def _test_clamp_scalar_max(test_case, shape, device):
input = flow.Tensor(np.random.randn(*shape),
dtype=flow.float32,
device=flow.device(device))
of_out = flow.clamp(input, None, 0.5)
np_out = np.clip(input.numpy(), None, 0.5)
test_case.assertTrue(np.allclose(of_out.numpy(), np_out, 1e-5, 1e-5))
def _test_clamp_backward(test_case, shape, device):
x = flow.Tensor(
np.random.randn(*shape),
dtype=flow.float32,
device=flow.device(device),
requires_grad=True,
)
y = flow.clamp(x, 0.1, 0.5).sum()
y.backward()
test_case.assertTrue(
np.allclose(x.grad.numpy(), _numpy_clamp_grad(x.numpy(), 0.1, 0.5),
1e-5, 1e-5))
def _test_clamp_integral(test_case, shape, device):
input = flow.Tensor(np.random.randint(3, 10, (shape)),
device=flow.device(device))
of_out = flow.clamp(input, 1, 5)
np_out = np.clip(input.numpy(), 1, 5)
test_case.assertTrue(np.allclose(of_out.numpy(), np_out, 1e-5, 1e-5))