mirror of https://github.com/pybind/pybind11.git
First draft of Eigen::Tensor support (#4201)
* First draft of Eigen::Tensor support * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix build errors * Weird allocator stuff? * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Remove unused + additional allocator junk * Disable warning * Use constexpr * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * clang tidy fixes * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Resolve comments * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Remove auto constexpr function * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Try again for older C++ * Oops forgot constexpr * Move to new files as suggested * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix weird tests * Fix nits * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Oops, forgot import * Fix clang 3.6 bug * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * More comprehensive test suite * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Refactor allocators to make things more clear * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Switch to std::copy * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Switch to DSizes instead of array * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Address feedback * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Fix python + dummy c++ change to trigger build * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Alignment * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Add include guard * Forgot inline * Fix compiler warning * Remove bad test * Better type signatures * Add guards to make compiler requirements more explicit * style: pre-commit fixes * Force rerun of tests due to flake * style: pre-commit fixes * Keep pragmas & all related comments together, add PLEASE KEEP IN SYNC * Move headers out of detail * style: pre-commit fixes * Fix cmake * Improve casting * style: pre-commit fixes * Add a ton more tests + refactor * Improve names * style: pre-commit fixes * Update include/pybind11/eigen/tensor.h Co-authored-by: Aaron Gokaslan <skylion.aaron@gmail.com> * Fix tests * style: pre-commit fixes * Update * Add a test to verify that strange numpy arrays work * Fix dumb compiler warning * Better tests * Better tests * Fix tests * style: pre-commit fixes * More test fixes * style: pre-commit fixes * A ton more test coverage * Fix tests * style: pre-commit fixes * style: pre-commit fixes * Add back constexpr * Another test * style: pre-commit fixes * Improve tests * Whoops * Less magic numbers * Update tests/test_eigen_tensor.py Co-authored-by: Sergiu Deitsch <sergiud@users.noreply.github.com> * Update tests/test_eigen_tensor.py Co-authored-by: Sergiu Deitsch <sergiud@users.noreply.github.com> * style: pre-commit fixes * Fix tests * style: pre-commit fixes * Fix memory leak * style: pre-commit fixes * Fix order * style: pre-commit fixes * Add test to make sure unsafe casts fail * Minor bug fix to work on 32 bit machines * Implement convert flag * style: pre-commit fixes * Switch to correct TensorMap const use * style: pre-commit fixes * Support older versions of eigen * Weird c++ compilers * Fix Eigen bug * Fix another eigen bug * Yet another eigen bug * Potential flakes? * style: pre-commit fixes * Rerun tests with dummy exception to find out what is going on * Another dummy test run * Ablate more * Found the broken test? * One step closer * one step further * Double check * one thing at a time * Give up and disable the test * Clang lies about being gcc * Oops, fix matrix test * style: pre-commit fixes * Add tests to verify scalar conversions * style: pre-commit fixes * Fix nits * Support no_array * Fix tests * style: pre-commit fixes * Silence compiler warning * Improve build system for ancient compilers * Make clang happy * Make gcc happy * Implement Skylion's suggestions * Fix warning * Inline const pointer check * Implement suggestions * style: pre-commit fixes * Improve tests * Typo * style: pre-commit fixes * Support Google's build environment * style: pre-commit fixes * Update include/pybind11/eigen/tensor.h Co-authored-by: Aaron Gokaslan <skylion.aaron@gmail.com> * style: pre-commit fixes * Test cleanup per Skylion * Switch to remvove_cv_t * Cleaner test * style: pre-commit fixes * Remove tensor from eigen.h, update tests * style: pre-commit fixes Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Ralf W. Grosse-Kunstleve <rwgk@google.com> Co-authored-by: Aaron Gokaslan <aaronGokaslan@gmail.com> Co-authored-by: Aaron Gokaslan <skylion.aaron@gmail.com> Co-authored-by: Sergiu Deitsch <sergiud@users.noreply.github.com>
This commit is contained in:
Lalaland
GitHub
parent
b926396bdf
commit
fab1eebe2c
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@ -43,3 +43,4 @@ pybind11Targets.cmake
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/pybind11/share/*
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/docs/_build/*
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.ipynb_checkpoints/
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tests/main.cpp
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@ -120,6 +120,8 @@ set(PYBIND11_HEADERS
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include/pybind11/complex.h
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include/pybind11/options.h
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include/pybind11/eigen.h
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include/pybind11/eigen/matrix.h
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include/pybind11/eigen/tensor.h
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include/pybind11/embed.h
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include/pybind11/eval.h
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include/pybind11/gil.h
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@ -9,705 +9,4 @@
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#pragma once
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/* HINT: To suppress warnings originating from the Eigen headers, use -isystem.
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See also:
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https://stackoverflow.com/questions/2579576/i-dir-vs-isystem-dir
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https://stackoverflow.com/questions/1741816/isystem-for-ms-visual-studio-c-compiler
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*/
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#include "numpy.h"
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// The C4127 suppression was introduced for Eigen 3.4.0. In theory we could
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// make it version specific, or even remove it later, but considering that
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// 1. C4127 is generally far more distracting than useful for modern template code, and
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// 2. we definitely want to ignore any MSVC warnings originating from Eigen code,
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// it is probably best to keep this around indefinitely.
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#if defined(_MSC_VER)
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# pragma warning(push)
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# pragma warning(disable : 4127) // C4127: conditional expression is constant
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# pragma warning(disable : 5054) // https://github.com/pybind/pybind11/pull/3741
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// C5054: operator '&': deprecated between enumerations of different types
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#elif defined(__MINGW32__)
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# pragma GCC diagnostic push
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# pragma GCC diagnostic ignored "-Wmaybe-uninitialized"
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#endif
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#include <Eigen/Core>
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#include <Eigen/SparseCore>
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#if defined(_MSC_VER)
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# pragma warning(pop)
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#elif defined(__MINGW32__)
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# pragma GCC diagnostic pop
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#endif
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// Eigen prior to 3.2.7 doesn't have proper move constructors--but worse, some classes get implicit
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// move constructors that break things. We could detect this an explicitly copy, but an extra copy
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// of matrices seems highly undesirable.
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static_assert(EIGEN_VERSION_AT_LEAST(3, 2, 7),
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"Eigen support in pybind11 requires Eigen >= 3.2.7");
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PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
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// Provide a convenience alias for easier pass-by-ref usage with fully dynamic strides:
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using EigenDStride = Eigen::Stride<Eigen::Dynamic, Eigen::Dynamic>;
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template <typename MatrixType>
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using EigenDRef = Eigen::Ref<MatrixType, 0, EigenDStride>;
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template <typename MatrixType>
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using EigenDMap = Eigen::Map<MatrixType, 0, EigenDStride>;
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PYBIND11_NAMESPACE_BEGIN(detail)
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#if EIGEN_VERSION_AT_LEAST(3, 3, 0)
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using EigenIndex = Eigen::Index;
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template <typename Scalar, int Flags, typename StorageIndex>
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using EigenMapSparseMatrix = Eigen::Map<Eigen::SparseMatrix<Scalar, Flags, StorageIndex>>;
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#else
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using EigenIndex = EIGEN_DEFAULT_DENSE_INDEX_TYPE;
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template <typename Scalar, int Flags, typename StorageIndex>
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using EigenMapSparseMatrix = Eigen::MappedSparseMatrix<Scalar, Flags, StorageIndex>;
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#endif
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// Matches Eigen::Map, Eigen::Ref, blocks, etc:
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template <typename T>
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using is_eigen_dense_map = all_of<is_template_base_of<Eigen::DenseBase, T>,
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std::is_base_of<Eigen::MapBase<T, Eigen::ReadOnlyAccessors>, T>>;
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template <typename T>
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using is_eigen_mutable_map = std::is_base_of<Eigen::MapBase<T, Eigen::WriteAccessors>, T>;
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template <typename T>
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using is_eigen_dense_plain
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= all_of<negation<is_eigen_dense_map<T>>, is_template_base_of<Eigen::PlainObjectBase, T>>;
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template <typename T>
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using is_eigen_sparse = is_template_base_of<Eigen::SparseMatrixBase, T>;
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// Test for objects inheriting from EigenBase<Derived> that aren't captured by the above. This
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// basically covers anything that can be assigned to a dense matrix but that don't have a typical
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// matrix data layout that can be copied from their .data(). For example, DiagonalMatrix and
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// SelfAdjointView fall into this category.
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template <typename T>
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using is_eigen_other
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= all_of<is_template_base_of<Eigen::EigenBase, T>,
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negation<any_of<is_eigen_dense_map<T>, is_eigen_dense_plain<T>, is_eigen_sparse<T>>>>;
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// Captures numpy/eigen conformability status (returned by EigenProps::conformable()):
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template <bool EigenRowMajor>
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struct EigenConformable {
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bool conformable = false;
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EigenIndex rows = 0, cols = 0;
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EigenDStride stride{0, 0}; // Only valid if negativestrides is false!
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bool negativestrides = false; // If true, do not use stride!
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// NOLINTNEXTLINE(google-explicit-constructor)
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EigenConformable(bool fits = false) : conformable{fits} {}
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// Matrix type:
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EigenConformable(EigenIndex r, EigenIndex c, EigenIndex rstride, EigenIndex cstride)
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: conformable{true}, rows{r}, cols{c},
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// TODO: when Eigen bug #747 is fixed, remove the tests for non-negativity.
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// http://eigen.tuxfamily.org/bz/show_bug.cgi?id=747
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stride{EigenRowMajor ? (rstride > 0 ? rstride : 0)
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: (cstride > 0 ? cstride : 0) /* outer stride */,
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EigenRowMajor ? (cstride > 0 ? cstride : 0)
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: (rstride > 0 ? rstride : 0) /* inner stride */},
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negativestrides{rstride < 0 || cstride < 0} {}
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// Vector type:
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EigenConformable(EigenIndex r, EigenIndex c, EigenIndex stride)
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: EigenConformable(r, c, r == 1 ? c * stride : stride, c == 1 ? r : r * stride) {}
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template <typename props>
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bool stride_compatible() const {
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// To have compatible strides, we need (on both dimensions) one of fully dynamic strides,
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// matching strides, or a dimension size of 1 (in which case the stride value is
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// irrelevant). Alternatively, if any dimension size is 0, the strides are not relevant
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// (and numpy ≥ 1.23 sets the strides to 0 in that case, so we need to check explicitly).
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if (negativestrides) {
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return false;
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}
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if (rows == 0 || cols == 0) {
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return true;
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}
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return (props::inner_stride == Eigen::Dynamic || props::inner_stride == stride.inner()
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|| (EigenRowMajor ? cols : rows) == 1)
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&& (props::outer_stride == Eigen::Dynamic || props::outer_stride == stride.outer()
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|| (EigenRowMajor ? rows : cols) == 1);
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}
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// NOLINTNEXTLINE(google-explicit-constructor)
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operator bool() const { return conformable; }
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};
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template <typename Type>
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struct eigen_extract_stride {
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using type = Type;
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};
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template <typename PlainObjectType, int MapOptions, typename StrideType>
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struct eigen_extract_stride<Eigen::Map<PlainObjectType, MapOptions, StrideType>> {
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using type = StrideType;
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};
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template <typename PlainObjectType, int Options, typename StrideType>
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struct eigen_extract_stride<Eigen::Ref<PlainObjectType, Options, StrideType>> {
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using type = StrideType;
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};
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// Helper struct for extracting information from an Eigen type
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template <typename Type_>
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struct EigenProps {
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using Type = Type_;
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using Scalar = typename Type::Scalar;
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using StrideType = typename eigen_extract_stride<Type>::type;
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static constexpr EigenIndex rows = Type::RowsAtCompileTime, cols = Type::ColsAtCompileTime,
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size = Type::SizeAtCompileTime;
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static constexpr bool row_major = Type::IsRowMajor,
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vector
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= Type::IsVectorAtCompileTime, // At least one dimension has fixed size 1
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fixed_rows = rows != Eigen::Dynamic, fixed_cols = cols != Eigen::Dynamic,
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fixed = size != Eigen::Dynamic, // Fully-fixed size
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dynamic = !fixed_rows && !fixed_cols; // Fully-dynamic size
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template <EigenIndex i, EigenIndex ifzero>
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using if_zero = std::integral_constant<EigenIndex, i == 0 ? ifzero : i>;
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static constexpr EigenIndex inner_stride
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= if_zero<StrideType::InnerStrideAtCompileTime, 1>::value,
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outer_stride = if_zero < StrideType::OuterStrideAtCompileTime,
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vector ? size
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: row_major ? cols
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: rows > ::value;
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static constexpr bool dynamic_stride
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= inner_stride == Eigen::Dynamic && outer_stride == Eigen::Dynamic;
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static constexpr bool requires_row_major
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= !dynamic_stride && !vector && (row_major ? inner_stride : outer_stride) == 1;
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static constexpr bool requires_col_major
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= !dynamic_stride && !vector && (row_major ? outer_stride : inner_stride) == 1;
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// Takes an input array and determines whether we can make it fit into the Eigen type. If
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// the array is a vector, we attempt to fit it into either an Eigen 1xN or Nx1 vector
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// (preferring the latter if it will fit in either, i.e. for a fully dynamic matrix type).
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static EigenConformable<row_major> conformable(const array &a) {
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const auto dims = a.ndim();
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if (dims < 1 || dims > 2) {
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return false;
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}
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if (dims == 2) { // Matrix type: require exact match (or dynamic)
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EigenIndex np_rows = a.shape(0), np_cols = a.shape(1),
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np_rstride = a.strides(0) / static_cast<ssize_t>(sizeof(Scalar)),
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np_cstride = a.strides(1) / static_cast<ssize_t>(sizeof(Scalar));
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if ((PYBIND11_SILENCE_MSVC_C4127(fixed_rows) && np_rows != rows)
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|| (PYBIND11_SILENCE_MSVC_C4127(fixed_cols) && np_cols != cols)) {
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return false;
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}
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return {np_rows, np_cols, np_rstride, np_cstride};
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}
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// Otherwise we're storing an n-vector. Only one of the strides will be used, but
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// whichever is used, we want the (single) numpy stride value.
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const EigenIndex n = a.shape(0),
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stride = a.strides(0) / static_cast<ssize_t>(sizeof(Scalar));
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if (vector) { // Eigen type is a compile-time vector
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if (PYBIND11_SILENCE_MSVC_C4127(fixed) && size != n) {
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return false; // Vector size mismatch
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}
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return {rows == 1 ? 1 : n, cols == 1 ? 1 : n, stride};
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}
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if (fixed) {
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// The type has a fixed size, but is not a vector: abort
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return false;
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}
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if (fixed_cols) {
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// Since this isn't a vector, cols must be != 1. We allow this only if it exactly
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// equals the number of elements (rows is Dynamic, and so 1 row is allowed).
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if (cols != n) {
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return false;
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}
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return {1, n, stride};
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} // Otherwise it's either fully dynamic, or column dynamic; both become a column vector
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if (PYBIND11_SILENCE_MSVC_C4127(fixed_rows) && rows != n) {
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return false;
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}
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return {n, 1, stride};
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}
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static constexpr bool show_writeable
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= is_eigen_dense_map<Type>::value && is_eigen_mutable_map<Type>::value;
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static constexpr bool show_order = is_eigen_dense_map<Type>::value;
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static constexpr bool show_c_contiguous = show_order && requires_row_major;
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static constexpr bool show_f_contiguous
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= !show_c_contiguous && show_order && requires_col_major;
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static constexpr auto descriptor
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= const_name("numpy.ndarray[") + npy_format_descriptor<Scalar>::name + const_name("[")
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+ const_name<fixed_rows>(const_name<(size_t) rows>(), const_name("m")) + const_name(", ")
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+ const_name<fixed_cols>(const_name<(size_t) cols>(), const_name("n")) + const_name("]")
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+
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// For a reference type (e.g. Ref<MatrixXd>) we have other constraints that might need to
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// be satisfied: writeable=True (for a mutable reference), and, depending on the map's
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// stride options, possibly f_contiguous or c_contiguous. We include them in the
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// descriptor output to provide some hint as to why a TypeError is occurring (otherwise
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// it can be confusing to see that a function accepts a 'numpy.ndarray[float64[3,2]]' and
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// an error message that you *gave* a numpy.ndarray of the right type and dimensions.
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const_name<show_writeable>(", flags.writeable", "")
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+ const_name<show_c_contiguous>(", flags.c_contiguous", "")
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+ const_name<show_f_contiguous>(", flags.f_contiguous", "") + const_name("]");
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};
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// Casts an Eigen type to numpy array. If given a base, the numpy array references the src data,
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// otherwise it'll make a copy. writeable lets you turn off the writeable flag for the array.
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template <typename props>
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handle
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eigen_array_cast(typename props::Type const &src, handle base = handle(), bool writeable = true) {
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constexpr ssize_t elem_size = sizeof(typename props::Scalar);
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array a;
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if (props::vector) {
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a = array({src.size()}, {elem_size * src.innerStride()}, src.data(), base);
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} else {
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a = array({src.rows(), src.cols()},
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{elem_size * src.rowStride(), elem_size * src.colStride()},
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src.data(),
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base);
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}
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if (!writeable) {
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array_proxy(a.ptr())->flags &= ~detail::npy_api::NPY_ARRAY_WRITEABLE_;
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}
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return a.release();
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}
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// Takes an lvalue ref to some Eigen type and a (python) base object, creating a numpy array that
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// reference the Eigen object's data with `base` as the python-registered base class (if omitted,
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// the base will be set to None, and lifetime management is up to the caller). The numpy array is
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// non-writeable if the given type is const.
