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https://gitlab.com/libeigen/eigen.git
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Eigen cost model part 1. This implements a basic recursive framework to estimate the cost of evaluating tensor expressions.
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@@ -159,6 +159,9 @@ struct TensorEvaluator<const TensorImagePatchOp<Rows, Cols, ArgType>, Device>
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typedef TensorEvaluator<const TensorImagePatchOp<Rows, Cols, ArgType>,
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Device> Self;
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typedef TensorEvaluator<ArgType, Device> Impl;
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typedef typename XprType::CoeffReturnType CoeffReturnType;
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typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
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static const int PacketSize = internal::unpacket_traits<PacketReturnType>::size;
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enum {
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IsAligned = false,
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@@ -307,9 +310,6 @@ struct TensorEvaluator<const TensorImagePatchOp<Rows, Cols, ArgType>, Device>
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}
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}
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typedef typename XprType::CoeffReturnType CoeffReturnType;
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typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; }
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar* /*data*/) {
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@@ -362,15 +362,14 @@ struct TensorEvaluator<const TensorImagePatchOp<Rows, Cols, ArgType>, Device>
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template<int LoadMode>
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(Index index) const
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{
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const Index packetSize = internal::unpacket_traits<PacketReturnType>::size;
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EIGEN_STATIC_ASSERT(packetSize > 1, YOU_MADE_A_PROGRAMMING_MISTAKE)
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eigen_assert(index+packetSize-1 < dimensions().TotalSize());
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EIGEN_STATIC_ASSERT(PacketSize > 1, YOU_MADE_A_PROGRAMMING_MISTAKE)
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eigen_assert(index+PacketSize-1 < dimensions().TotalSize());
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if (m_in_row_strides != 1 || m_in_col_strides != 1 || m_row_inflate_strides != 1 || m_col_inflate_strides != 1) {
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return packetWithPossibleZero(index);
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}
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const Index indices[2] = {index, index + packetSize - 1};
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const Index indices[2] = {index, index + PacketSize - 1};
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const Index patchIndex = indices[0] / m_fastPatchStride;
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if (patchIndex != indices[1] / m_fastPatchStride) {
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return packetWithPossibleZero(index);
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@@ -434,12 +433,24 @@ struct TensorEvaluator<const TensorImagePatchOp<Rows, Cols, ArgType>, Device>
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Index rowInflateStride() const { return m_row_inflate_strides; }
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Index colInflateStride() const { return m_col_inflate_strides; }
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost
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costPerCoeff(bool vectorized) const {
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// We conservatively estimate the cost for the code path where the computed
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// index is inside the original image and
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// TensorEvaluator<ArgType, Device>::CoordAccess is false.
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const double compute_cost = 3 * TensorOpCost::DivCost<Index>() +
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6 * TensorOpCost::MulCost<Index>() +
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8 * TensorOpCost::MulCost<Index>();
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return m_impl.costPerCoeff(vectorized) +
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TensorOpCost(0, 0, compute_cost, vectorized, PacketSize);
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}
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protected:
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packetWithPossibleZero(Index index) const
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{
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const int packetSize = internal::unpacket_traits<PacketReturnType>::size;
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EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[packetSize];
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for (int i = 0; i < packetSize; ++i) {
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const int PacketSize = internal::unpacket_traits<PacketReturnType>::size;
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EIGEN_ALIGN_MAX typename internal::remove_const<CoeffReturnType>::type values[PacketSize];
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for (int i = 0; i < PacketSize; ++i) {
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values[i] = coeff(index+i);
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}
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PacketReturnType rslt = internal::pload<PacketReturnType>(values);
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