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Commit 9103ecfe authored by Maxence Naud's avatar Maxence Naud Committed by Maxence Naud
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Resolve "Missing ``dilation`` parameter in Conv[DepthWise] forward kernel"

parent 445eb637
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3 merge requests!118v0.4.0,!108v0.4.0,!92Export refactor
Pipeline #57393 failed
Stage: static_analysis
    Stage: build
      Stage: test
        Stage: coverage
          Stage: release
            This commit is part of merge request !92. Comments created here will be created in the context of that merge request.
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            include/aidge/backend/cpu/operator/ConvDepthWiseImpl_kernels.hpp
            + 26
            − 16
            View file @ 9103ecfe
            ......@@ -38,7 +38,7 @@ namespace Aidge {
            */
            template <class I, class W, class B, class O>
            void ConvDepthWiseImpl1D_cpu_forward_kernel(const std::array<DimSize_t, 1>& strideDims,
            const std::array<DimSize_t, 1>& /*dilationDims*/,
            const std::array<DimSize_t, 1>& dilationDims,
            const std::array<DimSize_t, 1>& kernelDims,
            const std::array<DimSize_t, 3>& inputDims,
            const void *input_,
            ......@@ -56,6 +56,8 @@ void ConvDepthWiseImpl1D_cpu_forward_kernel(const std::array<DimSize_t, 1>& stri
            const std::size_t oxSize =
            static_cast<std::size_t>(std::floor(static_cast<float>(inputDims[2] - kernelDims[0] + strideDims[0]) /
            static_cast<float>(strideDims[0])));
            const DimSize_t dilated_kernel_x = dilationDims[0]*(kernelDims[0] - 1) + 1;
            // TODO: kernel computation
            // output (batch, outCh, Xout, Yout)
            ......@@ -71,15 +73,17 @@ void ConvDepthWiseImpl1D_cpu_forward_kernel(const std::array<DimSize_t, 1>& stri
            const std::size_t iIndex = (ch + batch*inputDims[1]) * inputDims[2];
            const std::size_t wIndex = ch * kernelDims[0];
            for (std::size_t ox = 0; ox < oxSize; ++ox) {
            const signedsize difx = static_cast<signedsize>(- 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>(inputDims[2]) + difx) < 0 ? 0 : ((inputDims[2] + difx) > kernelDims[0] ? kernelDims[0] : inputDims[2] + difx);
            // const signedsize difx = static_cast<signedsize>(- 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>(inputDims[2]) + difx) < 0 ? 0 : ((inputDims[2] + difx) > kernelDims[0] ? kernelDims[0] : 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 * strideDims[0]);
            for (std::size_t sx = sxMin; sx < sxMax; ++sx) {
            for (std::size_t sx = sxMin; sx*dilationDims[0] < sxMax; ++sx) {
            output[oIndexFull] += weights[wIndex + sx] *
            input[iIndex + static_cast<std::size_t>(ix+static_cast<signedsize>(sx))];
            input[iIndex + static_cast<std::size_t>(ix+static_cast<signedsize>(sx*dilationDims[0]))];
            }
            }
            }
            ......@@ -113,7 +117,7 @@ REGISTRAR(ConvDepthWiseImpl1D_cpu,
            */
            template <class I, class W, class B, class O>
            void ConvDepthWiseImpl2D_cpu_forward_kernel(const std::array<DimSize_t, 2>& strideDims,
            const std::array<DimSize_t, 2>& /*dilationDims*/,
            const std::array<DimSize_t, 2>& dilationDims,
            const std::array<DimSize_t, 2>& kernelDims,
            const std::array<DimSize_t, 4>& inputDims,
            const void *input_,
            ......@@ -132,10 +136,12 @@ void ConvDepthWiseImpl2D_cpu_forward_kernel(const std::array<DimSize_t, 2>& stri
            const std::size_t oxSize =
            static_cast<std::size_t>(std::floor(static_cast<float>(inputDims[2] - kernelDims[0] + strideDims[0]) /
            static_cast<float>(strideDims[0])));
            const DimSize_t dilated_kernel_x = dilationDims[0]*(kernelDims[0] - 1) + 1;
            // output W size
            const std::size_t oySize =
            static_cast<std::size_t>(std::floor(static_cast<float>(inputDims[3] - kernelDims[1] + strideDims[1]) /
            static_cast<float>(strideDims[1])));
            const DimSize_t dilated_kernel_y = dilationDims[1]*(kernelDims[1] - 1) + 1;
            // TODO: kernel computation
            // output (batch, outCh, Xout, Yout)
            ......@@ -151,13 +157,17 @@ void ConvDepthWiseImpl2D_cpu_forward_kernel(const std::array<DimSize_t, 2>& stri
            const std::size_t iIndex = (ch + batch*inputDims[1]) * inputDims[2] * inputDims[3];
            const std::size_t wIndex = ch * kernelDims[0] * kernelDims[1];
            for (std::size_t ox = 0; ox < oxSize; ++ox) {
