def test_inverse(self):
M = torch.randn(5,5)
MI = torch.inverse(M)
E = torch.eye(5)
self.assertFalse(MI.is_contiguous(), 'MI is contiguous')
self.assertEqual(E, torch.mm(M, MI), 1e-8, 'inverse value')
self.assertEqual(E, torch.mm(MI, M), 1e-8, 'inverse value')
MII = torch.Tensor(5, 5)
torch.inverse(MII, M)
self.assertFalse(MII.is_contiguous(), 'MII is contiguous')
self.assertEqual(MII, MI, 0, 'inverse value in-place')
# second call, now that MII is transposed
torch.inverse(MII, M)
self.assertFalse(MII.is_contiguous(), 'MII is contiguous')
self.assertEqual(MII, MI, 0, 'inverse value in-place')
python类inverse()的实例源码
def test_inverse(self):
M = torch.randn(5, 5)
MI = torch.inverse(M)
E = torch.eye(5)
self.assertFalse(MI.is_contiguous(), 'MI is contiguous')
self.assertEqual(E, torch.mm(M, MI), 1e-8, 'inverse value')
self.assertEqual(E, torch.mm(MI, M), 1e-8, 'inverse value')
MII = torch.Tensor(5, 5)
torch.inverse(M, out=MII)
self.assertFalse(MII.is_contiguous(), 'MII is contiguous')
self.assertEqual(MII, MI, 0, 'inverse value in-place')
# second call, now that MII is transposed
torch.inverse(M, out=MII)
self.assertFalse(MII.is_contiguous(), 'MII is contiguous')
self.assertEqual(MII, MI, 0, 'inverse value in-place')
def test_inverse(self):
M = torch.randn(5, 5)
MI = torch.inverse(M)
E = torch.eye(5)
self.assertFalse(MI.is_contiguous(), 'MI is contiguous')
self.assertEqual(E, torch.mm(M, MI), 1e-8, 'inverse value')
self.assertEqual(E, torch.mm(MI, M), 1e-8, 'inverse value')
MII = torch.Tensor(5, 5)
torch.inverse(M, out=MII)
self.assertFalse(MII.is_contiguous(), 'MII is contiguous')
self.assertEqual(MII, MI, 0, 'inverse value in-place')
# second call, now that MII is transposed
torch.inverse(M, out=MII)
self.assertFalse(MII.is_contiguous(), 'MII is contiguous')
self.assertEqual(MII, MI, 0, 'inverse value in-place')
def test_inverse(self):
M = torch.randn(5, 5)
MI = torch.inverse(M)
E = torch.eye(5)
self.assertFalse(MI.is_contiguous(), 'MI is contiguous')
self.assertEqual(E, torch.mm(M, MI), 1e-8, 'inverse value')
self.assertEqual(E, torch.mm(MI, M), 1e-8, 'inverse value')
MII = torch.Tensor(5, 5)
torch.inverse(M, out=MII)
self.assertFalse(MII.is_contiguous(), 'MII is contiguous')
self.assertEqual(MII, MI, 0, 'inverse value in-place')
# second call, now that MII is transposed
torch.inverse(M, out=MII)
self.assertFalse(MII.is_contiguous(), 'MII is contiguous')
self.assertEqual(MII, MI, 0, 'inverse value in-place')
def test_inverse(self):
M = torch.randn(5, 5)
MI = torch.inverse(M)
E = torch.eye(5)
self.assertFalse(MI.is_contiguous(), 'MI is contiguous')
self.assertEqual(E, torch.mm(M, MI), 1e-8, 'inverse value')
self.assertEqual(E, torch.mm(MI, M), 1e-8, 'inverse value')
MII = torch.Tensor(5, 5)
torch.inverse(M, out=MII)
self.assertFalse(MII.is_contiguous(), 'MII is contiguous')
self.assertEqual(MII, MI, 0, 'inverse value in-place')
# second call, now that MII is transposed
torch.inverse(M, out=MII)
self.assertFalse(MII.is_contiguous(), 'MII is contiguous')
self.assertEqual(MII, MI, 0, 'inverse value in-place')
def tset_potri(self):
a=torch.Tensor(((6.80, -2.11, 5.66, 5.97, 8.23),
(-6.05, -3.30, 5.36, -4.44, 1.08),
(-0.45, 2.58, -2.70, 0.27, 9.04),
(8.32, 2.71, 4.35, -7.17, 2.14),
(-9.67, -5.14, -7.26, 6.08, -6.87))).t()
# make sure 'a' is symmetric PSD
a = a * a.t()