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template <typename props, typename Type>
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handle eigen_ref_array(Type &src, handle parent = none()) {
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// none here is to get past array's should-we-copy detection, which currently always
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// copies when there is no base. Setting the base to None should be harmless.
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return eigen_array_cast<props>(src, parent, !std::is_const<Type>::value);
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}
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// Takes a pointer to some dense, plain Eigen type, builds a capsule around it, then returns a
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// numpy array that references the encapsulated data with a python-side reference to the capsule to
|
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// tie its destruction to that of any dependent python objects. Const-ness is determined by
|
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// whether or not the Type of the pointer given is const.
|
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template <typename props, typename Type, typename = enable_if_t<is_eigen_dense_plain<Type>::value>>
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handle eigen_encapsulate(Type *src) {
|
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capsule base(src, [](void *o) { delete static_cast<Type *>(o); });
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return eigen_ref_array<props>(*src, base);
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}
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|
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// Type caster for regular, dense matrix types (e.g. MatrixXd), but not maps/refs/etc. of dense
|
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// types.
|
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template <typename Type>
|
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struct type_caster<Type, enable_if_t<is_eigen_dense_plain<Type>::value>> {
|
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using Scalar = typename Type::Scalar;
|
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using props = EigenProps<Type>;
|
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|
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bool load(handle src, bool convert) {
|
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// If we're in no-convert mode, only load if given an array of the correct type
|
||||
if (!convert && !isinstance<array_t<Scalar>>(src)) {
|
||||
return false;
|
||||
}
|
||||
|
||||
// Coerce into an array, but don't do type conversion yet; the copy below handles it.
|
||||
auto buf = array::ensure(src);
|
||||
|
||||
if (!buf) {
|
||||
return false;
|
||||
}
|
||||
|
||||
auto dims = buf.ndim();
|
||||
if (dims < 1 || dims > 2) {
|
||||
return false;
|
||||
}
|
||||
|
||||
auto fits = props::conformable(buf);
|
||||
if (!fits) {
|
||||
return false;
|
||||
}
|
||||
|
||||
// Allocate the new type, then build a numpy reference into it
|
||||
value = Type(fits.rows, fits.cols);
|
||||
auto ref = reinterpret_steal<array>(eigen_ref_array<props>(value));
|
||||
if (dims == 1) {
|
||||
ref = ref.squeeze();
|
||||
} else if (ref.ndim() == 1) {
|
||||
buf = buf.squeeze();
|
||||
}
|
||||
|
||||
int result = detail::npy_api::get().PyArray_CopyInto_(ref.ptr(), buf.ptr());
|
||||
|
||||
if (result < 0) { // Copy failed!
|
||||
PyErr_Clear();
|
||||
return false;
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
private:
|
||||
// Cast implementation
|
||||
template <typename CType>
|
||||
static handle cast_impl(CType *src, return_value_policy policy, handle parent) {
|
||||
switch (policy) {
|
||||
case return_value_policy::take_ownership:
|
||||
case return_value_policy::automatic:
|
||||
return eigen_encapsulate<props>(src);
|
||||
case return_value_policy::move:
|
||||
return eigen_encapsulate<props>(new CType(std::move(*src)));
|
||||
case return_value_policy::copy:
|
||||
return eigen_array_cast<props>(*src);
|
||||
case return_value_policy::reference:
|
||||
case return_value_policy::automatic_reference:
|
||||
return eigen_ref_array<props>(*src);
|
||||
case return_value_policy::reference_internal:
|
||||
return eigen_ref_array<props>(*src, parent);
|
||||
default:
|
||||
throw cast_error("unhandled return_value_policy: should not happen!");
|
||||
};
|
||||
}
|
||||
|
||||
public:
|
||||
// Normal returned non-reference, non-const value:
|
||||
static handle cast(Type &&src, return_value_policy /* policy */, handle parent) {
|
||||
return cast_impl(&src, return_value_policy::move, parent);
|
||||
}
|
||||
// If you return a non-reference const, we mark the numpy array readonly:
|
||||
static handle cast(const Type &&src, return_value_policy /* policy */, handle parent) {
|
||||
return cast_impl(&src, return_value_policy::move, parent);
|
||||
}
|
||||
// lvalue reference return; default (automatic) becomes copy
|
||||
static handle cast(Type &src, return_value_policy policy, handle parent) {
|
||||
if (policy == return_value_policy::automatic
|
||||
|| policy == return_value_policy::automatic_reference) {
|
||||
policy = return_value_policy::copy;
|
||||
}
|
||||
return cast_impl(&src, policy, parent);
|
||||
}
|
||||
// const lvalue reference return; default (automatic) becomes copy
|
||||
static handle cast(const Type &src, return_value_policy policy, handle parent) {
|
||||
if (policy == return_value_policy::automatic
|
||||
|| policy == return_value_policy::automatic_reference) {
|
||||
policy = return_value_policy::copy;
|
||||
}
|
||||
return cast(&src, policy, parent);
|
||||
}
|
||||
// non-const pointer return
|
||||
static handle cast(Type *src, return_value_policy policy, handle parent) {
|
||||
return cast_impl(src, policy, parent);
|
||||
}
|
||||
// const pointer return
|
||||
static handle cast(const Type *src, return_value_policy policy, handle parent) {
|
||||
return cast_impl(src, policy, parent);
|
||||
}
|
||||
|
||||
static constexpr auto name = props::descriptor;
|
||||
|
||||
// NOLINTNEXTLINE(google-explicit-constructor)
|
||||
operator Type *() { return &value; }
|
||||
// NOLINTNEXTLINE(google-explicit-constructor)
|
||||
operator Type &() { return value; }
|
||||
// NOLINTNEXTLINE(google-explicit-constructor)
|
||||
operator Type &&() && { return std::move(value); }
|
||||
template <typename T>
|
||||
using cast_op_type = movable_cast_op_type<T>;
|
||||
|
||||
private:
|
||||
Type value;
|
||||
};
|
||||
|
||||
// Base class for casting reference/map/block/etc. objects back to python.
|
||||
template <typename MapType>
|
||||
struct eigen_map_caster {
|
||||
private:
|
||||
using props = EigenProps<MapType>;
|
||||
|
||||
public:
|
||||
// Directly referencing a ref/map's data is a bit dangerous (whatever the map/ref points to has
|
||||
// to stay around), but we'll allow it under the assumption that you know what you're doing
|
||||
// (and have an appropriate keep_alive in place). We return a numpy array pointing directly at
|
||||
// the ref's data (The numpy array ends up read-only if the ref was to a const matrix type.)
|
||||
// Note that this means you need to ensure you don't destroy the object in some other way (e.g.
|
||||
// with an appropriate keep_alive, or with a reference to a statically allocated matrix).
|
||||
static handle cast(const MapType &src, return_value_policy policy, handle parent) {
|
||||
switch (policy) {
|
||||
case return_value_policy::copy:
|
||||
return eigen_array_cast<props>(src);
|
||||
case return_value_policy::reference_internal:
|
||||
return eigen_array_cast<props>(src, parent, is_eigen_mutable_map<MapType>::value);
|
||||
case return_value_policy::reference:
|
||||
case return_value_policy::automatic:
|
||||
case return_value_policy::automatic_reference:
|
||||
return eigen_array_cast<props>(src, none(), is_eigen_mutable_map<MapType>::value);
|
||||
default:
|
||||
// move, take_ownership don't make any sense for a ref/map:
|
||||
pybind11_fail("Invalid return_value_policy for Eigen Map/Ref/Block type");
|
||||
}
|
||||
}
|
||||
|
||||
static constexpr auto name = props::descriptor;
|
||||
|
||||
// Explicitly delete these: support python -> C++ conversion on these (i.e. these can be return
|
||||
// types but not bound arguments). We still provide them (with an explicitly delete) so that
|
||||
// you end up here if you try anyway.
|
||||
bool load(handle, bool) = delete;
|
||||
operator MapType() = delete;
|
||||
template <typename>
|
||||
using cast_op_type = MapType;
|
||||
};
|
||||
|
||||
// We can return any map-like object (but can only load Refs, specialized next):
|
||||
template <typename Type>
|
||||
struct type_caster<Type, enable_if_t<is_eigen_dense_map<Type>::value>> : eigen_map_caster<Type> {};
|
||||
|
||||
// Loader for Ref<...> arguments. See the documentation for info on how to make this work without
|
||||
// copying (it requires some extra effort in many cases).
|
||||
template <typename PlainObjectType, typename StrideType>
|
||||
struct type_caster<
|
||||
Eigen::Ref<PlainObjectType, 0, StrideType>,
|
||||
enable_if_t<is_eigen_dense_map<Eigen::Ref<PlainObjectType, 0, StrideType>>::value>>
|
||||
: public eigen_map_caster<Eigen::Ref<PlainObjectType, 0, StrideType>> {
|
||||
private:
|
||||
using Type = Eigen::Ref<PlainObjectType, 0, StrideType>;
|
||||
using props = EigenProps<Type>;
|
||||
using Scalar = typename props::Scalar;
|
||||
using MapType = Eigen::Map<PlainObjectType, 0, StrideType>;
|
||||
using Array
|
||||
= array_t<Scalar,
|
||||
array::forcecast
|
||||
| ((props::row_major ? props::inner_stride : props::outer_stride) == 1
|
||||
? array::c_style
|
||||
: (props::row_major ? props::outer_stride : props::inner_stride) == 1
|
||||
? array::f_style
|
||||
: 0)>;
|
||||
static constexpr bool need_writeable = is_eigen_mutable_map<Type>::value;
|
||||
// Delay construction (these have no default constructor)
|
||||
std::unique_ptr<MapType> map;
|
||||
std::unique_ptr<Type> ref;
|
||||
// Our array. When possible, this is just a numpy array pointing to the source data, but
|
||||
// sometimes we can't avoid copying (e.g. input is not a numpy array at all, has an
|
||||
// incompatible layout, or is an array of a type that needs to be converted). Using a numpy
|
||||
// temporary (rather than an Eigen temporary) saves an extra copy when we need both type
|
||||
// conversion and storage order conversion. (Note that we refuse to use this temporary copy
|
||||
// when loading an argument for a Ref<M> with M non-const, i.e. a read-write reference).
|
||||
Array copy_or_ref;
|
||||
|
||||
public:
|
||||
bool load(handle src, bool convert) {
|
||||
// First check whether what we have is already an array of the right type. If not, we
|
||||
// can't avoid a copy (because the copy is also going to do type conversion).
|
||||
bool need_copy = !isinstance<Array>(src);
|
||||
|
||||
EigenConformable<props::row_major> fits;
|
||||
if (!need_copy) {
|
||||
// We don't need a converting copy, but we also need to check whether the strides are
|
||||
// compatible with the Ref's stride requirements
|
||||
auto aref = reinterpret_borrow<Array>(src);
|
||||
|
||||
if (aref && (!need_writeable || aref.writeable())) {
|
||||
fits = props::conformable(aref);
|
||||
if (!fits) {
|
||||
return false; // Incompatible dimensions
|
||||
}
|
||||
if (!fits.template stride_compatible<props>()) {
|
||||
need_copy = true;
|
||||
} else {
|
||||
copy_or_ref = std::move(aref);
|
||||
}
|
||||
} else {
|
||||
need_copy = true;
|
||||
}
|
||||
}
|
||||
|
||||
if (need_copy) {
|
||||
// We need to copy: If we need a mutable reference, or we're not supposed to convert
|
||||
// (either because we're in the no-convert overload pass, or because we're explicitly
|
||||
// instructed not to copy (via `py::arg().noconvert()`) we have to fail loading.
|
||||
if (!convert || need_writeable) {
|
||||
return false;
|
||||
}
|
||||
|
||||
Array copy = Array::ensure(src);
|
||||
if (!copy) {
|
||||
return false;
|
||||
}
|
||||
fits = props::conformable(copy);
|
||||
if (!fits || !fits.template stride_compatible<props>()) {
|
||||
return false;
|
||||
}
|
||||
copy_or_ref = std::move(copy);
|
||||
loader_life_support::add_patient(copy_or_ref);
|
||||
}
|
||||
|
||||
ref.reset();
|
||||
map.reset(new MapType(data(copy_or_ref),
|
||||
fits.rows,
|
||||
fits.cols,
|
||||
make_stride(fits.stride.outer(), fits.stride.inner())));
|
||||
ref.reset(new Type(*map));
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
// NOLINTNEXTLINE(google-explicit-constructor)
|
||||
operator Type *() { return ref.get(); }
|
||||
// NOLINTNEXTLINE(google-explicit-constructor)
|
||||
operator Type &() { return *ref; }
|
||||
template <typename _T>
|
||||
using cast_op_type = pybind11::detail::cast_op_type<_T>;
|
||||
|
||||
private:
|
||||
template <typename T = Type, enable_if_t<is_eigen_mutable_map<T>::value, int> = 0>
|
||||
Scalar *data(Array &a) {
|
||||
return a.mutable_data();
|
||||
}
|
||||
|
||||
template <typename T = Type, enable_if_t<!is_eigen_mutable_map<T>::value, int> = 0>
|
||||
const Scalar *data(Array &a) {
|
||||
return a.data();
|
||||
}
|
||||
|
||||
// Attempt to figure out a constructor of `Stride` that will work.
|
||||
// If both strides are fixed, use a default constructor:
|
||||
template <typename S>
|
||||
using stride_ctor_default = bool_constant<S::InnerStrideAtCompileTime != Eigen::Dynamic
|
||||
&& S::OuterStrideAtCompileTime != Eigen::Dynamic
|
||||
&& std::is_default_constructible<S>::value>;
|
||||
// Otherwise, if there is a two-index constructor, assume it is (outer,inner) like
|
||||
// Eigen::Stride, and use it:
|
||||
template <typename S>
|
||||
using stride_ctor_dual
|
||||
= bool_constant<!stride_ctor_default<S>::value
|
||||
&& std::is_constructible<S, EigenIndex, EigenIndex>::value>;
|
||||
// Otherwise, if there is a one-index constructor, and just one of the strides is dynamic, use
|
||||
// it (passing whichever stride is dynamic).
|
||||
template <typename S>
|
||||
using stride_ctor_outer
|
||||
= bool_constant<!any_of<stride_ctor_default<S>, stride_ctor_dual<S>>::value
|
||||
&& S::OuterStrideAtCompileTime == Eigen::Dynamic
|
||||
&& S::InnerStrideAtCompileTime != Eigen::Dynamic
|
||||
&& std::is_constructible<S, EigenIndex>::value>;
|
||||
template <typename S>
|
||||
using stride_ctor_inner
|
||||
= bool_constant<!any_of<stride_ctor_default<S>, stride_ctor_dual<S>>::value
|
||||
&& S::InnerStrideAtCompileTime == Eigen::Dynamic
|
||||
&& S::OuterStrideAtCompileTime != Eigen::Dynamic
|
||||
&& std::is_constructible<S, EigenIndex>::value>;
|
||||
|
||||
template <typename S = StrideType, enable_if_t<stride_ctor_default<S>::value, int> = 0>
|
||||
static S make_stride(EigenIndex, EigenIndex) {
|
||||
return S();
|
||||
}
|
||||
template <typename S = StrideType, enable_if_t<stride_ctor_dual<S>::value, int> = 0>
|
||||
static S make_stride(EigenIndex outer, EigenIndex inner) {
|
||||
return S(outer, inner);
|
||||
}
|
||||
template <typename S = StrideType, enable_if_t<stride_ctor_outer<S>::value, int> = 0>
|
||||
static S make_stride(EigenIndex outer, EigenIndex) {
|
||||
return S(outer);
|
||||
}
|
||||
template <typename S = StrideType, enable_if_t<stride_ctor_inner<S>::value, int> = 0>
|
||||
static S make_stride(EigenIndex, EigenIndex inner) {
|
||||
return S(inner);
|
||||
}
|
||||
};
|
||||
|
||||
// type_caster for special matrix types (e.g. DiagonalMatrix), which are EigenBase, but not
|
||||
// EigenDense (i.e. they don't have a data(), at least not with the usual matrix layout).
|
||||
// load() is not supported, but we can cast them into the python domain by first copying to a
|
||||
// regular Eigen::Matrix, then casting that.
|
||||
template <typename Type>
|
||||
struct type_caster<Type, enable_if_t<is_eigen_other<Type>::value>> {
|
||||
protected:
|
||||
using Matrix
|
||||
= Eigen::Matrix<typename Type::Scalar, Type::RowsAtCompileTime, Type::ColsAtCompileTime>;
|
||||
using props = EigenProps<Matrix>;
|
||||
|
||||
public:
|
||||
static handle cast(const Type &src, return_value_policy /* policy */, handle /* parent */) {
|
||||
handle h = eigen_encapsulate<props>(new Matrix(src));
|
||||
return h;
|
||||
}
|
||||
static handle cast(const Type *src, return_value_policy policy, handle parent) {
|
||||
return cast(*src, policy, parent);
|
||||
}
|
||||
|
||||
static constexpr auto name = props::descriptor;
|
||||
|
||||
// Explicitly delete these: support python -> C++ conversion on these (i.e. these can be return
|
||||
// types but not bound arguments). We still provide them (with an explicitly delete) so that
|
||||
// you end up here if you try anyway.
