@ti.kernel
def test():
for i in range(n):
s = 0
s += 45
print(s)
val[i] = s + i
print(val[i])
test()
for i in range(n):
assert val[i] == i + 45
@ti.test(arch=ti.get_host_arch_list())
@ti.must_throw(Exception)
def test_loop_var_life():
@ti.kernel
def test():
for i in ti.static(range(8)):
pass
print(i)
test()
@ti.test(arch=ti.get_host_arch_list())
@ti.must_throw(Exception)
def test_loop_var_life_double_iters():
@ti.kernel
def _test_scalar_ndarray(dtype, shape):
x = ti.ndarray(dtype, shape)
if isinstance(shape, tuple):
assert x.shape == shape
else:
assert x.shape == (shape, )
assert x.element_shape == ()
assert x.dtype == dtype
@pytest.mark.parametrize('dtype', data_types)
@pytest.mark.parametrize('shape', ndarray_shapes)
@pytest.mark.skipif(not ti.has_pytorch(), reason='Pytorch not installed.')
@ti.test(arch=ti.get_host_arch_list())
def test_scalar_ndarray_torch(dtype, shape):
_test_scalar_ndarray(dtype, shape)
@pytest.mark.parametrize('dtype', data_types)
@pytest.mark.parametrize('shape', ndarray_shapes)
@ti.test(arch=ti.get_host_arch_list(), ndarray_use_torch=False)
def test_scalar_ndarray(dtype, shape):
_test_scalar_ndarray(dtype, shape)
def _test_vector_ndarray(n, dtype, shape):
x = ti.Vector.ndarray(n, dtype, shape)
if isinstance(shape, tuple):
