diff --git a/include/aidge/operator/ReduceMean.hpp b/include/aidge/operator/ReduceMean.hpp
index 5c5531f343661f3e536d2519615e030c559a8256..ab27e4e0233052f7cc155ed0375175a27d3edcf5 100644
--- a/include/aidge/operator/ReduceMean.hpp
+++ b/include/aidge/operator/ReduceMean.hpp
@@ -12,14 +12,11 @@
#ifndef AIDGE_CORE_OPERATOR_REDUCEMEAN_H_
#define AIDGE_CORE_OPERATOR_REDUCEMEAN_H_
-#include <algorithm> // std::for_each, std::sort
-#include <array>
#include <cstdint> // std::int32_t
#include <memory>
-#include <stdexcept> // std::runtime_error
+#include <string>
#include <vector>
-#include "aidge/data/Tensor.hpp"
#include "aidge/graph/Node.hpp"
#include "aidge/operator/OperatorTensor.hpp"
#include "aidge/operator/Producer.hpp"
@@ -31,21 +28,20 @@
namespace Aidge {
enum class ReduceMeanAttr { Axes, KeepDims };
-template <DimIdx_t DIM>
class ReduceMean_Op : public OperatorTensor,
- public Registrable<ReduceMean_Op<DIM>, std::string, std::shared_ptr<OperatorImpl>(const ReduceMean_Op<DIM> &)>,
- public StaticAttributes<ReduceMeanAttr, std::array<std::int32_t, DIM>, DimSize_t> {
+ public Registrable<ReduceMean_Op, std::string, std::shared_ptr<OperatorImpl>(const ReduceMean_Op &)>,
+ public StaticAttributes<ReduceMeanAttr, std::vector<std::int32_t>, DimSize_t> {
public:
static const std::string Type;
ReduceMean_Op() = delete;
- using Attributes_ = StaticAttributes<ReduceMeanAttr, std::array<std::int32_t, DIM>, DimSize_t>;
+ using Attributes_ = StaticAttributes<ReduceMeanAttr, std::vector<std::int32_t>, DimSize_t>;
template <ReduceMeanAttr e>
using attr = typename Attributes_::template attr<e>;
- constexpr ReduceMean_Op(const std::array<std::int32_t, DIM> &axes, DimSize_t keep_dims)
+ ReduceMean_Op(const std::vector<std::int32_t>& axes, DimSize_t keep_dims)
: OperatorTensor(Type, 1, 0, 1),
Attributes_(attr<ReduceMeanAttr::Axes>(axes),
attr<ReduceMeanAttr::KeepDims>(keep_dims)) {}
@@ -54,13 +50,13 @@ class ReduceMean_Op : public OperatorTensor,
* @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)
+ ReduceMean_Op(const ReduceMean_Op& op)
: OperatorTensor(op),
Attributes_(op)
{
if (op.mImpl){
- SET_IMPL_MACRO(ReduceMean_Op<DIM>, *this, op.backend());
- }else{
+ SET_IMPL_MACRO(ReduceMean_Op, *this, op.backend());
+ } else {
mImpl = nullptr;
}
}
@@ -70,72 +66,51 @@ class ReduceMean_Op : public OperatorTensor,
* @see Operator::ReduceMean_Op
*/
std::shared_ptr<Operator> clone() const override {
- return std::make_shared<ReduceMean_Op<DIM>>(*this);
+ return std::make_shared<ReduceMean_Op>(*this);
}
- void computeOutputDims() override final {
- if (!getInput(0)) {
- AIDGE_THROW_OR_ABORT(std::runtime_error, "Every input should be associated with a Tensor");
- }
- if (!getInput(0)->empty()) {
- // make Axes attribute positive
- std::array<std::int32_t, DIM>& axes = this->template getAttr<ReduceMeanAttr::Axes>();
- std::for_each(axes.begin(), axes.end(), [&] (std::int32_t& val) {
- if (val < 0)
- val+=static_cast<std::int32_t>(getInput(0)->nbDims());
- });
- std::sort(axes.begin(), axes.end());
-
- // build output dimensions
- std::vector<DimSize_t> outDims = getInput(0)->dims();
- if (this->template getAttr<ReduceMeanAttr::KeepDims>()) {
- std::for_each(axes.begin(), axes.end(), [&outDims] (const std::int32_t& val) { outDims[val] = 1; });
- }
- else {
- for (auto it = axes.crbegin(); it != axes.crend(); ++it)
- outDims.erase(outDims.begin() + static_cast<std::size_t>(*it));
- }
-
- mOutputs[0]->resize((outDims.size()>0) ? outDims : std::vector<DimSize_t>({1}));
-
- }
- }
+ void computeOutputDims() override final;
- void setBackend(const std::string &name, DeviceIdx_t device = 0) override {
- SET_IMPL_MACRO(ReduceMean_Op<DIM>, *this, name);
- mOutputs[0]->setBackend(name, device);
- }
+ void setBackend(const std::string &name, DeviceIdx_t device = 0) override final;
- static const std::vector<std::string> getInputsName(){
+ static const std::vector<std::string> getInputsName() {
return {"data_input"};
}
- static const std::vector<std::string> getOutputsName(){
+ 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<std::int32_t, DIM> &axes,
+/**
+ * @brief Compute the mean value of a Tensor over the provided axes. Dimensions
+ * may be reduced by erasing the provided axes or not.
+ *
+ * @param axes Dimensions over which data mean should be computed.
+ * @param keep_dims Whether or not reduced dimensions are to be erased.
+ * @param name Name of the Operator.
+ * @return std::shared_ptr<Node> Node containing the Operator.
+ */
+inline std::shared_ptr<Node> ReduceMean(const std::vector<std::int32_t> &axes,
DimSize_t keep_dims=1,
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);
+ 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);
}
// 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(
- std::int32_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);
-}
-
-template <DimIdx_t DIM>
-const std::string ReduceMean_Op<DIM>::Type = "ReduceMean";
+// template <DimSize_t DIM>
+// inline std::shared_ptr<Node> ReduceMean(
+// std::int32_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);
+// }
+
+// template <DimIdx_t DIM>
+// const std::string ReduceMean_Op::Type = "ReduceMean";
} // namespace Aidge