|
||||
bool load(handle, bool) = delete;
|
||||
operator Type() = delete;
|
||||
template <typename>
|
||||
using cast_op_type = Type;
|
||||
};
|
||||
|
||||
template <typename Type>
|
||||
struct type_caster<Type, enable_if_t<is_eigen_sparse<Type>::value>> {
|
||||
using Scalar = typename Type::Scalar;
|
||||
using StorageIndex = remove_reference_t<decltype(*std::declval<Type>().outerIndexPtr())>;
|
||||
using Index = typename Type::Index;
|
||||
static constexpr bool rowMajor = Type::IsRowMajor;
|
||||
|
||||
bool load(handle src, bool) {
|
||||
if (!src) {
|
||||
return false;
|
||||
}
|
||||
|
||||
auto obj = reinterpret_borrow<object>(src);
|
||||
object sparse_module = module_::import("scipy.sparse");
|
||||
object matrix_type = sparse_module.attr(rowMajor ? "csr_matrix" : "csc_matrix");
|
||||
|
||||
if (!type::handle_of(obj).is(matrix_type)) {
|
||||
try {
|
||||
obj = matrix_type(obj);
|
||||
} catch (const error_already_set &) {
|
||||
return false;
|
||||
}
|
||||
}
|
||||
|
||||
auto values = array_t<Scalar>((object) obj.attr("data"));
|
||||
auto innerIndices = array_t<StorageIndex>((object) obj.attr("indices"));
|
||||
auto outerIndices = array_t<StorageIndex>((object) obj.attr("indptr"));
|
||||
auto shape = pybind11::tuple((pybind11::object) obj.attr("shape"));
|
||||
auto nnz = obj.attr("nnz").cast<Index>();
|
||||
|
||||
if (!values || !innerIndices || !outerIndices) {
|
||||
return false;
|
||||
}
|
||||
|
||||
value = EigenMapSparseMatrix<Scalar,
|
||||
Type::Flags &(Eigen::RowMajor | Eigen::ColMajor),
|
||||
StorageIndex>(shape[0].cast<Index>(),
|
||||
shape[1].cast<Index>(),
|
||||
std::move(nnz),
|
||||
outerIndices.mutable_data(),
|
||||
innerIndices.mutable_data(),
|
||||
values.mutable_data());
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
static handle cast(const Type &src, return_value_policy /* policy */, handle /* parent */) {
|
||||
const_cast<Type &>(src).makeCompressed();
|
||||
|
||||
object matrix_type
|
||||
= module_::import("scipy.sparse").attr(rowMajor ? "csr_matrix" : "csc_matrix");
|
||||
|
||||
array data(src.nonZeros(), src.valuePtr());
|
||||
array outerIndices((rowMajor ? src.rows() : src.cols()) + 1, src.outerIndexPtr());
|
||||
array innerIndices(src.nonZeros(), src.innerIndexPtr());
|
||||
|
||||
return matrix_type(pybind11::make_tuple(
|
||||
std::move(data), std::move(innerIndices), std::move(outerIndices)),
|
||||
pybind11::make_tuple(src.rows(), src.cols()))
|
||||
.release();
|
||||
}
|
||||
|
||||
PYBIND11_TYPE_CASTER(Type,
|
||||
const_name<(Type::IsRowMajor) != 0>("scipy.sparse.csr_matrix[",
|
||||
"scipy.sparse.csc_matrix[")
|
||||
+ npy_format_descriptor<Scalar>::name + const_name("]"));
|
||||
};
|
||||
|
||||
PYBIND11_NAMESPACE_END(detail)
|
||||
PYBIND11_NAMESPACE_END(PYBIND11_NAMESPACE)
|
||||
#include "eigen/matrix.h"
|
||||
|
|
|
|||
|
|
@ -0,0 +1,713 @@
|
|||
/*
|
||||
pybind11/eigen/matrix.h: Transparent conversion for dense and sparse Eigen matrices
|
||||
|
||||
Copyright (c) 2016 Wenzel Jakob <wenzel.jakob@epfl.ch>
|
||||
|
||||
All rights reserved. Use of this source code is governed by a
|
||||
BSD-style license that can be found in the LICENSE file.
|
||||
*/
|
||||
|
||||
#pragma once
|
||||
|
||||
#include "../numpy.h"
|
||||
|
||||
// Similar to comments & pragma block in eigen_tensor.h. PLEASE KEEP IN SYNC.
|
||||
/* HINT: To suppress warnings originating from the Eigen headers, use -isystem.
|
||||
See also:
|
||||
https://stackoverflow.com/questions/2579576/i-dir-vs-isystem-dir
|
||||
https://stackoverflow.com/questions/1741816/isystem-for-ms-visual-studio-c-compiler
|
||||
*/
|
||||
// The C4127 suppression was introduced for Eigen 3.4.0. In theory we could
|
||||
// make it version specific, or even remove it later, but considering that
|
||||
// 1. C4127 is generally far more distracting than useful for modern template code, and
|
||||
// 2. we definitely want to ignore any MSVC warnings originating from Eigen code,
|
||||
// it is probably best to keep this around indefinitely.
|
||||
#if defined(_MSC_VER)
|
||||
# pragma warning(push)
|
||||
# pragma warning(disable : 4127) // C4127: conditional expression is constant
|
||||
# pragma warning(disable : 5054) // https://github.com/pybind/pybind11/pull/3741
|
||||
// C5054: operator '&': deprecated between enumerations of different types
|
||||
#elif defined(__MINGW32__)
|
||||
# pragma GCC diagnostic push
|
||||
# pragma GCC diagnostic ignored "-Wmaybe-uninitialized"
|
||||
#endif
|
||||
|
||||
#include <Eigen/Core>
|
||||
#include <Eigen/SparseCore>
|
||||
|
||||
#if defined(_MSC_VER)
|
||||
# pragma warning(pop)
|
||||
#elif defined(__MINGW32__)
|
||||
# pragma GCC diagnostic pop
|
||||
#endif
|
||||
|
||||
// Eigen prior to 3.2.7 doesn't have proper move constructors--but worse, some classes get implicit
|
||||
// move constructors that break things. We could detect this an explicitly copy, but an extra copy
|
||||
// of matrices seems highly undesirable.
|
||||
static_assert(EIGEN_VERSION_AT_LEAST(3, 2, 7),
|
||||
"Eigen matrix support in pybind11 requires Eigen >= 3.2.7");
|
||||
|
||||
PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
|
||||
|
||||
// Provide a convenience alias for easier pass-by-ref usage with fully dynamic strides:
|
||||
using EigenDStride = Eigen::Stride<Eigen::Dynamic, Eigen::Dynamic>;
|
||||
template <typename MatrixType>
|
||||
using EigenDRef = Eigen::Ref<MatrixType, 0, EigenDStride>;
|
||||
template <typename MatrixType>
|
||||
using EigenDMap = Eigen::Map<MatrixType, 0, EigenDStride>;
|
||||
|
||||
PYBIND11_NAMESPACE_BEGIN(detail)
|
||||
|
||||
#if EIGEN_VERSION_AT_LEAST(3, 3, 0)
|
||||
using EigenIndex = Eigen::Index;
|
||||
template <typename Scalar, int Flags, typename StorageIndex>
|
||||
using EigenMapSparseMatrix = Eigen::Map<Eigen::SparseMatrix<Scalar, Flags, StorageIndex>>;
|
||||
#else
|
||||
using EigenIndex = EIGEN_DEFAULT_DENSE_INDEX_TYPE;
|
||||
template <typename Scalar, int Flags, typename StorageIndex>
|
||||
using EigenMapSparseMatrix = Eigen::MappedSparseMatrix<Scalar, Flags, StorageIndex>;
|
||||
#endif
|
||||
|
||||
// Matches Eigen::Map, Eigen::Ref, blocks, etc:
|
||||
template <typename T>
|
||||
using is_eigen_dense_map = all_of<is_template_base_of<Eigen::DenseBase, T>,
|
||||
std::is_base_of<Eigen::MapBase<T, Eigen::ReadOnlyAccessors>, T>>;
|
||||
template <typename T>
|
||||
using is_eigen_mutable_map = std::is_base_of<Eigen::MapBase<T, Eigen::WriteAccessors>, T>;
|
||||
template <typename T>
|
||||
using is_eigen_dense_plain
|
||||
= all_of<negation<is_eigen_dense_map<T>>, is_template_base_of<Eigen::PlainObjectBase, T>>;
|
||||
template <typename T>
|
||||
using is_eigen_sparse = is_template_base_of<Eigen::SparseMatrixBase, T>;
|
||||
// Test for objects inheriting from EigenBase<Derived> that aren't captured by the above. This
|
||||
// basically covers anything that can be assigned to a dense matrix but that don't have a typical
|
||||
// matrix data layout that can be copied from their .data(). For example, DiagonalMatrix and
|
||||
// SelfAdjointView fall into this category.
|
||||
template <typename T>
|
||||
using is_eigen_other
|
||||
= all_of<is_template_base_of<Eigen::EigenBase, T>,
|
||||
negation<any_of<is_eigen_dense_map<T>, is_eigen_dense_plain<T>, is_eigen_sparse<T>>>>;
|
||||
|
||||
// Captures numpy/eigen conformability status (returned by EigenProps::conformable()):
|
||||
template <bool EigenRowMajor>
|
||||
struct EigenConformable {
|
||||
bool conformable = false;
|
||||
EigenIndex rows = 0, cols = 0;
|
||||
EigenDStride stride{0, 0}; // Only valid if negativestrides is false!
|
||||
bool negativestrides = false; // If true, do not use stride!
|
||||
|
||||
// NOLINTNEXTLINE(google-explicit-constructor)
|
||||
EigenConformable(bool fits = false) : conformable{fits} {}
|
||||
// Matrix type:
|
||||
EigenConformable(EigenIndex r, EigenIndex c, EigenIndex rstride, EigenIndex cstride)
|
||||
: conformable{true}, rows{r}, cols{c},
|
||||
// TODO: when Eigen bug #747 is fixed, remove the tests for non-negativity.
|
||||
// http://eigen.tuxfamily.org/bz/show_bug.cgi?id=747
|
||||
stride{EigenRowMajor ? (rstride > 0 ? rstride : 0)
|
||||
: (cstride > 0 ? cstride : 0) /* outer stride */,
|
||||
EigenRowMajor ? (cstride > 0 ? cstride : 0)
|
||||
: (rstride > 0 ? rstride : 0) /* inner stride */},
|
||||
negativestrides{rstride < 0 || cstride < 0} {}
|
||||
// Vector type:
|
||||
EigenConformable(EigenIndex r, EigenIndex c, EigenIndex stride)
|
||||
: EigenConformable(r, c, r == 1 ? c * stride : stride, c == 1 ? r : r * stride) {}
|
||||
|
||||
template <typename props>
|
||||
bool stride_compatible() const {
|
||||
// To have compatible strides, we need (on both dimensions) one of fully dynamic strides,
|
||||
// matching strides, or a dimension size of 1 (in which case the stride value is
|
||||
// irrelevant). Alternatively, if any dimension size is 0, the strides are not relevant
|
||||
// (and numpy ≥ 1.23 sets the strides to 0 in that case, so we need to check explicitly).
|
||||
if (negativestrides) {
|
||||
return false;
|
||||
}
|
||||
if (rows == 0 || cols == 0) {
|
||||
return true;
|
||||
}
|
||||
return (props::inner_stride == Eigen::Dynamic || props::inner_stride == stride.inner()
|
||||
|| (EigenRowMajor ? cols : rows) == 1)
|
||||
&& (props::outer_stride == Eigen::Dynamic || props::outer_stride == stride.outer()
|
||||
|| (EigenRowMajor ? rows : cols) == 1);
|
||||
}
|
||||
// NOLINTNEXTLINE(google-explicit-constructor)
|
||||
operator bool() const { return conformable; }
|
||||
};
|
||||
|
||||
template <typename Type>
|
||||
struct eigen_extract_stride {
|
||||
using type = Type;
|
||||
};
|
||||
template <typename PlainObjectType, int MapOptions, typename StrideType>
|
||||
struct eigen_extract_stride<Eigen::Map<PlainObjectType, MapOptions, StrideType>> {
|
||||
using type = StrideType;
|
||||
};
|
||||
template <typename PlainObjectType, int Options, typename StrideType>
|
||||
struct eigen_extract_stride<Eigen::Ref<PlainObjectType, Options, StrideType>> {
|
||||
using type = StrideType;
|
||||
};
|
||||
|
||||
// Helper struct for extracting information from an Eigen type
|
||||
template <typename Type_>
|
||||
struct EigenProps {
|
||||
using Type = Type_;
|
||||
using Scalar = typename Type::Scalar;
|
||||
using StrideType = typename eigen_extract_stride<Type>::type;
|
||||
static constexpr EigenIndex rows = Type::RowsAtCompileTime, cols = Type::ColsAtCompileTime,
|
||||
size = Type::SizeAtCompileTime;
|
||||
static constexpr bool row_major = Type::IsRowMajor,
|
||||
vector
|
||||
= Type::IsVectorAtCompileTime, // At least one dimension has fixed size 1
|
||||
fixed_rows = rows != Eigen::Dynamic, fixed_cols = cols != Eigen::Dynamic,
|
||||
fixed = size != Eigen::Dynamic, // Fully-fixed size
|
||||
dynamic = !fixed_rows && !fixed_cols; // Fully-dynamic size
|
||||
|
||||
template <EigenIndex i, EigenIndex ifzero>
|
||||
using if_zero = std::integral_constant<EigenIndex, i == 0 ? ifzero : i>;
|
||||
static constexpr EigenIndex inner_stride
|
||||
= if_zero<StrideType::InnerStrideAtCompileTime, 1>::value,
|
||||
outer_stride = if_zero < StrideType::OuterStrideAtCompileTime,
|
||||
vector ? size
|
||||
: row_major ? cols
|
||||
: rows > ::value;
|
||||
static constexpr bool dynamic_stride
|
||||
= inner_stride == Eigen::Dynamic && outer_stride == Eigen::Dynamic;
|
||||
static constexpr bool requires_row_major
|
||||
= !dynamic_stride && !vector && (row_major ? inner_stride : outer_stride) == 1;
|
||||
static constexpr bool requires_col_major
|
||||
= !dynamic_stride && !vector && (row_major ? outer_stride : inner_stride) == 1;
|
||||
|
||||
// Takes an input array and determines whether we can make it fit into the Eigen type. If
|
||||
// the array is a vector, we attempt to fit it into either an Eigen 1xN or Nx1 vector
|
||||
// (preferring the latter if it will fit in either, i.e. for a fully dynamic matrix type).
|
||||
static EigenConformable<row_major> conformable(const array &a) {
|
||||
const auto dims = a.ndim();
|
||||
if (dims < 1 || dims > 2) {
|
||||
return false;
|
||||
}
|
||||
|
||||
if (dims == 2) { // Matrix type: require exact match (or dynamic)
|
||||
|
||||
EigenIndex np_rows = a.shape(0), np_cols = a.shape(1),
|
||||
np_rstride = a.strides(0) / static_cast<ssize_t>(sizeof(Scalar)),
|
||||
np_cstride = a.strides(1) / static_cast<ssize_t>(sizeof(Scalar));
|
||||
if ((PYBIND11_SILENCE_MSVC_C4127(fixed_rows) && np_rows != rows)
|
||||
|| (PYBIND11_SILENCE_MSVC_C4127(fixed_cols) && np_cols != cols)) {
|
||||
return false;
|
||||
}
|
||||
|
||||
return {np_rows, np_cols, np_rstride, np_cstride};
|
||||
}
|
||||
|
||||
// Otherwise we're storing an n-vector. Only one of the strides will be used, but
|
||||
// whichever is used, we want the (single) numpy stride value.
|
||||
const EigenIndex n = a.shape(0),
|
||||
stride = a.strides(0) / static_cast<ssize_t>(sizeof(Scalar));
|
||||
|
||||
if (vector) { // Eigen type is a compile-time vector
|
||||
if (PYBIND11_SILENCE_MSVC_C4127(fixed) && size != n) {
|
||||
return false; // Vector size mismatch
|
||||
}
|
||||
return {rows == 1 ? 1 : n, cols == 1 ? 1 : n, stride};
|
||||
}
|
||||
if (fixed) {
|
||||
// The type has a fixed size, but is not a vector: abort
|
||||
return false;
|
||||
}
|
||||
if (fixed_cols) {
|
||||
// Since this isn't a vector, cols must be != 1. We allow this only if it exactly
|
||||
// equals the number of elements (rows is Dynamic, and so 1 row is allowed).
