python柱状图zzt_Python torch.diag方法代碼示例
本文整理匯總了Python中torch.diag方法的典型用法代碼示例。如果您正苦於以下問題：Python torch.diag方法的具體用法？Python torch.diag怎麽用？Python torch.diag使用的例子？那麽恭喜您, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在模塊torch的用法示例。
在下文中一共展示了torch.diag方法的29個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於我們的係統推薦出更棒的Python代碼示例。
示例1: fuse_conv_and_bn
點讚 6
# 需要導入模塊: import torch [as 別名]
# 或者: from torch import diag [as 別名]
def fuse_conv_and_bn(conv, bn):
# https://tehnokv.com/posts/fusing-batchnorm-and-conv/
with torch.no_grad():
# init
fusedconv = torch.nn.Conv2d(conv.in_channels,
conv.out_channels,
kernel_size=conv.kernel_size,
stride=conv.stride,
padding=conv.padding,
bias=True)
# prepare filters
w_conv = conv.weight.clone().view(conv.out_channels, -1)
w_bn = torch.diag(bn.weight.div(torch.sqrt(bn.eps + bn.running_var)))
fusedconv.weight.copy_(torch.mm(w_bn, w_conv).view(fusedconv.weight.size()))
# prepare spatial bias
if conv.bias is not None:
b_conv = conv.bias
else:
b_conv = torch.zeros(conv.weight.size(0))
b_bn = bn.bias - bn.weight.mul(bn.running_mean).div(torch.sqrt(bn.running_var + bn.eps))
fusedconv.bias.copy_(b_conv + b_bn)
return fusedconv
開發者ID:zbyuan，項目名稱:pruning_yolov3，代碼行數:27，
示例2: _mix_rbf_kernel
點讚 6
# 需要導入模塊: import torch [as 別名]
# 或者: from torch import diag [as 別名]
def _mix_rbf_kernel(X, Y, sigma_list):
assert(X.size(0) == Y.size(0))
m = X.size(0)
Z = torch.cat((X, Y), 0)
ZZT = torch.mm(Z, Z.t())
diag_ZZT = torch.diag(ZZT).unsqueeze(1)
Z_norm_sqr = diag_ZZT.expand_as(ZZT)
exponent = Z_norm_sqr - 2 * ZZT + Z_norm_sqr.t()
K = 0.0
for sigma in sigma_list:
gamma = 1.0 / (2 * sigma**2)
K += torch.exp(-gamma * exponent)
return K[:m, :m], K[:m, m:], K[m:, m:], len(sigma_list)
開發者ID:jindongwang，項目名稱:transferlearning，代碼行數:18，
示例3: construct_diag
點讚 6
# 需要導入模塊: import torch [as 別名]
# 或者: from torch import diag [as 別名]
def construct_diag(x: torch.Tensor):
"""
Constructs a diagonal matrix based on batched data. Solution found here:
https://stackoverflow.com/questions/47372508/how-to-construct-a-3d-tensor-where-every-2d-sub-tensor-is-a-diagonal-matrix-in-p
Do note that it only considers the last axis.
:param x: The tensor
"""
if x.dim() < 1:
return x
elif x.shape[-1] < 2:
return x.unsqueeze(-1)
elif x.dim() < 2:
return torch.diag(x)
b = torch.eye(x.size(-1), device=x.device)
c = x.unsqueeze(-1).expand(*x.size(), x.size(-1))
return c * b
開發者ID:tingiskhan，項目名稱:pyfilter，代碼行數:21，
示例4: test_UnscentedTransform2D
點讚 6
# 需要導入模塊: import torch [as 別名]
# 或者: from torch import diag [as 別名]
def test_UnscentedTransform2D(self):
# ===== 2D model ===== #
mat = torch.eye(2)
scale = torch.diag(mat)
norm = Normal(0., 1.)
