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Commit 00a0a61c authored by Maxence Naud's avatar Maxence Naud
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[Add] 'dilation' parameter to Conv[DepthWise] forward kernel

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Pipeline #56893 failed
Stage: static_analysis
    Stage: build
      Stage: test
        Stage: coverage
          Stage: release
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            include/aidge/backend/cpu/operator/ConvDepthWiseImpl_kernels.hpp
            + 26
            − 16
            View file @ 00a0a61c
            ...@@ -38,7 +38,7 @@ namespace Aidge { ...@@ -38,7 +38,7 @@ namespace Aidge {
            */ */
            template <class I, class W, class B, class O> template <class I, class W, class B, class O>
            void ConvDepthWiseImpl1D_cpu_forward_kernel(const std::array<DimSize_t, 1>& strideDims, 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, 1>& kernelDims,
            const std::array<DimSize_t, 3>& inputDims, const std::array<DimSize_t, 3>& inputDims,
            const void *input_, const void *input_,
            ...@@ -56,6 +56,8 @@ void ConvDepthWiseImpl1D_cpu_forward_kernel(const std::array<DimSize_t, 1>& stri ...@@ -56,6 +56,8 @@ void ConvDepthWiseImpl1D_cpu_forward_kernel(const std::array<DimSize_t, 1>& stri
            const std::size_t oxSize = 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] - kernelDims[0] + strideDims[0]) /
            static_cast<float>(strideDims[0]))); static_cast<float>(strideDims[0])));
            const DimSize_t dilated_kernel_x = dilationDims[0]*(kernelDims[0] - 1) + 1;
            // TODO: kernel computation // TODO: kernel computation
            // output (batch, outCh, Xout, Yout) // output (batch, outCh, Xout, Yout)
            ...@@ -71,15 +73,17 @@ void ConvDepthWiseImpl1D_cpu_forward_kernel(const std::array<DimSize_t, 1>& stri ...@@ -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 iIndex = (ch + batch*inputDims[1]) * inputDims[2];
            const std::size_t wIndex = ch * kernelDims[0]; const std::size_t wIndex = ch * kernelDims[0];
            for (std::size_t ox = 0; ox < oxSize; ++ox) { for (std::size_t ox = 0; ox < oxSize; ++ox) {
            const signedsize difx = static_cast<signedsize>(- ox * strideDims[0]); // 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 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 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 std::size_t oIndexFull = oIndex + ox;
            const signedsize ix = static_cast<signedsize>(ox * strideDims[0]); 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] * 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, ...@@ -113,7 +117,7 @@ REGISTRAR(ConvDepthWiseImpl1D_cpu,
            */ */
            template <class I, class W, class B, class O> template <class I, class W, class B, class O>
            void ConvDepthWiseImpl2D_cpu_forward_kernel(const std::array<DimSize_t, 2>& strideDims, 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, 2>& kernelDims,
            const std::array<DimSize_t, 4>& inputDims, const std::array<DimSize_t, 4>& inputDims,
            const void *input_, const void *input_,
            ...@@ -132,10 +136,12 @@ void ConvDepthWiseImpl2D_cpu_forward_kernel(const std::array<DimSize_t, 2>& stri ...@@ -132,10 +136,12 @@ void ConvDepthWiseImpl2D_cpu_forward_kernel(const std::array<DimSize_t, 2>& stri
            const std::size_t oxSize = 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] - kernelDims[0] + strideDims[0]) /
            static_cast<float>(strideDims[0]))); static_cast<float>(strideDims[0])));
            const DimSize_t dilated_kernel_x = dilationDims[0]*(kernelDims[0] - 1) + 1;
            // output W size // output W size
            const std::size_t oySize = 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] - kernelDims[1] + strideDims[1]) /
            static_cast<float>(strideDims[1]))); static_cast<float>(strideDims[1])));
            const DimSize_t dilated_kernel_y = dilationDims[1]*(kernelDims[1] - 1) + 1;
            // TODO: kernel computation // TODO: kernel computation
            // output (batch, outCh, Xout, Yout) // output (batch, outCh, Xout, Yout)
            ...@@ -151,13 +157,17 @@ void ConvDepthWiseImpl2D_cpu_forward_kernel(const std::array<DimSize_t, 2>& stri ...@@ -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 iIndex = (ch + batch*inputDims[1]) * inputDims[2] * inputDims[3];
            const std::size_t wIndex = ch * kernelDims[0] * kernelDims[1]; const std::size_t wIndex = ch * kernelDims[0] * kernelDims[1];
            for (std::size_t ox = 0; ox < oxSize; ++ox) { for (std::size_t ox = 0; ox < oxSize; ++ox) {
            const signedsize difx = static_cast<signedsize>(- ox * strideDims[0]); // 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 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 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) { for (std::size_t oy = 0; oy < oySize; ++oy) {
            const signedsize dify = static_cast<signedsize>(- oy * strideDims[1]); // 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 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 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 std::size_t oIndexFull = oIndex + ox*oySize + oy;
            const signedsize ix = static_cast<signedsize>(ox * strideDims[0]); const signedsize ix = static_cast<signedsize>(ox * strideDims[0]);
            const signedsize iy = static_cast<signedsize>(oy * strideDims[1]); 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 ...@@ -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] + 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)]); 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 { } else {
            for (std::size_t sx = sxMin; sx < sxMax; ++sx) { for (std::size_t sx = sxMin; sx*dilationDims[0] < sxMax; ++sx) {