            const signedsize difx = static_cast<signedsize>(- 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>(inputDims[2]) + difx) < 0 ? 0 : ((inputDims[2] + difx) > kernelDims[0] ? kernelDims[0] : inputDims[2] + difx);
            // const signedsize difx = static_cast<signedsize>(- 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>(inputDims[2]) + difx) < 0 ? 0 : ((inputDims[2] + difx) > kernelDims[0] ? kernelDims[0] : inputDims[2] + difx);
            const std::size_t sxMin = 0;
            const std::size_t sxMax = dilated_kernel_x;
            for (std::size_t oy = 0; oy < oySize; ++oy) {
            const signedsize dify = static_cast<signedsize>(- 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>(inputDims[3]) + dify) < 0 ? 0 : ((inputDims[3] + dify) > kernelDims[1] ? kernelDims[1] : inputDims[3] + dify);
            // const signedsize dify = static_cast<signedsize>(- 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>(inputDims[3]) + dify) < 0 ? 0 : ((inputDims[3] + dify) > kernelDims[1] ? kernelDims[1] : inputDims[3] + dify);
            const std::size_t syMin = 0;
            const std::size_t syMax = dilated_kernel_y;
            const std::size_t oIndexFull = oIndex + ox*oySize + oy;
            const signedsize ix = static_cast<signedsize>(ox * strideDims[0]);
            const signedsize iy = static_cast<signedsize>(oy * strideDims[1]);
            ......@@ -173,10 +183,10 @@ void ConvDepthWiseImpl2D_cpu_forward_kernel(const std::array<DimSize_t, 2>& stri
            weights[wIndex + 2*kernelDims[1] + 1] * input[iIndex + static_cast<std::size_t>(ix+2)*inputDims[3] + static_cast<std::size_t>(iy+1)] +
            weights[wIndex + 2*kernelDims[1] + 2] * input[iIndex + static_cast<std::size_t>(ix+2)*inputDims[3] + static_cast<std::size_t>(iy+2)]);
            } else {
            for (std::size_t sx = sxMin; sx < sxMax; ++sx) {
            for (std::size_t sy = syMin; sy < syMax; ++sy) {
            for (std::size_t sx = sxMin; sx*dilationDims[0] < sxMax; ++sx) {
            for (std::size_t sy = syMin; sy*dilationDims[1] < syMax; ++sy) {
            output[oIndexFull] += weights[wIndex + sx*kernelDims[1] + sy] *
            input[iIndex + static_cast<std::size_t>(ix+static_cast<signedsize>(sx))*inputDims[3] + static_cast<std::size_t>(iy+static_cast<signedsize>(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]))];
            }
            }
            }
            ......
            include/aidge/backend/cpu/operator/ConvImpl_kernels.hpp
            + 29
            − 19
            View file @ 9103ecfe
            ......@@ -40,7 +40,7 @@ namespace Aidge {
            */
            template <class I, class W, class B, class O>
            void ConvImpl1D_cpu_forward_kernel(const std::array<DimSize_t, 1>& strideDims,
            const std::array<DimSize_t, 1>& /*dilationDims*/,
            const std::array<DimSize_t, 1>& dilationDims,
            const std::array<DimSize_t, 1>& kernelDims,
            const std::array<DimSize_t, 3>& inputDims,
            DimSize_t outChannels,
            ......@@ -57,8 +57,9 @@ void ConvImpl1D_cpu_forward_kernel(const std::array<DimSize_t, 1>& strideDims,
            // output H size
            const std::size_t oxSize =
            static_cast<std::size_t>(std::floor(static_cast<float>(inputDims[2] - kernelDims[0] + strideDims[0]) /
            static_cast<std::size_t>(std::floor(static_cast<float>(inputDims[2] - dilationDims[0]*(kernelDims[0] - 1) - 1 + strideDims[0]) /
            static_cast<float>(strideDims[0])));
            const DimSize_t dilated_kernel_x = dilationDims[0]*(kernelDims[0] - 1) + 1;
            // TODO: kernel computation
            // output (batch, outCh, Xout, Yout)
            ......@@ -76,15 +77,17 @@ void ConvImpl1D_cpu_forward_kernel(const std::array<DimSize_t, 1>& strideDims,
            const std::size_t iIndex = (inCh + batch*inputDims[1]) * inputDims[2];
            const std::size_t wIndex = (inCh + outCh*inputDims[1]) * kernelDims[0];
            for (std::size_t ox = 0; ox < oxSize; ++ox) {
            const signedsize difx = static_cast<signedsize>(- 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>(inputDims[2]) + difx) < 0 ? 0 : ((inputDims[2] + difx) > kernelDims[0] ? kernelDims[0] : inputDims[2] + difx);
            // const signedsize difx = static_cast<signedsize>(- 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>(inputDims[2]) + difx) < 0 ? 0 : ((inputDims[2] + difx) > kernelDims[0] ? kernelDims[0] : 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 * strideDims[0]);
            for (std::size_t sx = sxMin; sx < sxMax; ++sx) {
            for (std::size_t sx = sxMin; sx*dilationDims[0] < sxMax; ++sx) {
