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Adding TensorVolumePatchOP.h for sycl
This commit is contained in:
Mehdi Goli
parent
0b7875f137
commit
2fa2b617a9
@ -177,6 +177,16 @@ KERNELBROKERCONVERTIMAGEPATCHOP()
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#undef KERNELBROKERCONVERTIMAGEPATCHOP
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/// specialisation of the \ref ConvertToDeviceExpression struct when the node type is TensorVolumePatchOp
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#define KERNELBROKERCONVERTVOLUMEPATCHOP(CVQual)\
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template<DenseIndex Plannes, DenseIndex Rows, DenseIndex Cols, typename XprType>\
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struct ConvertToDeviceExpression<CVQual TensorVolumePatchOp<Plannes, Rows, Cols, XprType> >{\
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typedef CVQual TensorVolumePatchOp<Plannes, Rows, Cols, typename ConvertToDeviceExpression<XprType>::Type> Type;\
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};
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KERNELBROKERCONVERTVOLUMEPATCHOP(const)
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KERNELBROKERCONVERTVOLUMEPATCHOP()
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#undef KERNELBROKERCONVERTVOLUMEPATCHOP
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} // namespace internal
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@ -404,6 +404,28 @@ SYCLTENSORIMAGEPATCHOPEXPR(const)
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SYCLTENSORIMAGEPATCHOPEXPR()
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#undef SYCLTENSORIMAGEPATCHOPEXPR
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// TensorVolumePatchOp
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#define SYCLTENSORVOLUMEPATCHOPEXPR(CVQual)\
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template<DenseIndex Planes, DenseIndex Rows, DenseIndex Cols, typename OrigXprType, typename XprType, typename... Params>\
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struct ExprConstructor<CVQual TensorVolumePatchOp<Planes, Rows, Cols, OrigXprType>, CVQual TensorVolumePatchOp<Planes, Rows, Cols, XprType>, Params... > {\
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typedef ExprConstructor<OrigXprType, XprType, Params...> my_xpr_type;\
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typedef CVQual TensorVolumePatchOp<Planes, Rows, Cols, typename my_xpr_type::Type> Type;\
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my_xpr_type xprExpr;\
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Type expr;\
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template <typename FuncDetector>\
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ExprConstructor(FuncDetector &funcD, const utility::tuple::Tuple<Params...> &t)\
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: xprExpr(funcD.xprExpr, t), expr(xprExpr.expr, funcD.m_patch_planes, funcD.m_patch_rows, funcD.m_patch_cols, funcD.m_plane_strides, funcD.m_row_strides, funcD.m_col_strides,\
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funcD.m_in_plane_strides, funcD.m_in_row_strides, funcD.m_in_col_strides,funcD.m_plane_inflate_strides, funcD.m_row_inflate_strides, funcD.m_col_inflate_strides, \
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funcD.m_padding_top_z, funcD.m_padding_bottom_z, funcD.m_padding_top, funcD.m_padding_bottom, funcD.m_padding_left, funcD.m_padding_right, funcD.m_padding_value,\
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funcD.m_padding_type, funcD.m_padding_explicit){\
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}\
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};
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SYCLTENSORVOLUMEPATCHOPEXPR(const)
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SYCLTENSORVOLUMEPATCHOPEXPR()
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#undef SYCLTENSORVOLUMEPATCHOPEXPR
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// TensorLayoutSwapOp
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#define SYCLTENSORLAYOUTSWAPOPEXPR(CVQual)\
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@ -240,6 +240,21 @@ SYCLTENSORIMAGEPATCHOPEXTACC()
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#undef SYCLTENSORIMAGEPATCHOPEXTACC
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// specialisation of the \ref ExtractAccessor struct when the node type is
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/// TensorVolumePatchOp.
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#define SYCLTENSORVOLUMEPATCHOPEXTACC(CVQual)\
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template<DenseIndex Planes, DenseIndex Rows, DenseIndex Cols, typename XprType, typename Dev>\
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struct ExtractAccessor<TensorEvaluator<CVQual TensorVolumePatchOp<Planes, Rows, Cols, XprType>, Dev> >{\
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static inline auto getTuple(cl::sycl::handler& cgh, const TensorEvaluator<CVQual TensorVolumePatchOp<Planes, Rows, Cols, XprType>, Dev>& eval)\
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RETURN_CPP11(AccessorConstructor::getTuple(cgh, eval.impl()))\
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};
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SYCLTENSORVOLUMEPATCHOPEXTACC(const)
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SYCLTENSORVOLUMEPATCHOPEXTACC()
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#undef SYCLTENSORVOLUMEPATCHOPEXTACC
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// specialisation of the \ref ExtractAccessor struct when the node type is
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/// TensorLayoutSwapOp.