mvn = MultivariateNormal(torch.zeros(2), torch.eye(2))
mvnlinear = AffineProcess((fmvn, g), (mat, scale), mvn, mvn)
mvnoblinear = AffineObservations((fomvn, gomvn), (1.,), norm)
mvnmodel = StateSpaceModel(mvnlinear, mvnoblinear)
# ===== Perform unscented transform ===== #
uft = UnscentedFilterTransform(mvnmodel)
res = uft.initialize(3000)
p = uft.predict(res)
c = uft.correct(0., p)
assert isinstance(c.x_dist(), MultivariateNormal) and c.x_dist().mean.shape == torch.Size([3000, 2])
開發者ID:tingiskhan，項目名稱:pyfilter，代碼行數:21，
示例5: __init__
點讚 6
# 需要導入模塊: import torch [as 別名]
# 或者: from torch import diag [as 別名]
def __init__(self,in_channel):
super(InvConv,self).__init__()
weight=np.random.randn(in_channel,in_channel)
q,_=linalg.qr(weight)
w_p,w_l,w_u=linalg.lu(q.astype(np.float32))
w_s=np.diag(w_u)
w_u=np.triu(w_u,1)
u_mask=np.triu(np.ones_like(w_u),1)
l_mask=u_mask.T
self.register_buffer('w_p',torch.from_numpy(w_p))
self.register_buffer('u_mask',torch.from_numpy(u_mask))
self.register_buffer('l_mask',torch.from_numpy(l_mask))
self.register_buffer('l_eye',torch.eye(l_mask.shape[0]))
self.register_buffer('s_sign',torch.sign(torch.from_numpy(w_s)))
self.w_l=torch.nn.Parameter(torch.from_numpy(w_l))
self.w_s=torch.nn.Parameter(torch.log(1e-7+torch.abs(torch.from_numpy(w_s))))
self.w_u=torch.nn.Parameter(torch.from_numpy(w_u))
self.weight=None
self.invweight=None
return
開發者ID:joansj，項目名稱:blow，代碼行數:26，
示例6: forward
點讚 6
# 需要導入模塊: import torch [as 別名]
# 或者: from torch import diag [as 別名]
def forward(self, input):
laplacian = input.exp() + self.eps
output = input.clone()
for b in range(input.size(0)):
lap = laplacian[b].masked_fill(
Variable(torch.eye(input.size(1)).cuda().ne(0)), 0)
lap = -lap + torch.diag(lap.sum(0))
# store roots on diagonal
lap[0] = input[b].diag().exp()
inv_laplacian = lap.inverse()
factor = inv_laplacian.diag().unsqueeze(1)\
.expand_as(input[b]).transpose(0, 1)
term1 = input[b].exp().mul(factor).clone()
term2 = input[b].exp().mul(inv_laplacian.transpose(0, 1)).clone()
term1[:, 0] = 0
term2[0] = 0
output[b] = term1 - term2
roots_output = input[b].diag().exp().mul(
inv_laplacian.transpose(0, 1)[0])
output[b] = output[b] + torch.diag(roots_output)
return output
開發者ID:xiadingZ，項目名稱:video-caption-openNMT.pytorch，代碼行數:24，
示例7: __expm__
點讚 6
# 需要導入模塊: import torch [as 別名]
# 或者: from torch import diag [as 別名]
def __expm__(self, matrix, symmetric):
r"""Calculates matrix exponential.
Args:
matrix (Tensor): Matrix to take exponential of.
symmetric (bool): Specifies whether the matrix is symmetric.
