diff --git a/include/aidge/backend/cpu/operator/ConvDepthWiseImpl_kernels.hpp b/include/aidge/backend/cpu/operator/ConvDepthWiseImpl_kernels.hpp index 28ed8969aa415ab4151d038869594376480eba43..46ae59877bee1b87a9a17be242434d3caca7aae2 100644 --- a/include/aidge/backend/cpu/operator/ConvDepthWiseImpl_kernels.hpp +++ b/include/aidge/backend/cpu/operator/ConvDepthWiseImpl_kernels.hpp @@ -149,7 +149,6 @@ void ConvDepthWiseImpl2D_cpu_forward_kernel(const std::array<DimSize_t, 2>& stri // input (batch, ch, Xin, Yin) // weight (outCh, ch, kernelX, kernelY) // does not take Dilation attribute into account - using signedsize = std::make_signed<std::size_t>::type; const std::size_t outChannels_s = oxSize * oySize; if (dilated_kernel_x ==3 && dilated_kernel_y == 3) { @@ -232,13 +231,13 @@ void ConvDepthWiseImpl2D_cpu_forward_kernel(const std::array<DimSize_t, 2>& stri for (std::size_t oy = 0; oy < oySize; ++oy) { const std::size_t oIndexFull = ox*oySize + oy; - const signedsize ix = static_cast<signedsize>(ox * strideDims[0]); - const signedsize iy = static_cast<signedsize>(oy * strideDims[1]); + const std::size_t ix = ox * strideDims[0]; + const std::size_t iy = oy * strideDims[1]; for (std::size_t sx = 0; sx*dilationDims[0] < dilated_kernel_x; ++sx) { for (std::size_t sy = 0; sy*dilationDims[1] < dilated_kernel_y; ++sy) { output[oIndexFull] += weights[wIndex + sx*kernelDims[1] + sy] * - input[iIndex + static_cast<std::size_t>(ix+static_cast<signedsize>(sx*dilationDims[0]))*inputDims[3] + static_cast<std::size_t>(iy+static_cast<signedsize>(sy*dilationDims[1]))]; + input[iIndex + static_cast<std::size_t>(ix + sx*dilationDims[0])*inputDims[3] + static_cast<std::size_t>(iy + sy*dilationDims[1])]; } } } diff --git a/include/aidge/backend/cpu/operator/ConvImpl_kernels.hpp b/include/aidge/backend/cpu/operator/ConvImpl_kernels.hpp index b4abac19143d8222cf632757f1c9d4a532cb3661..e3b709bf308288a93fd72865a2fdef0e58908134 100644 --- a/include/aidge/backend/cpu/operator/ConvImpl_kernels.hpp +++ b/include/aidge/backend/cpu/operator/ConvImpl_kernels.hpp @@ -158,7 +158,6 @@ void ConvImpl2D_cpu_forward_kernel(const std::array<DimSize_t, 2>& strideDims, // weight (outCh, inCh, kernelX, kernelY) // does not take Dilation attribute into account const std::size_t outChannels_s = oxSize * oySize; - using signedsize = std::make_signed<std::size_t>::type; if (dilated_kernel_x == 3 && dilated_kernel_y == 3) { for (std::size_t batch = 0; batch < inputDims[0]; ++batch) { diff --git a/include/aidge/backend/cpu/operator/GridSampleImpl_kernels.hpp b/include/aidge/backend/cpu/operator/GridSampleImpl_kernels.hpp index fa390e4e9585225ab15b39651198cb3aaae77edb..3461b254b7beecf3e7a41e90a7e40d3f6ecf6a36 100644 --- a/include/aidge/backend/cpu/operator/GridSampleImpl_kernels.hpp +++ b/include/aidge/backend/cpu/operator/GridSampleImpl_kernels.hpp @@ -65,7 +65,7 @@ static float update_normalized_coord_with_padding(float coord, Aidge::GridSample return coord; } -static inline std::int64_t update_unnormalized_coord_with_padding(std::int64_t coord, std::int64_t size, Aidge::GridSample_Op::PaddingMode padding_mode) { +static std::int64_t update_unnormalized_coord_with_padding(std::int64_t coord, std::int64_t size, Aidge::GridSample_Op::PaddingMode padding_mode) { if (!in_bound(coord, 0, size)) { // out of bound. switch padding mode if (padding_mode == Aidge::GridSample_Op::PaddingMode::Border) { diff --git a/src/operator/ConvDepthWiseImpl.cpp b/src/operator/ConvDepthWiseImpl.cpp index d86bba8d1abf348eb25e2d9c69d04b5c33a8a176..9b4ca3ad50d4b1db3367d39381191cf6d8b01314 100644 --- a/src/operator/ConvDepthWiseImpl.cpp +++ b/src/operator/ConvDepthWiseImpl.cpp @@ -65,7 +65,6 @@ void Aidge::ConvDepthWiseImpl2D_cpu::forward() { AIDGE_ASSERT(op_.getInput(0), "missing input #0 in ConvDepthWise Operator"); AIDGE_ASSERT(op_.getInput(1), "missing input #1 in ConvDepthWise Operator"); - AIDGE_ASSERT(op_.getInput(2), "missing input #2 in ConvDepthWise Operator"); AIDGE_ASSERT((op_.getInput(0)->nbDims() == 4), "support for 4-dimensions tensors only");