test_autograd.py 文件源码

def test_mlpg_gradcheck():
    # MLPG is performed dimention by dimention, so static_dim 1 is enough,
    # 2 just for in case.
    static_dim = 2
    T = 10

    for windows in _get_windows_set():
        torch.manual_seed(1234)
        means = Variable(torch.rand(T, static_dim * len(windows)),
                         requires_grad=True)
        inputs = (means,)

        # Unit variances case
        variances = torch.ones(static_dim * len(windows)
                               ).expand(T, static_dim * len(windows))

        assert gradcheck(MLPG(variances, windows),
                         inputs, eps=1e-3, atol=1e-3)

        # Rand variances case
        variances = torch.rand(static_dim * len(windows)
                               ).expand(T, static_dim * len(windows))

        assert gradcheck(MLPG(variances, windows),
                         inputs, eps=1e-3, atol=1e-3)