test_autograd.py 文件源码

def test_functional_mlpg():
    static_dim = 2
    T = 5

    for windows in _get_windows_set():
        torch.manual_seed(1234)
        means = torch.rand(T, static_dim * len(windows))
        variances = torch.ones(static_dim * len(windows))

        y = G.mlpg(means.numpy(), variances.numpy(), windows)
        y = Variable(torch.from_numpy(y), requires_grad=False)

        means = Variable(means, requires_grad=True)

        # mlpg
        y_hat = AF.mlpg(means, variances, windows)
        assert np.allclose(y.data.numpy(), y_hat.data.numpy())

        # Test backward pass
        nn.MSELoss()(y_hat, y).backward()

        # unit_variance_mlpg
        R = torch.from_numpy(G.unit_variance_mlpg_matrix(windows, T))
        y_hat = AF.unit_variance_mlpg(R, means)
        assert np.allclose(y.data.numpy(), y_hat.data.numpy())

        nn.MSELoss()(y_hat, y).backward()

        # Test 3D tensor inputs
        y_hat = AF.unit_variance_mlpg(R, means.view(1, -1, means.size(-1)))
        assert np.allclose(
            y.data.numpy(), y_hat.data.view(-1, static_dim).numpy())

        nn.MSELoss()(y_hat.view(-1, static_dim), y).backward()