/* tests/eigen.cpp -- automatic conversion of Eigen types Copyright (c) 2016 Wenzel Jakob All rights reserved. Use of this source code is governed by a BSD-style license that can be found in the LICENSE file. */ #include #include #include "constructor_stats.h" #include "pybind11_tests.h" #if defined(_MSC_VER) # pragma warning(disable : 4996) // C4996: std::unary_negation is deprecated #endif #include using MatrixXdR = Eigen::Matrix; // Sets/resets a testing reference matrix to have values of 10*r + c, where r and c are the // (1-based) row/column number. template void reset_ref(M &x) { for (int i = 0; i < x.rows(); i++) { for (int j = 0; j < x.cols(); j++) { x(i, j) = 11 + 10 * i + j; } } } // Returns a static, column-major matrix Eigen::MatrixXd &get_cm() { static Eigen::MatrixXd *x; if (!x) { x = new Eigen::MatrixXd(3, 3); reset_ref(*x); } return *x; } // Likewise, but row-major MatrixXdR &get_rm() { static MatrixXdR *x; if (!x) { x = new MatrixXdR(3, 3); reset_ref(*x); } return *x; } // Resets the values of the static matrices returned by get_cm()/get_rm() void reset_refs() { reset_ref(get_cm()); reset_ref(get_rm()); } // Returns element 2,1 from a matrix (used to test copy/nocopy) double get_elem(const Eigen::Ref &m) { return m(2, 1); }; // Returns a matrix with 10*r + 100*c added to each matrix element (to help test that the matrix // reference is referencing rows/columns correctly). template Eigen::MatrixXd adjust_matrix(MatrixArgType m) { Eigen::MatrixXd ret(m); for (int c = 0; c < m.cols(); c++) { for (int r = 0; r < m.rows(); r++) { ret(r, c) += 10 * r + 100 * c; // NOLINT(clang-analyzer-core.uninitialized.Assign) } } return ret; } struct CustomOperatorNew { CustomOperatorNew() = default; Eigen::Matrix4d a = Eigen::Matrix4d::Zero(); Eigen::Matrix4d b = Eigen::Matrix4d::Identity(); EIGEN_MAKE_ALIGNED_OPERATOR_NEW; }; TEST_SUBMODULE(eigen_matrix, m) { using FixedMatrixR = Eigen::Matrix; using FixedMatrixC = Eigen::Matrix; using DenseMatrixR = Eigen::Matrix; using DenseMatrixC = Eigen::Matrix; using FourRowMatrixC = Eigen::Matrix; using FourColMatrixC = Eigen::Matrix; using FourRowMatrixR = Eigen::Matrix; using FourColMatrixR = Eigen::Matrix; using SparseMatrixR = Eigen::SparseMatrix; using SparseMatrixC = Eigen::SparseMatrix; // various tests m.def("double_col", [](const Eigen::VectorXf &x) -> Eigen::VectorXf { return 2.0f * x; }); m.def("double_row", [](const Eigen::RowVectorXf &x) -> Eigen::RowVectorXf { return 2.0f * x; }); m.def("double_complex", [](const Eigen::VectorXcf &x) -> Eigen::VectorXcf { return 2.0f * x; }); m.def("double_threec", [](py::EigenDRef x) { x *= 2; }); m.def("double_threer", [](py::EigenDRef x) { x *= 2; }); m.def("double_mat_cm", [](const Eigen::MatrixXf &x) -> Eigen::MatrixXf { return 2.0f * x; }); m.def("double_mat_rm", [](const DenseMatrixR &x) -> DenseMatrixR { return 2.0f * x; }); // test_eigen_ref_to_python // Different ways of passing via Eigen::Ref; the first and second are the Eigen-recommended m.def("cholesky1", [](const Eigen::Ref &x) -> Eigen::MatrixXd { return x.llt().matrixL(); }); m.def("cholesky2", [](const Eigen::Ref &x) -> Eigen::MatrixXd { return x.llt().matrixL(); }); m.def("cholesky3", [](const Eigen::Ref &x) -> Eigen::MatrixXd { return x.llt().matrixL(); }); m.def("cholesky4", [](const Eigen::Ref &x) -> Eigen::MatrixXd { return x.llt().matrixL(); }); // test_eigen_ref_mutators // Mutators: these add some value to the given element using Eigen, but Eigen should be mapping // into the numpy array data and so the result should show up there. There are three versions: // one that works on a contiguous-row matrix (numpy's default), one for a contiguous-column // matrix, and one for any matrix. auto add_rm = [](Eigen::Ref x, int r, int c, double v) { x(r, c) += v; }; auto add_cm = [](Eigen::Ref x, int