:rtype: (:class:`Tensor`)
"""
if symmetric:
e, V = torch.symeig(matrix, eigenvectors=True)
diff_mat = V @ torch.diag(e.exp()) @ V.t()
else:
diff_mat_np = expm(matrix.cpu().numpy())
diff_mat = torch.Tensor(diff_mat_np).to(matrix.device)
return diff_mat
開發者ID:rusty1s，項目名稱:pytorch_geometric，代碼行數:18，
示例8: regularizer_orth2
點讚 6
# 需要導入模塊: import torch [as 別名]
# 或者: from torch import diag [as 別名]
def regularizer_orth2(m):
"""
# ----------------------------------------
# Applies regularization to the training by performing the
# orthogonalization technique described in the paper
# This function is to be called by the torch.nn.Module.apply() method,
# which applies svd_orthogonalization() to every layer of the model.
# usage: net.apply(regularizer_orth2)
# ----------------------------------------
"""
classname = m.__class__.__name__
if classname.find('Conv') != -1:
w = m.weight.data.clone()
c_out, c_in, f1, f2 = w.size()
# dtype = m.weight.data.type()
w = w.permute(2, 3, 1, 0).contiguous().view(f1*f2*c_in, c_out)
u, s, v = torch.svd(w)
s_mean = s.mean()
s[s > 1.5*s_mean] = s[s > 1.5*s_mean] - 1e-4
s[s < 0.5*s_mean] = s[s < 0.5*s_mean] + 1e-4
w = torch.mm(torch.mm(u, torch.diag(s)), v.t())
m.weight.data = w.view(f1, f2, c_in, c_out).permute(3, 2, 0, 1) # .type(dtype)
else:
pass
開發者ID:cszn，項目名稱:KAIR，代碼行數:26，
示例9: get_loadings
點讚 6
# 需要導入模塊: import torch [as 別名]
# 或者: from torch import diag [as 別名]
def get_loadings(self) -> np.ndarray:
"""Extract per-gene weights (for each Z, shape is genes by dim(Z)) in the linear decoder."""
# This is BW, where B is diag(b) batch norm, W is weight matrix
if self.use_batch_norm is True:
w = self.decoder.factor_regressor.fc_layers[0][0].weight
bn = self.decoder.factor_regressor.fc_layers[0][1]
sigma = torch.sqrt(bn.running_var + bn.eps)
gamma = bn.weight
b = gamma / sigma
bI = torch.diag(b)
loadings = torch.matmul(bI, w)
else:
loadings = self.decoder.factor_regressor.fc_layers[0][0].weight
loadings = loadings.detach().cpu().numpy()
if self.n_batch > 1:
loadings = loadings[:, : -self.n_batch]
return loadings
開發者ID:YosefLab，項目名稱:scVI，代碼行數:20，
示例10: _regularizer
點讚 6
# 需要導入模塊: import torch [as 別名]
# 或者: from torch import diag [as 別名]
def _regularizer(self, mu_z, std_z):
kld_z = self.z_prior_stdv.log() - std_z.log() + (std_z ** 2 + (mu_z.pow(2) - self.z_prior_mean)) /\
(2 * self.z_prior_stdv.pow(2)) - 0.5
regularizer_loss = kld_z.sum()
regularizer_loss = self.beta * regularizer_loss
cov_mu_z = self._get_covariance_mu_z(mu_z)
if self.mode == "i":
dipvae_regularizer_loss = self._get_dipvae_regularizer(cov_mu_z, self.lambda_offdiag, self.lambda_diag)
elif self.mode == "ii":
cov_z = cov_mu_z + torch.mean(torch.diag(std_z**2), dim=0)
dipvae_regularizer_loss = self._get_dipvae_regularizer(cov_z, self.lambda_offdiag, self.lambda_diag)
else:
raise NotImplementedError("Unsupported dipvae mode.")
