我们从Python开源项目中，提取了以下17个代码示例，用于说明如何使用torch.tril()。
def __init__(self, nFeatures, args): super().__init__() nHidden, neq, nineq = 2*nFeatures-1,0,2*nFeatures-2 assert(neq==0) # self.fc1 = nn.Linear(nFeatures, nHidden) self.M = Variable(torch.tril(torch.ones(nHidden, nHidden)).cuda()) Q = 1e-8*torch.eye(nHidden) Q[:nFeatures,:nFeatures] = torch.eye(nFeatures) self.L = Variable(torch.potrf(Q)) self.D = Parameter(0.3*torch.randn(nFeatures-1, nFeatures)) # self.lam = Parameter(20.*torch.ones(1)) self.h = Variable(torch.zeros(nineq)) self.nFeatures = nFeatures self.nHidden = nHidden self.neq = neq self.nineq = nineq self.args = args
def __init__(self, nHidden=50, nineq=200, neq=0, eps=1e-4): super(LenetOptNet, self).__init__() self.conv1 = nn.Conv2d(1, 20, kernel_size=5) self.conv2 = nn.Conv2d(20, 50, kernel_size=5) self.qp_o = nn.Linear(50*4*4, nHidden) self.qp_z0 = nn.Linear(50*4*4, nHidden) self.qp_s0 = nn.Linear(50*4*4, nineq) assert(neq==0) self.M = Variable(torch.tril(torch.ones(nHidden, nHidden)).cuda()) self.L = Parameter(torch.tril(torch.rand(nHidden, nHidden).cuda())) self.G = Parameter(torch.Tensor(nineq,nHidden).uniform_(-1,1).cuda()) # self.z0 = Parameter(torch.zeros(nHidden).cuda()) # self.s0 = Parameter(torch.ones(nineq).cuda()) self.nHidden = nHidden self.nineq = nineq self.neq = neq self.eps = eps
def forward(self, L, z): ''' :param L: batch_size (B) x latent_size^2 (L^2) :param z: batch_size (B) x latent_size (L) :return: z_new = L*z ''' # L->tril(L) L_matrix = L.view( -1, self.args.z1_size, self.args.z1_size ) # resize to get B x L x L LTmask = torch.tril( torch.ones(self.args.z1_size, self.args.z1_size), k=-1 ) # lower-triangular mask matrix (1s in lower triangular part) I = Variable( torch.eye(self.args.z1_size, self.args.z1_size).expand(L_matrix.size(0), self.args.z1_size, self.args.z1_size) ) if self.args.cuda: LTmask = LTmask.cuda() I = I.cuda() LTmask = Variable(LTmask) LTmask = LTmask.unsqueeze(0).expand( L_matrix.size(0), self.args.z1_size, self.args.z1_size ) # 1 x L x L -> B x L x L LT = torch.mul( L_matrix, LTmask ) + I # here we get a batch of lower-triangular matrices with ones on diagonal # z_new = L * z z_new = torch.bmm( LT , z.unsqueeze(2) ).squeeze(2) # B x L x L * B x L x 1 -> B x L return z_new
def test_tril(self): x = torch.rand(SIZE, SIZE) res1 = torch.tril(x) res2 = torch.Tensor() torch.tril(res2, x) self.assertEqual(res1, res2, 0)
def __init__(self, nFeatures, nHidden, nCls, bn, nineq=200, neq=0, eps=1e-4): super().__init__() self.nFeatures = nFeatures self.nHidden = nHidden self.bn = bn self.nCls = nCls if bn: self.bn1 = nn.BatchNorm1d(nHidden) self.bn2 = nn.BatchNorm1d(nCls) self.fc1 = nn.Linear(nFeatures, nHidden) self.fc2 = nn.Linear(nHidden, nCls) # self.qp_z0 = nn.Linear(nCls, nCls) # self.qp_s0 = nn.Linear(nCls, nineq) assert(neq==0) self.M = Variable(torch.tril(torch.ones(nCls, nCls)).cuda()) self.L = Parameter(torch.tril(torch.rand(nCls, nCls).cuda())) self.G = Parameter(torch.Tensor(nineq,nCls).uniform_(-1,1).cuda()) self.z0 = Parameter(torch.zeros(nCls).cuda()) self.s0 = Parameter(torch.ones(nineq).cuda()) self.nineq = nineq self.neq = neq self.eps = eps
def test_tril(self): x = torch.rand(SIZE, SIZE) res1 = torch.tril(x) res2 = torch.Tensor() torch.tril(x, out=res2) self.assertEqual(res1, res2, 0)
def _make_cov(S): L = torch.tril(torch.rand(S, S)) return torch.mm(L, L.t())
def test_potrf(self): root = Variable(torch.tril(torch.rand(S, S)), requires_grad=True) def run_test(upper): def func(root): x = torch.mm(root, root.t()) return torch.potrf(x, upper) gradcheck(func, [root]) gradgradcheck(func, [root]) run_test(upper=True) run_test(upper=False)
