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https://github.com/pybind/pybind11.git
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5fd5074a0b
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.
81 lines
3.3 KiB
Python
81 lines
3.3 KiB
Python
#!/usr/bin/env python
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from __future__ import print_function
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import sys
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sys.path.append('.')
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from example import fixed_r, fixed_c
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from example import fixed_passthrough_r, fixed_passthrough_c
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from example import dense_r, dense_c
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from example import dense_passthrough_r, dense_passthrough_c
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from example import sparse_r, sparse_c
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from example import sparse_passthrough_r, sparse_passthrough_c
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from example import double_row, double_col
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from example import double_mat_cm, double_mat_rm
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from example import cholesky1, cholesky2, cholesky3, cholesky4, cholesky5, cholesky6
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try:
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import numpy as np
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import scipy
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except ImportError:
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# NumPy missing: skip test
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exit(99)
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ref = np.array(
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[[0, 3, 0, 0, 0, 11],
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[22, 0, 0, 0, 17, 11],
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[7, 5, 0, 1, 0, 11],
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[0, 0, 0, 0, 0, 11],
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[0, 0, 14, 0, 8, 11]])
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def check(mat):
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return 'OK' if np.sum(abs(mat - ref)) == 0 else 'NOT OK'
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print("should_give_NOT_OK = %s" % check(ref[:, ::-1]))
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print("fixed_r = %s" % check(fixed_r()))
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print("fixed_c = %s" % check(fixed_c()))
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print("pt_r(fixed_r) = %s" % check(fixed_passthrough_r(fixed_r())))
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print("pt_c(fixed_c) = %s" % check(fixed_passthrough_c(fixed_c())))
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print("pt_r(fixed_c) = %s" % check(fixed_passthrough_r(fixed_c())))
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print("pt_c(fixed_r) = %s" % check(fixed_passthrough_c(fixed_r())))
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print("dense_r = %s" % check(dense_r()))
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print("dense_c = %s" % check(dense_c()))
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print("pt_r(dense_r) = %s" % check(dense_passthrough_r(dense_r())))
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print("pt_c(dense_c) = %s" % check(dense_passthrough_c(dense_c())))
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print("pt_r(dense_c) = %s" % check(dense_passthrough_r(dense_c())))
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print("pt_c(dense_r) = %s" % check(dense_passthrough_c(dense_r())))
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print("sparse_r = %s" % check(sparse_r()))
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print("sparse_c = %s" % check(sparse_c()))
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print("pt_r(sparse_r) = %s" % check(sparse_passthrough_r(sparse_r())))
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print("pt_c(sparse_c) = %s" % check(sparse_passthrough_c(sparse_c())))
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print("pt_r(sparse_c) = %s" % check(sparse_passthrough_r(sparse_c())))
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print("pt_c(sparse_r) = %s" % check(sparse_passthrough_c(sparse_r())))
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def check_got_vs_ref(got_x, ref_x):
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return 'OK' if np.array_equal(got_x, ref_x) else 'NOT OK'
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counting_mat = np.arange(9.0, dtype=np.float32).reshape((3, 3))
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first_row = counting_mat[0, :]
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first_col = counting_mat[:, 0]
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print("double_row(first_row) = %s" % check_got_vs_ref(double_row(first_row), 2.0 * first_row))
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print("double_col(first_row) = %s" % check_got_vs_ref(double_col(first_row), 2.0 * first_row))
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print("double_row(first_col) = %s" % check_got_vs_ref(double_row(first_col), 2.0 * first_col))
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print("double_col(first_col) = %s" % check_got_vs_ref(double_col(first_col), 2.0 * first_col))
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counting_3d = np.arange(27.0, dtype=np.float32).reshape((3, 3, 3))
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slices = [counting_3d[0, :, :], counting_3d[:, 0, :], counting_3d[:, :, 0]]
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for slice_idx, ref_mat in enumerate(slices):
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print("double_mat_cm(%d) = %s" % (slice_idx, check_got_vs_ref(double_mat_cm(ref_mat), 2.0 * ref_mat)))
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print("double_mat_rm(%d) = %s" % (slice_idx, check_got_vs_ref(double_mat_rm(ref_mat), 2.0 * ref_mat)))
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i = 1
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for chol in [cholesky1, cholesky2, cholesky3, cholesky4, cholesky5, cholesky6]:
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mymat = chol(np.array([[1,2,4], [2,13,23], [4,23,77]]))
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print("cholesky" + str(i) + " " + ("OK" if (mymat == np.array([[1,0,0], [2,3,0], [4,5,6]])).all() else "NOT OKAY"))
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i += 1
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