(axes, keepDims, noopWithEmptyAxes, name);
}, py::arg("axes") = std::vector<std::int32_t>(),
py::arg("keep_dims") = 1,
+ py::arg("noop_with_empty_axes") = 0,
py::arg("name") = "");
}
diff --git a/python_binding/operator/pybind_ReduceSum.cpp b/python_binding/operator/pybind_ReduceSum.cpp
index 5a2c3eb1f5bbccce6a84e1a453047492c67b33bd..3a71d8cf092659cf26d1960b39b3f2fbe2d4b228 100644
--- a/python_binding/operator/pybind_ReduceSum.cpp
+++ b/python_binding/operator/pybind_ReduceSum.cpp
@@ -28,7 +28,7 @@ void init_ReduceSum(py::module &m) {
const std::string pyClassName("ReduceSumOp");
py::class_<ReduceSum_Op, std::shared_ptr<ReduceSum_Op>, OperatorTensor>(
m, pyClassName.c_str(), py::multiple_inheritance())
- .def(py::init<std::vector<std::int32_t>, DimSize_t>(), py::arg("axes"), py::arg("keep_dims"))
+ .def(py::init<std::vector<std::int32_t>, DimSize_t, DimSize_t>(), py::arg("axes"), py::arg("keep_dims"), py::arg("noop_with_empty_axes"))
.def_static("get_inputs_name", &ReduceSum_Op::getInputsName)
.def_static("get_outputs_name", &ReduceSum_Op::getOutputsName)
;
@@ -36,10 +36,12 @@ void init_ReduceSum(py::module &m) {
m.def("ReduceSum", [](const std::vector<int>& axes,
DimSize_t keepDims,
+ DimSize_t noopWithEmptyAxes,
const std::string& name) {
- return ReduceSum(axes, keepDims, name);
+ return ReduceSum(axes, keepDims, noopWithEmptyAxes, name);
}, py::arg("axes") = std::vector<std::int32_t>(),
py::arg("keep_dims") = 1,
+ py::arg("noop_with_empty_axes") = 0,
py::arg("name") = "");
}
} // namespace Aidge
diff --git a/src/operator/ReduceMean.cpp b/src/operator/ReduceMean.cpp
index 0e8cae9e951709a0c8e68bf04c5b1a0b9239ae21..7bbd2a60f8d7bfb3e49b507c01289646480f0b85 100644
--- a/src/operator/ReduceMean.cpp
+++ b/src/operator/ReduceMean.cpp
@@ -37,14 +37,20 @@ bool Aidge::ReduceMean_Op::forwardDims(bool /*allowDataDependency*/) {
});
std::sort(axes.begin(), axes.end());
- if (axes.empty()) // if no axes are provided, reduce on all axes
+ // build output dimensions
+ std::vector<DimSize_t> outDims = getInput(0)->dims();
+
+ if (axes.empty())
{
+ if(mAttributes->template getAttr<ReduceMeanAttr::NoopWithEmptyAxes>()) {
+ mOutputs[0]->resize(outDims);
+ return true;
+ }
+ // if no axes are provided and NoopWithEmptyAxes is false, reduce on all axes
axes.resize(getInput(0)->nbDims());
std::iota(axes.begin(), axes.end(), 0);
}
- // build output dimensions
- std::vector<DimSize_t> outDims = getInput(0)->dims();
if (mAttributes->template getAttr<ReduceMeanAttr::KeepDims>()) {
std::for_each(axes.cbegin(), axes.cend(), [&outDims] (const std::int32_t& val) { outDims[val] = 1; });
}
diff --git a/src/operator/ReduceSum.cpp b/src/operator/ReduceSum.cpp
index 317292632504cd63cb21a6827c0dc1fb87af2a42..aa8271f4c1696d46274e536e14d255525d848f80 100644
--- a/src/operator/ReduceSum.cpp
+++ b/src/operator/ReduceSum.cpp
@@ -37,14 +37,20 @@ bool Aidge::ReduceSum_Op::forwardDims(bool /*allowDataDependency*/) {
});
std::sort(axes.begin(), axes.end());
- if (axes.empty()) // if no axes are provided, reduce on all axes
+ // build output dimensions
+ std::vector<DimSize_t> outDims = getInput(0)->dims();
+
+ if (axes.empty())
{
+ if(mAttributes->template getAttr<ReduceSumAttr::NoopWithEmptyAxes>()) {
+ mOutputs[0]->resize(outDims);
+ return true;
+ }
+ // if no axes are provided and NoopWithEmptyAxes is false, reduce on all axes
axes.resize(getInput(0)->nbDims());
std::iota(axes.begin(), axes.end(), 0);
}
- // build output dimensions
- std::vector<DimSize_t> outDims = getInput(0)->dims();
if (mAttributes->template getAttr<ReduceSumAttr::KeepDims>()) {
std::for_each(axes.cbegin(), axes.cend(), [&outDims] (const std::int32_t& val) { outDims[val] = 1; });
}