|
||||
if (cols != n) {
|
||||
return false;
|
||||
}
|
||||
return {1, n, stride};
|
||||
} // Otherwise it's either fully dynamic, or column dynamic; both become a column vector
|
||||
if (PYBIND11_SILENCE_MSVC_C4127(fixed_rows) && rows != n) {
|
||||
return false;
|
||||
}
|
||||
return {n, 1, stride};
|
||||
}
|
||||
|
||||
static constexpr bool show_writeable
|
||||
= is_eigen_dense_map<Type>::value && is_eigen_mutable_map<Type>::value;
|
||||
static constexpr bool show_order = is_eigen_dense_map<Type>::value;
|
||||
static constexpr bool show_c_contiguous = show_order && requires_row_major;
|
||||
static constexpr bool show_f_contiguous
|
||||
= !show_c_contiguous && show_order && requires_col_major;
|
||||
|
||||
static constexpr auto descriptor
|
||||
= const_name("numpy.ndarray[") + npy_format_descriptor<Scalar>::name + const_name("[")
|
||||
+ const_name<fixed_rows>(const_name<(size_t) rows>(), const_name("m")) + const_name(", ")
|
||||
+ const_name<fixed_cols>(const_name<(size_t) cols>(), const_name("n")) + const_name("]")
|
||||
+
|
||||
// For a reference type (e.g. Ref<MatrixXd>) we have other constraints that might need to
|
||||
// be satisfied: writeable=True (for a mutable reference), and, depending on the map's
|
||||
// stride options, possibly f_contiguous or c_contiguous. We include them in the
|
||||
// descriptor output to provide some hint as to why a TypeError is occurring (otherwise
|
||||
// it can be confusing to see that a function accepts a 'numpy.ndarray[float64[3,2]]' and
|
||||
// an error message that you *gave* a numpy.ndarray of the right type and dimensions.
|
||||
const_name<show_writeable>(", flags.writeable", "")
|
||||
+ const_name<show_c_contiguous>(", flags.c_contiguous", "")
|
||||
+ const_name<show_f_contiguous>(", flags.f_contiguous", "") + const_name("]");
|
||||
};
|
||||
|
||||
// Casts an Eigen type to numpy array. If given a base, the numpy array references the src data,
|
||||
// otherwise it'll make a copy. writeable lets you turn off the writeable flag for the array.
|
||||
template <typename props>
|
||||
handle
|
||||
eigen_array_cast(typename props::Type const &src, handle base = handle(), bool writeable = true) {
|
||||
constexpr ssize_t elem_size = sizeof(typename props::Scalar);
|
||||
array a;
|
||||
if (props::vector) {
|
||||
a = array({src.size()}, {elem_size * src.innerStride()}, src.data(), base);
|
||||
} else {
|
||||
a = array({src.rows(), src.cols()},
|
||||
{elem_size * src.rowStride(), elem_size * src.colStride()},
|
||||
src.data(),
|
||||
base);
|
||||
}
|
||||
|
||||
if (!writeable) {
|
||||
array_proxy(a.ptr())->flags &= ~detail::npy_api::NPY_ARRAY_WRITEABLE_;
|
||||
}
|
||||
|
||||
return a.release();
|
||||
}
|
||||
|
||||
// Takes an lvalue ref to some Eigen type and a (python) base object, creating a numpy array that
|
||||
// reference the Eigen object's data with `base` as the python-registered base class (if omitted,
|
||||
// the base will be set to None, and lifetime management is up to the caller). The numpy array is
|
||||
// non-writeable if the given type is const.
|
||||
template <typename props, typename Type>
|
||||
handle eigen_ref_array(Type &src, handle parent = none()) {
|
||||
// none here is to get past array's should-we-copy detection, which currently always
|
||||
// copies when there is no base. Setting the base to None should be harmless.
|
||||
return eigen_array_cast<props>(src, parent, !std::is_const<Type>::value);
|
||||
}
|
||||
|
||||
// Takes a pointer to some dense, plain Eigen type, builds a capsule around it, then returns a
|
||||
// numpy array that references the encapsulated data with a python-side reference to the capsule to
|
||||
// tie its destruction to that of any dependent python objects. Const-ness is determined by
|
||||
// whether or not the Type of the pointer given is const.
|
||||
template <typename props, typename Type, typename = enable_if_t<is_eigen_dense_plain<Type>::value>>
|
||||
handle eigen_encapsulate(Type *src) {
|
||||
capsule base(src, [](void *o) { delete static_cast<Type *>(o); });
|
||||
return eigen_ref_array<props>(*src, base);
|
||||
}
|
||||
|
||||
// Type caster for regular, dense matrix types (e.g. MatrixXd), but not maps/refs/etc. of dense
|
||||
// types.
|
||||
template <typename Type>
|
||||
struct type_caster<Type, enable_if_t<is_eigen_dense_plain<Type>::value>> {
|
||||
using Scalar = typename Type::Scalar;
|
||||
using props = EigenProps<Type>;
|
||||
|
||||
bool load(handle src, bool convert) {
|
||||
// If we're in no-convert mode, only load if given an array of the correct type
|
||||
if (!convert && !isinstance<array_t<Scalar>>(src)) {
|
||||
return false;
|
||||
}
|
||||
|
||||
// Coerce into an array, but don't do type conversion yet; the copy below handles it.
|
||||
auto buf = array::ensure(src);
|
||||
|
||||
if (!buf) {
|
||||
return false;
|
||||
}
|
||||
|
||||
auto dims = buf.ndim();
|
||||
if (dims < 1 || dims > 2) {
|
||||
return false;
|
||||
}
|
||||
|
||||
auto fits = props::conformable(buf);
|
||||
if (!fits) {
|
||||
return false;
|
||||
}
|
||||
|
||||
// Allocate the new type, then build a numpy reference into it
|
||||
value = Type(fits.rows, fits.cols);
|
||||
auto ref = reinterpret_steal<array>(eigen_ref_array<props>(value));
|
||||
if (dims == 1) {
|
||||
ref = ref.squeeze();
|
||||
} else if (ref.ndim() == 1) {
|
||||
buf = buf.squeeze();
|
||||
}
|
||||
|
||||
int result = detail::npy_api::get().PyArray_CopyInto_(ref.ptr(), buf.ptr());
|
||||
|
||||
if (result < 0) { // Copy failed!
|
||||
PyErr_Clear();
|
||||
return false;
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
private:
|
||||
// Cast implementation
|
||||
template <typename CType>
|
||||
static handle cast_impl(CType *src, return_value_policy policy, handle parent) {
|
||||
switch (policy) {
|
||||
case return_value_policy::take_ownership:
|
||||
case return_value_policy::automatic:
|
||||
return eigen_encapsulate<props>(src);
|
||||
case return_value_policy::move:
|
||||
return eigen_encapsulate<props>(new CType(std::move(*src)));
|
||||
case return_value_policy::copy:
|
||||
return eigen_array_cast<props>(*src);
|
||||
case return_value_policy::reference:
|
||||
case return_value_policy::automatic_reference:
|
||||
return eigen_ref_array<props>(*src);
|
||||
case return_value_policy::reference_internal:
|
||||
return eigen_ref_array<props>(*src, parent);
|
||||
default:
|
||||
throw cast_error("unhandled return_value_policy: should not happen!");
|
||||
};
|
||||
}
|
||||
|
||||
public:
|
||||
// Normal returned non-reference, non-const value:
|
||||
static handle cast(Type &&src, return_value_policy /* policy */, handle parent) {
|
||||
return cast_impl(&src, return_value_policy::move, parent);
|
||||
}
|
||||
// If you return a non-reference const, we mark the numpy array readonly:
|
||||
static handle cast(const Type &&src, return_value_policy /* policy */, handle parent) {
|
||||
return cast_impl(&src, return_value_policy::move, parent);
|
||||
}
|
||||
// lvalue reference return; default (automatic) becomes copy
|
||||
static handle cast(Type &src, return_value_policy policy, handle parent) {
|
||||
if (policy == return_value_policy::automatic
|
||||
|| policy == return_value_policy::automatic_reference) {
|
||||
policy = return_value_policy::copy;
|
||||
}
|
||||
return cast_impl(&src, policy, parent);
|
||||
}
|
||||
// const lvalue reference return; default (automatic) becomes copy
|
||||
static handle cast(const Type &src, return_value_policy policy, handle parent) {
|
||||
if (policy == return_value_policy::automatic
|
||||
|| policy == return_value_policy::automatic_reference) {
|
||||
policy = return_value_policy::copy;
|
||||
}
|
||||
return cast(&src, policy, parent);
|
||||
}
|
||||
// non-const pointer return
|
||||
static handle cast(Type *src, return_value_policy policy, handle parent) {
|
||||
return cast_impl(src, policy, parent);
|
||||
}
|
||||
// const pointer return
|
||||
static handle cast(const Type *src, return_value_policy policy, handle parent) {
|
||||
return cast_impl(src, policy, parent);
|
||||
}
|
||||
|
||||
static constexpr auto name = props::descriptor;
|
||||
|
||||
// NOLINTNEXTLINE(google-explicit-constructor)
|
||||
operator Type *() { return &value; }
|
||||
// NOLINTNEXTLINE(google-explicit-constructor)
|
||||
operator Type &() { return value; }
|
||||
// NOLINTNEXTLINE(google-explicit-constructor)
|
||||
operator Type &&() && { return std::move(value); }
|
||||
template <typename T>
|
||||
using cast_op_type = movable_cast_op_type<T>;
|
||||
|
||||
private:
|
||||
Type value;
|
||||
};
|
||||
|
||||
// Base class for casting reference/map/block/etc. objects back to python.
|
||||
template <typename MapType>
|
||||
struct eigen_map_caster {
|
||||
private:
|
||||
using props = EigenProps<MapType>;
|
||||
|
||||
public:
|
||||
// Directly referencing a ref/map's data is a bit dangerous (whatever the map/ref points to has
|
||||
// to stay around), but we'll allow it under the assumption that you know what you're doing
|
||||
// (and have an appropriate keep_alive in place). We return a numpy array pointing directly at
|
||||
// the ref's data (The numpy array ends up read-only if the ref was to a const matrix type.)
|
||||
// Note that this means you need to ensure you don't destroy the object in some other way (e.g.
|
||||
// with an appropriate keep_alive, or with a reference to a statically allocated matrix).
|
||||
static handle cast(const MapType &src, return_value_policy policy, handle parent) {
|
||||
switch (policy) {
|
||||
case return_value_policy::copy:
|
||||
return eigen_array_cast<props>(src);
|
||||
case return_value_policy::reference_internal:
|
||||
return eigen_array_cast<props>(src, parent, is_eigen_mutable_map<MapType>::value);
|
||||
case return_value_policy::reference:
|
||||
case return_value_policy::automatic:
|
||||
case return_value_policy::automatic_reference:
|
||||
return eigen_array_cast<props>(src, none(), is_eigen_mutable_map<MapType>::value);
|
||||
default:
|
||||
// move, take_ownership don't make any sense for a ref/map:
|
||||
pybind11_fail("Invalid return_value_policy for Eigen Map/Ref/Block type");
|
||||
}
|
||||
}
|
||||
|
||||
static constexpr auto name = props::descriptor;
|
||||
|
||||
// Explicitly delete these: support python -> C++ conversion on these (i.e. these can be return
|
||||
// types but not bound arguments). We still provide them (with an explicitly delete) so that
|
||||
// you end up here if you try anyway.
|
||||
bool load(handle, bool) = delete;
|
||||
operator MapType() = delete;
|
||||
template <typename>
|
||||
using cast_op_type = MapType;
|
||||
};
|
||||
|
||||
// We can return any map-like object (but can only load Refs, specialized next):
|
||||
template <typename Type>
|
||||
struct type_caster<Type, enable_if_t<is_eigen_dense_map<Type>::value>> : eigen_map_caster<Type> {};
|
||||
|
||||
// Loader for Ref<...> arguments. See the documentation for info on how to make this work without
|
||||
// copying (it requires some extra effort in many cases).
|
||||
template <typename PlainObjectType, typename StrideType>
|
||||
struct type_caster<
|
||||
Eigen::Ref<PlainObjectType, 0, StrideType>,
|
||||
enable_if_t<is_eigen_dense_map<Eigen::Ref<PlainObjectType, 0, StrideType>>::value>>
|
||||
: public eigen_map_caster<Eigen::Ref<PlainObjectType, 0, StrideType>> {
|
||||
private:
|
||||
using Type = Eigen::Ref<PlainObjectType, 0, StrideType>;
|
||||
using props = EigenProps<Type>;
|
||||
using Scalar = typename props::Scalar;
|
||||
using MapType = Eigen::Map<PlainObjectType, 0, StrideType>;
|
||||
using Array
|
||||
= array_t<Scalar,
|
||||
array::forcecast
|
||||
| ((props::row_major ? props::inner_stride : props::outer_stride) == 1
|
||||
? array::c_style
|
||||
: (props::row_major ? props::outer_stride : props::inner_stride) == 1
|
||||
? array::f_style
|
||||
: 0)>;
|
||||
static constexpr bool need_writeable = is_eigen_mutable_map<Type>::value;
|
||||
// Delay construction (these have no default constructor)
|
||||
std::unique_ptr<MapType> map;
|
||||
std::unique_ptr<Type> ref;
|
||||
// Our array. When possible, this is just a numpy array pointing to the source data, but
|
||||
// sometimes we can't avoid copying (e.g. input is not a numpy array at all, has an
|
||||
// incompatible layout, or is an array of a type that needs to be converted). Using a numpy
|
||||
// temporary (rather than an Eigen temporary) saves an extra copy when we need both type
|
||||
// conversion and storage order conversion. (Note that we refuse to use this temporary copy
|
||||
// when loading an argument for a Ref<M> with M non-const, i.e. a read-write reference).
|
||||
Array copy_or_ref;
|
||||
|
||||
public:
|
||||
bool load(handle src, bool convert) {
|
||||
// First check whether what we have is already an array of the right type. If not, we
|
||||
// can't avoid a copy (because the copy is also going to do type conversion).
|
||||
bool need_copy = !isinstance<Array>(src);
|
||||
|
||||
EigenConformable<props::row_major> fits;
|
||||
if (!need_copy) {
|
||||
// We don't need a converting copy, but we also need to check whether the strides are
|
||||
// compatible with the Ref's stride requirements
|
||||
auto aref = reinterpret_borrow<Array>(src);
|
||||
|
||||
if (aref && (!need_writeable || aref.writeable())) {
|
||||
fits = props::conformable(aref);
|
||||
if (!fits) {
|
||||
return false; // Incompatible dimensions
|
||||
}
|
||||
if (!fits.template stride_compatible<props>()) {
|
||||
need_copy = true;
|
||||
} else {
|
||||
copy_or_ref = std::move(aref);
|
||||
}
|
||||
} else {
|
||||
need_copy = true;
|
||||
}
|
||||
}
|
||||
|
||||
if (need_copy) {
|
||||
// We need to copy: If we need a mutable reference, or we're not supposed to convert
|
||||
// (either because we're in the no-convert overload pass, or because we're explicitly
|
||||
// instructed not to copy (via `py::arg().noconvert()`) we have to fail loading.
|
||||
if (!convert || need_writeable) {
|
||||
return false;
|
||||
}
|
||||
|
||||
Array copy = Array::ensure(src);
|
||||
if (!copy) {
|
||||
return false;
|
||||
}
|
||||
fits = props::conformable(copy);
|
||||
if (!fits || !fits.template stride_compatible<props>()) {
|
||||
return false;
|
||||
}
|
||||
copy_or_ref = std::move(copy);
|
||||
loader_life_support::add_patient(copy_or_ref);
|
||||
}
|
||||
|
||||
ref.reset();
|
||||
map.reset(new MapType(data(copy_or_ref),
|
||||
fits.rows,
|
||||
fits.cols,
|
||||
make_stride(fits.stride.outer(), fits.stride.inner())));
|
||||
ref.reset(new Type(*map));
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
// NOLINTNEXTLINE(google-explicit-constructor)
|
||||
operator Type *() { return ref.get(); }
|
||||
// NOLINTNEXTLINE(google-explicit-constructor)
|
||||
operator Type &() { return *ref; }
|
||||
template <typename _T>
|
||||
using cast_op_type = pybind11::detail::cast_op_type<_T>;
|
||||
|
||||
private:
|
||||
template <typename T = Type, enable_if_t<is_eigen_mutable_map<T>::value, int> = 0>
|
||||
Scalar *data(Array &a) {
|
||||
return a.mutable_data();
|
||||
}
|
||||
|
||||
template <typename T = Type, enable_if_t<!is_eigen_mutable_map<T>::value, int> = 0>
|
||||
const Scalar *data(Array &a) {
|
||||
return a.data();
|
||||
}
|
||||
|
||||
// Attempt to figure out a constructor of `Stride` that will work.
|
||||
// If both strides are fixed, use a default constructor:
|
||||
template <typename S>
|
||||
using stride_ctor_default = bool_constant<S::InnerStrideAtCompileTime != Eigen::Dynamic
|
||||
&& S::OuterStrideAtCompileTime != Eigen::Dynamic
|
||||
&& std::is_default_constructible<S>::value>;
|
||||
// Otherwise, if there is a two-index constructor, assume it is (outer,inner) like
|
||||
// Eigen::Stride, and use it:
|
||||
template <typename S>
|
||||
using stride_ctor_dual
|
||||
= bool_constant<!stride_ctor_default<S>::value
|
||||
&& std::is_constructible<S, EigenIndex, EigenIndex>::value>;
|
||||
// Otherwise, if there is a one-index constructor, and just one of the strides is dynamic, use
|
||||
// it (passing whichever stride is dynamic).