# compute inverse directly
inv0 = torch.inverse(a)
# default case
chol = torch.potrf(a)
inv1 = torch.potri(chol)
self.assertLessEqual(inv0.dist(inv1), 1e-12)
# upper Triangular Test
chol = torch.potrf(a, 'U')
inv1 = torch.potri(chol, 'U')
self.assertLessEqual(inv0.dist(inv1), 1e-12)
# lower Triangular Test
chol = torch.potrf(a, 'L')
inv1 = torch.potri(chol, 'L')
self.assertLessEqual(inv0.dist(inv1), 1e-12)
def tset_potri(self):
a = torch.Tensor(((6.80, -2.11, 5.66, 5.97, 8.23),
(-6.05, -3.30, 5.36, -4.44, 1.08),
(-0.45, 2.58, -2.70, 0.27, 9.04),
(8.32, 2.71, 4.35, -7.17, 2.14),
(-9.67, -5.14, -7.26, 6.08, -6.87))).t()
# make sure 'a' is symmetric PSD
a = a * a.t()
# compute inverse directly
inv0 = torch.inverse(a)
# default case
chol = torch.potrf(a)
inv1 = torch.potri(chol)
self.assertLessEqual(inv0.dist(inv1), 1e-12)
# upper Triangular Test
chol = torch.potrf(a, 'U')
inv1 = torch.potri(chol, 'U')
self.assertLessEqual(inv0.dist(inv1), 1e-12)
# lower Triangular Test
chol = torch.potrf(a, 'L')
inv1 = torch.potri(chol, 'L')
self.assertLessEqual(inv0.dist(inv1), 1e-12)
def tset_potri(self):
a = torch.Tensor(((6.80, -2.11, 5.66, 5.97, 8.23),
(-6.05, -3.30, 5.36, -4.44, 1.08),
(-0.45, 2.58, -2.70, 0.27, 9.04),
(8.32, 2.71, 4.35, -7.17, 2.14),
(-9.67, -5.14, -7.26, 6.08, -6.87))).t()
# make sure 'a' is symmetric PSD
a = a * a.t()
# compute inverse directly
inv0 = torch.inverse(a)
# default case
chol = torch.potrf(a)
inv1 = torch.potri(chol)
self.assertLessEqual(inv0.dist(inv1), 1e-12)
# upper Triangular Test
chol = torch.potrf(a, 'U')
inv1 = torch.potri(chol, 'U')
self.assertLessEqual(inv0.dist(inv1), 1e-12)
# lower Triangular Test
chol = torch.potrf(a, 'L')
inv1 = torch.potri(chol, 'L')
self.assertLessEqual(inv0.dist(inv1), 1e-12)
def forward(ctx, input):
inverse = torch.inverse(input)
ctx.save_for_backward(inverse)
return inverse
def backward(ctx, grad_output):
inverse, = ctx.saved_variables
return -torch.mm(inverse.t(), torch.mm(grad_output, inverse.t()))
def tset_potri(self):
a = torch.Tensor(((6.80, -2.11, 5.66, 5.97, 8.23),
(-6.05, -3.30, 5.36, -4.44, 1.08),
(-0.45, 2.58, -2.70, 0.27, 9.04),
(8.32, 2.71, 4.35, -7.17, 2.14),
(-9.67, -5.14, -7.26, 6.08, -6.87))).t()
# make sure 'a' is symmetric PSD
a = a * a.t()
# compute inverse directly
inv0 = torch.inverse(a)
# default case
chol = torch.potrf(a)
inv1 = torch.potri(chol)
self.assertLessEqual(inv0.dist(inv1), 1e-12)
# upper Triangular Test
chol = torch.potrf(a, 'U')
inv1 = torch.potri(chol, 'U')
self.assertLessEqual(inv0.dist(inv1), 1e-12)
# lower Triangular Test
chol = torch.potrf(a, 'L')
inv1 = torch.potri(chol, 'L')
self.assertLessEqual(inv0.dist(inv1), 1e-12)
def tset_potri(self):
a = torch.Tensor(((6.80, -2.11, 5.66, 5.97, 8.23),
(-6.05, -3.30, 5.36, -4.44, 1.08),
(-0.45, 2.58, -2.70, 0.27, 9.04),
(8.32, 2.71, 4.35, -7.17, 2.14),
(-9.67, -5.14, -7.26, 6.08, -6.87))).t()
# make sure 'a' is symmetric PSD
a = a * a.t()
# compute inverse directly
inv0 = torch.inverse(a)
# default case
chol = torch.potrf(a)
inv1 = torch.potri(chol)
self.assertLessEqual(inv0.dist(inv1), 1e-12)
# upper Triangular Test
chol = torch.potrf(a, 'U')
inv1 = torch.potri(chol, 'U')
self.assertLessEqual(inv0.dist(inv1), 1e-12)
# lower Triangular Test
chol = torch.potrf(a, 'L')
inv1 = torch.potri(chol, 'L')
self.assertLessEqual(inv0.dist(inv1), 1e-12)