def test_trtrs(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() b = torch.Tensor(((4.02, 6.19, -8.22, -7.57, -3.03), (-1.56, 4.00, -8.67, 1.75, 2.86), (9.81, -4.09, -4.57, -8.61, 8.99))).t() U = torch.triu(a) L = torch.tril(a) # solve Ux = b x = torch.trtrs(b, U)[0] self.assertLessEqual(b.dist(torch.mm(U, x)), 1e-12) x = torch.trtrs(b, U, True, False, False)[0] self.assertLessEqual(b.dist(torch.mm(U, x)), 1e-12) # solve Lx = b x = torch.trtrs(b, L, False)[0] self.assertLessEqual(b.dist(torch.mm(L, x)), 1e-12) x = torch.trtrs(b, L, False, False, False)[0] self.assertLessEqual(b.dist(torch.mm(L, x)), 1e-12) # solve U'x = b x = torch.trtrs(b, U, True, True)[0] self.assertLessEqual(b.dist(torch.mm(U.t(), x)), 1e-12) x = torch.trtrs(b, U, True, True, False)[0] self.assertLessEqual(b.dist(torch.mm(U.t(), x)), 1e-12) # solve U'x = b by manual transposition y = torch.trtrs(b, U.t(), False, False)[0] self.assertLessEqual(x.dist(y), 1e-12) # solve L'x = b x = torch.trtrs(b, L, False, True)[0] self.assertLessEqual(b.dist(torch.mm(L.t(), x)), 1e-12) x = torch.trtrs(b, L, False, True, False)[0] self.assertLessEqual(b.dist(torch.mm(L.t(), x)), 1e-12) # solve L'x = b by manual transposition y = torch.trtrs(b, L.t(), True, False)[0] self.assertLessEqual(x.dist(y), 1e-12) # test reuse res1 = torch.trtrs(b,a)[0] ta = torch.Tensor() tb = torch.Tensor() torch.trtrs(tb,ta,b,a) self.assertEqual(res1, tb, 0) tb.zero_() torch.trtrs(tb,ta,b,a) self.assertEqual(res1, tb, 0)
def __init__(self, nFeatures, args): super(OptNet, self).__init__() nHidden, neq, nineq = 2*nFeatures-1,0,2*nFeatures-2 assert(neq==0) self.fc1 = nn.Linear(nFeatures, nHidden) self.M = Variable(torch.tril(torch.ones(nHidden, nHidden)).cuda()) if args.tvInit: Q = 1e-8*torch.eye(nHidden) Q[:nFeatures,:nFeatures] = torch.eye(nFeatures) self.L = Parameter(torch.potrf(Q)) D = torch.zeros(nFeatures-1, nFeatures) D[:nFeatures-1,:nFeatures-1] = torch.eye(nFeatures-1) D[:nFeatures-1,1:nFeatures] -= torch.eye(nFeatures-1) G_ = block((( D, -torch.eye(nFeatures-1)), (-D, -torch.eye(nFeatures-1)))) self.G = Parameter(G_) self.s0 = Parameter(torch.ones(2*nFeatures-2)+1e-6*torch.randn(2*nFeatures-2)) G_pinv = (G_.t().mm(G_)+1e-5*torch.eye(nHidden)).inverse().mm(G_.t()) self.z0 = Parameter(-G_pinv.mv(self.s0.data)+1e-6*torch.randn(nHidden)) lam = 21.21 W_fc1, b_fc1 = self.fc1.weight, self.fc1.bias W_fc1.data[:,:] = 1e-3*torch.randn((2*nFeatures-1, nFeatures)) # W_fc1.data[:,:] = 0.0 W_fc1.data[:nFeatures,:nFeatures] += -torch.eye(nFeatures) # b_fc1.data[:] = torch.zeros(2*nFeatures-1) b_fc1.data[:] = 0.0 b_fc1.data[nFeatures:2*nFeatures-1] = lam else: self.L = Parameter(torch.tril(torch.rand(nHidden, nHidden))) self.G = Parameter(torch.Tensor(nineq,nHidden).uniform_(-1,1)) self.z0 = Parameter(torch.zeros(nHidden)) self.s0 = Parameter(torch.ones(nineq)) self.nFeatures = nFeatures self.nHidden = nHidden self.neq = neq self.nineq = nineq self.args = args
def test_trtrs(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() b = torch.Tensor(((4.02, 6.19, -8.22, -7.57, -3.03), (-1.56, 4.00, -8.67, 1.75, 2.86), (9.81, -4.09, -4.57, -8.61, 8.99))).t() U = torch.triu(a) L = torch.tril(a) # solve Ux = b x = torch.trtrs(b, U)[0] self.assertLessEqual(b.dist(torch.mm(U, x)), 1e-12) x = torch.trtrs(b, U, True, False, False)[0] self.assertLessEqual(b.dist(torch.mm(U, x)), 1e-12) # solve Lx = b x = torch.trtrs(b, L, False)[0] self.assertLessEqual(b.dist(torch.mm(L, x)), 1e-12) x = torch.trtrs(b, L, False, False, False)[0] self.assertLessEqual(b.dist(torch.mm(L, x)), 1e-12) # solve U'x = b x = torch.trtrs(b, U, True, True)[0] self.assertLessEqual(b.dist(torch.mm(U.t(), x)), 1e-12) x = torch.trtrs(b, U, True, True, False)[0] self.assertLessEqual(b.dist(torch.mm(U.t(), x)), 1e-12) # solve U'x = b by manual transposition y = torch.trtrs(b, U.t(), False, False)[0] self.assertLessEqual(x.dist(y), 1e-12) # solve L'x = b x = torch.trtrs(b, L, False, True)[0] self.assertLessEqual(b.dist(torch.mm(L.t(), x)), 1e-12) x = torch.trtrs(b, L, False, True, False)[0] self.assertLessEqual(b.dist(torch.mm(L.t(), x)), 1e-12) # solve L'x = b by manual transposition y = torch.trtrs(b, L.t(), True, False)[0] self.assertLessEqual(x.dist(y), 1e-12) # test reuse res1 = torch.trtrs(b, a)[0] ta = torch.Tensor() tb = torch.Tensor() torch.trtrs(b, a, out=(tb, ta)) self.assertEqual(res1, tb, 0) tb.zero_() torch.trtrs(b, a, out=(tb, ta)) self.assertEqual(res1, tb, 0)
