python中cd的用法_Python torch.cdist方法代码示例
本文整理汇总了Python中torch.cdist方法的典型用法代码示例。如果您正苦于以下问题：Python torch.cdist方法的具体用法？Python torch.cdist怎么用？Python torch.cdist使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在模块torch的用法示例。
在下文中一共展示了torch.cdist方法的15个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于我们的系统推荐出更棒的Python代码示例。
示例1: euclidean_distances
点赞 6
# 需要导入模块: import torch [as 别名]
# 或者: from torch import cdist [as 别名]
def euclidean_distances(X, Z=None):
"""computes the pairwise euclidean distances between the samples matrices *X* and *Z*.
Parameters
----------
X: torch tensor of shape (n_samples_1, n_features)
Z: torch tensor of shape (n_samples_2, n_features)
Returns
-------
D: torch tensor of shape (n_samples_1, n_samples_2),
the distances matrix.
"""
X, Z = check_pairwise_X_Z(X, Z)
return torch.cdist(X, Z)
开发者ID:IvanoLauriola，项目名称:MKLpy，代码行数:18，
示例2: test_knn
点赞 6
# 需要导入模块: import torch [as 别名]
# 或者: from torch import cdist [as 别名]
def test_knn():
x = th.randn(8, 3)
kg = dgl.nn.KNNGraph(3)
d = th.cdist(x, x)
def check_knn(g, x, start, end):
for v in range(start, end):
src, _ = g.in_edges(v)
src = set(src.numpy())
i = v - start
src_ans = set(th.topk(d[start:end, start:end][i], 3, largest=False)[1].numpy() + start)
assert src == src_ans
g = kg(x)
check_knn(g, x, 0, 8)
g = kg(x.view(2, 4, 3))
check_knn(g, x, 0, 4)
check_knn(g, x, 4, 8)
kg = dgl.nn.SegmentedKNNGraph(3)
g = kg(x, [3, 5])
check_knn(g, x, 0, 3)
check_knn(g, x, 3, 8)
开发者ID:dmlc，项目名称:dgl，代码行数:26，
示例3: order_points
点赞 6
# 需要导入模块: import torch [as 别名]
# 或者: from torch import cdist [as 别名]
def order_points(pts):
pts_reorder = []
for idx, pt in enumerate(pts):
idx = torch.argsort(pt[:, 0])
xSorted = pt[idx, :]
leftMost = xSorted[:2, :]
rightMost = xSorted[2:, :]
leftMost = leftMost[torch.argsort(leftMost[:, 1]), :]
(tl, bl) = leftMost
D = torch.cdist(tl[np.newaxis], rightMost)[0]
(br, tr) = rightMost[torch.argsort(D, descending=True), :]
pts_reorder.append(torch.stack([tl, tr, br, bl]))
return torch.stack([p for p in pts_reorder])
开发者ID:NVIDIA，项目名称:retinanet-examples，代码行数:19，
示例4: _euclidian
点赞 6
# 需要导入模块: import torch [as 别名]
# 或者: from torch import cdist [as 别名]
def _euclidian(x, y):
"""
Helper function to calculate euclidian distance between torch.tensors x and y: sqrt(|x-y|**2)
Based on torch.cdist
Parameters
----------
x : torch.tensor
2D tensor of size m x f
y : torch.tensor
2D tensor of size n x f
Returns
-------
torch.tensor
2D tensor of size m x n
"""
return torch.cdist(x, y)
开发者ID:helmholtz-analytics，项目名称:heat，代码行数:20，
示例5: _gaussian
点赞 6
# 需要导入模块: import torch [as 别名]
# 或者: from torch import cdist [as 别名]
def _gaussian(x, y, sigma=1.0):
"""
Helper function to calculate gaussian distance between torch.tensors x and y: exp(-(|x-y|**2/2sigma**2)
Based on torch.cdist
Parameters
----------
x : torch.tensor
2D tensor of size m x f
y : torch.tensor
2D tensor of size n x f
sigma: float, default=1.0
scaling factor for gaussian kernel
Returns
-------
torch.tensor
2D tensor of size m x n
"""
d2 = _euclidian(x, y) ** 2
result = torch.exp(-d2 / (2 * sigma * sigma))
return result
开发者ID:helmholtz-analytics，项目名称:heat，代码行数:24，
示例6: _sample
点赞 6
# 需要导入模块: import torch [as 别名]
# 或者: from torch import cdist [as 别名]
def _sample(self, features: Tensor, labels: List[int]) -> TTripletsIds:
"""
This method samples the hardest triplets inside the batch.