|
||||
template <typename S>
|
||||
using stride_ctor_outer
|
||||
= bool_constant<!any_of<stride_ctor_default<S>, stride_ctor_dual<S>>::value
|
||||
&& S::OuterStrideAtCompileTime == Eigen::Dynamic
|
||||
&& S::InnerStrideAtCompileTime != Eigen::Dynamic
|
||||
&& std::is_constructible<S, EigenIndex>::value>;
|
||||
template <typename S>
|
||||
using stride_ctor_inner
|
||||
= bool_constant<!any_of<stride_ctor_default<S>, stride_ctor_dual<S>>::value
|
||||
&& S::InnerStrideAtCompileTime == Eigen::Dynamic
|
||||
&& S::OuterStrideAtCompileTime != Eigen::Dynamic
|
||||
&& std::is_constructible<S, EigenIndex>::value>;
|
||||
|
||||
template <typename S = StrideType, enable_if_t<stride_ctor_default<S>::value, int> = 0>
|
||||
static S make_stride(EigenIndex, EigenIndex) {
|
||||
return S();
|
||||
}
|
||||
template <typename S = StrideType, enable_if_t<stride_ctor_dual<S>::value, int> = 0>
|
||||
static S make_stride(EigenIndex outer, EigenIndex inner) {
|
||||
return S(outer, inner);
|
||||
}
|
||||
template <typename S = StrideType, enable_if_t<stride_ctor_outer<S>::value, int> = 0>
|
||||
static S make_stride(EigenIndex outer, EigenIndex) {
|
||||
return S(outer);
|
||||
}
|
||||
template <typename S = StrideType, enable_if_t<stride_ctor_inner<S>::value, int> = 0>
|
||||
static S make_stride(EigenIndex, EigenIndex inner) {
|
||||
return S(inner);
|
||||
}
|
||||
};
|
||||
|
||||
// type_caster for special matrix types (e.g. DiagonalMatrix), which are EigenBase, but not
|
||||
// EigenDense (i.e. they don't have a data(), at least not with the usual matrix layout).
|
||||
// load() is not supported, but we can cast them into the python domain by first copying to a
|
||||
// regular Eigen::Matrix, then casting that.
|
||||
template <typename Type>
|
||||
struct type_caster<Type, enable_if_t<is_eigen_other<Type>::value>> {
|
||||
protected:
|
||||
using Matrix
|
||||
= Eigen::Matrix<typename Type::Scalar, Type::RowsAtCompileTime, Type::ColsAtCompileTime>;
|
||||
using props = EigenProps<Matrix>;
|
||||
|
||||
public:
|
||||
static handle cast(const Type &src, return_value_policy /* policy */, handle /* parent */) {
|
||||
handle h = eigen_encapsulate<props>(new Matrix(src));
|
||||
return h;
|
||||
}
|
||||
static handle cast(const Type *src, return_value_policy policy, handle parent) {
|
||||
return cast(*src, policy, parent);
|
||||
}
|
||||
|
||||
static constexpr auto name = props::descriptor;
|
||||
|
||||
// Explicitly delete these: support python -> C++ conversion on these (i.e. these can be return
|
||||
// types but not bound arguments). We still provide them (with an explicitly delete) so that
|
||||
// you end up here if you try anyway.
|
||||
bool load(handle, bool) = delete;
|
||||
operator Type() = delete;
|
||||
template <typename>
|
||||
using cast_op_type = Type;
|
||||
};
|
||||
|
||||
template <typename Type>
|
||||
struct type_caster<Type, enable_if_t<is_eigen_sparse<Type>::value>> {
|
||||
using Scalar = typename Type::Scalar;
|
||||
using StorageIndex = remove_reference_t<decltype(*std::declval<Type>().outerIndexPtr())>;
|
||||
using Index = typename Type::Index;
|
||||
static constexpr bool rowMajor = Type::IsRowMajor;
|
||||
|
||||
bool load(handle src, bool) {
|
||||
if (!src) {
|
||||
return false;
|
||||
}
|
||||
|
||||
auto obj = reinterpret_borrow<object>(src);
|
||||
object sparse_module = module_::import("scipy.sparse");
|
||||
object matrix_type = sparse_module.attr(rowMajor ? "csr_matrix" : "csc_matrix");
|
||||
|
||||
if (!type::handle_of(obj).is(matrix_type)) {
|
||||
try {
|
||||
obj = matrix_type(obj);
|
||||
} catch (const error_already_set &) {
|
||||
return false;
|
||||
}
|
||||
}
|
||||
|
||||
auto values = array_t<Scalar>((object) obj.attr("data"));
|
||||
auto innerIndices = array_t<StorageIndex>((object) obj.attr("indices"));
|
||||
auto outerIndices = array_t<StorageIndex>((object) obj.attr("indptr"));
|
||||
auto shape = pybind11::tuple((pybind11::object) obj.attr("shape"));
|
||||
auto nnz = obj.attr("nnz").cast<Index>();
|
||||
|
||||
if (!values || !innerIndices || !outerIndices) {
|
||||
return false;
|
||||
}
|
||||
|
||||
value = EigenMapSparseMatrix<Scalar,
|
||||
Type::Flags &(Eigen::RowMajor | Eigen::ColMajor),
|
||||
StorageIndex>(shape[0].cast<Index>(),
|
||||
shape[1].cast<Index>(),
|
||||
std::move(nnz),
|
||||
outerIndices.mutable_data(),
|
||||
innerIndices.mutable_data(),
|
||||
values.mutable_data());
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
static handle cast(const Type &src, return_value_policy /* policy */, handle /* parent */) {
|
||||
const_cast<Type &>(src).makeCompressed();
|
||||
|
||||
object matrix_type
|
||||
= module_::import("scipy.sparse").attr(rowMajor ? "csr_matrix" : "csc_matrix");
|
||||
|
||||
array data(src.nonZeros(), src.valuePtr());
|
||||
array outerIndices((rowMajor ? src.rows() : src.cols()) + 1, src.outerIndexPtr());
|
||||
array innerIndices(src.nonZeros(), src.innerIndexPtr());
|
||||
|
||||
return matrix_type(pybind11::make_tuple(
|
||||
std::move(data), std::move(innerIndices), std::move(outerIndices)),
|
||||
pybind11::make_tuple(src.rows(), src.cols()))
|
||||
.release();
|
||||
}
|
||||
|
||||
PYBIND11_TYPE_CASTER(Type,
|
||||
const_name<(Type::IsRowMajor) != 0>("scipy.sparse.csr_matrix[",
|
||||
"scipy.sparse.csc_matrix[")
|
||||
+ npy_format_descriptor<Scalar>::name + const_name("]"));
|
||||
};
|
||||
|
||||
PYBIND11_NAMESPACE_END(detail)
|
||||
PYBIND11_NAMESPACE_END(PYBIND11_NAMESPACE)
|
||||
|
|
@ -0,0 +1,518 @@
|
|||
/*
|
||||
pybind11/eigen/tensor.h: Transparent conversion for Eigen tensors
|
||||
|
||||
All rights reserved. Use of this source code is governed by a
|
||||
BSD-style license that can be found in the LICENSE file.
|
||||
*/
|
||||
|
||||
#pragma once
|
||||
|
||||
#include "../numpy.h"
|
||||
|
||||
#if defined(__GNUC__) && !defined(__clang__) && !defined(__INTEL_COMPILER)
|
||||
static_assert(__GNUC__ > 5, "Eigen Tensor support in pybind11 requires GCC > 5.0");
|
||||
#endif
|
||||
|
||||
#if defined(_MSC_VER)
|
||||
# pragma warning(push)
|
||||
# pragma warning(disable : 4554) // Tensor.h warning
|
||||
# pragma warning(disable : 4127) // Tensor.h warning
|
||||
#elif defined(__MINGW32__)
|
||||
# pragma GCC diagnostic push
|
||||
# pragma GCC diagnostic ignored "-Wmaybe-uninitialized"
|
||||
#endif
|
||||
|
||||
#include <unsupported/Eigen/CXX11/Tensor>
|
||||
|
||||
#if defined(_MSC_VER)
|
||||
# pragma warning(pop)
|
||||
#elif defined(__MINGW32__)
|
||||
# pragma GCC diagnostic pop
|
||||
#endif
|
||||
|
||||
static_assert(EIGEN_VERSION_AT_LEAST(3, 3, 0),
|
||||
"Eigen Tensor support in pybind11 requires Eigen >= 3.3.0");
|
||||
|
||||
PYBIND11_NAMESPACE_BEGIN(PYBIND11_NAMESPACE)
|
||||
|
||||
PYBIND11_NAMESPACE_BEGIN(detail)
|
||||
|
||||
inline bool is_tensor_aligned(const void *data) {
|
||||
return (reinterpret_cast<std::size_t>(data) % EIGEN_DEFAULT_ALIGN_BYTES) == 0;
|
||||
}
|
||||
|
||||
template <typename T>
|
||||
constexpr int compute_array_flag_from_tensor() {
|
||||
static_assert((static_cast<int>(T::Layout) == static_cast<int>(Eigen::RowMajor))
|
||||
|| (static_cast<int>(T::Layout) == static_cast<int>(Eigen::ColMajor)),
|
||||
"Layout must be row or column major");
|
||||
return (static_cast<int>(T::Layout) == static_cast<int>(Eigen::RowMajor)) ? array::c_style
|
||||
: array::f_style;
|
||||
}
|
||||
|
||||
template <typename T>
|
||||
struct eigen_tensor_helper {};
|
||||
|
||||
template <typename Scalar_, int NumIndices_, int Options_, typename IndexType>
|
||||
struct eigen_tensor_helper<Eigen::Tensor<Scalar_, NumIndices_, Options_, IndexType>> {
|
||||
using Type = Eigen::Tensor<Scalar_, NumIndices_, Options_, IndexType>;
|
||||
using ValidType = void;
|
||||
|
||||
static Eigen::DSizes<typename Type::Index, Type::NumIndices> get_shape(const Type &f) {
|
||||
return f.dimensions();
|
||||
}
|
||||
|
||||
static constexpr bool
|
||||
is_correct_shape(const Eigen::DSizes<typename Type::Index, Type::NumIndices> & /*shape*/) {
|
||||
return true;
|
||||
}
|
||||
|
||||
template <typename T>
|
||||
struct helper {};
|
||||
|
||||
template <size_t... Is>
|
||||
struct helper<index_sequence<Is...>> {
|
||||
static constexpr auto value = concat(const_name(((void) Is, "?"))...);
|
||||
};
|
||||
|
||||
static constexpr auto dimensions_descriptor
|
||||
= helper<decltype(make_index_sequence<Type::NumIndices>())>::value;
|
||||
|
||||
template <typename... Args>
|
||||
static Type *alloc(Args &&...args) {
|
||||
return new Type(std::forward<Args>(args)...);
|
||||
}
|
||||
|
||||
static void free(Type *tensor) { delete tensor; }
|
||||
};
|
||||
|
||||
template <typename Scalar_, typename std::ptrdiff_t... Indices, int Options_, typename IndexType>
|
||||
struct eigen_tensor_helper<
|
||||
Eigen::TensorFixedSize<Scalar_, Eigen::Sizes<Indices...>, Options_, IndexType>> {
|
||||
using Type = Eigen::TensorFixedSize<Scalar_, Eigen::Sizes<Indices...>, Options_, IndexType>;
|
||||
using ValidType = void;
|
||||
|
||||
static constexpr Eigen::DSizes<typename Type::Index, Type::NumIndices>
|
||||
get_shape(const Type & /*f*/) {
|
||||
return get_shape();
|
||||
}
|
||||
|
||||
static constexpr Eigen::DSizes<typename Type::Index, Type::NumIndices> get_shape() {
|
||||
return Eigen::DSizes<typename Type::Index, Type::NumIndices>(Indices...);
|
||||
}
|
||||
|
||||
static bool
|
||||
is_correct_shape(const Eigen::DSizes<typename Type::Index, Type::NumIndices> &shape) {
|
||||
return get_shape() == shape;
|
||||
}
|
||||
|
||||
static constexpr auto dimensions_descriptor = concat(const_name<Indices>()...);
|
||||
|
||||
template <typename... Args>
|
||||
static Type *alloc(Args &&...args) {
|
||||
Eigen::aligned_allocator<Type> allocator;
|
||||
return ::new (allocator.allocate(1)) Type(std::forward<Args>(args)...);
|
||||
}
|
||||
|
||||
static void free(Type *tensor) {
|
||||
Eigen::aligned_allocator<Type> allocator;
|
||||
tensor->~Type();
|
||||
allocator.deallocate(tensor, 1);
|
||||
}
|
||||
};
|
||||
|
||||
template <typename Type, bool ShowDetails, bool NeedsWriteable = false>
|
||||
struct get_tensor_descriptor {
|
||||
static constexpr auto details
|
||||
= const_name<NeedsWriteable>(", flags.writeable", "")
|
||||
+ const_name<static_cast<int>(Type::Layout) == static_cast<int>(Eigen::RowMajor)>(
|
||||
", flags.c_contiguous", ", flags.f_contiguous");
|
||||
static constexpr auto value
|
||||
= const_name("numpy.ndarray[") + npy_format_descriptor<typename Type::Scalar>::name
|
||||
+ const_name("[") + eigen_tensor_helper<remove_cv_t<Type>>::dimensions_descriptor
|
||||
+ const_name("]") + const_name<ShowDetails>(details, const_name("")) + const_name("]");
|
||||
};
|
||||
|
||||
// When EIGEN_AVOID_STL_ARRAY is defined, Eigen::DSizes<T, 0> does not have the begin() member
|
||||
// function. Falling back to a simple loop works around this issue.
|
||||
//
|
||||
// We need to disable the type-limits warning for the inner loop when size = 0.