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#define SYCLTENSORLAYOUTSWAPOPEXTACC(CVQual)\
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@ -344,6 +344,50 @@ FunctorExtractor(const TensorEvaluator<Self, Device>& expr)\
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SYCLEXTRFUNCIMAGEPATCHOP(const)
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SYCLEXTRFUNCIMAGEPATCHOP()
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#undef SYCLEXTRFUNCIMAGEPATCHOP
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/// TensorVolumePatchOp
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#define SYCLEXTRFUNCVOLUMEPATCHOP(CVQual)\
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template<DenseIndex Planes, DenseIndex Rows, DenseIndex Cols, typename XprType, typename Device>\
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struct FunctorExtractor<TensorEvaluator<CVQual TensorVolumePatchOp<Planes, Rows, Cols, XprType>, Device> >{\
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typedef CVQual TensorVolumePatchOp<Planes, Rows, Cols, XprType> Self;\
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FunctorExtractor<Eigen::TensorEvaluator<XprType, Device> > xprExpr;\
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const DenseIndex m_patch_planes;\
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const DenseIndex m_patch_rows;\
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const DenseIndex m_patch_cols;\
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const DenseIndex m_plane_strides;\
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const DenseIndex m_row_strides;\
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const DenseIndex m_col_strides;\
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const DenseIndex m_in_plane_strides;\
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const DenseIndex m_in_row_strides;\
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const DenseIndex m_in_col_strides;\
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const DenseIndex m_plane_inflate_strides;\
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const DenseIndex m_row_inflate_strides;\
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const DenseIndex m_col_inflate_strides;\
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const bool m_padding_explicit;\
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const DenseIndex m_padding_top_z;\
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const DenseIndex m_padding_bottom_z;\
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const DenseIndex m_padding_top;\
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const DenseIndex m_padding_bottom;\
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const DenseIndex m_padding_left;\
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const DenseIndex m_padding_right;\
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const PaddingType m_padding_type;\
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const typename Self::Scalar m_padding_value;\
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FunctorExtractor(const TensorEvaluator<Self, Device>& expr)\
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: xprExpr(expr.impl()), m_patch_planes(expr.xpr().patch_planes()), m_patch_rows(expr.xpr().patch_rows()), m_patch_cols(expr.xpr().patch_cols()),\
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m_plane_strides(expr.xpr().plane_strides()), m_row_strides(expr.xpr().row_strides()), m_col_strides(expr.xpr().col_strides()),\
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m_in_plane_strides(expr.xpr().in_plane_strides()), m_in_row_strides(expr.xpr().in_row_strides()), m_in_col_strides(expr.xpr().in_col_strides()),\
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m_plane_inflate_strides(expr.xpr().plane_inflate_strides()),m_row_inflate_strides(expr.xpr().row_inflate_strides()),\
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m_col_inflate_strides(expr.xpr().col_inflate_strides()), m_padding_explicit(expr.xpr().padding_explicit()),\
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m_padding_top_z(expr.xpr().padding_top_z()), m_padding_bottom_z(expr.xpr().padding_bottom_z()), \
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m_padding_top(expr.xpr().padding_top()), m_padding_bottom(expr.xpr().padding_bottom()), m_padding_left(expr.xpr().padding_left()),\
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m_padding_right(expr.xpr().padding_right()), m_padding_type(expr.xpr().padding_type()),m_padding_value(expr.xpr().padding_value()){}\
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};
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SYCLEXTRFUNCVOLUMEPATCHOP(const)
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SYCLEXTRFUNCVOLUMEPATCHOP()
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#undef SYCLEXTRFUNCVOLUMEPATCHOP
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/// template deduction function for FunctorExtractor
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template <typename Evaluator>
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auto inline extractFunctors(const Evaluator& evaluator)-> FunctorExtractor<Evaluator> {
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@ -151,7 +151,7 @@ CHIPPINGOPLEAFCOUNT(const)
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CHIPPINGOPLEAFCOUNT()
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#undef CHIPPINGOPLEAFCOUNT
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///TensorStridingSlicingOp
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#define SLICESTRIDEOPLEAFCOUNT(CVQual)\
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template<typename StartIndices, typename StopIndices, typename Strides, typename XprType>\
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struct LeafCount<CVQual TensorStridingSlicingOp<StartIndices, StopIndices, Strides, XprType> >:CategoryCount<XprType>{};
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@ -160,7 +160,7 @@ SLICESTRIDEOPLEAFCOUNT(const)
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SLICESTRIDEOPLEAFCOUNT()
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#undef SLICESTRIDEOPLEAFCOUNT
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//TensorImagePatchOp
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#define TENSORIMAGEPATCHOPLEAFCOUNT(CVQual)\
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template<DenseIndex Rows, DenseIndex Cols, typename XprType>\
