pybind11/tests/test_eigen.cpp
Jason Rhinelander 391c75447d Update all remaining tests to new test styles
This udpates all the remaining tests to the new test suite code and
comment styles started in #898.  For the most part, the test coverage
here is unchanged, with a few minor exceptions as noted below.

- test_constants_and_functions: this adds more overload tests with
  overloads with different number of arguments for more comprehensive
  overload_cast testing.  The test style conversion broke the overload
  tests under MSVC 2015, prompting the additional tests while looking
  for a workaround.

- test_eigen: this dropped the unused functions `get_cm_corners` and
  `get_cm_corners_const`--these same tests were duplicates of the same
  things provided (and used) via ReturnTester methods.

- test_opaque_types: this test had a hidden dependence on ExampleMandA
  which is now fixed by using the global UserType which suffices for the
  relevant test.

- test_methods_and_attributes: this required some additions to UserType
  to make it usable as a replacement for the test's previous SimpleType:
  UserType gained a value mutator, and the `value` property is not
  mutable (it was previously readonly).  Some overload tests were also
  added to better test overload_cast (as described above).

- test_numpy_array: removed the untemplated mutate_data/mutate_data_t:
  the templated versions with an empty parameter pack expand to the same
  thing.

- test_stl: this was already mostly in the new style; this just tweaks
  things a bit, localizing a class, and adding some missing
  `// test_whatever` comments.

- test_virtual_functions: like `test_stl`, this was mostly in the new
  test style already, but needed some `// test_whatever` comments.
  This commit also moves the inherited virtual example code to the end
  of the file, after the main set of tests (since it is less important
  than the other tests, and rather length); it also got renamed to
  `test_inherited_virtuals` (from `test_inheriting_repeat`) because it
  tests both inherited virtual approaches, not just the repeat approach.
2017-08-05 18:46:22 -04:00