            for (std::size_t sy = syMin; sy < syMax; ++sy) { for (std::size_t sy = syMin; sy*dilationDims[1] < syMax; ++sy) {
            output[oIndexFull] += weights[wIndex + sx*kernelDims[1] + 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 @ 00a0a61c
            ...@@ -40,7 +40,7 @@ namespace Aidge { ...@@ -40,7 +40,7 @@ namespace Aidge {
            */ */
            template <class I, class W, class B, class O> template <class I, class W, class B, class O>
            void ConvImpl1D_cpu_forward_kernel(const std::array<DimSize_t, 1>& strideDims, 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, 1>& kernelDims,
            const std::array<DimSize_t, 3>& inputDims, const std::array<DimSize_t, 3>& inputDims,
            DimSize_t outChannels, DimSize_t outChannels,
            ...@@ -57,8 +57,9 @@ void ConvImpl1D_cpu_forward_kernel(const std::array<DimSize_t, 1>& strideDims, ...@@ -57,8 +57,9 @@ void ConvImpl1D_cpu_forward_kernel(const std::array<DimSize_t, 1>& strideDims,
            // output H size // output H size
            const std::size_t oxSize = 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]))); static_cast<float>(strideDims[0])));
            const DimSize_t dilated_kernel_x = dilationDims[0]*(kernelDims[0] - 1) + 1;
            // TODO: kernel computation // TODO: kernel computation
            // output (batch, outCh, Xout, Yout) // output (batch, outCh, Xout, Yout)
            ...@@ -76,15 +77,17 @@ void ConvImpl1D_cpu_forward_kernel(const std::array<DimSize_t, 1>& strideDims, ...@@ -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 iIndex = (inCh + batch*inputDims[1]) * inputDims[2];
            const std::size_t wIndex = (inCh + outCh*inputDims[1]) * kernelDims[0]; const std::size_t wIndex = (inCh + outCh*inputDims[1]) * kernelDims[0];
            for (std::size_t ox = 0; ox < oxSize; ++ox) { for (std::size_t ox = 0; ox < oxSize; ++ox) {
            const signedsize difx = static_cast<signedsize>(- ox * strideDims[0]); // 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 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 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 std::size_t oIndexFull = oIndex + ox;
            const signedsize ix = static_cast<signedsize>(ox * strideDims[0]); 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] * 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, ...@@ -122,7 +125,7 @@ REGISTRAR(ConvImpl1D_cpu,
            */ */
            template <class I, class W, class B, class O> template <class I, class W, class B, class O>
            void ConvImpl2D_cpu_forward_kernel(const std::array<DimSize_t, 2>& strideDims, 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, 2>& kernelDims,
            const std::array<DimSize_t, 4> &inputDims, const std::array<DimSize_t, 4> &inputDims,
            DimSize_t outChannels, DimSize_t outChannels,
            ...@@ -139,12 +142,15 @@ void ConvImpl2D_cpu_forward_kernel(const std::array<DimSize_t, 2>& strideDims, ...@@ -139,12 +142,15 @@ void ConvImpl2D_cpu_forward_kernel(const std::array<DimSize_t, 2>& strideDims,
            // output H size // output H size
            const std::size_t oxSize = 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]))); static_cast<float>(strideDims[0])));
            const DimSize_t dilated_kernel_x = dilationDims[0]*(kernelDims[0] - 1) + 1;
            // output W size // output W size
            const std::size_t oySize = 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]))); static_cast<float>(strideDims[1])));
            const DimSize_t dilated_kernel_y = dilationDims[1]*(kernelDims[1] - 1) + 1;
            // TODO: kernel computation // TODO: kernel computation
            // output (batch, outCh, Xout, Yout) // output (batch, outCh, Xout, Yout)
            ...@@ -162,13 +168,17 @@ void ConvImpl2D_cpu_forward_kernel(const std::array<DimSize_t, 2>& strideDims, ...@@ -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 iIndex = (inCh + batch*inputDims[1]) * inputDims[2] * inputDims[3];
            const std::size_t wIndex = (inCh + outCh*inputDims[1]) * kernelDims[0] * kernelDims[1]; const std::size_t wIndex = (inCh + outCh*inputDims[1]) * kernelDims[0] * kernelDims[1];
            for (std::size_t ox = 0; ox < oxSize; ++ox) { for (std::size_t ox = 0; ox < oxSize; ++ox) {
            const signedsize difx = static_cast<signedsize>(- ox * strideDims[0]); // 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 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 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) { for (std::size_t oy = 0; oy < oySize; ++oy) {
            const signedsize dify = static_cast<signedsize>(- oy * strideDims[1]); // 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 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 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 std::size_t oIndexFull = oIndex + ox*oySize + oy;
            const signedsize ix = static_cast<signedsize>(ox * strideDims[0]); const signedsize ix = static_cast<signedsize>(ox * strideDims[0]);
            const signedsize iy = static_cast<signedsize>(oy * strideDims[1]); 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, ...@@ -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] + 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)]); 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 { } else {
            for (std::size_t sx = sxMin; sx < sxMax; ++sx) { for (std::size_t sx = sxMin; sx*dilationDims[0] < sxMax; ++sx) {
            for (std::size_t sy = syMin; sy < syMax; ++sy) { for (std::size_t sy = syMin; sy*dilationDims[1] < syMax; ++sy) {
            output[oIndexFull] += weights[wIndex + sx*kernelDims[1] + 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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