return regularizer_loss + dipvae_regularizer_loss
開發者ID:IBM，項目名稱:AIX360，代碼行數:21，
示例11: __init__
點讚 6
# 需要導入模塊: import torch [as 別名]
# 或者: from torch import diag [as 別名]
def __init__(self, num_inputs):
super(LUInvertibleMM, self).__init__()
self.W = torch.Tensor(num_inputs, num_inputs)
nn.init.orthogonal_(self.W)
self.L_mask = torch.tril(torch.ones(self.W.size()), -1)
self.U_mask = self.L_mask.t().clone()
P, L, U = sp.linalg.lu(self.W.numpy())
self.P = torch.from_numpy(P)
self.L = nn.Parameter(torch.from_numpy(L))
self.U = nn.Parameter(torch.from_numpy(U))
S = np.diag(U)
sign_S = np.sign(S)
log_S = np.log(abs(S))
self.sign_S = torch.from_numpy(sign_S)
self.log_S = nn.Parameter(torch.from_numpy(log_S))
self.I = torch.eye(self.L.size(0))
開發者ID:ikostrikov，項目名稱:pytorch-flows，代碼行數:21，
示例12: forward
點讚 6
# 需要導入模塊: import torch [as 別名]
# 或者: from torch import diag [as 別名]
def forward(self, inputs, cond_inputs=None, mode='direct'):
if str(self.L_mask.device) != str(self.L.device):
self.L_mask = self.L_mask.to(self.L.device)
self.U_mask = self.U_mask.to(self.L.device)
self.I = self.I.to(self.L.device)
self.P = self.P.to(self.L.device)
self.sign_S = self.sign_S.to(self.L.device)
L = self.L * self.L_mask + self.I
U = self.U * self.U_mask + torch.diag(
self.sign_S * torch.exp(self.log_S))
W = self.P @ L @ U
if mode == 'direct':
return inputs @ W, self.log_S.sum().unsqueeze(0).unsqueeze(
0).repeat(inputs.size(0), 1)
else:
return inputs @ torch.inverse(
W), -self.log_S.sum().unsqueeze(0).unsqueeze(0).repeat(
inputs.size(0), 1)
開發者ID:ikostrikov，項目名稱:pytorch-flows，代碼行數:22，
示例13: test_np
點讚 6
# 需要導入模塊: import torch [as 別名]
# 或者: from torch import diag [as 別名]
def test_np():
npr.seed(0)
nx, nineq, neq = 4, 6, 7
Q = npr.randn(nx, nx)
G = npr.randn(nineq, nx)
A = npr.randn(neq, nx)
D = np.diag(npr.rand(nineq))
K_ = np.bmat((
(Q, np.zeros((nx, nineq)), G.T, A.T),
(np.zeros((nineq, nx)), D, np.eye(nineq), np.zeros((nineq, neq))),
(G, np.eye(nineq), np.zeros((nineq, nineq + neq))),
(A, np.zeros((neq, nineq + nineq + neq)))
))
K = block((
(Q, 0, G.T, A.T),
(0, D, 'I', 0),
(G, 'I', 0, 0),
(A, 0, 0, 0)
))
assert np.allclose(K_, K)
開發者ID:bamos，項目名稱:block，代碼行數:26，
示例14: loss_l2
點讚 6
# 需要導入模塊: import torch [as 別名]
# 或者: from torch import diag [as 別名]
def loss_l2(self, l2=0):
"""L2 loss centered around mu_init, scaled optionally per-source.
In other words, diagonal Tikhonov regularization,
||D(\mu-\mu_{init})||_2^2
where D is diagonal.
