def test_batch_diag(self):
left_interp_indices = torch.LongTensor([[2, 3], [3, 4],
[4, 5]]).repeat(5, 1, 1)
left_interp_values = torch.tensor([[1, 1], [1, 1], [1, 1]],
dtype=torch.float).repeat(5, 1, 1)
right_interp_indices = torch.LongTensor([[0, 1], [1, 2],
[2, 3]]).repeat(5, 1, 1)
right_interp_values = torch.tensor([[1, 1], [1, 1], [1, 1]],
dtype=torch.float).repeat(5, 1, 1)
base_lazy_tensor_mat = torch.randn(5, 6, 6)
base_lazy_tensor_mat = base_lazy_tensor_mat.transpose(
1, 2).matmul(base_lazy_tensor_mat)
base_lazy_tensor_mat.requires_grad = True
base_lazy_tensor = NonLazyTensor(base_lazy_tensor_mat)
interp_lazy_tensor = InterpolatedLazyTensor(base_lazy_tensor,
left_interp_indices,
left_interp_values,
right_interp_indices,
right_interp_values)
actual = interp_lazy_tensor.evaluate()
actual_diag = torch.stack([
actual[0].diag(), actual[1].diag(), actual[2].diag(),
actual[3].diag(), actual[4].diag()
])
self.assertTrue(approx_equal(actual_diag, interp_lazy_tensor.diag()))
def test_batch_sample(self):
left_interp_indices = torch.LongTensor([[2, 3], [3, 4],
[4, 5]]).repeat(5, 1, 1)
left_interp_values = torch.tensor([[1, 1], [1, 1], [1, 1]],
dtype=torch.float).repeat(5, 1, 1)
base_lazy_tensor_mat = torch.randn(5, 6, 6)
base_lazy_tensor_mat = base_lazy_tensor_mat.transpose(
1, 2).matmul(base_lazy_tensor_mat)
base_lazy_tensor_mat.requires_grad = True
base_lazy_tensor = NonLazyTensor(base_lazy_tensor_mat)
interp_lazy_tensor = InterpolatedLazyTensor(base_lazy_tensor,
left_interp_indices,
left_interp_values,
left_interp_indices,
left_interp_values)
actual = interp_lazy_tensor.evaluate()
samples = interp_lazy_tensor.zero_mean_mvn_samples(10000)
sample_covar = samples.unsqueeze(-1).matmul(
samples.unsqueeze(-2)).mean(0)
self.assertLess(((sample_covar - actual).abs() /
actual.abs().clamp(1e-5, 1e5)).max().item(), 2e-1)
def test_diag(self):
left_interp_indices = torch.LongTensor([[2, 3], [3, 4], [4, 5]])
left_interp_values = torch.tensor([[1, 1], [1, 1], [1, 1]],
dtype=torch.float)
right_interp_indices = torch.LongTensor([[0, 1], [1, 2], [2, 3]])
right_interp_values = torch.tensor([[1, 1], [1, 1], [1, 1]],
dtype=torch.float)
base_lazy_tensor_mat = torch.randn(6, 6)
base_lazy_tensor_mat = base_lazy_tensor_mat.t().matmul(
base_lazy_tensor_mat)
base_lazy_tensor_mat.requires_grad = True
base_lazy_tensor = NonLazyTensor(base_lazy_tensor_mat)
interp_lazy_tensor = InterpolatedLazyTensor(base_lazy_tensor,
left_interp_indices,
left_interp_values,
right_interp_indices,
right_interp_values)
actual = interp_lazy_tensor.evaluate()
self.assertTrue(approx_equal(actual.diag(), interp_lazy_tensor.diag()))
