knx/examples/knxPython/pybind11/tests/test_eigen_tensor.py
2024-06-29 16:50:08 +02:00

291 lines
9.2 KiB
Python

from __future__ import annotations
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:
import eigen_tensor_avoid_stl_array as avoid
submodules += [avoid.c_style, avoid.f_style]
except ImportError as e:
# Ensure config, build, toolchain, etc. issues are not masked here:
msg = (
"import eigen_tensor_avoid_stl_array FAILED, while "
"import pybind11_tests.eigen_tensor succeeded. "
"Please ensure that "
"test_eigen_tensor.cpp & "
"eigen_tensor_avoid_stl_array.cpp "
"are built together (or both are not built if Eigen is not available)."
)
raise RuntimeError(msg) from e
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("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))
)
bad_options = "C" if m.needed_options == "F" else "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)
)
temp = np.zeros((3, 5, 2), dtype=np.float64, order=m.needed_options)
with pytest.raises(
TypeError, match=r"^round_trip_view_tensor\(\): incompatible function arguments"
):
m.round_trip_view_tensor(
temp[:, ::-1, :],
)
temp = np.zeros((3, 5, 2), dtype=np.float64, order=m.needed_options)
temp.setflags(write=False)
with pytest.raises(
TypeError, match=r"^round_trip_view_tensor\(\): incompatible function arguments"
):
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[?, ?, ?]]"
)