def test_log_det_only(self):
# Forward pass
with gpytorch.settings.num_trace_samples(1000):
res = NonLazyTensor(self.mat).log_det()
actual = self.mat_clone.logdet()
self.assertAlmostEqual(res.item(), actual.item(), places=1)
# Backward
actual.backward()
res.backward()
self.assertLess(
torch.max((self.mat_clone.grad - self.mat.grad).abs()).item(),
1e-1)
def test_log_det_only(self):
# Forward pass
with gpytorch.settings.num_trace_samples(1000):
res = NonLazyTensor(self.mat_var).log_det()
actual = self.mat_var_clone.det().log()
self.assertAlmostEqual(res.item(), actual.item(), places=1)
# Backward
actual.backward()
res.backward()
self.assertTrue(
approx_equal(self.mat_var_clone.grad,
self.mat_var.grad,
epsilon=1e-1))
def test_inv_quad_only_vector(self):
# Forward pass
res = NonLazyTensor(self.mat_var).inv_quad(self.vec_var)
actual = self.mat_var_clone.inverse().matmul(self.vec_var_clone).mul(
self.vec_var_clone).sum()
self.assertAlmostEqual(res.item(), actual.item(), places=1)
# Backward
actual.backward()
res.backward()
self.assertTrue(
approx_equal(self.mat_var_clone.grad,
self.mat_var.grad,
epsilon=1e-1))
self.assertTrue(
approx_equal(self.vec_var_clone.grad, self.vec_var.grad))
def test_inv_quad_only_vector(self):
# Forward pass
res = NonLazyTensor(self.mat).inv_quad(self.vec)
actual = self.mat_clone.inverse().matmul(self.vec_clone).mul(
self.vec_clone).sum()
self.assertAlmostEqual(res.item(), actual.item(), places=1)
# Backward
actual.backward()
res.backward()
self.assertLess(
torch.max((self.mat_clone.grad - self.mat.grad).abs()).item(),
1e-3)
self.assertLess(
torch.max((self.vec_clone.grad - self.vec.grad).abs()).item(),
1e-3)
