# 需要导入模块: import torch_sparse [as 别名]# 或者: from torch_sparse importspspmm[as 别名]defStAS(index_A, value_A, index_S, value_S, device, N, kN):r"""StAS: a function which returns new edge weights for the pooled graph using the formula S^{T}AS"""index_A, value_A =...
首先,如果您反复执行可以产生重复条目(例如torch.sparse.FloatTensor.add())的操作,则应偶尔将您的稀疏张量合并,以防止它们变得太大。 其次,一些运营商将取决于它们是否被合并或不产生不同的值(例如, torch.sparse.FloatTensor._values()和 torch.sparse.FloatTensor._indices(),以及 torch.Tensor._sparse_mask())。
Example: import torch indices = torch.tensor([[0,1], [0,1]]) values = torch.tensor([2,3]) shape = torch.Size((2,2)) s = torch.sparse.FloatTensor(indic
torch.spmm()/torch.sparse.mm() 稀疏矩阵相乘 稀疏矩阵:矩阵中非零元素的个数远远小于矩阵元素的总数,并且非零元素的分布没有规律,则称该矩阵为稀疏矩阵(sparse matrix) 如果矩阵是稀疏矩阵,没法直接用torch.mm相乘 torch.sparse.mm(a,b)用法上类似torch.mm(a,b),但a是稀疏矩阵,b是普通矩阵或稀疏矩阵 pyth...
from torch_sparse import spspmm, spmm ModuleNotFoundError: No module named 'torch_sparse' I follow the solution of#542,I have successfully installed related packaegs using the follow command: pip install --verbose --no-cache-dir torch-scatter==1.1.2 ...
import torch import numpy as np import scipy.sparse as sp import torch.nn.functional as F from torch_geometric.nn import GCNConv,GATConv,SAGEConv from torch_geometric.datasets import Planetoid download.pytorch.org/wh 如果安装不成功,请手动下载安装。 5.3 数据预处理 def encode_onehot(labels): #...
该版本稀疏矩阵类支持稀疏矩阵和稀疏矩阵的乘积torch.sparse.mm(sparse, sparse/dense);(1.8.0支持,之前版本不支持) 矩阵元素乘torch.mul(sparse,sparse),此处两个sparse的row,col,size需要一致。 稀疏矩阵支持转置。Sparse.matrix.t() 稀疏矩阵支持整行索引,支持Sparse.matrix[row_index];稀疏矩阵不支持具体位置位...
torch.spmm(self.S, x) x = x.reshape(-1) x = self.fc(x) return x if __name__ == "__main__": X = torch.ones(4, 2, dtype=torch.float).cuda() y = torch.zeros(4, dtype=torch.float).cuda() sparseTest = SparseTest() sparseTest = sparseTest.cuda() sparseTest = torch....
printtorch.sparse.FloatTensor(2,3).size()# (2L, 3L)printtorch.sparse.FloatTensor(2,3).is_coalesced()# False add() add_() clone() dim() div() div_() get_device() hspmm() mm() mul() mul_() resizeAs_() size() spadd() ...
spmm(adj, support) # return output + self.bias Example #8Source File: nipa_q_net_node.py From DeepRobust with MIT License 6 votes def get_graph_embedding(self, adj): if self.node_features.data.is_sparse: node_embed = torch.spmm(self.node_features, self.w_n2l) else: node_embed...