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struct LeafCount<CVQual TensorImagePatchOp<Rows, Cols, XprType> >:CategoryCount<XprType>{};
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@ -170,6 +170,15 @@ TENSORIMAGEPATCHOPLEAFCOUNT(const)
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TENSORIMAGEPATCHOPLEAFCOUNT()
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#undef TENSORIMAGEPATCHOPLEAFCOUNT
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// TensorVolumePatchOp
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#define TENSORVOLUMEPATCHOPLEAFCOUNT(CVQual)\
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template<DenseIndex Planes, DenseIndex Rows, DenseIndex Cols, typename XprType>\
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struct LeafCount<CVQual TensorVolumePatchOp<Planes, Rows, Cols, XprType> >:CategoryCount<XprType>{};
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TENSORVOLUMEPATCHOPLEAFCOUNT(const)
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TENSORVOLUMEPATCHOPLEAFCOUNT()
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#undef TENSORVOLUMEPATCHOPLEAFCOUNT
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} /// namespace TensorSycl
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} /// namespace internal
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@ -235,6 +235,20 @@ SYCLTENSORIMAGEPATCHOP()
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#undef SYCLTENSORIMAGEPATCHOP
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/// specialisation of the \ref PlaceHolderExpression when the node is
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/// TensorVolumePatchOp
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#define SYCLTENSORVOLUMEPATCHOP(CVQual)\
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template<DenseIndex Planes, DenseIndex Rows, DenseIndex Cols, typename XprType, size_t N>\
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struct PlaceHolderExpression<CVQual TensorVolumePatchOp<Planes,Rows, Cols, XprType>, N> {\
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typedef CVQual TensorVolumePatchOp<Planes,Rows, Cols, typename CalculateIndex <N, XprType>::ArgType> Type;\
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};
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SYCLTENSORVOLUMEPATCHOP(const)
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SYCLTENSORVOLUMEPATCHOP()
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#undef SYCLTENSORVOLUMEPATCHOP
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/// template deduction for \ref PlaceHolderExpression struct
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template <typename Expr>
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struct createPlaceHolderExpression {
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@ -65,12 +65,8 @@ class TensorVolumePatchOp : public TensorBase<TensorVolumePatchOp<Planes, Rows,
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DenseIndex in_plane_strides, DenseIndex in_row_strides, DenseIndex in_col_strides,
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DenseIndex plane_inflate_strides, DenseIndex row_inflate_strides, DenseIndex col_inflate_strides,
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PaddingType padding_type, Scalar padding_value)
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: m_xpr(expr), m_patch_planes(patch_planes), m_patch_rows(patch_rows), m_patch_cols(patch_cols),
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m_plane_strides(plane_strides), m_row_strides(row_strides), m_col_strides(col_strides),
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m_in_plane_strides(in_plane_strides), m_in_row_strides(in_row_strides), m_in_col_strides(in_col_strides),
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m_plane_inflate_strides(plane_inflate_strides), m_row_inflate_strides(row_inflate_strides), m_col_inflate_strides(col_inflate_strides),
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m_padding_explicit(false), m_padding_top_z(0), m_padding_bottom_z(0), m_padding_top(0), m_padding_bottom(0), m_padding_left(0), m_padding_right(0),
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m_padding_type(padding_type), m_padding_value(padding_value) {}
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: TensorVolumePatchOp(expr, patch_planes, patch_rows, patch_cols, plane_strides, row_strides, col_strides, in_plane_strides, in_row_strides, in_col_strides,
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plane_inflate_strides, row_inflate_strides, col_inflate_strides, 0,0,0,0,0,0,padding_value, padding_type, false) {}
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorVolumePatchOp(const XprType& expr, DenseIndex patch_planes, DenseIndex patch_rows, DenseIndex patch_cols,
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DenseIndex plane_strides, DenseIndex row_strides, DenseIndex col_strides,
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@ -79,14 +75,14 @@ class TensorVolumePatchOp : public TensorBase<TensorVolumePatchOp<Planes, Rows,
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DenseIndex padding_top_z, DenseIndex padding_bottom_z,
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DenseIndex padding_top, DenseIndex padding_bottom,
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DenseIndex padding_left, DenseIndex padding_right,
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Scalar padding_value)
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Scalar padding_value, PaddingType padding_type=PADDING_VALID, bool padding_explicit=true)
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: m_xpr(expr), m_patch_planes(patch_planes), m_patch_rows(patch_rows), m_patch_cols(patch_cols),
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m_plane_strides(plane_strides), m_row_strides(row_strides), m_col_strides(col_strides),
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m_in_plane_strides(in_plane_strides), m_in_row_strides(in_row_strides), m_in_col_strides(in_col_strides),
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m_plane_inflate_strides(plane_inflate_strides), m_row_inflate_strides(row_inflate_strides), m_col_inflate_strides(col_inflate_strides),
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m_padding_explicit(true), m_padding_top_z(padding_top_z), m_padding_bottom_z(padding_bottom_z), m_padding_top(padding_top), m_padding_bottom(padding_bottom),
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m_padding_explicit(padding_explicit), m_padding_top_z(padding_top_z), m_padding_bottom_z(padding_bottom_z), m_padding_top(padding_top), m_padding_bottom(padding_bottom),
