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https://github.com/paboyle/Grid.git
synced 2025-11-03 21:44:33 +00:00
Hdf5 writing of scalar (i.e. no Grid subtypes) Eigen::Tensor works. But issues when adding Eigen::Tensor to serialisable object.
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@@ -55,6 +55,12 @@ namespace Grid {
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template <typename U>
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typename std::enable_if<!std::is_base_of<Serializable, U>::value, void>::type
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write(const std::string& s, const U &output);
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template <typename U>
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void write(const std::string &s, const iScalar<U> &output);
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template <typename U, int N>
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void write(const std::string &s, const iVector<U, N> &output);
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template <typename U, int N>
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void write(const std::string &s, const iMatrix<U, N> &output);
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template <typename Scalar_, int NumIndices_, int Options_, typename IndexType_>
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typename std::enable_if<std::is_arithmetic<Scalar_>::value || Grid::is_complex<Scalar_>::value, void>::type
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write(const std::string &s, const Eigen::Tensor<Scalar_, NumIndices_, Options_, IndexType_> &output);
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@@ -64,13 +70,7 @@ namespace Grid {
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void write(const std::string &s, const Eigen::Tensor<iVector<U, N>, NumIndices_, Options_, IndexType_> &output);
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template<typename U, int N, int NumIndices_, int Options_, typename IndexType_>
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void write(const std::string &s, const Eigen::Tensor<iMatrix<U, N>, NumIndices_, Options_, IndexType_> &output);
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template <typename U>
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void write(const std::string &s, const iScalar<U> &output);
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template <typename U, int N>
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void write(const std::string &s, const iVector<U, N> &output);
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template <typename U, int N>
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void write(const std::string &s, const iMatrix<U, N> &output);
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void scientificFormat(const bool set);
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bool isScientific(void);
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void setPrecision(const unsigned int prec);
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@@ -152,43 +152,6 @@ namespace Grid {
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upcast->writeDefault(s, output);
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}
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// Eigen::Tensors of arithmetic/complex base type
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template <typename T>
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template <typename Scalar_, int NumIndices_, int Options_, typename IndexType_>
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typename std::enable_if<std::is_arithmetic<Scalar_>::value || Grid::is_complex<Scalar_>::value, void>::type
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Writer<T>::write(const std::string &s, const Eigen::Tensor<Scalar_, NumIndices_, Options_, IndexType_> &output)
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{
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//upcast->writeDefault(s, tensorToVec(output));
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std::cout << "I really should add code to write Eigen::Tensor (arithmetic/complex) ..." << std::endl;
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}
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// Eigen::Tensors of iScalar<U>
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template <typename T>
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template<typename U, int NumIndices_, int Options_, typename IndexType_>
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void Writer<T>::write(const std::string &s, const Eigen::Tensor<iScalar<U>, NumIndices_, Options_, IndexType_> &output)
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{
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//upcast->writeDefault(s, tensorToVec(output));
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std::cout << "I really should add code to write Eigen::Tensor (iScalar) ..." << std::endl;
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}
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// Eigen::Tensors of iVector<U, N>
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template <typename T>
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template<typename U, int N, int NumIndices_, int Options_, typename IndexType_>
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void Writer<T>::write(const std::string &s, const Eigen::Tensor<iVector<U, N>, NumIndices_, Options_, IndexType_> &output)
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{
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//upcast->writeDefault(s, tensorToVec(output));
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std::cout << "I really should add code to write Eigen::Tensor (iVector) ..." << std::endl;
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}
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// Eigen::Tensors of iMatrix<U, N>
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template <typename T>
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template<typename U, int N, int NumIndices_, int Options_, typename IndexType_>
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void Writer<T>::write(const std::string &s, const Eigen::Tensor<iMatrix<U, N>, NumIndices_, Options_, IndexType_> &output)
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{
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//upcast->writeDefault(s, tensorToVec(output));
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std::cout << "I really should add code to write Eigen::Tensor (iMatrix) ..." << std::endl;
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}
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template <typename T>
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template <typename U>
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@@ -211,6 +174,83 @@ namespace Grid {
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upcast->writeDefault(s, tensorToVec(output));
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}
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// Eigen::Tensors of arithmetic/complex base type
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template <typename T>
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template <typename Scalar_, int NumIndices_, int Options_, typename IndexType_>
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typename std::enable_if<std::is_arithmetic<Scalar_>::value || Grid::is_complex<Scalar_>::value, void>::type
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Writer<T>::write(const std::string &s, const Eigen::Tensor<Scalar_, NumIndices_, Options_, IndexType_> &output)
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{
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typedef Eigen::Tensor<Scalar_, NumIndices_, Options_, IndexType_> Tensor;
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const typename Tensor::Index NumElements{output.size()};
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assert( NumElements > 0 );
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if( NumElements == 1 )
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upcast->writeDefault(s, * output.data());
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else {
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// Create a single, flat vector to hold all the data
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std::vector<Scalar_> flat(NumElements);
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// We're not interested in trivial dimensions, i.e. dimensions = 1