|
||||
|
||||
#if defined(__GNUC__)
|
||||
# pragma GCC diagnostic push
|
||||
# pragma GCC diagnostic ignored "-Wtype-limits"
|
||||
#endif
|
||||
|
||||
template <typename T, int size>
|
||||
std::vector<T> convert_dsizes_to_vector(const Eigen::DSizes<T, size> &arr) {
|
||||
std::vector<T> result(size);
|
||||
|
||||
for (size_t i = 0; i < size; i++) {
|
||||
result[i] = arr[i];
|
||||
}
|
||||
|
||||
return result;
|
||||
}
|
||||
|
||||
template <typename T, int size>
|
||||
Eigen::DSizes<T, size> get_shape_for_array(const array &arr) {
|
||||
Eigen::DSizes<T, size> result;
|
||||
const T *shape = arr.shape();
|
||||
for (size_t i = 0; i < size; i++) {
|
||||
result[i] = shape[i];
|
||||
}
|
||||
|
||||
return result;
|
||||
}
|
||||
|
||||
#if defined(__GNUC__)
|
||||
# pragma GCC diagnostic pop
|
||||
#endif
|
||||
|
||||
template <typename Type>
|
||||
struct type_caster<Type, typename eigen_tensor_helper<Type>::ValidType> {
|
||||
using Helper = eigen_tensor_helper<Type>;
|
||||
static constexpr auto temp_name = get_tensor_descriptor<Type, false>::value;
|
||||
PYBIND11_TYPE_CASTER(Type, temp_name);
|
||||
|
||||
bool load(handle src, bool convert) {
|
||||
if (!convert) {
|
||||
if (!isinstance<array>(src)) {
|
||||
return false;
|
||||
}
|
||||
array temp = array::ensure(src);
|
||||
if (!temp) {
|
||||
return false;
|
||||
}
|
||||
|
||||
if (!convert && !temp.dtype().is(dtype::of<typename Type::Scalar>())) {
|
||||
return false;
|
||||
}
|
||||
}
|
||||
|
||||
array_t<typename Type::Scalar, compute_array_flag_from_tensor<Type>()> arr(
|
||||
reinterpret_borrow<object>(src));
|
||||
|
||||
if (arr.ndim() != Type::NumIndices) {
|
||||
return false;
|
||||
}
|
||||
auto shape = get_shape_for_array<typename Type::Index, Type::NumIndices>(arr);
|
||||
|
||||
if (!Helper::is_correct_shape(shape)) {
|
||||
return false;
|
||||
}
|
||||
|
||||
#if EIGEN_VERSION_AT_LEAST(3, 4, 0)
|
||||
auto data_pointer = arr.data();
|
||||
#else
|
||||
// Handle Eigen bug
|
||||
auto data_pointer = const_cast<typename Type::Scalar *>(arr.data());
|
||||
#endif
|
||||
|
||||
if (is_tensor_aligned(arr.data())) {
|
||||
value = Eigen::TensorMap<const Type, Eigen::Aligned>(data_pointer, shape);
|
||||
} else {
|
||||
value = Eigen::TensorMap<const Type>(data_pointer, shape);
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
static handle cast(Type &&src, return_value_policy policy, handle parent) {
|
||||
if (policy == return_value_policy::reference
|
||||
|| policy == return_value_policy::reference_internal) {
|
||||
pybind11_fail("Cannot use a reference return value policy for an rvalue");
|
||||
}
|
||||
return cast_impl(&src, return_value_policy::move, parent);
|
||||
}
|
||||
|
||||
static handle cast(const Type &&src, return_value_policy policy, handle parent) {
|
||||
if (policy == return_value_policy::reference
|
||||
|| policy == return_value_policy::reference_internal) {
|
||||
pybind11_fail("Cannot use a reference return value policy for an rvalue");
|
||||
}
|
||||
return cast_impl(&src, return_value_policy::move, parent);
|
||||
}
|
||||
|
||||
static handle cast(Type &src, return_value_policy policy, handle parent) {
|
||||
if (policy == return_value_policy::automatic
|
||||
|| policy == return_value_policy::automatic_reference) {
|
||||
policy = return_value_policy::copy;
|
||||
}
|
||||
return cast_impl(&src, policy, parent);
|
||||
}
|
||||
|
||||
static handle cast(const Type &src, return_value_policy policy, handle parent) {
|
||||
if (policy == return_value_policy::automatic
|
||||
|| policy == return_value_policy::automatic_reference) {
|
||||
policy = return_value_policy::copy;
|
||||
}
|
||||
return cast(&src, policy, parent);
|
||||
}
|
||||
|
||||
static handle cast(Type *src, return_value_policy policy, handle parent) {
|
||||
if (policy == return_value_policy::automatic) {
|
||||
policy = return_value_policy::take_ownership;
|
||||
} else if (policy == return_value_policy::automatic_reference) {
|
||||
policy = return_value_policy::reference;
|
||||
}
|
||||
return cast_impl(src, policy, parent);
|
||||
}
|
||||
|
||||
static handle cast(const Type *src, return_value_policy policy, handle parent) {
|
||||
if (policy == return_value_policy::automatic) {
|
||||
policy = return_value_policy::take_ownership;
|
||||
} else if (policy == return_value_policy::automatic_reference) {
|
||||
policy = return_value_policy::reference;
|
||||
}
|
||||
return cast_impl(src, policy, parent);
|
||||
}
|
||||
|
||||
template <typename C>
|
||||
static handle cast_impl(C *src, return_value_policy policy, handle parent) {
|
||||
object parent_object;
|
||||
bool writeable = false;
|
||||
switch (policy) {
|
||||
case return_value_policy::move:
|
||||
if (std::is_const<C>::value) {
|
||||
pybind11_fail("Cannot move from a constant reference");
|
||||
}
|
||||
|
||||
src = Helper::alloc(std::move(*src));
|
||||
|
||||
parent_object
|
||||
= capsule(src, [](void *ptr) { Helper::free(reinterpret_cast<Type *>(ptr)); });
|
||||
writeable = true;
|
||||
break;
|
||||
|
||||
case return_value_policy::take_ownership:
|
||||
if (std::is_const<C>::value) {
|
||||
// This cast is ugly, and might be UB in some cases, but we don't have an
|
||||
// alterantive here as we must free that memory
|
||||
Helper::free(const_cast<Type *>(src));
|
||||
pybind11_fail("Cannot take ownership of a const reference");
|
||||
}
|
||||
|
||||
parent_object
|
||||
= capsule(src, [](void *ptr) { Helper::free(reinterpret_cast<Type *>(ptr)); });
|
||||
writeable = true;
|
||||
break;
|
||||
|
||||
case return_value_policy::copy:
|
||||
writeable = true;
|
||||
break;
|
||||
|
||||
case return_value_policy::reference:
|
||||
parent_object = none();
|
||||
writeable = !std::is_const<C>::value;
|
||||
break;
|
||||
|
||||
case return_value_policy::reference_internal:
|
||||
// Default should do the right thing
|
||||
if (!parent) {
|
||||
pybind11_fail("Cannot use reference internal when there is no parent");
|
||||
}
|
||||
parent_object = reinterpret_borrow<object>(parent);
|
||||
writeable = !std::is_const<C>::value;
|
||||
break;
|
||||
|
||||
default:
|
||||
pybind11_fail("pybind11 bug in eigen.h, please file a bug report");
|
||||
}
|
||||
|
||||
auto result = array_t<typename Type::Scalar, compute_array_flag_from_tensor<Type>()>(
|
||||
convert_dsizes_to_vector(Helper::get_shape(*src)), src->data(), parent_object);
|
||||
|
||||
if (!writeable) {
|
||||
array_proxy(result.ptr())->flags &= ~detail::npy_api::NPY_ARRAY_WRITEABLE_;
|
||||
}
|
||||
|
||||
return result.release();
|
||||
}
|
||||
};
|
||||
|
||||
template <typename StoragePointerType,
|
||||
bool needs_writeable,
|
||||
enable_if_t<!needs_writeable, bool> = true>
|
||||
StoragePointerType get_array_data_for_type(array &arr) {
|
||||
#if EIGEN_VERSION_AT_LEAST(3, 4, 0)
|
||||
return reinterpret_cast<StoragePointerType>(arr.data());
|
||||
#else
|
||||
// Handle Eigen bug
|
||||
return reinterpret_cast<StoragePointerType>(const_cast<void *>(arr.data()));
|
||||
#endif
|
||||
}
|
||||
|
||||
template <typename StoragePointerType,
|
||||
bool needs_writeable,
|
||||
enable_if_t<needs_writeable, bool> = true>
|
||||
StoragePointerType get_array_data_for_type(array &arr) {
|
||||
return reinterpret_cast<StoragePointerType>(arr.mutable_data());
|
||||
}
|
||||
|
||||
template <typename T, typename = void>
|
||||
struct get_storage_pointer_type;
|
||||
|
||||
template <typename MapType>
|
||||
struct get_storage_pointer_type<MapType, void_t<typename MapType::StoragePointerType>> {
|
||||
using SPT = typename MapType::StoragePointerType;
|
||||
};
|
||||
|
||||
template <typename MapType>
|
||||
struct get_storage_pointer_type<MapType, void_t<typename MapType::PointerArgType>> {
|
||||
using SPT = typename MapType::PointerArgType;
|
||||
};
|
||||
|
||||
template <typename Type, int Options>
|
||||
struct type_caster<Eigen::TensorMap<Type, Options>,
|
||||
typename eigen_tensor_helper<remove_cv_t<Type>>::ValidType> {
|
||||
using MapType = Eigen::TensorMap<Type, Options>;
|
||||
using Helper = eigen_tensor_helper<remove_cv_t<Type>>;
|
||||
|
||||
bool load(handle src, bool /*convert*/) {
|
||||
// Note that we have a lot more checks here as we want to make sure to avoid copies
|
||||
if (!isinstance<array>(src)) {
|
||||
return false;
|
||||
}
|
||||
auto arr = reinterpret_borrow<array>(src);
|
||||
if ((arr.flags() & compute_array_flag_from_tensor<Type>()) == 0) {
|
||||
return false;
|
||||
}
|
||||
|
||||
if (!arr.dtype().is(dtype::of<typename Type::Scalar>())) {
|
||||
return false;
|
||||
}
|
||||
|
||||
if (arr.ndim() != Type::NumIndices) {
|
||||
return false;
|
||||
}
|
||||
|
||||
constexpr bool is_aligned = (Options & Eigen::Aligned) != 0;
|
||||
|
||||
if (PYBIND11_SILENCE_MSVC_C4127(is_aligned) && !is_tensor_aligned(arr.data())) {
|
||||
return false;
|
||||
}
|
||||
|
||||
auto shape = get_shape_for_array<typename Type::Index, Type::NumIndices>(arr);
|
||||
|
||||
if (!Helper::is_correct_shape(shape)) {
|
||||
return false;
|
||||
}
|
||||
|
||||
if (PYBIND11_SILENCE_MSVC_C4127(needs_writeable) && !arr.writeable()) {
|
||||
return false;
|
||||
}
|
||||
|
||||
auto result = get_array_data_for_type<typename get_storage_pointer_type<MapType>::SPT,
|
||||
needs_writeable>(arr);
|
||||
|
||||
value.reset(new MapType(std::move(result), std::move(shape)));
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
static handle cast(MapType &&src, return_value_policy policy, handle parent) {
|
||||
return cast_impl(&src, policy, parent);
|
||||
}
|
||||
|
||||
static handle cast(const MapType &&src, return_value_policy policy, handle parent) {
|
||||
return cast_impl(&src, policy, parent);
|
||||
}
|
||||
|
||||
static handle cast(MapType &src, return_value_policy policy, handle parent) {
|
||||
if (policy == return_value_policy::automatic
|
||||
|| policy == return_value_policy::automatic_reference) {
|
||||
policy = return_value_policy::copy;
|
||||
}
|
||||
return cast_impl(&src, policy, parent);
|
||||
}
|
||||
|
||||
static handle cast(const MapType &src, return_value_policy policy, handle parent) {
|
||||
if (policy == return_value_policy::automatic
|
||||
|| policy == return_value_policy::automatic_reference) {
|
||||
policy = return_value_policy::copy;
|
||||
}
|
||||
return cast(&src, policy, parent);
|
||||
}
|
||||
|
||||
static handle cast(MapType *src, return_value_policy policy, handle parent) {
|
||||
if (policy == return_value_policy::automatic) {
|
||||
policy = return_value_policy::take_ownership;
|
||||
} else if (policy == return_value_policy::automatic_reference) {
|
||||
policy = return_value_policy::reference;
|
||||
}
|
||||
return cast_impl(src, policy, parent);
|
||||
}
|
||||
|
||||
static handle cast(const MapType *src, return_value_policy policy, handle parent) {
|
||||
if (policy == return_value_policy::automatic) {
|
||||
policy = return_value_policy::take_ownership;
|
||||
} else if (policy == return_value_policy::automatic_reference) {
|
||||
policy = return_value_policy::reference;
|
||||
}
|
||||
return cast_impl(src, policy, parent);
|
||||
}
|
||||
|
||||
template <typename C>
|
||||
static handle cast_impl(C *src, return_value_policy policy, handle parent) {
|
||||
object parent_object;
|
||||
constexpr bool writeable = !std::is_const<C>::value;
|
||||
switch (policy) {
|
||||
case return_value_policy::reference:
|
||||
parent_object = none();
|
||||
break;
|
||||
|
||||
case return_value_policy::reference_internal:
|
||||
// Default should do the right thing
|
||||
if (!parent) {
|
||||
pybind11_fail("Cannot use reference internal when there is no parent");
|
||||
}
|
||||
parent_object = reinterpret_borrow<object>(parent);
|
||||
break;
|
||||
|
||||
case return_value_policy::take_ownership:
|
||||
delete src;
|
||||
// fallthrough
|
||||
default:
|
||||
// move, take_ownership don't make any sense for a ref/map:
|
||||
pybind11_fail("Invalid return_value_policy for Eigen Map type, must be either "
|
||||
"reference or reference_internal");
|
||||
}
|
||||
|
||||
auto result = array_t<typename Type::Scalar, compute_array_flag_from_tensor<Type>()>(
|
||||
convert_dsizes_to_vector(Helper::get_shape(*src)),
|
||||
src->data(),
|
||||
std::move(parent_object));
|
||||
|
||||
if (!writeable) {
|
||||
array_proxy(result.ptr())->flags &= ~detail::npy_api::NPY_ARRAY_WRITEABLE_;
|
||||
}
|
||||
|
||||
return result.release();
|
||||
}
|
||||
|
||||
#if EIGEN_VERSION_AT_LEAST(3, 4, 0)
|
||||
|
||||
static constexpr bool needs_writeable = !std::is_const<typename std::remove_pointer<
|
||||
typename get_storage_pointer_type<MapType>::SPT>::type>::value;
|
||||
#else
|
||||
// Handle Eigen bug
|
||||
static constexpr bool needs_writeable = !std::is_const<Type>::value;
|
||||
#endif
|
||||
|
||||
protected:
|
||||
// TODO: Move to std::optional once std::optional has more support
|
||||
std::unique_ptr<MapType> value;
|
||||
|
||||
public:
|
||||
static constexpr auto name = get_tensor_descriptor<Type, true, needs_writeable>::value;
|
||||
explicit operator MapType *() { return value.get(); }
|
||||
explicit operator MapType &() { return *value; }
|
||||
explicit operator MapType &&() && { return std::move(*value); }
|
||||
|
||||
template <typename T_>
|
||||
using cast_op_type = ::pybind11::detail::movable_cast_op_type<T_>;
|
||||
};
|
||||
|
||||
PYBIND11_NAMESPACE_END(detail)
|
||||
PYBIND11_NAMESPACE_END(PYBIND11_NAMESPACE)
|
||||
|
|
@ -128,7 +128,9 @@ set(PYBIND11_TEST_FILES
|
|||
test_custom_type_casters
|
||||
test_custom_type_setup
|
||||
test_docstring_options
|
||||
test_eigen
|
||||
test_eigen_matrix
|
||||
test_eigen_tensor
|
||||
test_eigen_tensor_avoid_stl_array.cpp
|
||||
test_enum
|
||||
test_eval
|
||||
test_exceptions
|
||||
|
|
@ -233,7 +235,10 @@ list(GET PYBIND11_EIGEN_VERSION_AND_HASH 1 PYBIND11_EIGEN_VERSION_HASH)
|
|||
# Check if Eigen is available; if not, remove from PYBIND11_TEST_FILES (but
|
||||
# keep it in PYBIND11_PYTEST_FILES, so that we get the "eigen is not installed"
|
||||
# skip message).
|
||||
list(FIND PYBIND11_TEST_FILES test_eigen.cpp PYBIND11_TEST_FILES_EIGEN_I)
|
||||
list(FIND PYBIND11_TEST_FILES test_eigen_matrix.cpp PYBIND11_TEST_FILES_EIGEN_I)
|
||||
if(PYBIND11_TEST_FILES_EIGEN_I EQUAL -1)
|
||||
list(FIND PYBIND11_TEST_FILES test_eigen_tensor.cpp PYBIND11_TEST_FILES_EIGEN_I)
|
||||
endif()
|
||||
if(PYBIND11_TEST_FILES_EIGEN_I GREATER -1)
|
||||
# Try loading via newer Eigen's Eigen3Config first (bypassing tools/FindEigen3.cmake).
|
||||
# Eigen 3.3.1+ exports a cmake 3.0+ target for handling dependency requirements, but also
|
||||
|
|
@ -289,12 +294,37 @@ if(PYBIND11_TEST_FILES_EIGEN_I GREATER -1)
|
|||
endif()
|
||||
message(STATUS "Building tests with Eigen v${EIGEN3_VERSION}")
|
||||
else()
|
||||
list(FIND PYBIND11_TEST_FILES test_eigen_matrix.cpp PYBIND11_TEST_FILES_EIGEN_I)
|
||||
if(PYBIND11_TEST_FILES_EIGEN_I GREATER -1)
|
||||
list(REMOVE_AT PYBIND11_TEST_FILES ${PYBIND11_TEST_FILES_EIGEN_I})
|
||||
endif()
|
||||
|
||||
list(FIND PYBIND11_TEST_FILES test_eigen_tensor.cpp PYBIND11_TEST_FILES_EIGEN_I)
|
||||
if(PYBIND11_TEST_FILES_EIGEN_I GREATER -1)
|
||||
list(REMOVE_AT PYBIND11_TEST_FILES ${PYBIND11_TEST_FILES_EIGEN_I})
|
||||
endif()
|
||||
list(FIND PYBIND11_TEST_FILES test_eigen_tensor_avoid_stl_array.cpp
|
||||
PYBIND11_TEST_FILES_EIGEN_I)
|
||||
if(PYBIND11_TEST_FILES_EIGEN_I GREATER -1)
|
||||
list(REMOVE_AT PYBIND11_TEST_FILES ${PYBIND11_TEST_FILES_EIGEN_I})
|
||||
endif()
|
||||
message(
|
||||
STATUS "Building tests WITHOUT Eigen, use -DDOWNLOAD_EIGEN=ON on CMake 3.11+ to download")
|
||||
endif()
|
||||
endif()
|
||||
|
||||
# Some code doesn't support gcc 4
|
||||
if(CMAKE_CXX_COMPILER_ID STREQUAL "GNU" AND CMAKE_CXX_COMPILER_VERSION VERSION_LESS 5.0)
|
||||
list(FIND PYBIND11_TEST_FILES test_eigen_tensor.cpp PYBIND11_TEST_FILES_EIGEN_I)
|
||||
if(PYBIND11_TEST_FILES_EIGEN_I GREATER -1)
|
||||
list(REMOVE_AT PYBIND11_TEST_FILES ${PYBIND11_TEST_FILES_EIGEN_I})
|
||||
endif()
|
||||
list(FIND PYBIND11_TEST_FILES test_eigen_tensor_avoid_stl_array.cpp PYBIND11_TEST_FILES_EIGEN_I)
|
||||
if(PYBIND11_TEST_FILES_EIGEN_I GREATER -1)
|
||||
list(REMOVE_AT PYBIND11_TEST_FILES ${PYBIND11_TEST_FILES_EIGEN_I})
|
||||
endif()
|
||||
endif()
|
||||
|
||||
# Optional dependency for some tests (boost::variant is only supported with version >= 1.56)
|
||||
find_package(Boost 1.56)
|
||||
|
||||
|
|
|
|||
|
|
@ -55,6 +55,11 @@ detail_headers = {
|
|||
"include/pybind11/detail/typeid.h",
|
||||
}
|
||||
|
||||
eigen_headers = {
|
||||
"include/pybind11/eigen/matrix.h",
|
||||
"include/pybind11/eigen/tensor.h",
|
||||
}
|
||||
|
||||
stl_headers = {
|
||||
"include/pybind11/stl/filesystem.h",
|
||||
}
|
||||
|
|
@ -82,7 +87,7 @@ py_files = {
|
|||
"setup_helpers.py",
|
||||
}
|
||||
|
||||
headers = main_headers | detail_headers | stl_headers
|
||||
headers = main_headers | detail_headers | eigen_headers | stl_headers
|
||||
src_files = headers | cmake_files | pkgconfig_files
|
||||
all_files = src_files | py_files
|
||||
|
||||
|
|
@ -92,6 +97,7 @@ sdist_files = {
|
|||
"pybind11/include",
|
||||
"pybind11/include/pybind11",
|
||||
"pybind11/include/pybind11/detail",
|
||||
"pybind11/include/pybind11/eigen",
|
||||
"pybind11/include/pybind11/stl",
|
||||
"pybind11/share",
|
||||
"pybind11/share/cmake",
|
||||
|
|
|
|||
|
|
@ -7,7 +7,7 @@
|
|||
BSD-style license that can be found in the LICENSE file.