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m_padding_left(padding_left), m_padding_right(padding_right),
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m_padding_type(PADDING_VALID), m_padding_value(padding_value) {}
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m_padding_type(padding_type), m_padding_value(padding_value) {}
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EIGEN_DEVICE_FUNC
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DenseIndex patch_planes() const { return m_patch_planes; }
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@ -183,9 +179,13 @@ struct TensorEvaluator<const TensorVolumePatchOp<Planes, Rows, Cols, ArgType>, D
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CoordAccess = false,
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RawAccess = false
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};
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#ifdef __SYCL_DEVICE_ONLY__
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator( const XprType op, const Device& device)
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#else
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator( const XprType& op, const Device& device)
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#endif
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device)
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: m_impl(op.expression(), device)
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: m_impl(op.expression(), device), m_op(op)
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{
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EIGEN_STATIC_ASSERT((NumDims >= 5), YOU_MADE_A_PROGRAMMING_MISTAKE);
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@ -321,7 +321,9 @@ struct TensorEvaluator<const TensorVolumePatchOp<Planes, Rows, Cols, ArgType>, D
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m_outputPlanesRows = m_outputPlanes * m_outputRows;
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// Fast representations of different variables.
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// printf("THis is m_otherStride: %lu\n", m_otherStride );
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m_fastOtherStride = internal::TensorIntDivisor<Index>(m_otherStride);
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m_fastPatchStride = internal::TensorIntDivisor<Index>(m_patchStride);
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m_fastColStride = internal::TensorIntDivisor<Index>(m_colStride);
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m_fastRowStride = internal::TensorIntDivisor<Index>(m_rowStride);
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@ -338,7 +340,6 @@ struct TensorEvaluator<const TensorVolumePatchOp<Planes, Rows, Cols, ArgType>, D
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m_fastOutputDepth = internal::TensorIntDivisor<Index>(m_dimensions[NumDims-1]);
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}
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}
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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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@ -352,6 +353,7 @@ struct TensorEvaluator<const TensorVolumePatchOp<Planes, Rows, Cols, ArgType>, D
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const
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{
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// Patch index corresponding to the passed in index.
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const Index patchIndex = index / m_fastPatchStride;
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@ -505,6 +507,8 @@ struct TensorEvaluator<const TensorVolumePatchOp<Planes, Rows, Cols, ArgType>, D
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EIGEN_DEVICE_FUNC Scalar* data() const { return NULL; }
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const TensorEvaluator<ArgType, Device>& impl() const { return m_impl; }
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// required by sycl
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const XprType& xpr() const { return m_op; }
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Index planePaddingTop() const { return m_planePaddingTop; }
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Index rowPaddingTop() const { return m_rowPaddingTop; }
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@ -600,6 +604,8 @@ struct TensorEvaluator<const TensorVolumePatchOp<Planes, Rows, Cols, ArgType>, D
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Scalar m_paddingValue;
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TensorEvaluator<ArgType, Device> m_impl;
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// required by sycl
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XprType m_op;
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};
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@ -167,11 +167,12 @@ if(EIGEN_TEST_CXX11)
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ei_add_test_sycl(cxx11_tensor_convolution_sycl "-std=c++11")
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ei_add_test_sycl(cxx11_tensor_striding_sycl "-std=c++11")
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ei_add_test_sycl(cxx11_tensor_chipping_sycl "-std=c++11")
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ei_add_test_sycl(cxx11_tensor_patch_sycl "-std=c++11")
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ei_add_test_sycl(cxx11_tensor_layout_swap_sycl "-std=c++11")
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ei_add_test_sycl(cxx11_tensor_image_patchOP_sycl "-std=c++11")
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ei_add_test_sycl(cxx11_tensor_inflation_sycl "-std=c++11")
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ei_add_test_sycl(cxx11_tensor_generator_sycl "-std=c++11")
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ei_add_test_sycl(cxx11_tensor_patch_sycl "-std=c++11")
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ei_add_test_sycl(cxx11_tensor_image_patchOP_sycl "-std=c++11")
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ei_add_test_sycl(cxx11_tensor_volume_patchOP_sycl "-std=c++11")
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endif(EIGEN_TEST_SYCL)
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# It should be safe to always run these tests as there is some fallback code for
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# older compiler that don't support cxx11.
