Function test_func_backends

test/test_backend.py:284–733

(nx)

def test_func_backends(nx):
    rnd = np.random.RandomState(0)
    M = rnd.randn(10, 3)
    SquareM = rnd.randn(10, 10)
    v = rnd.randn(3)
    val = np.array([1.0])

    M1 = rnd.randn(1, 2, 10, 10)
    M2 = rnd.randn(3, 1, 10, 10)

    # Sparse tensors test
    sp_row = np.array([0, 3, 1, 0, 3])
    sp_col = np.array([0, 3, 1, 2, 2])
    sp_data = np.array([4, 5, 7, 9, 0], dtype=np.float64)

    lst_tot = []

    for nx in [ot.backend.NumpyBackend(), nx]:
        print("Backend: ", nx.__name__)

        lst_b = []
        lst_name = []

        Mb = nx.from_numpy(M)
        SquareMb = nx.from_numpy(SquareM)
        vb = nx.from_numpy(v)

        M1b = nx.from_numpy(M1)
        M2b = nx.from_numpy(M2)

        val = nx.from_numpy(val)

        sp_rowb = nx.from_numpy(sp_row)
        sp_colb = nx.from_numpy(sp_col)
        sp_datab = nx.from_numpy(sp_data)

        A = nx.set_gradients(val, v, v)

        lst_b.append(nx.to_numpy(A))
        lst_name.append("set_gradients")

        A = nx.detach(Mb)
        A, B = nx.detach(Mb, Mb)
        lst_b.append(nx.to_numpy(A))
        lst_name.append("detach")

        A = nx.zeros((10, 3))
        A = nx.zeros((10, 3), type_as=Mb)
        lst_b.append(nx.to_numpy(A))
        lst_name.append("zeros")

        A = nx.ones((10, 3))
        A = nx.ones((10, 3), type_as=Mb)
        lst_b.append(nx.to_numpy(A))
        lst_name.append("ones")

        A = nx.arange(10, 1, 2)
        lst_b.append(nx.to_numpy(A))