            output[oIndexFull] += weights[wIndex + sx] *
            input[iIndex + static_cast<std::size_t>(ix+static_cast<signedsize>(sx))];
            input[iIndex + static_cast<std::size_t>(ix+static_cast<signedsize>(sx*dilationDims[0]))];
            }
            }
            }
            ......@@ -122,7 +125,7 @@ REGISTRAR(ConvImpl1D_cpu,
            */
            template <class I, class W, class B, class O>
            void ConvImpl2D_cpu_forward_kernel(const std::array<DimSize_t, 2>& strideDims,
            const std::array<DimSize_t, 2>& /*dilationDims*/,
            const std::array<DimSize_t, 2>& dilationDims,
            const std::array<DimSize_t, 2>& kernelDims,
            const std::array<DimSize_t, 4> &inputDims,
            DimSize_t outChannels,
            ......@@ -139,12 +142,15 @@ void ConvImpl2D_cpu_forward_kernel(const std::array<DimSize_t, 2>& strideDims,
            // output H size
            const std::size_t oxSize =
            static_cast<std::size_t>(std::floor(static_cast<float>(inputDims[2] - kernelDims[0] + strideDims[0]) /
            static_cast<std::size_t>(std::floor(static_cast<float>(inputDims[2] - dilationDims[0]*(kernelDims[0] - 1) - 1 + strideDims[0]) /
            static_cast<float>(strideDims[0])));
            const DimSize_t dilated_kernel_x = dilationDims[0]*(kernelDims[0] - 1) + 1;
            // output W size
            const std::size_t oySize =
            static_cast<std::size_t>(std::floor(static_cast<float>(inputDims[3] - kernelDims[1] + strideDims[1]) /
            static_cast<std::size_t>(std::floor(static_cast<float>(inputDims[3] - dilationDims[1]*(kernelDims[1] - 1) - 1 + strideDims[1]) /
            static_cast<float>(strideDims[1])));
            const DimSize_t dilated_kernel_y = dilationDims[1]*(kernelDims[1] - 1) + 1;
            // TODO: kernel computation
            // output (batch, outCh, Xout, Yout)
            ......@@ -162,13 +168,17 @@ void ConvImpl2D_cpu_forward_kernel(const std::array<DimSize_t, 2>& strideDims,
            const std::size_t iIndex = (inCh + batch*inputDims[1]) * inputDims[2] * inputDims[3];
            const std::size_t wIndex = (inCh + outCh*inputDims[1]) * kernelDims[0] * kernelDims[1];
            for (std::size_t ox = 0; ox < oxSize; ++ox) {
            const signedsize difx = static_cast<signedsize>(- 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>(inputDims[2]) + difx) < 0 ? 0 : ((inputDims[2] + difx) > kernelDims[0] ? kernelDims[0] : inputDims[2] + difx);
            // const signedsize difx = static_cast<signedsize>(- 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>(inputDims[2]) + difx) < 0 ? 0 : ((inputDims[2] + difx) > kernelDims[0] ? kernelDims[0] : inputDims[2] + difx);
            const std::size_t sxMin = 0;
            const std::size_t sxMax = dilated_kernel_x;
            for (std::size_t oy = 0; oy < oySize; ++oy) {
            const signedsize dify = static_cast<signedsize>(- 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>(inputDims[3]) + dify) < 0 ? 0 : ((inputDims[3] + dify) > kernelDims[1] ? kernelDims[1] : inputDims[3] + dify);
            // const signedsize dify = static_cast<signedsize>(- 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>(inputDims[3]) + dify) < 0 ? 0 : ((inputDims[3] + dify) > kernelDims[1] ? kernelDims[1] : inputDims[3] + dify);
            const std::size_t syMin = 0;
            const std::size_t syMax = dilated_kernel_y;
            const std::size_t oIndexFull = oIndex + ox*oySize + oy;
            const signedsize ix = static_cast<signedsize>(ox * strideDims[0]);
            const signedsize iy = static_cast<signedsize>(oy * strideDims[1]);
            ......@@ -184,10 +194,10 @@ void ConvImpl2D_cpu_forward_kernel(const std::array<DimSize_t, 2>& strideDims,
            weights[wIndex + 2*kernelDims[1] + 1] * input[iIndex + static_cast<std::size_t>(ix+2)*inputDims[3] + static_cast<std::size_t>(iy+1)] +
            weights[wIndex + 2*kernelDims[1] + 2] * input[iIndex + static_cast<std::size_t>(ix+2)*inputDims[3] + static_cast<std::size_t>(iy+2)]);
            } else {
            for (std::size_t sx = sxMin; sx < sxMax; ++sx) {
            for (std::size_t sy = syMin; sy < syMax; ++sy) {
            for (std::size_t sx = sxMin; sx*dilationDims[0] < sxMax; ++sx) {
            for (std::size_t sy = syMin; sy*dilationDims[1] < syMax; ++sy) {
            output[oIndexFull] += weights[wIndex + sx*kernelDims[1] + sy] *
            input[iIndex + static_cast<std::size_t>(ix+static_cast<signedsize>(sx))*inputDims[3] + static_cast<std::size_t>(iy+static_cast<signedsize>(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]))];
            }
            }
            }
            ......
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