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222
unsupported/test/cxx11_tensor_volume_patchOP_sycl.cpp
Normal file
222
unsupported/test/cxx11_tensor_volume_patchOP_sycl.cpp
Normal file
@ -0,0 +1,222 @@
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// This file is part of Eigen, a lightweight C++ template library
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// for linear algebra.
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//
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// Copyright (C) 2016
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// Mehdi Goli Codeplay Software Ltd.
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// Ralph Potter Codeplay Software Ltd.
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// Luke Iwanski Codeplay Software Ltd.
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// Contact: <eigen@codeplay.com>
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//
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// This Source Code Form is subject to the terms of the Mozilla
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// Public License v. 2.0. If a copy of the MPL was not distributed
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// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
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#define EIGEN_TEST_NO_LONGDOUBLE
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#define EIGEN_TEST_NO_COMPLEX
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#define EIGEN_TEST_FUNC cxx11_tensor_volume_patchOP_sycl
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#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int64_t
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#define EIGEN_USE_SYCL
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#include "main.h"
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#include <unsupported/Eigen/CXX11/Tensor>
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using Eigen::Tensor;
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static const int DataLayout = ColMajor;
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template <typename DataType, typename IndexType>
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static void test_single_voxel_patch_sycl(const Eigen::SyclDevice& sycl_device)
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{
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IndexType sizeDim0 = 4;
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IndexType sizeDim1 = 2;
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IndexType sizeDim2 = 3;
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IndexType sizeDim3 = 5;
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IndexType sizeDim4 = 7;
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array<IndexType, 5> tensorColMajorRange = {{sizeDim0, sizeDim1, sizeDim2, sizeDim3, sizeDim4}};
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array<IndexType, 5> tensorRowMajorRange = {{sizeDim4, sizeDim3, sizeDim2, sizeDim1, sizeDim0}};
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Tensor<DataType, 5, DataLayout,IndexType> tensor_col_major(tensorColMajorRange);
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Tensor<DataType, 5, RowMajor,IndexType> tensor_row_major(tensorRowMajorRange);
|
||||
tensor_col_major.setRandom();
|
||||
|
||||
|
||||
DataType* gpu_data_col_major = static_cast<DataType*>(sycl_device.allocate(tensor_col_major.size()*sizeof(DataType)));
|
||||
DataType* gpu_data_row_major = static_cast<DataType*>(sycl_device.allocate(tensor_row_major.size()*sizeof(DataType)));
|
||||
