def _test_pow(test_case, shape, device):
input = flow.Tensor(np.random.randn(*shape),
dtype=flow.float32,
device=flow.device(device))
of_out = flow.pow(input, 2.1)
np_out = np.power(input.numpy(), 2.1)
test_case.assertTrue(
np.allclose(of_out.numpy(), np_out, 1e-5, 1e-5, equal_nan=True))
def _test_pow_elementwise_impl(test_case, shape, scalar, device):
np_input_x = 10 * np.random.rand(*shape)
np_input_y = np.random.randint(1, 3, shape) + np.random.randn(*shape)
of_input_x = flow.Tensor(np_input_x, dtype=flow.float32, device=flow.device(device))
of_input_y = flow.Tensor(np_input_y, dtype=flow.float32, device=flow.device(device))
of_out = flow.pow(of_input_x, of_input_y)
np_out = np.power(np_input_x, np_input_y)
test_case.assertTrue(np.allclose(of_out.numpy(), np_out, 1e-4, 1e-4))
def _test_pow_backward(test_case, shape, device):
x = flow.Tensor(
np.random.randn(*shape),
dtype=flow.float32,
device=flow.device(device),
requires_grad=True,
)
y = flow.pow(x, 2.34)
z = y.sum()
z.backward()
np_grad = 2.34 * x.numpy()**(2.34 - 1)
test_case.assertTrue(
np.allclose(x.grad.numpy(), np_grad, 1e-5, 1e-5, equal_nan=True))
def test_y_grad():
of_input_x = flow.Tensor(
np_input_x, dtype=flow.float32, device=flow.device(device)
)
of_input_y = flow.Tensor(
np_input_y,
dtype=flow.float32,
device=flow.device(device),
requires_grad=True,
)
of_out = flow.pow(of_input_x, of_input_y)
of_out_sum = of_out.sum()
of_out_sum.backward()
test_case.assertTrue(
np.allclose(of_input_y.grad.numpy(), np_y_grad, 1e-4, 1e-4)
)
def test_pow(test_case):
input = flow.Tensor(np.array([1, 2, 3, 4, 5, 6]), dtype=flow.float32)
of_out = flow.pow(input, 2.1)
np_out = np.power(input.numpy(), 2.1)
test_case.assertTrue(np.allclose(of_out.numpy(), np_out, 1e-5, 1e-5))
def _test_pow_scalar_impl(test_case, shape, scalar, device):
np_input = 10 * np.random.rand(*shape)
of_input = flow.Tensor(np_input, dtype=flow.float32, device=flow.device(device))
of_out = flow.pow(of_input, scalar)
np_out = np.power(np_input, scalar)
test_case.assertTrue(np.allclose(of_out.numpy(), np_out, 1e-4, 1e-4))
