Python torch.cdist方法代碼示例

# 需要導入模塊: import torch [as 別名]
# 或者: from torch import cdist [as 別名]
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) 

# 需要導入模塊: import torch [as 別名]
# 或者: from torch import cdist [as 別名]
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) 

# 需要導入模塊: import torch [as 別名]
# 或者: from torch import cdist [as 別名]
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]) 

# 需要導入模塊: import torch [as 別名]
# 或者: from torch import cdist [as 別名]
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) 

# 需要導入模塊: import torch [as 別名]
# 或者: from torch import cdist [as 別名]
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 

# 需要導入模塊: import torch [as 別名]
# 或者: from torch import cdist [as 別名]
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 

# 需要導入模塊: import torch [as 別名]
# 或者: from torch import cdist [as 別名]
def batched_l1_dist(a, b):
    res = th.cdist(a, b, p=1)
    return res 

# 需要導入模塊: import torch [as 別名]
# 或者: from torch import cdist [as 別名]
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) 

# 需要導入模塊: import torch [as 別名]
# 或者: from torch import cdist [as 別名]
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]) 

# 需要導入模塊: import torch [as 別名]
# 或者: from torch import cdist [as 別名]
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() 

# 需要導入模塊: import torch [as 別名]
# 或者: from torch import cdist [as 別名]
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) 

# 需要導入模塊: import torch [as 別名]
# 或者: from torch import cdist [as 別名]
def cdist(X, Y=None, quadratic_expansion=False):
    if quadratic_expansion:
        return _dist(X, Y, _euclidian_fast)
    else:
        return _dist(X, Y, _euclidian) 

# 需要導入模塊: import torch [as 別名]
# 或者: from torch import cdist [as 別名]
def squared_euclidian_distance(a, b):
    return torch.cdist(a, b)**2 

# 需要導入模塊: import torch [as 別名]
# 或者: from torch import cdist [as 別名]
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 

# 需要導入模塊: import torch [as 別名]
# 或者: from torch import cdist [as 別名]
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