TensorMap<Tensor<DataType, 5, ColMajor, IndexType>> gpu_col_major(gpu_data_col_major, tensorColMajorRange);
|
||||
TensorMap<Tensor<DataType, 5, RowMajor, IndexType>> gpu_row_major(gpu_data_row_major, tensorRowMajorRange);
|
||||
|
||||
sycl_device.memcpyHostToDevice(gpu_data_col_major, tensor_col_major.data(),(tensor_col_major.size())*sizeof(DataType));
|
||||
gpu_row_major.device(sycl_device)=gpu_col_major.swap_layout();
|
||||
|
||||
|
||||
// single volume patch: ColMajor
|
||||
array<IndexType, 6> patchColMajorTensorRange={{sizeDim0,1, 1, 1, sizeDim1*sizeDim2*sizeDim3, sizeDim4}};
|
||||
Tensor<DataType, 6, DataLayout,IndexType> single_voxel_patch_col_major(patchColMajorTensorRange);
|
||||
size_t patchTensorBuffSize =single_voxel_patch_col_major.size()*sizeof(DataType);
|
||||
DataType* gpu_data_single_voxel_patch_col_major = static_cast<DataType*>(sycl_device.allocate(patchTensorBuffSize));
|
||||
TensorMap<Tensor<DataType, 6, DataLayout,IndexType>> gpu_single_voxel_patch_col_major(gpu_data_single_voxel_patch_col_major, patchColMajorTensorRange);
|
||||
gpu_single_voxel_patch_col_major.device(sycl_device)=gpu_col_major.extract_volume_patches(1, 1, 1);
|
||||
sycl_device.memcpyDeviceToHost(single_voxel_patch_col_major.data(), gpu_data_single_voxel_patch_col_major, patchTensorBuffSize);
|
||||
|
||||
|
||||
VERIFY_IS_EQUAL(single_voxel_patch_col_major.dimension(0), 4);
|
||||
VERIFY_IS_EQUAL(single_voxel_patch_col_major.dimension(1), 1);
|
||||
VERIFY_IS_EQUAL(single_voxel_patch_col_major.dimension(2), 1);
|
||||
VERIFY_IS_EQUAL(single_voxel_patch_col_major.dimension(3), 1);
|
||||
VERIFY_IS_EQUAL(single_voxel_patch_col_major.dimension(4), 2 * 3 * 5);
|
||||
VERIFY_IS_EQUAL(single_voxel_patch_col_major.dimension(5), 7);
|
||||
|
||||
array<IndexType, 6> patchRowMajorTensorRange={{sizeDim4, sizeDim1*sizeDim2*sizeDim3, 1, 1, 1, sizeDim0}};
|
||||
Tensor<DataType, 6, RowMajor,IndexType> single_voxel_patch_row_major(patchRowMajorTensorRange);
|
||||
patchTensorBuffSize =single_voxel_patch_row_major.size()*sizeof(DataType);
|
||||
DataType* gpu_data_single_voxel_patch_row_major = static_cast<DataType*>(sycl_device.allocate(patchTensorBuffSize));
|
||||
TensorMap<Tensor<DataType, 6, RowMajor,IndexType>> gpu_single_voxel_patch_row_major(gpu_data_single_voxel_patch_row_major, patchRowMajorTensorRange);
|
||||
gpu_single_voxel_patch_row_major.device(sycl_device)=gpu_row_major.extract_volume_patches(1, 1, 1);
|
||||
sycl_device.memcpyDeviceToHost(single_voxel_patch_row_major.data(), gpu_data_single_voxel_patch_row_major, patchTensorBuffSize);
|
||||
|
||||
VERIFY_IS_EQUAL(single_voxel_patch_row_major.dimension(0), 7);
|
||||
VERIFY_IS_EQUAL(single_voxel_patch_row_major.dimension(1), 2 * 3 * 5);
|
||||
VERIFY_IS_EQUAL(single_voxel_patch_row_major.dimension(2), 1);
|
||||
VERIFY_IS_EQUAL(single_voxel_patch_row_major.dimension(3), 1);
|
||||
VERIFY_IS_EQUAL(single_voxel_patch_row_major.dimension(4), 1);
|
||||
VERIFY_IS_EQUAL(single_voxel_patch_row_major.dimension(5), 4);
|
||||
|
||||
sycl_device.memcpyDeviceToHost(tensor_row_major.data(), gpu_data_row_major, (tensor_col_major.size())*sizeof(DataType));
|
||||
for (IndexType i = 0; i < tensor_col_major.size(); ++i) {
|
||||
VERIFY_IS_EQUAL(tensor_col_major.data()[i], single_voxel_patch_col_major.data()[i]);
|
||||
VERIFY_IS_EQUAL(tensor_row_major.data()[i], single_voxel_patch_row_major.data()[i]);
|
||||
VERIFY_IS_EQUAL(tensor_col_major.data()[i], tensor_row_major.data()[i]);
|
||||
}
|
||||
|
||||
|
||||
sycl_device.deallocate(gpu_data_col_major);
|
||||
sycl_device.deallocate(gpu_data_row_major);
|
||||
sycl_device.deallocate(gpu_data_single_voxel_patch_col_major);