Args:
features: has the shape of [batch_size, feature_size]
labels: labels of the samples in the batch
Returns:
the batch of the triplets in the order below:
(anchor, positive, negative)
"""
assert features.shape[0] == len(labels)
if self._need_norm:
features = normalize(samples=features)
dist_mat = torch.cdist(x1=features, x2=features, p=2)
ids_anchor, ids_pos, ids_neg = self._sample_from_distmat(
distmat=dist_mat, labels=labels
)
return ids_anchor, ids_pos, ids_neg
开发者ID:catalyst-team，项目名称:catalyst，代码行数:26，
示例7: batched_l1_dist
点赞 5
# 需要导入模块: import torch [as 别名]
# 或者: from torch import cdist [as 别名]
def batched_l1_dist(a, b):
res = th.cdist(a, b, p=1)
return res
开发者ID:dmlc，项目名称:dgl，代码行数:5，
示例8: score_emb
点赞 5
# 需要导入模块: import torch [as 别名]
# 或者: from torch import cdist [as 别名]
def score_emb(self, s_emb, p_emb, o_emb, combine: str):
n = p_emb.size(0)
if combine == "spo":
out = -F.pairwise_distance(s_emb + p_emb, o_emb, p=self._norm)
elif combine == "sp_":
out = -torch.cdist(s_emb + p_emb, o_emb, p=self._norm)
elif combine == "_po":
out = -torch.cdist(o_emb - p_emb, s_emb, p=self._norm)
else:
out = super().score_emb(s_emb, p_emb, o_emb, combine)
return out.view(n, -1)
开发者ID:uma-pi1，项目名称:kge，代码行数:13，
示例9: assign_by_euclidian_at_k
点赞 5
# 需要导入模块: import torch [as 别名]
# 或者: from torch import cdist [as 别名]
def assign_by_euclidian_at_k(X, T, k):
"""
X : [nb_samples x nb_features], e.g. 100 x 64 (embeddings)
k : for each sample, assign target labels of k nearest points
"""
distances = torch.cdist(X, X)
# get nearest points
indices = distances.topk(k + 1, largest=False)[1][:, 1: k + 1]
return np.array([[T[i] for i in ii] for ii in indices])
开发者ID:dichotomies，项目名称:proxy-nca，代码行数:11，
示例10: forward
点赞 5
# 需要导入模块: import torch [as 别名]
# 或者: from torch import cdist [as 别名]
def forward(self, X, T):
P = F.normalize(self.proxies, p = 2, dim = -1) * self.scaling_p
X = F.normalize(X, p = 2, dim = -1) * self.scaling_x
D = torch.cdist(X, P) ** 2
T = binarize_and_smooth_labels(T, len(P), self.smoothing_const)
# note that compared to proxy nca, positive included in denominator
loss = torch.sum(-T * F.log_softmax(-D, -1), -1)
return loss.mean()
开发者ID:dichotomies，项目名称:proxy-nca，代码行数:10，
示例11: compute_indices
点赞 5
# 需要导入模块: import torch [as 别名]
# 或者: from torch import cdist [as 别名]
def compute_indices(inputs_orig, codebook):
bi = []
SZ = 10000
for i in range(0, inputs_orig.size(0), SZ):
inputs = inputs_orig[i:i + SZ]
# NxK
distances_matrix = torch.cdist(inputs, codebook)
# Nx1
indic = torch.min(distances_matrix, dim=-1)[1].unsqueeze(1)
bi.append(indic)
return torch.cat(bi, dim=0)
开发者ID:Vermeille，项目名称:Torchelie，代码行数:13，代码来源:vq.py
示例12: cdist
点赞 5
# 需要导入模块: import torch [as 别名]
# 或者: from torch import cdist [as 别名]
def cdist(X, Y=None, quadratic_expansion=False):
if quadratic_expansion:
return _dist(X, Y, _euclidian_fast)
else:
return _dist(X, Y, _euclidian)
开发者ID:helmholtz-analytics，项目名称:heat，代码行数:7，
示例13: squared_euclidian_distance
点赞 5
