test_torch.py 文件源码

def test_svd(self):
        a=torch.Tensor(((8.79,  6.11, -9.15,  9.57, -3.49,  9.84),
                        (9.93,  6.91, -7.93,  1.64,  4.02,  0.15),
                        (9.83,  5.04,  4.86,  8.83,  9.80, -8.99),
                        (5.45, -0.27,  4.85,  0.74, 10.00, -6.02),
                        (3.16,  7.98,  3.01,  5.80,  4.27, -5.31))).t().clone()
        u, s, v = torch.svd(a)
        uu = torch.Tensor()
        ss = torch.Tensor()
        vv = torch.Tensor()
        uuu, sss, vvv = torch.svd(uu, ss, vv, a)
        self.assertEqual(u, uu, 0, 'torch.svd')
        self.assertEqual(u, uuu, 0, 'torch.svd')
        self.assertEqual(s, ss, 0, 'torch.svd')
        self.assertEqual(s, sss, 0, 'torch.svd')
        self.assertEqual(v, vv, 0, 'torch.svd')
        self.assertEqual(v, vvv, 0, 'torch.svd')

        # test reuse
        X = torch.randn(4, 4)
        U, S, V = torch.svd(X)
        Xhat = torch.mm(U, torch.mm(S.diag(), V.t()))
        self.assertEqual(X, Xhat, 1e-8, 'USV\' wrong')

        self.assertFalse(U.is_contiguous(), 'U is contiguous')
        torch.svd(U, S, V, X)
        Xhat = torch.mm(U, torch.mm(S.diag(), V.t()))
        self.assertEqual(X, Xhat, 1e-8, 'USV\' wrong')

        # test non-contiguous
        X = torch.randn(5, 5)
        U = torch.zeros(5, 2, 5)[:,1]
        S = torch.zeros(5, 2)[:,1]
        V = torch.zeros(5, 2, 5)[:,1]

        self.assertFalse(U.is_contiguous(), 'U is contiguous')
        self.assertFalse(S.is_contiguous(), 'S is contiguous')
        self.assertFalse(V.is_contiguous(), 'V is contiguous')
        torch.svd(U, S, V, X)
        Xhat = torch.mm(U, torch.mm(S.diag(), V.t()))
        self.assertEqual(X, Xhat, 1e-8, 'USV\' wrong')