diff --git a/include/aidge/backend/cpu/operator/ConvImpl.hpp b/include/aidge/backend/cpu/operator/ConvImpl.hpp
index 5dfd923957738138561808895280ef1d7059b6d4..4bb2aaaf558aa2f9152d24e8db8ae072a34ef7d1 100644
--- a/include/aidge/backend/cpu/operator/ConvImpl.hpp
+++ b/include/aidge/backend/cpu/operator/ConvImpl.hpp
@@ -20,21 +20,22 @@
#include "aidge/utils/Types.h"
namespace Aidge {
+
// Operator implementation entry point for the backend
using Conv1D_Op = Conv_Op<1>;
using ConvImpl1D_cpu = OperatorImpl_cpu<Conv1D_Op,
- void(const DimSize_t &,
- const DimSize_t &,
- const DimSize_t &,
+ void(const std::array<DimSize_t,1> &,
+ const std::array<DimSize_t,1> &,
+ const std::array<DimSize_t,1> &,
const std::array<DimSize_t, 3> &,
DimSize_t,
const void *,
const void *,
const void *,
void *),
- void(const DimSize_t &,
- const DimSize_t &,
- const DimSize_t &,
+ void(const std::array<DimSize_t,1> &,
+ const std::array<DimSize_t,1> &,
+ const std::array<DimSize_t,1> &,
const std::array<DimSize_t, 3> &,
const std::array<DimSize_t, 3> &,
const void *,
diff --git a/include/aidge/backend/cpu/operator/ConvImpl_kernels.hpp b/include/aidge/backend/cpu/operator/ConvImpl_kernels.hpp
index 36b6d4e23c21cd8349ed9ad8263b7336615c99c8..274f5f4f8d373ade68698d4325b089cf7986f259 100644
--- a/include/aidge/backend/cpu/operator/ConvImpl_kernels.hpp
+++ b/include/aidge/backend/cpu/operator/ConvImpl_kernels.hpp
@@ -37,9 +37,9 @@ using std::array;
* @param output_ Output Tensor.
*/
template <class I, class W, class B, class O>
-void ConvImpl1D_cpu_forward_kernel(const DimSize_t &strideDim,
- const DimSize_t &dilationDim,
- const DimSize_t &kernelDim,
+void ConvImpl1D_cpu_forward_kernel(const array<DimSize_t, 1> &strideDim,
+ const array<DimSize_t, 1> &dilationDim,
+ const array<DimSize_t, 1> &kernelDim,
const std::array<DimSize_t, 3> &inputDims,
DimSize_t outChannels,
const void *input_,
@@ -54,10 +54,10 @@ void ConvImpl1D_cpu_forward_kernel(const DimSize_t &strideDim,
// output H size
const std::size_t oxSize = static_cast<std::size_t>(std::floor(
- static_cast<float>(inputDims[2] - dilationDim * (kernelDim - 1) - 1 +
- strideDim) /
- static_cast<float>(strideDim)));
- const DimSize_t dilated_kernel_x = dilationDim * (kernelDim - 1) + 1;
+ static_cast<float>(inputDims[2] - dilationDim[0] * (kernelDim[0] - 1) -
+ 1 + strideDim[0]) /
+ static_cast<float>(strideDim[0])));
+ const DimSize_t dilated_kernel_x = dilationDim[0] * (kernelDim[0] - 1) + 1;
using signedsize = std::make_signed<std::size_t>::type;
for (std::size_t batch = 0; batch < inputDims[0]; ++batch) {
@@ -70,28 +70,28 @@ void ConvImpl1D_cpu_forward_kernel(const DimSize_t &strideDim,
const std::size_t iIndex =
(inCh + batch * inputDims[1]) * inputDims[2];
const std::size_t wIndex =
- (inCh + outCh * inputDims[1]) * kernelDim;
+ (inCh + outCh * inputDims[1]) * kernelDim[0];
for (std::size_t ox = 0; ox < oxSize; ++ox) {
// const signedsize difx = static_cast<signedsize>(- ox *
- // strideDims); const std::size_t sxMin =
+ // strideDim[0s); const std::size_t sxMin =
// static_cast<std::size_t>(std::max(difx, signedsize(0)));
// const std::size_t sxMax =
// (static_cast<signedsize>(inputDims[2]) + difx) < 0 ? 0 :
- // ((inputDims[2] + difx) > kernelDims[0] ? kernelDims :
- // inputDims[2] + difx);
+ // ((inputDims[2] + difx) > kernelDim[0s[0] ? kernelDim[0s
+ // : inputDims[2] + difx);
const std::size_t sxMin = 0;
const std::size_t sxMax = dilated_kernel_x;
const std::size_t oIndexFull = oIndex + ox;
const signedsize ix =
- static_cast<signedsize>(ox * strideDim);
+ static_cast<signedsize>(ox * strideDim[0]);
- for (std::size_t sx = sxMin; sx * dilationDim < sxMax;
+ for (std::size_t sx = sxMin; sx * dilationDim[0] < sxMax;
++sx) {
output[oIndexFull] +=
weights[wIndex + sx] *
input[iIndex + static_cast<std::size_t>(
ix + static_cast<signedsize>(
- sx * dilationDim))];
+ sx * dilationDim[0]))];
}
}
}
@@ -135,9 +135,9 @@ void ConvImpl1D_cpu_forward_kernel(const DimSize_t &strideDim,
* @param[inout] iGrad gradients of the input to update
*/
template <class I, class W, class O>
-void conv1DBackwardInput(const DimSize_t &stride,
- const DimSize_t &dilation,
- const DimSize_t &kDim,
+void conv1DBackwardInput(const array<DimSize_t, 1> &stride,
+ const array<DimSize_t, 1> &dilation,
+ const array<DimSize_t, 1> &kDim,
const array<DimSize_t, 2> &kStrides,
const W *weights,
const array<DimSize_t, 3> &oDims,
diff --git a/src/operator/ConvImpl.cpp b/src/operator/ConvImpl.cpp
index 8ce62dcb0ebe22823928e19e98f596be93c28d9d..d23a9968ffb424b4639e0fcd2629a3a1cc2e11c3 100644
--- a/src/operator/ConvImpl.cpp
+++ b/src/operator/ConvImpl.cpp
@@ -41,9 +41,9 @@ void Aidge::ConvImpl1D_cpu::forward() {
// Call kernel
impl.forward(
- op_.strideDims()[0],
- op_.dilationDims()[0],
- op_.kernelDims()[0],
+ op_.strideDims(),
+ op_.dilationDims(),
+ op_.kernelDims(),
op_.getInput(0)->template dims<3>(), // input dimensions
dynamic_cast<const Conv_Op<1> &>(mOp).outChannels(), // outChannels
input0.getImpl()->rawPtr(), // input