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Add support for Eigen::Ref<...> function arguments

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Eigen::Ref is a common way to pass eigen dense types without needing a
template, e.g. the single definition `void
func(Eigen::Ref<Eigen::MatrixXd> x)` can be called with any double
matrix-like object.

The current pybind11 eigen support fails with internal errors if
attempting to bind a function with an Eigen::Ref<...> argument because
Eigen::Ref<...> satisfies the "is_eigen_dense" requirement, but can't
compile if actually used: Eigen::Ref<...> itself is not default
constructible, and so the argument std::tuple containing an
Eigen::Ref<...> isn't constructible, which results in compilation
failure.

This commit adds support for Eigen::Ref<...> by giving it its own
type_caster implementation which consists of an internal type_caster of
the referenced type, load/cast methods that dispatch to the internal
type_caster, and a unique_ptr to an Eigen::Ref<> instance that gets
set during load().

There is, of course, no performance advantage for pybind11-using code of
using Eigen::Ref<...>--we are allocating a matrix of the derived type
when loading it--but this has the advantage of allowing pybind11 to bind
transparently to C++ methods taking Eigen::Refs.
This commit is contained in:
Jason Rhinelander
2016-08-03 16:50:22 -04:00
parent 7f9603fe24
commit 5fd5074a0b
4 changed files with 63 additions and 1 deletions
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8
example/eigen.py
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@@ -11,6 +11,7 @@ from example import sparse_r, sparse_c
from example import sparse_passthrough_r, sparse_passthrough_c
from example import double_row, double_col
from example import double_mat_cm, double_mat_rm
from example import cholesky1, cholesky2, cholesky3, cholesky4, cholesky5, cholesky6
try:
import numpy as np
import scipy
@@ -70,3 +71,10 @@ slices = [counting_3d[0, :, :], counting_3d[:, 0, :], counting_3d[:, :, 0]]
for slice_idx, ref_mat in enumerate(slices):
print("double_mat_cm(%d) = %s" % (slice_idx, check_got_vs_ref(double_mat_cm(ref_mat), 2.0 * ref_mat)))
print("double_mat_rm(%d) = %s" % (slice_idx, check_got_vs_ref(double_mat_rm(ref_mat), 2.0 * ref_mat)))
i = 1
for chol in [cholesky1, cholesky2, cholesky3, cholesky4, cholesky5, cholesky6]:
mymat = chol(np.array([[1,2,4], [2,13,23], [4,23,77]]))
print("cholesky" + str(i) + " " + ("OK" if (mymat == np.array([[1,0,0], [2,3,0], [4,5,6]])).all() else "NOT OKAY"))
i += 1