Args:
- l2: A float or np.array representing the per-source regularization
strengths to use
"""
if isinstance(l2, (int, float)):
D = l2 * torch.eye(self.d)
else:
D = torch.diag(torch.from_numpy(l2))
# Note that mu is a matrix and this is the *Frobenius norm*
return torch.norm(D @ (self.mu - self.mu_init)) ** 2
開發者ID:HazyResearch，項目名稱:metal，代碼行數:20，
示例15: _set_class_balance
點讚 6
# 需要導入模塊: import torch [as 別名]
# 或者: from torch import diag [as 別名]
def _set_class_balance(self, class_balance, Y_dev):
"""Set a prior for the class balance
In order of preference:
1) Use user-provided class_balance
2) Estimate balance from Y_dev
3) Assume uniform class distribution
"""
if class_balance is not None:
self.p = np.array(class_balance)
elif Y_dev is not None:
class_counts = Counter(Y_dev)
sorted_counts = np.array([v for k, v in sorted(class_counts.items())])
self.p = sorted_counts / sum(sorted_counts)
else:
self.p = (1 / self.k) * np.ones(self.k)
self.P = torch.diag(torch.from_numpy(self.p)).float()
開發者ID:HazyResearch，項目名稱:metal，代碼行數:19，
示例16: __init__
點讚 6
# 需要導入模塊: import torch [as 別名]
# 或者: from torch import diag [as 別名]
def __init__(self, params, eps=1e-2):
super(SolveNewsvendor, self).__init__()
k = len(params['d'])
self.Q = Variable(torch.diag(torch.Tensor(
[params['c_quad']] + [params['b_quad']]*k + [params['h_quad']]*k)) \
.cuda())
self.p = Variable(torch.Tensor(
[params['c_lin']] + [params['b_lin']]*k + [params['h_lin']]*k) \
.cuda())
self.G = Variable(torch.cat([
torch.cat([-torch.ones(k,1), -torch.eye(k), torch.zeros(k,k)], 1),
torch.cat([torch.ones(k,1), torch.zeros(k,k), -torch.eye(k)], 1),
-torch.eye(1 + 2*k)], 0).cuda())
self.h = Variable(torch.Tensor(
np.concatenate([-params['d'], params['d'], np.zeros(1+ 2*k)])).cuda())
self.one = Variable(torch.Tensor([1])).cuda()
self.eps_eye = eps * Variable(torch.eye(1 + 2*k).cuda()).unsqueeze(0)
開發者ID:locuslab，項目名稱:e2e-model-learning，代碼行數:19，
示例17: forward
點讚 6
# 需要導入模塊: import torch [as 別名]
# 或者: from torch import diag [as 別名]
def forward(self, y):
nBatch, k = y.size()
Q_scale = torch.cat([torch.diag(torch.cat(
[self.one, y[i], y[i]])).unsqueeze(0) for i in range(nBatch)], 0)
Q = self.Q.unsqueeze(0).expand_as(Q_scale).mul(Q_scale)
p_scale = torch.cat([Variable(torch.ones(nBatch,1).cuda()), y, y], 1)
p = self.p.unsqueeze(0).expand_as(p_scale).mul(p_scale)
G = self.G.unsqueeze(0).expand(nBatch, self.G.size(0), self.G.size(1))
h = self.h.unsqueeze(0).expand(nBatch, self.h.size(0))
e = Variable(torch.Tensor().cuda()).double()
out = QPFunction(verbose=False)\
(Q.double(), p.double(), G.double(), h.double(), e, e).float()
return out[:,:1]
開發者ID:locuslab，項目名稱:e2e-model-learning，代碼行數:18，
示例18: forward
點讚 6
# 需要導入模塊: import torch [as 別名]
# 或者: from torch import diag [as 別名]
def forward(self, y):
nBatch, k = y.size()
eps2 = 1e-8
Q_scale = torch.cat([torch.diag(torch.cat(
[self.one, y[i]+eps2, y[i]+eps2])).unsqueeze(0) for i in range(nBatch)], 0)
Q = self.Q.unsqueeze(0).expand_as(Q_scale).mul(Q_scale)
p_scale = torch.cat([Variable(torch.ones(nBatch,1).cuda()), y, y], 1)
p = self.p.unsqueeze(0).expand_as(p_scale).mul(p_scale)
G = self.G.unsqueeze(0).expand(nBatch, self.G.size(0), self.G.size(1))
h = self.h.unsqueeze(0).expand(nBatch, self.h.size(0))
e = Variable(torch.Tensor().cuda()).double()
out = QPFunction(verbose=False)\
(Q.double(), p.double(), G.double(), h.double(), e, e).float()
return out[:,:1]
開發者ID:locuslab，項目名稱:e2e-model-learning，代碼行數:19，
示例19: symsqrt
點讚 6
# 需要導入模塊: import torch [as 別名]
# 或者: from torch import diag [as 別名]
def symsqrt(a, cond=None, return_rank=False, dtype=torch.float32):
"""Symmetric square root of a positive semi-definite matrix.
