def test_single_eval_neg_branin(self, cuda=False):
device = torch.device("cuda") if cuda else torch.device("cpu")
for dtype in (torch.float, torch.double):
X = torch.zeros(2, device=device, dtype=dtype)
res = neg_branin(X)
self.assertEqual(res.dtype, dtype)
self.assertEqual(res.device.type, device.type)
self.assertEqual(res.shape, torch.Size())
def test_batch_eval_neg_branin(self, cuda=False):
device = torch.device("cuda") if cuda else torch.device("cpu")
for dtype in (torch.float, torch.double):
X = torch.zeros(2, 2, device=device, dtype=dtype)
res = neg_branin(X)
self.assertEqual(res.dtype, dtype)
self.assertEqual(res.device.type, device.type)
self.assertEqual(res.shape, torch.Size([2]))
def test_neg_branin_global_maxima(self, cuda=False):
device = torch.device("cuda") if cuda else torch.device("cpu")
for dtype in (torch.float, torch.double):
X = torch.tensor(
GLOBAL_MAXIMIZERS, device=device, dtype=dtype, requires_grad=True
)
res = neg_branin(X)
for r in res:
self.assertAlmostEqual(r.item(), GLOBAL_MAXIMUM, places=4)
grad = torch.autograd.grad(res.sum(), X)[0]
self.assertLess(grad.abs().max().item(), 1e-4)
def test_neg_branin_global_maxima(self, cuda=False):
device = torch.device("cuda") if cuda else torch.device("cpu")
for dtype in (torch.float, torch.double):
X = torch.tensor(
GLOBAL_MAXIMIZERS, device=device, dtype=dtype, requires_grad=True
)
res = neg_branin(X)
res.sum().backward()
for r in res:
self.assertAlmostEqual(r.item(), GLOBAL_MAXIMUM, places=4)
self.assertLess(X.grad.abs().max().item(), 1e-4)
