Python torch 模块，symeig() 实例源码
我们从Python开源项目中，提取了以下7个代码示例，用于说明如何使用torch.symeig()。
def test_symeig(self):
xval = torch.rand(100,3)
cov = torch.mm(xval.t(), xval)
rese = torch.zeros(3)
resv = torch.zeros(3,3)
# First call to symeig
self.assertTrue(resv.is_contiguous(), 'resv is not contiguous')
torch.symeig(rese, resv, cov.clone(), True)
ahat = torch.mm(torch.mm(resv, torch.diag(rese)), resv.t())
self.assertEqual(cov, ahat, 1e-8, 'VeV\' wrong')
# Second call to symeig
self.assertFalse(resv.is_contiguous(), 'resv is contiguous')
torch.symeig(rese, resv, cov.clone(), True)
ahat = torch.mm(torch.mm(resv, torch.diag(rese)), resv.t())
self.assertEqual(cov, ahat, 1e-8, 'VeV\' wrong')
# test non-contiguous
X = torch.rand(5, 5)
X = X.t() * X
e = torch.zeros(4, 2).select(1, 1)
v = torch.zeros(4, 2, 4)[:,1]
self.assertFalse(v.is_contiguous(), 'V is contiguous')
self.assertFalse(e.is_contiguous(), 'E is contiguous')
torch.symeig(e, v, X, True)
Xhat = torch.mm(torch.mm(v, torch.diag(e)), v.t())
self.assertEqual(X, Xhat, 1e-8, 'VeV\' wrong')
def test_symeig(self):
xval = torch.rand(100, 3)
cov = torch.mm(xval.t(), xval)
rese = torch.zeros(3)
resv = torch.zeros(3, 3)
# First call to symeig
self.assertTrue(resv.is_contiguous(), 'resv is not contiguous')
torch.symeig(cov.clone(), True, out=(rese, resv))
ahat = torch.mm(torch.mm(resv, torch.diag(rese)), resv.t())
self.assertEqual(cov, ahat, 1e-8, 'VeV\' wrong')
# Second call to symeig
self.assertFalse(resv.is_contiguous(), 'resv is contiguous')
torch.symeig(cov.clone(), True, out=(rese, resv))
ahat = torch.mm(torch.mm(resv, torch.diag(rese)), resv.t())
self.assertEqual(cov, ahat, 1e-8, 'VeV\' wrong')
# test non-contiguous
X = torch.rand(5, 5)
X = X.t() * X
e = torch.zeros(4, 2).select(1, 1)
v = torch.zeros(4, 2, 4)[:, 1]
self.assertFalse(v.is_contiguous(), 'V is contiguous')
self.assertFalse(e.is_contiguous(), 'E is contiguous')
torch.symeig(X, True, out=(e, v))
Xhat = torch.mm(torch.mm(v, torch.diag(e)), v.t())
self.assertEqual(X, Xhat, 1e-8, 'VeV\' wrong')
def test_symeig(self):
xval = torch.rand(100, 3)
cov = torch.mm(xval.t(), xval)
rese = torch.zeros(3)
resv = torch.zeros(3, 3)
# First call to symeig
self.assertTrue(resv.is_contiguous(), 'resv is not contiguous')
torch.symeig(cov.clone(), True, out=(rese, resv))
ahat = torch.mm(torch.mm(resv, torch.diag(rese)), resv.t())
self.assertEqual(cov, ahat, 1e-8, 'VeV\' wrong')
# Second call to symeig
self.assertFalse(resv.is_contiguous(), 'resv is contiguous')
torch.symeig(cov.clone(), True, out=(rese, resv))
ahat = torch.mm(torch.mm(resv, torch.diag(rese)), resv.t())
self.assertEqual(cov, ahat, 1e-8, 'VeV\' wrong')
# test non-contiguous
X = torch.rand(5, 5)
X = X.t() * X
e = torch.zeros(4, 2).select(1, 1)
v = torch.zeros(4, 2, 4)[:, 1]
self.assertFalse(v.is_contiguous(), 'V is contiguous')
self.assertFalse(e.is_contiguous(), 'E is contiguous')
torch.symeig(X, True, out=(e, v))
Xhat = torch.mm(torch.mm(v, torch.diag(e)), v.t())
self.assertEqual(X, Xhat, 1e-8, 'VeV\' wrong')
def test_symeig(self):
xval = torch.rand(100, 3)
cov = torch.mm(xval.t(), xval)
rese = torch.zeros(3)
resv = torch.zeros(3, 3)
# First call to symeig
self.assertTrue(resv.is_contiguous(), 'resv is not contiguous')
torch.symeig(cov.clone(), True, out=(rese, resv))
ahat = torch.mm(torch.mm(resv, torch.diag(rese)), resv.t())
self.assertEqual(cov, ahat, 1e-8, 'VeV\' wrong')