|
||||
sycl_device.deallocate(gpu_data_single_voxel_patch_row_major);
|
||||
}
|
||||
|
||||
template <typename DataType, typename IndexType>
|
||||
static void test_entire_volume_patch_sycl(const Eigen::SyclDevice& sycl_device)
|
||||
{
|
||||
const int depth = 4;
|
||||
const int patch_z = 2;
|
||||
const int patch_y = 3;
|
||||
const int patch_x = 5;
|
||||
const int batch = 7;
|
||||
|
||||
array<IndexType, 5> tensorColMajorRange = {{depth, patch_z, patch_y, patch_x, batch}};
|
||||
array<IndexType, 5> tensorRowMajorRange = {{batch, patch_x, patch_y, patch_z, depth}};
|
||||
Tensor<DataType, 5, DataLayout,IndexType> tensor_col_major(tensorColMajorRange);
|
||||
Tensor<DataType, 5, RowMajor,IndexType> tensor_row_major(tensorRowMajorRange);
|
||||
tensor_col_major.setRandom();
|
||||
|
||||
|
||||
DataType* gpu_data_col_major = static_cast<DataType*>(sycl_device.allocate(tensor_col_major.size()*sizeof(DataType)));
|
||||
DataType* gpu_data_row_major = static_cast<DataType*>(sycl_device.allocate(tensor_row_major.size()*sizeof(DataType)));
|
||||
TensorMap<Tensor<DataType, 5, ColMajor, IndexType>> gpu_col_major(gpu_data_col_major, tensorColMajorRange);
|
||||
TensorMap<Tensor<DataType, 5, RowMajor, IndexType>> gpu_row_major(gpu_data_row_major, tensorRowMajorRange);
|
||||
|
||||
sycl_device.memcpyHostToDevice(gpu_data_col_major, tensor_col_major.data(),(tensor_col_major.size())*sizeof(DataType));
|
||||
gpu_row_major.device(sycl_device)=gpu_col_major.swap_layout();
|
||||
sycl_device.memcpyDeviceToHost(tensor_row_major.data(), gpu_data_row_major, (tensor_col_major.size())*sizeof(DataType));
|
||||
|
||||
|
||||
// single volume patch: ColMajor
|
||||
array<IndexType, 6> patchColMajorTensorRange={{depth,patch_z, patch_y, patch_x, patch_z*patch_y*patch_x, batch}};
|
||||
Tensor<DataType, 6, DataLayout,IndexType> entire_volume_patch_col_major(patchColMajorTensorRange);
|
||||
size_t patchTensorBuffSize =entire_volume_patch_col_major.size()*sizeof(DataType);
|
||||
DataType* gpu_data_entire_volume_patch_col_major = static_cast<DataType*>(sycl_device.allocate(patchTensorBuffSize));
|
||||
TensorMap<Tensor<DataType, 6, DataLayout,IndexType>> gpu_entire_volume_patch_col_major(gpu_data_entire_volume_patch_col_major, patchColMajorTensorRange);
|
||||
gpu_entire_volume_patch_col_major.device(sycl_device)=gpu_col_major.extract_volume_patches(patch_z, patch_y, patch_x);
|
||||
sycl_device.memcpyDeviceToHost(entire_volume_patch_col_major.data(), gpu_data_entire_volume_patch_col_major, patchTensorBuffSize);
|
||||
|
||||
|
||||
// Tensor<float, 5> tensor(depth, patch_z, patch_y, patch_x, batch);
|
||||
// tensor.setRandom();
|
||||
// Tensor<float, 5, RowMajor> tensor_row_major = tensor.swap_layout();
|
||||
|
||||
//Tensor<float, 6> entire_volume_patch;
|
||||
//entire_volume_patch = tensor.extract_volume_patches(patch_z, patch_y, patch_x);
|
||||
VERIFY_IS_EQUAL(entire_volume_patch_col_major.dimension(0), depth);
|
||||
VERIFY_IS_EQUAL(entire_volume_patch_col_major.dimension(1), patch_z);
|
||||
VERIFY_IS_EQUAL(entire_volume_patch_col_major.dimension(2), patch_y);
|
||||
VERIFY_IS_EQUAL(entire_volume_patch_col_major.dimension(3), patch_x);
|
||||
VERIFY_IS_EQUAL(entire_volume_patch_col_major.dimension(4), patch_z * patch_y * patch_x);
|
||||
VERIFY_IS_EQUAL(entire_volume_patch_col_major.dimension(5), batch);
|
||||
|
||||
// Tensor<float, 6, RowMajor> entire_volume_patch_row_major;
|
||||
//entire_volume_patch_row_major = tensor_row_major.extract_volume_patches(patch_z, patch_y, patch_x);
|
||||
|
||||
array<IndexType, 6> patchRowMajorTensorRange={{batch,patch_z*patch_y*patch_x, patch_x, patch_y, patch_z, depth}};
