def test_generic():
from jina.proto.ndarray.generic import GenericNdArray
from scipy.sparse import coo_matrix
row = np.array([0, 3, 1, 0])
col = np.array([0, 3, 1, 2])
data = np.array([4, 5, 7, 9])
a = coo_matrix((data, (row, col)), shape=(4, 4))
dense_a = a.toarray()
b = GenericNdArray(is_sparse=True)
b.value = a
dense_b = b.value.toarray()
np.testing.assert_equal(dense_b, dense_a)
c = np.random.random([10, 3, 4])
# without change of `is_sparse`, this should raise error
with pytest.raises(AttributeError):
b.value = c
b.is_sparse = False
b.value = c
np.testing.assert_equal(b.value, c)
def test_array2pb():
# i don't understand why is this set?
# os env should be available to that process-context only
if 'JINA_ARRAY_QUANT' in os.environ:
print(f'quant is on: {os.environ["JINA_ARRAY_QUANT"]}')
del os.environ['JINA_ARRAY_QUANT']
d = GenericNdArray()
d.value = e4
np.testing.assert_almost_equal(d.value, e4)
def test_array_protobuf_conversions_with_quantize(quantize, type):
random_array = np.random.rand(random.randrange(0, 50), random.randrange(0, 20)).astype(type)
d = GenericNdArray(quantize=quantize)
d.value = random_array
np.testing.assert_almost_equal(d.value, random_array, decimal=2)
def test_array_protobuf_conversions(type):
random_array = np.random.rand(random.randrange(0, 50), random.randrange(0, 20)).astype(type)
d = GenericNdArray()
d.value = random_array
np.testing.assert_almost_equal(d.value, random_array)