def test_outer_indexer_consistency_with_broadcast_indexes_vectorized():
def nonzero(x):
if isinstance(x, np.ndarray) and x.dtype.kind == 'b':
x = x.nonzero()[0]
return x
original = np.random.rand(10, 20, 30)
v = Variable(['i', 'j', 'k'], original)
I = ReturnItem()
# test orthogonally applied indexers
indexers = [
I[:], 0, -2, I[:3],
np.array([0, 1, 2, 3]),
np.array([0]),
np.arange(10) < 5
]
for i, j, k in itertools.product(indexers, repeat=3):
if isinstance(j, np.ndarray) and j.dtype.kind == 'b': # match size
j = np.arange(20) < 4
if isinstance(k, np.ndarray) and k.dtype.kind == 'b':
k = np.arange(30) < 8
_, expected, new_order = v._broadcast_indexes_vectorized((i, j, k))
expected_data = nputils.NumpyVIndexAdapter(v.data)[expected.tuple]
if new_order:
old_order = range(len(new_order))
expected_data = np.moveaxis(expected_data, old_order, new_order)
outer_index = (nonzero(i), nonzero(j), nonzero(k))
actual = indexing._outer_to_numpy_indexer(outer_index, v.shape)
actual_data = v.data[actual]
np.testing.assert_array_equal(actual_data, expected_data)
def test_outer_indexer_consistency_with_broadcast_indexes_vectorized():
def nonzero(x):
if isinstance(x, np.ndarray) and x.dtype.kind == 'b':
x = x.nonzero()[0]
return x
original = np.random.rand(10, 20, 30)
v = Variable(['i', 'j', 'k'], original)
I = ReturnItem() # noqa: E741 # allow ambiguous name
# test orthogonally applied indexers
indexers = [I[:], 0, -2, I[:3], np.array([0, 1, 2, 3]), np.array([0]),
np.arange(10) < 5]
for i, j, k in itertools.product(indexers, repeat=3):
if isinstance(j, np.ndarray) and j.dtype.kind == 'b': # match size
j = np.arange(20) < 4
if isinstance(k, np.ndarray) and k.dtype.kind == 'b':
k = np.arange(30) < 8
_, expected, new_order = v._broadcast_indexes_vectorized((i, j, k))
expected_data = nputils.NumpyVIndexAdapter(v.data)[expected.tuple]
if new_order:
old_order = range(len(new_order))
expected_data = np.moveaxis(expected_data, old_order,
new_order)
outer_index = indexing.OuterIndexer((nonzero(i), nonzero(j),
nonzero(k)))
actual = indexing._outer_to_numpy_indexer(outer_index, v.shape)
actual_data = v.data[actual]
np.testing.assert_array_equal(actual_data, expected_data)
