def test_batch_diag(self):
left_interp_indices = Variable(
torch.LongTensor([[2, 3], [3, 4], [4, 5]]).repeat(5, 1, 1))
left_interp_values = Variable(
torch.Tensor([[1, 1], [1, 1], [1, 1]]).repeat(5, 1, 1))
right_interp_indices = Variable(
torch.LongTensor([[0, 1], [1, 2], [2, 3]]).repeat(5, 1, 1))
right_interp_values = Variable(
torch.Tensor([[1, 1], [1, 1], [1, 1]]).repeat(5, 1, 1))
base_lazy_variable_mat = torch.randn(5, 6, 6)
base_lazy_variable_mat = base_lazy_variable_mat.transpose(
1, 2).matmul(base_lazy_variable_mat)
base_lazy_variable = NonLazyVariable(
Variable(base_lazy_variable_mat, requires_grad=True))
interp_lazy_var = InterpolatedLazyVariable(base_lazy_variable,
left_interp_indices,
left_interp_values,
right_interp_indices,
right_interp_values)
actual = interp_lazy_var.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.data,
interp_lazy_var.diag().data))
def test_diag(self):
left_interp_indices = Variable(torch.LongTensor([[2, 3], [3, 4], [4, 5]]))
left_interp_values = Variable(torch.Tensor([[1, 1], [1, 1], [1, 1]]))
right_interp_indices = Variable(torch.LongTensor([[0, 1], [1, 2], [2, 3]]))
right_interp_values = Variable(torch.Tensor([[1, 1], [1, 1], [1, 1]]))
base_lazy_variable_mat = torch.randn(6, 6)
base_lazy_variable_mat = (
base_lazy_variable_mat.t().
matmul(base_lazy_variable_mat)
)
base_lazy_variable = NonLazyVariable(
Variable(base_lazy_variable_mat, requires_grad=True)
)
interp_lazy_var = InterpolatedLazyVariable(
base_lazy_variable,
left_interp_indices,
left_interp_values,
right_interp_indices,
right_interp_values,
)
actual = interp_lazy_var.evaluate()
self.assertTrue(approx_equal(actual.diag().data, interp_lazy_var.diag().data))