318 lines
16 KiB
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/*
tests/eigen.cpp -- automatic conversion of Eigen types
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.
*/
#include "pybind11_tests.h"
#include "constructor_stats.h"
#include <pybind11/eigen.h>
#include <pybind11/stl.h>
#include <Eigen/Cholesky>
using MatrixXdR = Eigen::Matrix<double, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>;
// 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 <typename M> 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(Eigen::Ref<const Eigen::MatrixXd> 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 <typename MatrixArgType> 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;
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, 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>;
using DenseMatrixC = Eigen::Matrix<float, Eigen::Dynamic, Eigen::Dynamic>;
using FourRowMatrixC = Eigen::Matrix<float, 4, Eigen::Dynamic>;
using FourColMatrixC = Eigen::Matrix<float, Eigen::Dynamic, 4>;
using FourRowMatrixR = Eigen::Matrix<float, 4, Eigen::Dynamic>;
using FourColMatrixR = Eigen::Matrix<float, Eigen::Dynamic, 4>;
using SparseMatrixR = Eigen::SparseMatrix<float, Eigen::RowMajor>;
using SparseMatrixC = Eigen::SparseMatrix<float>;
m.attr("have_eigen") = true;
// 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<Eigen::Vector3f> x) { x *= 2; });
m.def("double_threer", [](py::EigenDRef<Eigen::RowVector3f> x) { x *= 2; });
m.def("double_mat_cm", [](Eigen::MatrixXf x) -> Eigen::MatrixXf { return 2.0f * x; });
m.def("double_mat_rm", [](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", [](Eigen::Ref<MatrixXdR> x) -> Eigen::MatrixXd { return x.llt().matrixL(); });
m.def("cholesky2", [](const Eigen::Ref<const MatrixXdR> &x) -> Eigen::MatrixXd { return x.llt().matrixL(); });
m.def("cholesky3", [](const Eigen::Ref<MatrixXdR> &x) -> Eigen::MatrixXd { return x.llt().matrixL(); });
m.def("cholesky4", [](Eigen::Ref<const MatrixXdR> 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<MatrixXdR> x, int r, int c, double v) { x(r,c) += v; };
auto add_cm = [](Eigen::Ref<Eigen::MatrixXd> 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<Eigen::MatrixXd> x, int r, int c, double v) { x(r,c) += v; });
// Return mutable references (numpy maps into eigen varibles)
m.def("get_cm_ref", []() { return Eigen::Ref<Eigen::MatrixXd>(get_cm()); });
m.def("get_rm_ref", []() { return Eigen::Ref<MatrixXdR>(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<const Eigen::MatrixXd>(get_cm()); });
m.def("get_rm_const_ref", []() { return Eigen::Ref<const MatrixXdR>(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<Eigen::MatrixXd> 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<Eigen::MatrixXd> 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<Eigen::MatrixXd> m) {
return py::EigenDMap<Eigen::MatrixXd>(
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<Eigen::MatrixXd> m) {
return py::EigenDMap<Eigen::MatrixXd>(
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<const Eigen::MatrixXd> &x) { return x.diagonal(); });
m.def("diagonal_1", [](const Eigen::Ref<const Eigen::MatrixXd> &x) { return x.diagonal<1>(); });
m.def("diagonal_n", [](const Eigen::Ref<const Eigen::MatrixXd> &x, int index) { return x.diagonal(index); });
// Return a block of a matrix (gives non-standard strides)
m.def("block", [](const Eigen::Ref<const Eigen::MatrixXd> &x, int start_row, int start_col, int block_rows, int block_cols) {
return x.block(start_row, start_col, block_rows, block_cols);
});
// 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); }
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<Eigen::MatrixXd> ref() { return mat; }
Eigen::Ref<const Eigen::MatrixXd> refConst() { return mat; }
Eigen::Block<Eigen::MatrixXd> block(int r, int c, int nrow, int ncol) { return mat.block(r, c, nrow, ncol); }
Eigen::Block<const Eigen::MatrixXd> blockConst(int r, int c, int nrow, int ncol) const { return mat.block(r, c, nrow, ncol); }
py::EigenDMap<Eigen::Matrix2d> corners() { return py::EigenDMap<Eigen::Matrix2d>(mat.data(),
py::EigenDStride(mat.outerStride() * (mat.outerSize()-1), mat.innerStride() * (mat.innerSize()-1))); }
py::EigenDMap<const Eigen::Matrix2d> cornersConst() const { return py::EigenDMap<const Eigen::Matrix2d>(mat.data(),
py::EigenDStride(mat.outerStride() * (mat.outerSize()-1), mat.innerStride() * (mat.innerSize()-1))); }
};
using rvp = py::return_value_policy;
py::class_<ReturnTester>(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<int, Eigen::Dynamic> 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<Eigen::Lower>();
});
// Returns a SelfAdjointView referencing the lower triangle of m
m.def("symmetric_upper", [](const Eigen::MatrixXi &m) {
return m.selfadjointView<Eigen::Upper>();
});
// 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); });
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", [](Eigen::Ref<FixedMatrixR>) {});
m.def("fixed_mutator_c", [](Eigen::Ref<FixedMatrixC>) {});
m.def("fixed_mutator_a", [](py::EigenDRef<FixedMatrixC>) {});
// 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 { return Eigen::SparseView<Eigen::MatrixXf>(mat); });
m.def("sparse_c", [mat]() -> SparseMatrixC { return Eigen::SparseView<Eigen::MatrixXf>(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<Eigen::MatrixXd>()(1, 0); });
m.def("cpp_ref_c", [](py::handle m) { return m.cast<Eigen::Ref<Eigen::MatrixXd>>()(1, 0); });
m.def("cpp_ref_r", [](py::handle m) { return m.cast<Eigen::Ref<MatrixXdR>>()(1, 0); });
m.def("cpp_ref_any", [](py::handle m) { return m.cast<py::EigenDRef<Eigen::MatrixXd>>()(1, 0); });
// 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", [](Eigen::Ref<const Eigen::MatrixXd> 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<const Eigen::Matrix<long, -1, -1, Eigen::RowMajor>> &m) -> long { return m(2, 1); },
py::arg().noconvert());
// test_issue738
// Issue #738: 1xN or Nx1 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.
m.def("iss738_f1", &adjust_matrix<const Eigen::Ref<const Eigen::MatrixXd> &>, py::arg().noconvert());
m.def("iss738_f2", &adjust_matrix<const Eigen::Ref<const Eigen::Matrix<double, -1, -1, Eigen::RowMajor>> &>, py::arg().noconvert());
// test_named_arguments
// Make sure named arguments are working properly:
m.def("matrix_multiply", [](const py::EigenDRef<const Eigen::MatrixXd> A, const py::EigenDRef<const Eigen::MatrixXd> 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_<CustomOperatorNew>(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", [](Eigen::Ref<const Eigen::VectorXd> v) {
py::module::import("numpy").attr("ones")(10);
return v(5);
});
m.def("get_elem_indirect", [](std::vector<Eigen::Ref<const Eigen::VectorXd>> v) {
py::module::import("numpy").attr("ones")(10);
return v[0](5);
});
}