torch.sparse.SparseTensor是PyTorch中用于创建稀疏张量的一个函数。稀疏张量是一种数据结构,用于存储那些大部分元素为零的张量,通过仅存储非零元素及其位置来节省内存和计算资源。然而,根据提供的信息,torch.sparse.SparseTensor已经被弃用,不再推荐使用。 3. 解释torch.sparse.SparseTensor(indices, values, shape, *,...
3]) val =torch.linspace(1, 8, 8) c = SparseTensor(row=row, col=col, value=val) print('...
# 需要導入模塊: import torch [as 別名]# 或者: from torch importsparse_coo_tensor[as 別名]def_compute_laplacian(self):"""Precomputes the graph Laplacian."""self._recompute_laplacian =Falseindices = [ (node, edge)fornode, edgesinenumerate(self.adjacency)foredgeinedges + [node] ] values =...
有篇关于Openfire导入到IntelliJ IDEA的文章（http://www.linuxidc.com/Linux/2015-01/112313.htm），...
def scipy_sparse_mat_to_torch_sparse_tensor(sparse_mx): """ 将scipy的sparse matrix转换成torch的sparse tensor. """ sparse_mx = sparse_mx.tocoo().astype(np.float32) indices = torch.from_numpy( np.vstack((sparse_mx.row, sparse_mx.col)).astype(np.int64)) values = torch.from_numpy(sp...
sparse_coo_tensor(edge_index, edge_attr, size) eye = torch.arange(start=0, end=num_nodes) eye = torch.stack([eye, eye]) eye = torch.sparse_coo_tensor(eye, torch.ones([num_nodes]), size) adj = adj.t() + adj + eye # greater than 1 when edge_index is already symmetrical adj...
🚀 The feature, motivation and pitch torch.clamp() fails on sparse tensors using SparseCPU backend. Among other things, this breaks gradient clipping with sparse tensors. import torch sparse_tensor = torch.sparse_coo_tensor([[1,2]], [1,5]...
While performing:- import torch import torch_sparse.SparseTensor as sparse I get the following error:- ModuleNotFoundError Traceback (most recent call last) in 1 import torch ---> 2 import torch_sparse.SparseTensor as sparse` ModuleNotF...
sparse_grad (bool,optional) – 如果为True,梯度w.r.t。input将是一个稀疏张量。 out (Tensor, optional) – 目标张量 Example: >>> t = torch.tensor([[1, 2], [3, 4]]) >>> torch.gather(dim=1, index=torch.tensor([[0, 0], [1, 0]])) ...
# 在坐标(0,2),(1,0),(1,2)处,赋值[3,4], [5,6], [7,8] i = [[0, 1, 1],[2, 0, 2]] v = [[3, 4], [5, 6], [7, 8]] s = torch.sparse_coo_tensor(i, v, (2, 3, 2)) s # s.shape (2,3,2),最后一个对应的就是v中元素的维度。 显然,这里就是在把value...