See https://github.com/pytorch/pytorch/issues/25481"""
s, u = torch.symeig(a, eigenvectors=True)
cond_dict = {torch.float32: 1e3 * 1.1920929e-07, torch.float64: 1E6 * 2.220446049250313e-16}
if cond in [None, -1]:
cond = cond_dict[dtype]
above_cutoff = (abs(s) > cond * torch.max(abs(s)))
psigma_diag = torch.sqrt(s[above_cutoff])
u = u[:, above_cutoff]
B = u @ torch.diag(psigma_diag) @ u.t()
if return_rank:
return B, len(psigma_diag)
else:
return B
開發者ID:cybertronai，項目名稱:autograd-hacks，代碼行數:22，
示例20: forward
點讚 6
# 需要導入模塊: import torch [as 別名]
# 或者: from torch import diag [as 別名]
def forward(self, input):
laplacian = input.exp() + self.eps
output = input.clone()
for b in range(input.size(0)):
lap = laplacian[b].masked_fill(
torch.eye(input.size(1)).cuda().ne(0), 0)
lap = -lap + torch.diag(lap.sum(0))
# store roots on diagonal
lap[0] = input[b].diag().exp()
inv_laplacian = lap.inverse()
factor = inv_laplacian.diag().unsqueeze(1)\
.expand_as(input[b]).transpose(0, 1)
term1 = input[b].exp().mul(factor).clone()
term2 = input[b].exp().mul(inv_laplacian.transpose(0, 1)).clone()
term1[:, 0] = 0
term2[0] = 0
output[b] = term1 - term2
roots_output = input[b].diag().exp().mul(
inv_laplacian.transpose(0, 1)[0])
output[b] = output[b] + torch.diag(roots_output)
return output
開發者ID:lizekang，項目名稱:ITDD，代碼行數:24，
示例21: normalize_adj_tensor
點讚 6
# 需要導入模塊: import torch [as 別名]
# 或者: from torch import diag [as 別名]
def normalize_adj_tensor(adj, sparse=False):
"""Normalize adjacency tensor matrix.
"""
device = torch.device("cuda" if adj.is_cuda else "cpu")
if sparse:
# TODO if this is too slow, uncomment the following code,
# but you need to install torch_scatter
# return normalize_sparse_tensor(adj)
adj = to_scipy(adj)
mx = normalize_adj(adj)
return sparse_mx_to_torch_sparse_tensor(mx).to(device)
else:
mx = adj + torch.eye(adj.shape[0]).to(device)
rowsum = mx.sum(1)
r_inv = rowsum.pow(-1/2).flatten()
r_inv[torch.isinf(r_inv)] = 0.
r_mat_inv = torch.diag(r_inv)
mx = r_mat_inv @ mx
mx = mx @ r_mat_inv
return mx
開發者ID:DSE-MSU，項目名稱:DeepRobust，代碼行數:22，
示例22: degree_normalize_adj_tensor
點讚 6
# 需要導入模塊: import torch [as 別名]
# 或者: from torch import diag [as 別名]
def degree_normalize_adj_tensor(adj, sparse=True):
"""degree_normalize_adj_tensor.
"""
device = torch.device("cuda" if adj.is_cuda else "cpu")
if sparse:
# return degree_normalize_sparse_tensor(adj)
adj = to_scipy(adj)
mx = degree_normalize_adj(adj)
return sparse_mx_to_torch_sparse_tensor(mx).to(device)
else:
mx = adj + torch.eye(adj.shape[0]).to(device)
rowsum = mx.sum(1)
r_inv = rowsum.pow(-1).flatten()
r_inv[torch.isinf(r_inv)] = 0.