|
||||
*/
|
||||
|
||||
#include <pybind11/eigen.h>
|
||||
#include <pybind11/eigen/matrix.h>
|
||||
#include <pybind11/stl.h>
|
||||
|
||||
#include "constructor_stats.h"
|
||||
|
|
@ -81,7 +81,7 @@ struct CustomOperatorNew {
|
|||
EIGEN_MAKE_ALIGNED_OPERATOR_NEW;
|
||||
};
|
||||
|
||||
TEST_SUBMODULE(eigen, m) {
|
||||
TEST_SUBMODULE(eigen_matrix, m) {
|
||||
using FixedMatrixR = Eigen::Matrix<float, 5, 6, Eigen::RowMajor>;
|
||||
using FixedMatrixC = Eigen::Matrix<float, 5, 6>;
|
||||
using DenseMatrixR = Eigen::Matrix<float, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>;
|
||||
|
|
@ -3,7 +3,7 @@ import pytest
|
|||
from pybind11_tests import ConstructorStats
|
||||
|
||||
np = pytest.importorskip("numpy")
|
||||
m = pytest.importorskip("pybind11_tests.eigen")
|
||||
m = pytest.importorskip("pybind11_tests.eigen_matrix")
|
||||
|
||||
|
||||
ref = np.array(
|
||||
|
|
@ -0,0 +1,16 @@
|
|||
/*
|
||||
tests/eigen_tensor.cpp -- automatic conversion of Eigen Tensor
|
||||
|
||||
All rights reserved. Use of this source code is governed by a
|
||||
BSD-style license that can be found in the LICENSE file.
|
||||
*/
|
||||
|
||||
constexpr const char *test_eigen_tensor_module_name = "eigen_tensor";
|
||||
|
||||
#define PYBIND11_TEST_EIGEN_TENSOR_NAMESPACE eigen_tensor
|
||||
|
||||
#ifdef EIGEN_AVOID_STL_ARRAY
|
||||
# undef EIGEN_AVOID_STL_ARRAY
|
||||
#endif
|
||||
|
||||
#include "test_eigen_tensor.inl"
|
||||
|
|
@ -0,0 +1,333 @@
|
|||
/*
|
||||
tests/eigen_tensor.cpp -- automatic conversion of Eigen Tensor
|
||||
|
||||
All rights reserved. Use of this source code is governed by a
|
||||
BSD-style license that can be found in the LICENSE file.
|
||||
*/
|
||||
|
||||
#include <pybind11/eigen/tensor.h>
|
||||
|
||||
#include "pybind11_tests.h"
|
||||
|
||||
namespace PYBIND11_TEST_EIGEN_TENSOR_NAMESPACE {
|
||||
|
||||
template <typename M>
|
||||
void reset_tensor(M &x) {
|
||||
for (int i = 0; i < x.dimension(0); i++) {
|
||||
for (int j = 0; j < x.dimension(1); j++) {
|
||||
for (int k = 0; k < x.dimension(2); k++) {
|
||||
x(i, j, k) = i * (5 * 2) + j * 2 + k;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
template <typename M>
|
||||
bool check_tensor(M &x) {
|
||||
for (int i = 0; i < x.dimension(0); i++) {
|
||||
for (int j = 0; j < x.dimension(1); j++) {
|
||||
for (int k = 0; k < x.dimension(2); k++) {
|
||||
if (x(i, j, k) != (i * (5 * 2) + j * 2 + k)) {
|
||||
return false;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
return true;
|
||||
}
|
||||
|
||||
template <int Options>
|
||||
Eigen::Tensor<double, 3, Options> &get_tensor() {
|
||||
static Eigen::Tensor<double, 3, Options> *x;
|
||||
|
||||
if (!x) {
|
||||
x = new Eigen::Tensor<double, 3, Options>(3, 5, 2);
|
||||
reset_tensor(*x);
|
||||
}
|
||||
|
||||
return *x;
|
||||
}
|
||||
|
||||
template <int Options>
|
||||
Eigen::TensorMap<Eigen::Tensor<double, 3, Options>> &get_tensor_map() {
|
||||
static Eigen::TensorMap<Eigen::Tensor<double, 3, Options>> *x;
|
||||
|
||||
if (!x) {
|
||||
x = new Eigen::TensorMap<Eigen::Tensor<double, 3, Options>>(get_tensor<Options>());
|
||||
}
|
||||
|
||||
return *x;
|
||||
}
|
||||
|
||||
template <int Options>
|
||||
Eigen::TensorFixedSize<double, Eigen::Sizes<3, 5, 2>, Options> &get_fixed_tensor() {
|
||||
static Eigen::TensorFixedSize<double, Eigen::Sizes<3, 5, 2>, Options> *x;
|
||||
|
||||
if (!x) {
|
||||
Eigen::aligned_allocator<Eigen::TensorFixedSize<double, Eigen::Sizes<3, 5, 2>, Options>>
|
||||
allocator;
|
||||
x = new (allocator.allocate(1))
|
||||
Eigen::TensorFixedSize<double, Eigen::Sizes<3, 5, 2>, Options>();
|
||||
reset_tensor(*x);
|
||||
}
|
||||
|
||||
return *x;
|
||||
}
|
||||
|
||||
template <int Options>
|
||||
const Eigen::Tensor<double, 3, Options> &get_const_tensor() {
|
||||
return get_tensor<Options>();
|
||||
}
|
||||
|
||||
template <int Options>
|
||||
struct CustomExample {
|
||||
CustomExample() : member(get_tensor<Options>()), view_member(member) {}
|
||||
|
||||
Eigen::Tensor<double, 3, Options> member;
|
||||
Eigen::TensorMap<Eigen::Tensor<double, 3, Options>> view_member;
|
||||
};
|
||||
|
||||
template <int Options>
|
||||
void init_tensor_module(pybind11::module &m) {
|
||||
const char *needed_options = "";
|
||||
if (PYBIND11_SILENCE_MSVC_C4127(Options == Eigen::ColMajor)) {
|
||||
needed_options = "F";
|
||||
} else {
|
||||
needed_options = "C";
|
||||
}
|
||||
m.attr("needed_options") = needed_options;
|
||||
|
||||
m.def("setup", []() {
|
||||
reset_tensor(get_tensor<Options>());
|
||||
reset_tensor(get_fixed_tensor<Options>());
|
||||
});
|
||||
|
||||
m.def("is_ok", []() {
|
||||
return check_tensor(get_tensor<Options>()) && check_tensor(get_fixed_tensor<Options>());
|
||||
});
|
||||
|
||||
py::class_<CustomExample<Options>>(m, "CustomExample")
|
||||
.def(py::init<>())
|
||||
.def_readonly(
|
||||
"member", &CustomExample<Options>::member, py::return_value_policy::reference_internal)
|
||||
.def_readonly("member_view",
|
||||
&CustomExample<Options>::view_member,
|
||||
py::return_value_policy::reference_internal);
|
||||
|
||||
m.def(
|
||||
"copy_fixed_tensor",
|
||||
[]() { return &get_fixed_tensor<Options>(); },
|
||||
py::return_value_policy::copy);
|
||||
|
||||
m.def(
|
||||
"copy_tensor", []() { return &get_tensor<Options>(); }, py::return_value_policy::copy);
|
||||
|
||||
m.def(
|
||||
"copy_const_tensor",
|
||||
[]() { return &get_const_tensor<Options>(); },
|
||||
py::return_value_policy::copy);
|
||||
|
||||
m.def(
|
||||
"move_fixed_tensor_copy",
|
||||
[]() -> Eigen::TensorFixedSize<double, Eigen::Sizes<3, 5, 2>, Options> {
|
||||
return get_fixed_tensor<Options>();
|
||||
},
|
||||
py::return_value_policy::move);
|
||||
|
||||
m.def(
|
||||
"move_tensor_copy",
|
||||
[]() -> Eigen::Tensor<double, 3, Options> { return get_tensor<Options>(); },
|
||||
py::return_value_policy::move);
|
||||
|
||||
m.def(
|
||||
"move_const_tensor",
|
||||
[]() -> const Eigen::Tensor<double, 3, Options> & { return get_const_tensor<Options>(); },
|
||||
py::return_value_policy::move);
|
||||
|
||||
m.def(
|
||||
"take_fixed_tensor",
|
||||
|
||||
[]() {
|
||||
Eigen::aligned_allocator<
|
||||
Eigen::TensorFixedSize<double, Eigen::Sizes<3, 5, 2>, Options>>
|
||||
allocator;
|
||||
return new (allocator.allocate(1))
|
||||
Eigen::TensorFixedSize<double, Eigen::Sizes<3, 5, 2>, Options>(
|
||||
get_fixed_tensor<Options>());
|
||||
},
|
||||
py::return_value_policy::take_ownership);
|
||||
|
||||
m.def(
|
||||
"take_tensor",
|
||||
[]() { return new Eigen::Tensor<double, 3, Options>(get_tensor<Options>()); },
|
||||
py::return_value_policy::take_ownership);
|
||||
|
||||
m.def(
|
||||
"take_const_tensor",
|
||||
[]() -> const Eigen::Tensor<double, 3, Options> * {
|
||||
return new Eigen::Tensor<double, 3, Options>(get_tensor<Options>());
|
||||
},
|
||||
py::return_value_policy::take_ownership);
|
||||
|
||||
m.def(
|
||||
"take_view_tensor",
|
||||
[]() -> const Eigen::TensorMap<Eigen::Tensor<double, 3, Options>> * {
|
||||
return new Eigen::TensorMap<Eigen::Tensor<double, 3, Options>>(get_tensor<Options>());
|
||||
},
|
||||
py::return_value_policy::take_ownership);
|
||||
|
||||
m.def(
|
||||
"reference_tensor",
|
||||
[]() { return &get_tensor<Options>(); },
|
||||
py::return_value_policy::reference);
|
||||
|
||||
m.def(
|
||||
"reference_tensor_v2",
|
||||
[]() -> Eigen::Tensor<double, 3, Options> & { return get_tensor<Options>(); },
|
||||
py::return_value_policy::reference);
|
||||
|
||||
m.def(
|
||||
"reference_tensor_internal",
|
||||
[]() { return &get_tensor<Options>(); },
|
||||
py::return_value_policy::reference_internal);
|
||||
|
||||
m.def(
|
||||
"reference_fixed_tensor",
|
||||
[]() { return &get_tensor<Options>(); },
|
||||
py::return_value_policy::reference);
|
||||
|
||||
m.def(
|
||||
"reference_const_tensor",
|
||||
[]() { return &get_const_tensor<Options>(); },
|
||||
py::return_value_policy::reference);
|
||||
|
||||
m.def(
|
||||
"reference_const_tensor_v2",
|
||||
[]() -> const Eigen::Tensor<double, 3, Options> & { return get_const_tensor<Options>(); },
|
||||
py::return_value_policy::reference);
|
||||
|
||||
m.def(
|
||||
"reference_view_of_tensor",
|
||||
[]() -> Eigen::TensorMap<Eigen::Tensor<double, 3, Options>> {
|
||||
return get_tensor_map<Options>();
|
||||
},
|
||||
py::return_value_policy::reference);
|
||||
|
||||
m.def(
|
||||
"reference_view_of_tensor_v2",
|
||||
// NOLINTNEXTLINE(readability-const-return-type)
|
||||
[]() -> const Eigen::TensorMap<Eigen::Tensor<double, 3, Options>> {
|
||||
return get_tensor_map<Options>(); // NOLINT(readability-const-return-type)
|
||||
}, // NOLINT(readability-const-return-type)
|
||||
py::return_value_policy::reference);
|
||||
|
||||
m.def(
|
||||
"reference_view_of_tensor_v3",
|
||||
[]() -> Eigen::TensorMap<Eigen::Tensor<double, 3, Options>> * {
|
||||
return &get_tensor_map<Options>();
|
||||
},
|
||||
py::return_value_policy::reference);
|
||||
|
||||
m.def(
|
||||
"reference_view_of_tensor_v4",
|
||||
[]() -> const Eigen::TensorMap<Eigen::Tensor<double, 3, Options>> * {
|
||||
return &get_tensor_map<Options>();
|
||||
},
|
||||
py::return_value_policy::reference);
|
||||
|
||||
m.def(
|
||||
"reference_view_of_tensor_v5",
|
||||
[]() -> Eigen::TensorMap<Eigen::Tensor<double, 3, Options>> & {
|
||||
return get_tensor_map<Options>();
|
||||
},
|
||||
py::return_value_policy::reference);
|
||||
|
||||
m.def(
|
||||
"reference_view_of_tensor_v6",
|
||||
[]() -> const Eigen::TensorMap<Eigen::Tensor<double, 3, Options>> & {
|
||||
return get_tensor_map<Options>();
|
||||
},
|
||||
py::return_value_policy::reference);
|
||||
|
||||
m.def(
|
||||
"reference_view_of_fixed_tensor",
|
||||
[]() {
|
||||
return Eigen::TensorMap<
|
||||
Eigen::TensorFixedSize<double, Eigen::Sizes<3, 5, 2>, Options>>(
|
||||
get_fixed_tensor<Options>());
|
||||
},
|
||||
py::return_value_policy::reference);
|
||||
|
||||
m.def("round_trip_tensor",
|
||||
[](const Eigen::Tensor<double, 3, Options> &tensor) { return tensor; });
|
||||
|
||||
m.def(
|
||||
"round_trip_tensor_noconvert",
|
||||
[](const Eigen::Tensor<double, 3, Options> &tensor) { return tensor; },
|
||||
py::arg("tensor").noconvert());
|
||||
|
||||
m.def("round_trip_tensor2",
|
||||
[](const Eigen::Tensor<int32_t, 3, Options> &tensor) { return tensor; });
|
||||
|
||||
m.def("round_trip_fixed_tensor",
|
||||
[](const Eigen::TensorFixedSize<double, Eigen::Sizes<3, 5, 2>, Options> &tensor) {
|
||||
return tensor;
|
||||
});
|
||||
|
||||
m.def(
|
||||
"round_trip_view_tensor",
|
||||
[](Eigen::TensorMap<Eigen::Tensor<double, 3, Options>> view) { return view; },
|
||||
py::return_value_policy::reference);
|
||||
|
||||
m.def(
|
||||
"round_trip_view_tensor_ref",
|
||||
[](Eigen::TensorMap<Eigen::Tensor<double, 3, Options>> &view) { return view; },
|
||||
py::return_value_policy::reference);
|
||||
|
||||
m.def(
|
||||
"round_trip_view_tensor_ptr",
|
||||
[](Eigen::TensorMap<Eigen::Tensor<double, 3, Options>> *view) { return view; },
|
||||
py::return_value_policy::reference);
|
||||
|
||||
m.def(
|
||||
"round_trip_aligned_view_tensor",
|
||||
[](Eigen::TensorMap<Eigen::Tensor<double, 3, Options>, Eigen::Aligned> view) {
|
||||
return view;
|
||||
},
|
||||
py::return_value_policy::reference);
|
||||
|
||||
m.def(
|
||||
"round_trip_const_view_tensor",
|
||||
[](Eigen::TensorMap<const Eigen::Tensor<double, 3, Options>> view) {
|
||||
return Eigen::Tensor<double, 3, Options>(view);
|
||||
},
|
||||
py::return_value_policy::move);
|
||||
|
||||
m.def(
|
||||
"round_trip_rank_0",
|
||||
[](const Eigen::Tensor<double, 0, Options> &tensor) { return tensor; },
|
||||
py::return_value_policy::move);
|
||||
|
||||
m.def(
|
||||
"round_trip_rank_0_noconvert",
|
||||
[](const Eigen::Tensor<double, 0, Options> &tensor) { return tensor; },
|
||||
py::arg("tensor").noconvert(),
|
||||
py::return_value_policy::move);
|
||||
|
||||
m.def(
|
||||
"round_trip_rank_0_view",
|
||||
[](Eigen::TensorMap<Eigen::Tensor<double, 0, Options>> &tensor) { return tensor; },
|
||||
py::return_value_policy::reference);
|
||||
}
|
||||
|
||||
void test_module(py::module_ &);
|
||||
test_initializer name(test_eigen_tensor_module_name, test_module);
|
||||
void test_module(py::module_ &m) {
|
||||
auto f_style = m.def_submodule("f_style");
|
||||
auto c_style = m.def_submodule("c_style");
|
||||
|
||||
init_tensor_module<Eigen::ColMajor>(f_style);
|
||||
init_tensor_module<Eigen::RowMajor>(c_style);
|
||||
}
|
||||
|
||||
} // namespace PYBIND11_TEST_EIGEN_TENSOR_NAMESPACE
|
||||
|
|
@ -0,0 +1,288 @@
|
|||
import sys
|
||||
|
||||
import pytest
|
||||
|
||||
np = pytest.importorskip("numpy")
|
||||
eigen_tensor = pytest.importorskip("pybind11_tests.eigen_tensor")
|
||||
submodules = [eigen_tensor.c_style, eigen_tensor.f_style]
|