# 需要导入模块: import torch [as 别名]
# 或者: from torch import cdist [as 别名]
def squared_euclidian_distance(a, b):
return torch.cdist(a, b)**2
开发者ID:arthurdouillard，项目名称:incremental_learning.pytorch，代码行数:4，
示例14: cluster
点赞 5
# 需要导入模块: import torch [as 别名]
# 或者: from torch import cdist [as 别名]
def cluster(self, lr=0.5, max_iter_p=10, max_iter_h=3000, lr_decay=200, early_stop=-1):
""" compute Wasserstein clustering
Args:
reg_type (int): specify regulazation term, 0 means no regularization
reg (int): regularization weight
max_iter_p (int): max num of iteration of clustering
max_iter_h (int): max num of updating h
lr (float): GD learning rate
lr_decay (float): learning rate decay
Returns:
idx (pytorch Tensor): assignment of e to p
pred_label_e (pytorch Tensor): labels of e that come from nearest p
See Also
--------
update_p : update p
update_map: compute optimal transportation
"""
e_idx, pred_label_e = None, None
for iter_p in range(max_iter_p):
dist = torch.cdist(self.data_p, self.data_e) ** 2
e_idx, pred_label_e = self.update_map(dist, max_iter_h, lr=lr, lr_decay=lr_decay, early_stop=early_stop)
if self.update_p(e_idx, iter_p):
break
return e_idx, pred_label_e
开发者ID:icemiliang，项目名称:pyvot，代码行数:29，
示例15: __init__
点赞 4
# 需要导入模块: import torch [as 别名]
# 或者: from torch import cdist [as 别名]
def __init__(self, data, sampling='unisquare', label=None, weight_p=None, thres=1e-5, ratio=100, verbose=True, device='cpu'):
""" set up parameters
Args:
thres float: threshold to break loops
ratio float: the ratio of num of e to the num of p
data pytorch Tensor: initial coordinates of p
label pytorch Tensor: labels of p
mass_p pytorch Tensor: weights of p
Atts:
thres float: Threshold to break loops
lr float: Learning rate
verbose bool: console output verbose flag
y pytorch floattensor: coordinates of p
label_y pytorch inttensor: labels of p
mass_p pytorch floattensor: mass of clusters of p
weight_p pytorch floattensor: dirac measure of p
"""
if not isinstance(data, torch.Tensor):
raise Exception('input is not a pytorch tensor')
if label and not isinstance(label, torch.Tensor):
raise Exception('label is neither a numpy array not a pytorch tensor')
if weight_p and not isinstance(weight_p, torch.Tensor):
raise Exception('label is neither a numpy array not a pytorch tensor')
self.data_p = data
self.data_p_original = self.data_p.clone()
num_p = data.shape[0]
self.label_p = label
self.weight_p = weight_p if weight_p is not None else torch.ones(num_p).double().to(device) / num_p
self.thres = thres
self.verbose = verbose
self.ratio = ratio
self.device = device
utils.assert_boundary(self.data_p)
num_e = int(self.ratio * num_p)
dim = self.data_p.shape[1]
self.data_e, _ = utils.random_sample(num_e, dim, sampling=sampling)
self.data_e = torch.from_numpy(self.data_e).double().to(self.device)
self.dist = torch.cdist(self.data_p, self.data_e, p=2).double().to(self.device)**2
开发者ID:icemiliang，项目名称:pyvot，代码行数:49，
注：本文中的torch.cdist方法示例整理自Github/MSDocs等源码及文档管理平台，相关代码片段筛选自各路编程大神贡献的开源项目，源码版权归原作者所有，传播和使用请参考对应项目的License；未经允许，请勿转载。