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const typename Tensor::Dimensions & DimsOriginal{output.dimensions()};
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assert(DimsOriginal.size() == NumIndices_);
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unsigned int TrivialDimCount{0};
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for(auto i : DimsOriginal ) {
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if( i <= 1 ) {
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TrivialDimCount++;
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assert( i == 1 ); // Not expecting dimension to be <= 0
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}
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}
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const unsigned int ReducedDimCount{NumIndices_ - TrivialDimCount};
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assert( ReducedDimCount > 0 ); // NB: We've already checked this is not a scalar
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// Save a flat vector of the non-trivial dimensions
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std::vector<size_t> ReducedDims(ReducedDimCount);
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unsigned int ui = 0;
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for(auto i : DimsOriginal ) {
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if( i > 1 ) {
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ReducedDims[ui] = static_cast<size_t>(i);
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assert( ReducedDims[ui] == i ); // check we didn't lose anything in the conversion
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ui++;
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}
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}
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// Now copy all the data to my flat vector
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// Regardless of the Eigen::Tensor storage order, the copy will be Row Major
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std::array<typename Tensor::Index, NumIndices_> MyIndex;
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for( int i = 0 ; i < NumIndices_ ; i++ ) MyIndex[i] = 0;
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for( typename Tensor::Index n = 0; n < NumElements; n++ ) {
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flat[n] = output( MyIndex );
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// Now increment the index
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for( int i = NumIndices_ - 1; i >= 0 && ++MyIndex[i] == DimsOriginal[i]; i-- )
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MyIndex[i] = 0;
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}
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upcast->template writeMultiDim<Scalar_>(s, ReducedDims, flat);
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}
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}
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// Eigen::Tensors of iScalar<U>
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template <typename T>
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template<typename U, int NumIndices_, int Options_, typename IndexType_>
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void Writer<T>::write(const std::string &s, const Eigen::Tensor<iScalar<U>, NumIndices_, Options_, IndexType_> &output)
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{
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//upcast->writeDefault(s, tensorToVec(output));
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std::cout << "I really should add code to write Eigen::Tensor (iScalar) ..." << std::endl;
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}
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// Eigen::Tensors of iVector<U, N>
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template <typename T>
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template<typename U, int N, int NumIndices_, int Options_, typename IndexType_>
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void Writer<T>::write(const std::string &s, const Eigen::Tensor<iVector<U, N>, NumIndices_, Options_, IndexType_> &output)
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{
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//upcast->writeDefault(s, tensorToVec(output));
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std::cout << "I really should add code to write Eigen::Tensor (iVector) ..." << std::endl;
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}
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// Eigen::Tensors of iMatrix<U, N>
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template <typename T>
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template<typename U, int N, int NumIndices_, int Options_, typename IndexType_>
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void Writer<T>::write(const std::string &s, const Eigen::Tensor<iMatrix<U, N>, NumIndices_, Options_, IndexType_> &output)
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{
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//upcast->writeDefault(s, tensorToVec(output));
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std::cout << "I really should add code to write Eigen::Tensor (iMatrix) ..." << std::endl;
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}
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template <typename T>
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void Writer<T>::scientificFormat(const bool set)
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{
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@@ -38,6 +38,8 @@ namespace Grid
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template <typename U>
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typename std::enable_if<!element<std::vector<U>>::is_number, void>::type
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writeDefault(const std::string &s, const std::vector<U> &x);
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template <typename U>
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void writeMultiDim(const std::string &s, const std::vector<size_t> & Dimensions, const std::vector<U> & DataRowMajor);
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H5NS::Group & getGroup(void);
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private:
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template <typename U>
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@@ -101,6 +103,35 @@ namespace Grid
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template <>
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void Hdf5Writer::writeDefault(const std::string &s, const std::string &x);
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template <typename U>
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void Hdf5Writer::writeMultiDim(const std::string &s, const std::vector<size_t> & Dimensions, const std::vector<U> & DataRowMajor)
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{
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// Hdf5 needs the dimensions as hsize_t
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std::vector<hsize_t> dim;
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for (auto &d: Dimensions)
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dim.push_back(d);
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// write to file
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H5NS::DataSpace dataSpace(dim.size(), dim.data());
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if (DataRowMajor.size() > dataSetThres_)
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{
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H5NS::DataSet dataSet;
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H5NS::DSetCreatPropList plist;
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plist.setChunk(dim.size(), dim.data());
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plist.setFletcher32();
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dataSet = group_.createDataSet(s, Hdf5Type<U>::type(), dataSpace, plist);
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dataSet.write(&DataRowMajor[0], Hdf5Type<U>::type());
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}
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else
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{
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H5NS::Attribute attribute;
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attribute = group_.createAttribute(s, Hdf5Type<U>::type(), dataSpace);
