def _test_cast_backward(test_case, device, shape):
np_arr = np.random.randn(*shape).astype(np.float32)
x = flow.Tensor(
np_arr, dtype=flow.float32, device=flow.device(device), requires_grad=True
)
y = flow.cast(x, flow.int8)
z = y.sum()
z.backward()
np_out = np_arr.astype(np.int8)
test_case.assertTrue(np.array_equal(x.grad.numpy(), np.ones(shape=shape)))
def _test_cast_int2float(test_case, device, shape):
np_arr = np.random.randn(*shape).astype(np.int8)
input = flow.Tensor(np_arr, dtype=flow.int8, device=flow.device(device))
output = flow.cast(input, flow.float32)
np_out = np_arr.astype(np.float32)
test_case.assertTrue(np.array_equal(output.numpy(), np_out))
def test_cast_int2float(test_case):
np_arr = np.random.randn(5, 2, 3, 4).astype(np.int8)
input = flow.Tensor(np_arr, dtype=flow.int8)
output = flow.cast(input, flow.float32)
np_out = np_arr.astype(np.float32)
test_case.assertTrue(np.array_equal(output.numpy(), np_out))