r_mat_inv = torch.diag(r_inv)
mx = r_mat_inv @ mx
return mx
開發者ID:DSE-MSU，項目名稱:DeepRobust，代碼行數:20，
示例23: prox_nuclear_truncated_2
點讚 6
# 需要導入模塊: import torch [as 別名]
# 或者: from torch import diag [as 別名]
def prox_nuclear_truncated_2(self, data, alpha, k=50):
import tensorly as tl
tl.set_backend('pytorch')
U, S, V = tl.truncated_svd(data.cpu(), n_eigenvecs=k)
U, S, V = torch.FloatTensor(U).cuda(), torch.FloatTensor(S).cuda(), torch.FloatTensor(V).cuda()
self.nuclear_norm = S.sum()
# print("nuclear norm: %.4f" % self.nuclear_norm)
S = torch.clamp(S-alpha, min=0)
indices = torch.tensor(range(0, U.shape[0]),range(0, U.shape[0])).cuda()
values = S
diag_S = torch.sparse.FloatTensor(indices, values, torch.Size(U.shape))
# diag_S = torch.diag(torch.clamp(S-alpha, min=0))
U = torch.spmm(U, diag_S)
V = torch.matmul(U, V)
return V
開發者ID:DSE-MSU，項目名稱:DeepRobust，代碼行數:18，
示例24: feature_smoothing
點讚 6
# 需要導入模塊: import torch [as 別名]
# 或者: from torch import diag [as 別名]
def feature_smoothing(self, adj, X):
adj = (adj.t() + adj)/2
rowsum = adj.sum(1)
r_inv = rowsum.flatten()
D = torch.diag(r_inv)
L = D - adj
r_inv = r_inv + 1e-3
r_inv = r_inv.pow(-1/2).flatten()
r_inv[torch.isinf(r_inv)] = 0.
r_mat_inv = torch.diag(r_inv)
# L = r_mat_inv @ L
L = r_mat_inv @ L @ r_mat_inv
XLXT = torch.matmul(torch.matmul(X.t(), L), X)
loss_smooth_feat = torch.trace(XLXT)
return loss_smooth_feat
開發者ID:DSE-MSU，項目名稱:DeepRobust，代碼行數:19，
示例25: zca_matrix
點讚 6
# 需要導入模塊: import torch [as 別名]
# 或者: from torch import diag [as 別名]
def zca_matrix(data_tensor):
"""
Helper function: compute ZCA whitening matrix across a dataset ~ (N, C, H, W).
"""
# 1. flatten dataset:
X = data_tensor.view(data_tensor.shape[0], -1)
# 2. zero-center the matrix:
X = rescale(X, -1., 1.)
# 3. compute covariances:
cov = torch.t(X) @ X
# 4. compute ZCA(X) == U @ (diag(1/S)) @ torch.t(V) where U, S, V = SVD(cov):
U, S, V = torch.svd(cov)
return (U @ torch.diag(torch.reciprocal(S)) @ torch.t(V))
開發者ID:paultsw，項目名稱:nice_pytorch，代碼行數:18，
示例26: forward
點讚 6
# 需要導入模塊: import torch [as 別名]
# 或者: from torch import diag [as 別名]
def forward(self, xs):
"""
Forward pass through all invertible coupling layers.
Args:
* xs: float tensor of shape (B,dim).
Returns:
* ys: float tensor of shape (B,dim).
"""
ys = self.layer1(xs)
ys = self.layer2(ys)
ys = self.layer3(ys)
ys = self.layer4(ys)
ys = torch.matmul(ys, torch.diag(torch.exp(self.scaling_diag)))
return ys
開發者ID:paultsw，項目名稱:nice_pytorch，代碼行數:18，
示例27: greedy_decoder
點讚 6
# 需要導入模塊: import torch [as 別名]
# 或者: from torch import diag [as 別名]
def greedy_decoder(arc_matrix, mask=None):
r"""
貪心解碼方式, 輸入圖, 輸出貪心解碼的parsing結果, 不保證合法的構成樹
:param arc_matrix: [batch, seq_len, seq_len] 輸入圖矩陣
:param mask: [batch, seq_len] 輸入圖的padding mask, 有內容的部分為 1, 否則為 0.