r, int c, double v) { x(r, c) += v; }; // Mutators (Eigen maps into numpy variables): m.def("add_rm", add_rm); // Only takes row-contiguous m.def("add_cm", add_cm); // Only takes column-contiguous // Overloaded versions that will accept either row or column contiguous: m.def("add1", add_rm); m.def("add1", add_cm); m.def("add2", add_cm); m.def("add2", add_rm); // This one accepts a matrix of any stride: m.def("add_any", [](py::EigenDRef x, int r, int c, double v) { x(r, c) += v; }); // Return mutable references (numpy maps into eigen variables) m.def("get_cm_ref", []() { return Eigen::Ref(get_cm()); }); m.def("get_rm_ref", []() { return Eigen::Ref(get_rm()); }); // The same references, but non-mutable (numpy maps into eigen variables, but is !writeable) m.def("get_cm_const_ref", []() { return Eigen::Ref(get_cm()); }); m.def("get_rm_const_ref", []() { return Eigen::Ref(get_rm()); }); m.def("reset_refs", reset_refs); // Restores get_{cm,rm}_ref to original values // Increments and returns ref to (same) matrix m.def( "incr_matrix", [](Eigen::Ref m, double v) { m += Eigen::MatrixXd::Constant(m.rows(), m.cols(), v); return m; }, py::return_value_policy::reference); // Same, but accepts a matrix of any strides m.def( "incr_matrix_any", [](py::EigenDRef m, double v) { m += Eigen::MatrixXd::Constant(m.rows(), m.cols(), v); return m; }, py::return_value_policy::reference); // Returns an eigen slice of even rows m.def( "even_rows", [](py::EigenDRef m) { return py::EigenDMap( m.data(), (m.rows() + 1) / 2, m.cols(), py::EigenDStride(m.outerStride(), 2 * m.innerStride())); }, py::return_value_policy::reference); // Returns an eigen slice of even columns m.def( "even_cols", [](py::EigenDRef m) { return py::EigenDMap( m.data(), m.rows(), (m.cols() + 1) / 2, py::EigenDStride(2 * m.outerStride(), m.innerStride())); }, py::return_value_policy::reference); // Returns diagonals: a vector-like object with an inner stride != 1 m.def("diagonal", [](const Eigen::Ref &x) { return x.diagonal(); }); m.def("diagonal_1", [](const Eigen::Ref &x) { return x.diagonal<1>(); }); m.def("diagonal_n", [](const Eigen::Ref &x, int index) { return x.diagonal(index); }); // Return a block of a matrix (gives non-standard strides) m.def("block", [m](const py::object &x_obj, int start_row, int start_col, int block_rows, int block_cols) { return m.attr("_block")(x_obj, x_obj, start_row, start_col, block_rows, block_cols); }); m.def( "_block", [](const py::object &x_obj, const Eigen::Ref &x, int start_row, int start_col, int block_rows, int block_cols) { // See PR #4217 for background. This test is a bit over the top, but might be useful // as a concrete example to point to when explaining the dangling reference trap. auto i0 = py::make_tuple(0, 0); auto x0_orig = x_obj[*i0].cast(); if (x(0, 0) != x0_orig) { throw std::runtime_error( "Something in the type_caster for Eigen::Ref is terribly wrong."); } double x0_mod = x0_orig + 1; x_obj[*i0] = x0_mod; auto copy_detected = (x(0, 0) != x0_mod); x_obj[*i0] = x0_orig; if (copy_detected) { throw std::runtime_error("type_caster for Eigen::Ref made a copy."); } return x.block(start_row, start_col, block_rows, block_cols); }, py::keep_alive<0, 1>()); // test_eigen_return_references, test_eigen_keepalive // return value referencing/copying tests: class ReturnTester { Eigen::MatrixXd mat = create(); public: ReturnTester() { print_created(this); } ~ReturnTester() { print_destroyed(this); } static Eigen::MatrixXd create() { return Eigen::MatrixXd::Ones(10, 10); } // NOLINTNEXTLINE(readability-const-return-type) static const Eigen::MatrixXd createConst() { return Eigen::MatrixXd::Ones(10, 10); } Eigen::MatrixXd &get() { return mat; } Eigen::MatrixXd *getPtr() { return &mat; } const Eigen::MatrixXd &view() { return mat; } const Eigen::MatrixXd *viewPtr() { return &mat; } Eigen::Ref ref() { return mat; } Eigen::Ref refConst() { return mat; } Eigen::Block block(int r, int c, int nrow, int ncol) { return mat.block(r, c, nrow, ncol); } Eigen::Block blockConst(int r, int c, int nrow, int ncol) const { return mat.block(r, c, nrow, ncol); } py::EigenDMap corners() { return py::EigenDMap( mat.data(), py::EigenDStride(mat.outerStride() * (mat.outerSize() - 1), mat.innerStride() * (mat.innerSize() - 1))); } py::EigenDMap cornersConst() const { return py::EigenDMap( mat.data(), py::EigenDStride(mat.outerStride() * (mat.outerSize() - 1), mat.innerStride() * (mat.innerSize() - 1))); } }; using rvp = py::return_value_policy; py::class_(m, "ReturnTester") .def(py::init<>()) .def_static("create", &ReturnTester::create) .def_static("create_const", &ReturnTester::createConst) .def("get", &ReturnTester::get, rvp::reference_internal) .def("get_ptr", &ReturnTester::getPtr, rvp::reference_internal) .def("view", &ReturnTester::view, rvp::reference_internal) .def("view_ptr", &ReturnTester::view, rvp::reference_internal) .def("copy_get", &ReturnTester::get) // Default rvp: copy .def("copy_view", &ReturnTester::view) // " .def("ref", &ReturnTester::ref) // Default for Ref is to reference .def("ref_const", &ReturnTester::refConst) // Likewise, but const .def("ref_safe", &ReturnTester::ref, rvp::reference_internal) .def("ref_const_safe", &ReturnTester::refConst, rvp::reference_internal) .def("copy_ref", &ReturnTester::ref, rvp::copy) .def("copy_ref_const", &ReturnTester::refConst, rvp::copy) .def("block", &ReturnTester::block) .def("block_safe", &ReturnTester::block, rvp::reference_internal) .def("block_const", &ReturnTester::blockConst, rvp::reference_internal) .def("copy_block", &ReturnTester::block, rvp::copy) .def("corners", &ReturnTester::corners, rvp::reference_internal) .def("corners_const", &ReturnTester::cornersConst, rvp::reference_internal); // test_special_matrix_objects // Returns a DiagonalMatrix with diagonal (1,2,3,...) m.def("incr_diag", [](int k) { Eigen::DiagonalMatrix m(k); for (int i = 0; i < k; i++) { m.diagonal()[i] = i + 1; } return m; }); // Returns a SelfAdjointView referencing the lower triangle of m m.def("symmetric_lower", [](const Eigen::MatrixXi &m) { return m.selfadjointView(); }); // Returns a SelfAdjointView referencing the lower triangle of m m.def("symmetric_upper", [](const Eigen::MatrixXi &m) { return m.selfadjointView(); }); // Test matrix for various functions below. Eigen::MatrixXf mat(5, 6); mat << 0, 3, 0, 0, 0, 11, 22, 0, 0, 0, 17, 11, 7, 5, 0, 1, 0, 11, 0, 0, 0, 0, 0, 11, 0, 0, 14, 0, 8, 11; // test_fixed, and various other tests m.def("fixed_r", [mat]() -> FixedMatrixR { return FixedMatrixR(mat); }); // Our Eigen does a hack which respects constness through the numpy writeable flag. // Therefore, the const return actually affects this type despite being an rvalue. // NOLINTNEXTLINE(readability-const-return-type) m.def("fixed_r_const", [mat]() -> const FixedMatrixR { return FixedMatrixR(mat); }); m.def("fixed_c", [mat]() -> FixedMatrixC { return FixedMatrixC(mat); }); m.def("fixed_copy_r", [](const FixedMatrixR &m) -> FixedMatrixR { return m; }); m.def("fixed_copy_c", [](const FixedMatrixC &m) -> FixedMatrixC { return m; }); // test_mutator_descriptors m.def("fixed_mutator_r", [](const Eigen::Ref &) {}); m.def("fixed_mutator_c", [](const Eigen::Ref &) {}); m.def("fixed_mutator_a", [](const py::EigenDRef &) {}); // test_dense m.def("dense_r", [mat]() -> DenseMatrixR { return DenseMatrixR(mat); }); m.def("dense_c", [mat]() -> DenseMatrixC { return DenseMatrixC(mat); }); m.def("dense_copy_r", [](const DenseMatrixR &m) -> DenseMatrixR { return m; }); m.def("dense_copy_c", [](const DenseMatrixC &m) -> DenseMatrixC { return m; }); // test_sparse, test_sparse_signature