||||
try:
|
||||
from pybind11_tests import eigen_tensor_avoid_stl_array as avoid
|
||||
|
||||
submodules += [avoid.c_style, avoid.f_style]
|
||||
except ImportError:
|
||||
pass
|
||||
|
||||
tensor_ref = np.empty((3, 5, 2), dtype=np.int64)
|
||||
|
||||
for i in range(tensor_ref.shape[0]):
|
||||
for j in range(tensor_ref.shape[1]):
|
||||
for k in range(tensor_ref.shape[2]):
|
||||
tensor_ref[i, j, k] = i * (5 * 2) + j * 2 + k
|
||||
|
||||
indices = (2, 3, 1)
|
||||
|
||||
|
||||
@pytest.fixture(autouse=True)
|
||||
def cleanup():
|
||||
for module in submodules:
|
||||
module.setup()
|
||||
|
||||
yield
|
||||
|
||||
for module in submodules:
|
||||
assert module.is_ok()
|
||||
|
||||
|
||||
def test_import_avoid_stl_array():
|
||||
pytest.importorskip("pybind11_tests.eigen_tensor_avoid_stl_array")
|
||||
assert len(submodules) == 4
|
||||
|
||||
|
||||
def assert_equal_tensor_ref(mat, writeable=True, modified=None):
|
||||
assert mat.flags.writeable == writeable
|
||||
|
||||
copy = np.array(tensor_ref)
|
||||
if modified is not None:
|
||||
copy[indices] = modified
|
||||
|
||||
np.testing.assert_array_equal(mat, copy)
|
||||
|
||||
|
||||
@pytest.mark.parametrize("m", submodules)
|
||||
@pytest.mark.parametrize("member_name", ["member", "member_view"])
|
||||
def test_reference_internal(m, member_name):
|
||||
|
||||
if not hasattr(sys, "getrefcount"):
|
||||
pytest.skip("No reference counting")
|
||||
foo = m.CustomExample()
|
||||
counts = sys.getrefcount(foo)
|
||||
mem = getattr(foo, member_name)
|
||||
assert_equal_tensor_ref(mem, writeable=False)
|
||||
new_counts = sys.getrefcount(foo)
|
||||
assert new_counts == counts + 1
|
||||
assert_equal_tensor_ref(mem, writeable=False)
|
||||
del mem
|
||||
assert sys.getrefcount(foo) == counts
|
||||
|
||||
|
||||
assert_equal_funcs = [
|
||||
"copy_tensor",
|
||||
"copy_fixed_tensor",
|
||||
"copy_const_tensor",
|
||||
"move_tensor_copy",
|
||||
"move_fixed_tensor_copy",
|
||||
"take_tensor",
|
||||
"take_fixed_tensor",
|
||||
"reference_tensor",
|
||||
"reference_tensor_v2",
|
||||
"reference_fixed_tensor",
|
||||
"reference_view_of_tensor",
|
||||
"reference_view_of_tensor_v3",
|
||||
"reference_view_of_tensor_v5",
|
||||
"reference_view_of_fixed_tensor",
|
||||
]
|
||||
|
||||
assert_equal_const_funcs = [
|
||||
"reference_view_of_tensor_v2",
|
||||
"reference_view_of_tensor_v4",
|
||||
"reference_view_of_tensor_v6",
|
||||
"reference_const_tensor",
|
||||
"reference_const_tensor_v2",
|
||||
]
|
||||
|
||||
|
||||
@pytest.mark.parametrize("m", submodules)
|
||||
@pytest.mark.parametrize("func_name", assert_equal_funcs + assert_equal_const_funcs)
|
||||
def test_convert_tensor_to_py(m, func_name):
|
||||
writeable = func_name in assert_equal_funcs
|
||||
assert_equal_tensor_ref(getattr(m, func_name)(), writeable=writeable)
|
||||
|
||||
|
||||
@pytest.mark.parametrize("m", submodules)
|
||||
def test_bad_cpp_to_python_casts(m):
|
||||
|
||||
with pytest.raises(
|
||||
RuntimeError, match="Cannot use reference internal when there is no parent"
|
||||
):
|
||||
m.reference_tensor_internal()
|
||||
|
||||
with pytest.raises(RuntimeError, match="Cannot move from a constant reference"):
|
||||
m.move_const_tensor()
|
||||
|
||||
with pytest.raises(
|
||||
RuntimeError, match="Cannot take ownership of a const reference"
|
||||
):
|
||||
m.take_const_tensor()
|
||||
|
||||
with pytest.raises(
|
||||
RuntimeError,
|
||||
match="Invalid return_value_policy for Eigen Map type, must be either reference or reference_internal",
|
||||
):
|
||||
m.take_view_tensor()
|
||||
|
||||
|
||||
@pytest.mark.parametrize("m", submodules)
|
||||
def test_bad_python_to_cpp_casts(m):
|
||||
|
||||
with pytest.raises(
|
||||
TypeError, match=r"^round_trip_tensor\(\): incompatible function arguments"
|
||||
):
|
||||
m.round_trip_tensor(np.zeros((2, 3)))
|
||||
|
||||
with pytest.raises(TypeError, match=r"^Cannot cast array data from dtype"):
|
||||
m.round_trip_tensor(np.zeros(dtype=np.str_, shape=(2, 3, 1)))
|
||||
|
||||
with pytest.raises(
|
||||
TypeError,
|
||||
match=r"^round_trip_tensor_noconvert\(\): incompatible function arguments",
|
||||
):
|
||||
m.round_trip_tensor_noconvert(tensor_ref)
|
||||
|
||||
assert_equal_tensor_ref(
|
||||
m.round_trip_tensor_noconvert(tensor_ref.astype(np.float64))
|
||||
)
|
||||
|
||||
if m.needed_options == "F":
|
||||
bad_options = "C"
|
||||
else:
|
||||
bad_options = "F"
|
||||
# Shape, dtype and the order need to be correct for a TensorMap cast
|
||||
with pytest.raises(
|
||||
TypeError, match=r"^round_trip_view_tensor\(\): incompatible function arguments"
|
||||
):
|
||||
m.round_trip_view_tensor(
|
||||
np.zeros((3, 5, 2), dtype=np.float64, order=bad_options)
|
||||
)
|
||||
|
||||
with pytest.raises(
|
||||
TypeError, match=r"^round_trip_view_tensor\(\): incompatible function arguments"
|
||||
):
|
||||
m.round_trip_view_tensor(
|
||||
np.zeros((3, 5, 2), dtype=np.float32, order=m.needed_options)
|
||||
)
|
||||
|
||||
with pytest.raises(
|
||||
TypeError, match=r"^round_trip_view_tensor\(\): incompatible function arguments"
|
||||
):
|
||||
m.round_trip_view_tensor(
|
||||
np.zeros((3, 5), dtype=np.float64, order=m.needed_options)
|
||||
)
|
||||
|
||||
with pytest.raises(
|
||||
TypeError, match=r"^round_trip_view_tensor\(\): incompatible function arguments"
|
||||
):
|
||||
temp = np.zeros((3, 5, 2), dtype=np.float64, order=m.needed_options)
|
||||
m.round_trip_view_tensor(
|
||||
temp[:, ::-1, :],
|
||||
)
|
||||
|
||||
with pytest.raises(
|
||||
TypeError, match=r"^round_trip_view_tensor\(\): incompatible function arguments"
|
||||
):
|
||||
temp = np.zeros((3, 5, 2), dtype=np.float64, order=m.needed_options)
|
||||
temp.setflags(write=False)
|
||||
m.round_trip_view_tensor(temp)
|
||||
|
||||
|
||||
@pytest.mark.parametrize("m", submodules)
|
||||
def test_references_actually_refer(m):
|
||||
|
||||
a = m.reference_tensor()
|
||||
temp = a[indices]
|
||||
a[indices] = 100
|
||||
assert_equal_tensor_ref(m.copy_const_tensor(), modified=100)
|
||||
a[indices] = temp
|
||||
assert_equal_tensor_ref(m.copy_const_tensor())
|
||||
|
||||
a = m.reference_view_of_tensor()
|
||||
a[indices] = 100
|
||||
assert_equal_tensor_ref(m.copy_const_tensor(), modified=100)
|
||||
a[indices] = temp
|
||||
assert_equal_tensor_ref(m.copy_const_tensor())
|
||||
|
||||
|
||||
@pytest.mark.parametrize("m", submodules)
|
||||
def test_round_trip(m):
|
||||
|
||||
assert_equal_tensor_ref(m.round_trip_tensor(tensor_ref))
|
||||
|
||||
with pytest.raises(TypeError, match="^Cannot cast array data from"):
|
||||
assert_equal_tensor_ref(m.round_trip_tensor2(tensor_ref))
|
||||
|
||||
assert_equal_tensor_ref(m.round_trip_tensor2(np.array(tensor_ref, dtype=np.int32)))
|
||||
assert_equal_tensor_ref(m.round_trip_fixed_tensor(tensor_ref))
|
||||
assert_equal_tensor_ref(m.round_trip_aligned_view_tensor(m.reference_tensor()))
|
||||
|
||||
copy = np.array(tensor_ref, dtype=np.float64, order=m.needed_options)
|
||||
assert_equal_tensor_ref(m.round_trip_view_tensor(copy))
|
||||
assert_equal_tensor_ref(m.round_trip_view_tensor_ref(copy))
|
||||
assert_equal_tensor_ref(m.round_trip_view_tensor_ptr(copy))
|
||||
copy.setflags(write=False)
|
||||
assert_equal_tensor_ref(m.round_trip_const_view_tensor(copy))
|
||||
|
||||
np.testing.assert_array_equal(
|
||||
tensor_ref[:, ::-1, :], m.round_trip_tensor(tensor_ref[:, ::-1, :])
|
||||
)
|
||||
|
||||
assert m.round_trip_rank_0(np.float64(3.5)) == 3.5
|
||||
assert m.round_trip_rank_0(3.5) == 3.5
|
||||
|
||||
with pytest.raises(
|
||||
TypeError,
|
||||
match=r"^round_trip_rank_0_noconvert\(\): incompatible function arguments",
|
||||
):
|
||||
m.round_trip_rank_0_noconvert(np.float64(3.5))
|
||||
|
||||
with pytest.raises(
|
||||
TypeError,
|
||||
match=r"^round_trip_rank_0_noconvert\(\): incompatible function arguments",
|
||||
):
|
||||
m.round_trip_rank_0_noconvert(3.5)
|
||||
|
||||
with pytest.raises(
|
||||
TypeError, match=r"^round_trip_rank_0_view\(\): incompatible function arguments"
|
||||
):
|
||||
m.round_trip_rank_0_view(np.float64(3.5))
|
||||
|
||||
with pytest.raises(
|
||||
TypeError, match=r"^round_trip_rank_0_view\(\): incompatible function arguments"
|
||||
):
|
||||
m.round_trip_rank_0_view(3.5)
|
||||
|
||||
|
||||
@pytest.mark.parametrize("m", submodules)
|
||||
def test_round_trip_references_actually_refer(m):
|
||||
|
||||
# Need to create a copy that matches the type on the C side
|
||||
copy = np.array(tensor_ref, dtype=np.float64, order=m.needed_options)
|
||||
a = m.round_trip_view_tensor(copy)
|
||||
temp = a[indices]
|
||||
a[indices] = 100
|
||||
assert_equal_tensor_ref(copy, modified=100)
|
||||
a[indices] = temp
|
||||
assert_equal_tensor_ref(copy)
|
||||
|
||||
|
||||
@pytest.mark.parametrize("m", submodules)
|
||||
def test_doc_string(m, doc):
|
||||
assert (
|
||||
doc(m.copy_tensor) == "copy_tensor() -> numpy.ndarray[numpy.float64[?, ?, ?]]"
|
||||
)
|
||||
assert (
|
||||
doc(m.copy_fixed_tensor)
|
||||
== "copy_fixed_tensor() -> numpy.ndarray[numpy.float64[3, 5, 2]]"
|
||||
)
|
||||
assert (
|
||||
doc(m.reference_const_tensor)
|
||||
== "reference_const_tensor() -> numpy.ndarray[numpy.float64[?, ?, ?]]"
|
||||
)
|
||||
|
||||
order_flag = f"flags.{m.needed_options.lower()}_contiguous"
|
||||
assert doc(m.round_trip_view_tensor) == (
|
||||
f"round_trip_view_tensor(arg0: numpy.ndarray[numpy.float64[?, ?, ?], flags.writeable, {order_flag}])"
|
||||
+ f" -> numpy.ndarray[numpy.float64[?, ?, ?], flags.writeable, {order_flag}]"
|
||||
)
|
||||
assert doc(m.round_trip_const_view_tensor) == (
|
||||
f"round_trip_const_view_tensor(arg0: numpy.ndarray[numpy.float64[?, ?, ?], {order_flag}])"
|
||||
+ " -> numpy.ndarray[numpy.float64[?, ?, ?]]"
|
||||
)
|
||||
|
|
@ -0,0 +1,16 @@
|
|||
/*
|
||||
tests/eigen_tensor.cpp -- automatic conversion of Eigen Tensor
|
||||
|
||||
All rights reserved. Use of this source code is governed by a
|
||||
BSD-style license that can be found in the LICENSE file.
|
||||
*/
|
||||
|
||||
constexpr const char *test_eigen_tensor_module_name = "eigen_tensor_avoid_stl_array";
|
||||
|
||||
#ifndef EIGEN_AVOID_STL_ARRAY
|
||||
# define EIGEN_AVOID_STL_ARRAY
|
||||
#endif
|
||||
|
||||
#define PYBIND11_TEST_EIGEN_TENSOR_NAMESPACE eigen_tensor_avoid_stl_array
|
||||
|
||||
#include "test_eigen_tensor.inl"
|
||||
|
|
@ -521,4 +521,6 @@ TEST_SUBMODULE(numpy_array, sm) {
|
|||
sm.def("test_fmt_desc_double", [](const py::array_t<double> &) {});
|
||||
sm.def("test_fmt_desc_const_float", [](const py::array_t<const float> &) {});
|
||||
sm.def("test_fmt_desc_const_double", [](const py::array_t<const double> &) {});
|
||||
|
||||
sm.def("round_trip_float", [](double d) { return d; });
|
||||
}
|
||||
|
|
|
|||
|
|
@ -585,3 +585,9 @@ def test_dtype_refcount_leak():
|
|||
m.ndim(a)
|
||||
after = getrefcount(dtype)
|
||||
assert after == before
|
||||
|
||||
|
||||
def test_round_trip_float():
|
||||
arr = np.zeros((), np.float64)
|
||||
arr[()] = 37.2
|
||||
assert m.round_trip_float(arr) == 37.2
|
||||
|
|
|
|||
|
|
@ -27,10 +27,11 @@ class InstallHeadersNested(install_headers):
|
|||
|
||||
main_headers = glob.glob("pybind11/include/pybind11/*.h")
|
||||
detail_headers = glob.glob("pybind11/include/pybind11/detail/*.h")
|
||||
eigen_headers = glob.glob("pybind11/include/pybind11/eigen/*.h")
|
||||
stl_headers = glob.glob("pybind11/include/pybind11/stl/*.h")
|
||||
cmake_files = glob.glob("pybind11/share/cmake/pybind11/*.cmake")
|
||||
pkgconfig_files = glob.glob("pybind11/share/pkgconfig/*.pc")
|
||||
headers = main_headers + detail_headers + stl_headers
|
||||
headers = main_headers + detail_headers + stl_headers + eigen_headers
|
||||
|
||||
cmdclass = {"install_headers": InstallHeadersNested}
|
||||
$extra_cmd
|
||||
|
|
@ -55,6 +56,7 @@ setup(
|
|||
(base + "share/pkgconfig", pkgconfig_files),
|
||||
(base + "include/pybind11", main_headers),
|
||||
(base + "include/pybind11/detail", detail_headers),
|
||||
(base + "include/pybind11/eigen", eigen_headers),
|
||||
(base + "include/pybind11/stl", stl_headers),
|
||||
],
|
||||
cmdclass=cmdclass,
|
||||
|
|
|
|||
|
|
@ -15,6 +15,7 @@ setup(
|
|||
"pybind11",
|
||||
"pybind11.include.pybind11",
|
||||
"pybind11.include.pybind11.detail",
|
||||
"pybind11.include.pybind11.eigen",
|
||||
"pybind11.include.pybind11.stl",
|
||||
"pybind11.share.cmake.pybind11",
|
||||
"pybind11.share.pkgconfig",
|
||||
|
|
@ -23,6 +24,7 @@ setup(
|
|||
"pybind11": ["py.typed"],
|
||||
"pybind11.include.pybind11": ["*.h"],
|
||||
"pybind11.include.pybind11.detail": ["*.h"],
|
||||
"pybind11.include.pybind11.eigen": ["*.h"],
|
||||
"pybind11.include.pybind11.stl": ["*.h"],
|
||||
"pybind11.share.cmake.pybind11": ["*.cmake"],
|
||||
"pybind11.share.pkgconfig": ["*.pc"],
|
||||
|
|
|
|||
Loading…
Reference in New Issue