|
||||
Tensor<DataType, 6, RowMajor,IndexType> entire_volume_patch_row_major(patchRowMajorTensorRange);
|
||||
patchTensorBuffSize =entire_volume_patch_row_major.size()*sizeof(DataType);
|
||||
DataType* gpu_data_entire_volume_patch_row_major = static_cast<DataType*>(sycl_device.allocate(patchTensorBuffSize));
|
||||
TensorMap<Tensor<DataType, 6, RowMajor,IndexType>> gpu_entire_volume_patch_row_major(gpu_data_entire_volume_patch_row_major, patchRowMajorTensorRange);
|
||||
gpu_entire_volume_patch_row_major.device(sycl_device)=gpu_row_major.extract_volume_patches(patch_z, patch_y, patch_x);
|
||||
sycl_device.memcpyDeviceToHost(entire_volume_patch_row_major.data(), gpu_data_entire_volume_patch_row_major, patchTensorBuffSize);
|
||||
|
||||
|
||||
VERIFY_IS_EQUAL(entire_volume_patch_row_major.dimension(0), batch);
|
||||
VERIFY_IS_EQUAL(entire_volume_patch_row_major.dimension(1), patch_z * patch_y * patch_x);
|
||||
VERIFY_IS_EQUAL(entire_volume_patch_row_major.dimension(2), patch_x);
|
||||
VERIFY_IS_EQUAL(entire_volume_patch_row_major.dimension(3), patch_y);
|
||||
VERIFY_IS_EQUAL(entire_volume_patch_row_major.dimension(4), patch_z);
|
||||
VERIFY_IS_EQUAL(entire_volume_patch_row_major.dimension(5), depth);
|
||||
|
||||
const int dz = patch_z - 1;
|
||||
const int dy = patch_y - 1;
|
||||
const int dx = patch_x - 1;
|
||||
|
||||
const int forward_pad_z = dz - dz / 2;
|
||||
const int forward_pad_y = dy - dy / 2;
|
||||
const int forward_pad_x = dx - dx / 2;
|
||||
|
||||
for (int pz = 0; pz < patch_z; pz++) {
|
||||
for (int py = 0; py < patch_y; py++) {
|
||||
for (int px = 0; px < patch_x; px++) {
|
||||
const int patchId = pz + patch_z * (py + px * patch_y);
|
||||
for (int z = 0; z < patch_z; z++) {
|
||||
for (int y = 0; y < patch_y; y++) {
|
||||
for (int x = 0; x < patch_x; x++) {
|
||||
for (int b = 0; b < batch; b++) {
|
||||
for (int d = 0; d < depth; d++) {
|
||||
float expected = 0.0f;
|
||||
float expected_row_major = 0.0f;
|
||||
const int eff_z = z - forward_pad_z + pz;
|
||||
const int eff_y = y - forward_pad_y + py;
|
||||
const int eff_x = x - forward_pad_x + px;
|
||||
if (eff_z >= 0 && eff_y >= 0 && eff_x >= 0 &&
|
||||
eff_z < patch_z && eff_y < patch_y && eff_x < patch_x) {
|
||||
expected = tensor_col_major(d, eff_z, eff_y, eff_x, b);
|
||||
expected_row_major = tensor_row_major(b, eff_x, eff_y, eff_z, d);
|
||||
}
|
||||
VERIFY_IS_EQUAL(entire_volume_patch_col_major(d, z, y, x, patchId, b), expected);
|
||||
VERIFY_IS_EQUAL(entire_volume_patch_row_major(b, patchId, x, y, z, d), expected_row_major);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
sycl_device.deallocate(gpu_data_col_major);
|
||||
sycl_device.deallocate(gpu_data_row_major);
|
||||
sycl_device.deallocate(gpu_data_entire_volume_patch_col_major);
|
||||
sycl_device.deallocate(gpu_data_entire_volume_patch_row_major);
|
||||
}
|
||||
|
||||
|
||||
|
||||
template<typename DataType, typename dev_Selector> void sycl_tensor_volume_patch_test_per_device(dev_Selector s){
|
||||
QueueInterface queueInterface(s);
|
||||
auto sycl_device = Eigen::SyclDevice(&queueInterface);
|
||||
std::cout << "Running on " << s.template get_info<cl::sycl::info::device::name>() << std::endl;
|
||||
test_single_voxel_patch_sycl<DataType, int64_t>(sycl_device);
|
||||
test_entire_volume_patch_sycl<DataType, int64_t>(sycl_device);
|
||||
}
|
||||
void test_cxx11_tensor_volume_patchOP_sycl()
|
||||
{
|
||||
for (const auto& device :Eigen::get_sycl_supported_devices()) {
|
||||
CALL_SUBTEST(sycl_tensor_volume_patch_test_per_device<float>(device));
|
||||
}
|
||||
}
|
||||
Loading…
Reference in New Issue
Block a user