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attribute.write(Hdf5Type<U>::type(), &DataRowMajor[0]);
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}
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}
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template <typename U>
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typename std::enable_if<element<std::vector<U>>::is_number, void>::type
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Hdf5Writer::writeDefault(const std::string &s, const std::vector<U> &x)
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@@ -110,34 +141,11 @@ namespace Grid
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// flatten the vector and getting dimensions
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Flatten<std::vector<U>> flat(x);
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std::vector<hsize_t> dim;
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std::vector<size_t> dim;
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const auto &flatx = flat.getFlatVector();
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for (auto &d: flat.getDim())
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{
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dim.push_back(d);
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}
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// write to file
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H5NS::DataSpace dataSpace(dim.size(), dim.data());
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if (flatx.size() > dataSetThres_)
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{
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H5NS::DataSet dataSet;
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H5NS::DSetCreatPropList plist;
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plist.setChunk(dim.size(), dim.data());
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plist.setFletcher32();
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dataSet = group_.createDataSet(s, Hdf5Type<Element>::type(), dataSpace, plist);
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dataSet.write(flatx.data(), Hdf5Type<Element>::type());
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}
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else
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{
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H5NS::Attribute attribute;
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attribute = group_.createAttribute(s, Hdf5Type<Element>::type(), dataSpace);
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attribute.write(Hdf5Type<Element>::type(), flatx.data());
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}
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writeMultiDim<Element>(s, dim, flatx);
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}
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template <typename U>
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@@ -53,6 +53,17 @@ namespace Grid {
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return os;
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}
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// std::vector<std:vector<...>> nested to specified Rank //////////////////////////////////
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template<typename T, unsigned int Rank>
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struct NestedStdVector {
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typedef typename std::vector<typename NestedStdVector<T, Rank - 1>::type> type;
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};
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template<typename T>
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struct NestedStdVector<T,0> {
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typedef T type;
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};
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// Grid scalar tensors to nested std::vectors //////////////////////////////////
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template <typename T>
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struct TensorToVec
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@@ -441,18 +441,21 @@ bool DebugEigenTest()
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}
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typedef iMatrix<Complex,7> OddBall;
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typedef Eigen::Tensor<int, 3, Eigen::RowMajor> TensorInt;
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typedef Eigen::Tensor<std::complex<double>, 3, Eigen::RowMajor> TensorComplex;
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typedef Eigen::Tensor<OddBall, 3, Eigen::RowMajor> TensorOddBall;
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typedef int TestScalar;
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//typedef std::complex<double> TestScalar;
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typedef Eigen::Tensor<TestScalar, 3, Eigen::RowMajor> TestTensor;
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// From Test_serialisation.cc
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class myclass: Serializable {
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public:
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GRID_SERIALIZABLE_CLASS_MEMBERS(myclass
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//, OddBall, critter
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//, TestTensor, critter
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, SpinColourVector, scv
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, SpinColourMatrix, scm
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);
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//myclass() : critter(7,3,2) {}
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};
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template <typename W, typename R, typename O>
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@@ -483,11 +486,31 @@ bool DebugIOTest(void) {
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ioTest<Hdf5Writer, Hdf5Reader, SpinColourMatrix>("iotest_matrix.h5", scm, "SpinColourMatrix");
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SpinColourVector scv;
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ioTest<Hdf5Writer, Hdf5Reader, SpinColourVector>("iotest_vector.h5", scv, "SpinColourVector");
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TestTensor t(3,6,2);
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TestScalar Val{1};
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const TestScalar Inc{1};
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for( int i = 0 ; i < 3 ; i++)
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for( int j = 0 ; j < 6 ; j++)
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for( int k = 0 ; k < 2 ; k++) {
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t(i,j,k) = Val;
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Val += Inc;
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}
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ioTest<Hdf5Writer, Hdf5Reader, TestTensor>("iotest_tensor.h5", t, "eigen_tensor_instance_name");
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TestTensor t2(t);
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auto bEqual = (t == t2).all();
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std::cout << "(t2 == t) = " << bEqual << std::endl;
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//if( * bEqual.data() != 0 )
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//std::cout << "t2 == t" << std::endl;
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//else
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//std::cout << "t2 != t" << std::endl;
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myclass o;
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ioTest<Hdf5Writer, Hdf5Reader, myclass>("iotest_object.h5", o, "myclass_object_instance_name");
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TensorInt t(3,6,2);
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ioTest<Hdf5Writer, Hdf5Reader, TensorInt>("iotest_tensor.h5", t, "eigen_tensor_instance_name");
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std::cout << "Wow!" << std::endl;
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return true;
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}
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#endif
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