m.def("sparse_r", [mat]() -> SparseMatrixR { // NOLINTNEXTLINE(clang-analyzer-core.uninitialized.UndefReturn) return Eigen::SparseView(mat); }); m.def("sparse_c", [mat]() -> SparseMatrixC { return Eigen::SparseView(mat); }); m.def("sparse_copy_r", [](const SparseMatrixR &m) -> SparseMatrixR { return m; }); m.def("sparse_copy_c", [](const SparseMatrixC &m) -> SparseMatrixC { return m; }); // test_partially_fixed m.def("partial_copy_four_rm_r", [](const FourRowMatrixR &m) -> FourRowMatrixR { return m; }); m.def("partial_copy_four_rm_c", [](const FourColMatrixR &m) -> FourColMatrixR { return m; }); m.def("partial_copy_four_cm_r", [](const FourRowMatrixC &m) -> FourRowMatrixC { return m; }); m.def("partial_copy_four_cm_c", [](const FourColMatrixC &m) -> FourColMatrixC { return m; }); // test_cpp_casting // Test that we can cast a numpy object to a Eigen::MatrixXd explicitly m.def("cpp_copy", [](py::handle m) { return m.cast()(1, 0); }); m.def("cpp_ref_c", [](py::handle m) { return m.cast>()(1, 0); }); m.def("cpp_ref_r", [](py::handle m) { return m.cast>()(1, 0); }); m.def("cpp_ref_any", [](py::handle m) { return m.cast>()(1, 0); }); // [workaround(intel)] ICC 20/21 breaks with py::arg().stuff, using py::arg{}.stuff works. // test_nocopy_wrapper // Test that we can prevent copying into an argument that would normally copy: First a version // that would allow copying (if types or strides don't match) for comparison: m.def("get_elem", &get_elem); // Now this alternative that calls the tells pybind to fail rather than copy: m.def( "get_elem_nocopy", [](const Eigen::Ref &m) -> double { return get_elem(m); }, py::arg{}.noconvert()); // Also test a row-major-only no-copy const ref: m.def( "get_elem_rm_nocopy", [](Eigen::Ref> &m) -> long { return m(2, 1); }, py::arg{}.noconvert()); // test_issue738, test_zero_length // Issue #738: 1×N or N×1 2D matrices were neither accepted nor properly copied with an // incompatible stride value on the length-1 dimension--but that should be allowed (without // requiring a copy!) because the stride value can be safely ignored on a size-1 dimension. // Similarly, 0×N or N×0 matrices were not accepted--again, these should be allowed since // they contain no data. This particularly affects numpy ≥ 1.23, which sets the strides to // 0 if any dimension size is 0. m.def("iss738_f1", &adjust_matrix &>, py::arg{}.noconvert()); m.def("iss738_f2", &adjust_matrix> &>, py::arg{}.noconvert()); // test_issue1105 // Issue #1105: when converting from a numpy two-dimensional (Nx1) or (1xN) value into a dense // eigen Vector or RowVector, the argument would fail to load because the numpy copy would // fail: numpy won't broadcast a Nx1 into a 1-dimensional vector. m.def("iss1105_col", [](const Eigen::VectorXd &) { return true; }); m.def("iss1105_row", [](const Eigen::RowVectorXd &) { return true; }); // test_named_arguments // Make sure named arguments are working properly: m.def( "matrix_multiply", [](const py::EigenDRef &A, const py::EigenDRef &B) -> Eigen::MatrixXd { if (A.cols() != B.rows()) { throw std::domain_error("Nonconformable matrices!"); } return A * B; }, py::arg("A"), py::arg("B")); // test_custom_operator_new py::class_(m, "CustomOperatorNew") .def(py::init<>()) .def_readonly("a", &CustomOperatorNew::a) .def_readonly("b", &CustomOperatorNew::b); // test_eigen_ref_life_support // In case of a failure (the caster's temp array does not live long enough), creating // a new array (np.ones(10)) increases the chances that the temp array will be garbage // collected and/or that its memory will be overridden with different values. m.def("get_elem_direct", [](const Eigen::Ref &v) { py::module_::import("numpy").attr("ones")(10); return v(5); }); m.def("get_elem_indirect", [](std::vector> v) { py::module_::import("numpy").attr("ones")(10); return v[0](5); }); }