若為 ``None`` 時, 默認為全1向量. Default: ``None``
:return heads: [batch, seq_len] 每個元素在樹中對應的head(parent)預測結果
"""
_, seq_len, _ = arc_matrix.shape
matrix = arc_matrix + torch.diag(arc_matrix.new(seq_len).fill_(-np.inf))
flip_mask = mask.eq(False)
matrix.masked_fill_(flip_mask.unsqueeze(1), -np.inf)
_, heads = torch.max(matrix, dim=2)
if mask is not None:
heads *= mask.long()
return heads
開發者ID:fastnlp，項目名稱:fastNLP，代碼行數:19，
示例28: preprocess
點讚 6
# 需要導入模塊: import torch [as 別名]
# 或者: from torch import diag [as 別名]
def preprocess(A):
# Get size of the adjacency matrix
size = A.size(1)
# Get the degrees for each node
degrees = torch.sum(A, dim=2)
# Create diagonal matrix D from the degrees of the nodes
D = Variable(torch.zeros(A.size(0),A.size(1),A.size(2))).cuda()
for i in range(D.size(0)):
D[i, :, :] = torch.diag(torch.pow(degrees[i,:], -0.5))
# Cholesky decomposition of D
# D = np.linalg.cholesky(D)
# Inverse of the Cholesky decomposition of D
# D = np.linalg.inv(D)
# Create an identity matrix of size x size
# Create A hat
# Return A_hat
A_normal = torch.matmul(torch.matmul(D,A), D)
# print(A_normal)
return A_normal
# a sequential GCN model, GCN with n layers
開發者ID:JiaxuanYou，項目名稱:graph-generation，代碼行數:26，
示例29: _mmd2
點讚 5
# 需要導入模塊: import torch [as 別名]
# 或者: from torch import diag [as 別名]
def _mmd2(K_XX, K_XY, K_YY, const_diagonal=False, biased=False):
m = K_XX.size(0) # assume X, Y are same shape
# Get the various sums of kernels that we'll use
# Kts drop the diagonal, but we don't need to compute them explicitly
if const_diagonal is not False:
diag_X = diag_Y = const_diagonal
sum_diag_X = sum_diag_Y = m * const_diagonal
else:
diag_X = torch.diag(K_XX) # (m,)
diag_Y = torch.diag(K_YY) # (m,)
sum_diag_X = torch.sum(diag_X)
sum_diag_Y = torch.sum(diag_Y)
Kt_XX_sums = K_XX.sum(dim=1) - diag_X # \tilde{K}_XX * e = K_XX * e - diag_X
Kt_YY_sums = K_YY.sum(dim=1) - diag_Y # \tilde{K}_YY * e = K_YY * e - diag_Y
K_XY_sums_0 = K_XY.sum(dim=0) # K_{XY}^T * e
Kt_XX_sum = Kt_XX_sums.sum() # e^T * \tilde{K}_XX * e
Kt_YY_sum = Kt_YY_sums.sum() # e^T * \tilde{K}_YY * e
K_XY_sum = K_XY_sums_0.sum() # e^T * K_{XY} * e
if biased:
mmd2 = ((Kt_XX_sum + sum_diag_X) / (m * m)
+ (Kt_YY_sum + sum_diag_Y) / (m * m)
- 2.0 * K_XY_sum / (m * m))
else:
mmd2 = (Kt_XX_sum / (m * (m - 1))
+ Kt_YY_sum / (m * (m - 1))
- 2.0 * K_XY_sum / (m * m))
return mmd2
開發者ID:jindongwang，項目名稱:transferlearning，代碼行數:34，
注：本文中的torch.diag方法示例整理自Github/MSDocs等源碼及文檔管理平台，相關代碼片段篩選自各路編程大神貢獻的開源項目，源碼版權歸原作者所有，傳播和使用請參考對應項目的License；未經允許，請勿轉載。