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Merge branch 'master' into sh_merge_master

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This commit is contained in:
Ralf W. Grosse-Kunstleve 2022-10-18 17:00:46 -07:00
commit 50da709678
17 changed files with 1944 additions and 713 deletions
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1
.gitignore vendored
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@ -43,3 +43,4 @@ pybind11Targets.cmake
/pybind11/share/*
/docs/_build/*
.ipynb_checkpoints/
tests/main.cpp

2
CMakeLists.txt
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@ -125,6 +125,8 @@ set(PYBIND11_HEADERS
include/pybind11/complex.h
include/pybind11/options.h
include/pybind11/eigen.h
include/pybind11/eigen/matrix.h
include/pybind11/eigen/tensor.h
include/pybind11/embed.h
include/pybind11/eval.h
include/pybind11/gil.h

706
include/pybind11/eigen.h
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@ -9,708 +9,4 @@
#pragma once
/* 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
*/
#include "numpy.h"
// 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 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);
case return_value_policy::_return_as_bytes:
pybind11_fail("return_value_policy::_return_as_bytes does not apply.");
break;
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"

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/*
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)

518
include/pybind11/eigen/tensor.h Normal file
View File

@ -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)

36
tests/CMakeLists.txt
View File

@ -145,7 +145,9 @@ 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_exc_namespace_visibility.py
@ -257,7 +259,10 @@ 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
@ -313,12 +318,37 @@ if(PYBIND11_TEST_FILES_EIGEN_I GREATER -1)
endif()
message(STATUS "Building tests with Eigen v${EIGEN3_VERSION}")
else()
list(REMOVE_AT PYBIND11_TEST_FILES ${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 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)

8
tests/extra_python_package/test_files.py
View File

@ -62,6 +62,11 @@ 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",
}
@ -89,7 +94,7 @@ 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
@ -99,6 +104,7 @@ sdist_files = {
"pybind11/include",
"pybind11/include/pybind11",
"pybind11/include/pybind11/detail",
"pybind11/include/pybind11/eigen",
"pybind11/include/pybind11/stl",
"pybind11/share",
"pybind11/share/cmake",

4
tests/test_eigen.cpp → tests/test_eigen_matrix.cpp
View File

@ -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>;

2
tests/test_eigen.py → tests/test_eigen_matrix.py
View File

@ -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(

16
tests/test_eigen_tensor.cpp Normal file
View File

@ -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"

333
tests/test_eigen_tensor.inl Normal file
View File

@ -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

288
tests/test_eigen_tensor.py Normal file
View File

@ -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[?, ?, ?]]"
)

16
tests/test_eigen_tensor_avoid_stl_array.cpp Normal file
View File

@ -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"

2
tests/test_numpy_array.cpp
View File

@ -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; });
}

6
tests/test_numpy_array.py
View File

@ -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

4
tools/setup_global.py.in
View File

@ -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,

2
tools/setup_main.py.in
View File

@ -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"],