# Second call to symeig
self.assertFalse(resv.is_contiguous(), 'resv is contiguous')
torch.symeig(cov.clone(), True, out=(rese, resv))
ahat = torch.mm(torch.mm(resv, torch.diag(rese)), resv.t())
self.assertEqual(cov, ahat, 1e-8, 'VeV\' wrong')
# test non-contiguous
X = torch.rand(5, 5)
X = X.t() * X
e = torch.zeros(4, 2).select(1, 1)
v = torch.zeros(4, 2, 4)[:, 1]
self.assertFalse(v.is_contiguous(), 'V is contiguous')
self.assertFalse(e.is_contiguous(), 'E is contiguous')
torch.symeig(X, True, out=(e, v))
Xhat = torch.mm(torch.mm(v, torch.diag(e)), v.t())
self.assertEqual(X, Xhat, 1e-8, 'VeV\' wrong')
def test_symeig(self):
# Small case
tensor = torch.randn(3, 3).cuda()
tensor = torch.mm(tensor, tensor.t())
eigval, eigvec = torch.symeig(tensor, eigenvectors=True)
self.assertEqual(tensor, torch.mm(torch.mm(eigvec, eigval.diag()), eigvec.t()))
# Large case
tensor = torch.randn(257, 257).cuda()
tensor = torch.mm(tensor, tensor.t())
eigval, eigvec = torch.symeig(tensor, eigenvectors=True)
self.assertEqual(tensor, torch.mm(torch.mm(eigvec, eigval.diag()), eigvec.t()))
def test_symeig(self):
xval = torch.rand(100, 3)
cov = torch.mm(xval.t(), xval)
rese = torch.zeros(3)
resv = torch.zeros(3, 3)
# First call to symeig
self.assertTrue(resv.is_contiguous(), 'resv is not contiguous')
torch.symeig(cov.clone(), True, out=(rese, resv))
ahat = torch.mm(torch.mm(resv, torch.diag(rese)), resv.t())
self.assertEqual(cov, ahat, 1e-8, 'VeV\' wrong')
# Second call to symeig
self.assertFalse(resv.is_contiguous(), 'resv is contiguous')
torch.symeig(cov.clone(), True, out=(rese, resv))
ahat = torch.mm(torch.mm(resv, torch.diag(rese)), resv.t())
self.assertEqual(cov, ahat, 1e-8, 'VeV\' wrong')
# test non-contiguous
X = torch.rand(5, 5)
X = X.t() * X
e = torch.zeros(4, 2).select(1, 1)
v = torch.zeros(4, 2, 4)[:, 1]
self.assertFalse(v.is_contiguous(), 'V is contiguous')
self.assertFalse(e.is_contiguous(), 'E is contiguous')
torch.symeig(X, True, out=(e, v))
Xhat = torch.mm(torch.mm(v, torch.diag(e)), v.t())
self.assertEqual(X, Xhat, 1e-8, 'VeV\' wrong')
def step(self):
# Add weight decay
if self.weight_decay > 0:
for p in self.model.parameters():
p.grad.data.add_(self.weight_decay, p.data)
updates = {}
for i, m in enumerate(self.modules):
assert len(list(m.parameters())
) == 1, "Can handle only one parameter at the moment"
classname = m.__class__.__name__
p = next(m.parameters())
la = self.damping + self.weight_decay
if self.steps % self.Tf == 0:
# My asynchronous implementation exists, I will add it later.
# Experimenting with different ways to this in PyTorch.
self.d_a[m], self.Q_a[m] = torch.symeig(
self.m_aa[m], eigenvectors=True)
self.d_g[m], self.Q_g[m] = torch.symeig(
self.m_gg[m], eigenvectors=True)
self.d_a[m].mul_((self.d_a[m] > 1e-6).float())
self.d_g[m].mul_((self.d_g[m] > 1e-6).float())
if classname == 'Conv2d':
p_grad_mat = p.grad.data.view(p.grad.data.size(0), -1)
else:
p_grad_mat = p.grad.data
v1 = self.Q_g[m].t() @ p_grad_mat @ self.Q_a[m]
v2 = v1 / (
self.d_g[m].unsqueeze(1) * self.d_a[m].unsqueeze(0) + la)
v = self.Q_g[m] @ v2 @ self.Q_a[m].t()
v = v.view(p.grad.data.size())
updates[p] = v
vg_sum = 0
for p in self.model.parameters():
v = updates[p]
vg_sum += (v * p.grad.data * self.lr * self.lr).sum()
nu = min(1, math.sqrt(self.kl_clip / vg_sum))
for p in self.model.parameters():
v = updates[p]
p.grad.data.copy_(v)
p.grad.data.mul_(nu)
self.optim.step()
self.steps += 1