[MaxPooling] Adapt MaxPoolImpl to latest change on attributes.

include/aidge/backend/cpu/operator/MaxPoolingImpl.hpp

@@ -29,11 +29,11 @@ namespace Aidge {
 class MaxPoolingImpl2DForward_cpu
     : public Registrable<MaxPoolingImpl2DForward_cpu,
                          std::tuple<DataType, DataType>,
-                         void(const MaxPooling_Op<2>::Parameters &, const std::array<DimSize_t, 4> &, const void *, void *)> {};
+                         void(const MaxPooling_Op<2>::Attrs &, const std::array<DimSize_t, 4> &, const void *, void *)> {};
 class MaxPoolingImpl2DBackward_cpu
     : public Registrable<MaxPoolingImpl2DBackward_cpu,
                          std::tuple<DataType, DataType>,
-                         void(const MaxPooling_Op<2>::Parameters &, const std::array<DimSize_t, 4> &, const void *, void *)> {};
+                         void(const MaxPooling_Op<2>::Attrs &, const std::array<DimSize_t, 4> &, const void *, void *)> {};
 
 class MaxPoolingImpl2D_cpu : public OperatorImpl {
    private:

include/aidge/backend/cpu/operator/MaxPoolingImpl_forward_kernels.hpp

@@ -26,13 +26,13 @@ namespace Aidge {
  * @brief Forward kernel for 2D MaxPoolingolution on CPU backend.
  * @tparam I Input data type.
  * @tparam O Output data type.
- * @param params tuple of Parameters from the Operator
+ * @param attrs tuple of Attributes from the Operator
  * @param dims Array of input dimensions.
  * @param input_ const input Tensor.
  * @param output_ Output Tensor.
  */
 template <class I, class O>
-void MaxPoolingImpl2D_cpu_forward_kernel(const MaxPooling_Op<2>::Parameters &params,
+void MaxPoolingImpl2D_cpu_forward_kernel(const MaxPooling_Op<2>::Attrs &attrs,
                                              const std::array<DimSize_t, 4> &dims,
                                              const void *input_,
                                              void *output_) {
@@ -40,15 +40,18 @@ void MaxPoolingImpl2D_cpu_forward_kernel(const MaxPooling_Op<2>::Parameters &par
     const I *input = static_cast<const I *>(input_);
     O *output = static_cast<O *>(output_);
 
+    std::array<DimSize_t, 2> strideDims  = std::get<0>(attrs);
+    std::array<DimSize_t, 2> kernelDims  = std::get<1>(attrs);
+    std::array<DimSize_t, 4> paddingDims = std::get<2>(attrs);
 
     // output H size
     const std::size_t oxSize =
-            static_cast<std::size_t>(std::floor(static_cast<float>(dims[2] + std::get<2>(params)[0] + std::get<2>(params)[2] - std::get<1>(params)[0] + std::get<0>(params)[0]) /
-                                static_cast<float>(std::get<0>(params)[0])));
+            static_cast<std::size_t>(std::floor(static_cast<float>(dims[2] + paddingDims[0] + paddingDims[2] - kernelDims[0] + strideDims[0]) /
+                                static_cast<float>(strideDims[0])));
     // output W size
     const std::size_t oySize =
-            static_cast<std::size_t>(std::floor(static_cast<float>(dims[3] + std::get<2>(params)[1] + std::get<2>(params)[3] - std::get<1>(params)[1] + std::get<0>(params)[1]) /
-                                static_cast<float>(std::get<0>(params)[1])));
+            static_cast<std::size_t>(std::floor(static_cast<float>(dims[3] + paddingDims[1] + paddingDims[3] - kernelDims[1] + strideDims[1]) /
+                                static_cast<float>(strideDims[1])));
 
     // TODO: kernel computation
     // output (batch, outCh, Xout, Yout)
@@ -61,16 +64,16 @@ void MaxPoolingImpl2D_cpu_forward_kernel(const MaxPooling_Op<2>::Parameters &par
             const std::size_t oIndex = (ch + batch*dims[1]) * oxSize * oySize;
             const std::size_t iIndex = (ch + batch*dims[1]) * dims[2] * dims[3];
             for (std::size_t ox = 0; ox < oxSize; ++ox) {
-                const signedsize difx = static_cast<signedsize>(std::get<2>(params)[0] - ox * std::get<0>(params)[0]);
+                const signedsize difx = static_cast<signedsize>(paddingDims[0] - ox * strideDims[0]);
                 const std::size_t sxMin = static_cast<std::size_t>(std::max(difx, signedsize(0)));
-                const std::size_t sxMax = (static_cast<signedsize>(dims[2]) + difx) < 0 ? 0 : ((dims[2] + difx) > std::get<1>(params)[0] ? std::get<1>(params)[0] : dims[2] + difx);
+                const std::size_t sxMax = (static_cast<signedsize>(dims[2]) + difx) < 0 ? 0 : ((dims[2] + difx) > kernelDims[0] ? kernelDims[0] : dims[2] + difx);
                 for (std::size_t oy = 0; oy < oySize; ++oy) {
-                    const signedsize dify = static_cast<signedsize>(std::get<2>(params)[1] - oy * std::get<0>(params)[1]);
+                    const signedsize dify = static_cast<signedsize>(paddingDims[1] - oy * strideDims[1]);
                     const std::size_t syMin = static_cast<std::size_t>(std::max(dify, signedsize(0)));
-                    const std::size_t syMax = (static_cast<signedsize>(dims[3]) + dify) < 0 ? 0 : ((dims[3] + dify) > std::get<1>(params)[1] ? std::get<1>(params)[1] : dims[3] + dify);
+                    const std::size_t syMax = (static_cast<signedsize>(dims[3]) + dify) < 0 ? 0 : ((dims[3] + dify) > kernelDims[1] ? kernelDims[1] : dims[3] + dify);
                     const std::size_t oIndexFull = oIndex + ox*oySize + oy;
-                    const std::size_t ix = ox * std::get<0>(params)[0];
-                    const std::size_t iy = oy * std::get<0>(params)[1];
+                    const std::size_t ix = ox * strideDims[0];
+                    const std::size_t iy = oy * strideDims[1];
 
                     I poolValue(0.0);
                     bool valid = false;

src/operator/MaxPoolingImpl.cpp

@@ -70,7 +70,7 @@ void Aidge::MaxPoolingImpl2D_cpu::forward() {
             Registrar<MaxPoolingImpl2DForward_cpu>::create({mOp.getInput(0)->dataType(), mOp.getOutput(0)->dataType()});
 
     // Call kernel
-    kernelFunc(mOp.getParams(),
+    kernelFunc(mOp.getStaticAttributes(),
                mOp.getInput(0)->dims<4>(),
                mOp.getInput(0)->getImpl()->rawPtr(),
                mOp.getOutput(0)->getImpl()->rawPtr());