coo_matrix((np.ones(edges.shape[0]), (edges[:, 0], edges[:, 1])), shape=(labels.shape[0], labels.shape[0]), dtype=np.float32) adj = adj + adj.T.multiply(adj.T > adj) - adj.multiply(adj.T > adj) features = normalize(features) # 特征值归一化 adj = normalize(adj + ...
1. 构建稀疏矩阵 官方直接torch构建(coo类型的稀疏矩阵) i =torch.LongTensor([[0, 1, 1],[2, 0, 2]]) #row, col v =torch.FloatTensor([3, 4, 5]) #data torch.sparse.FloatTensor(i, v, torch.Size([2,3])).to_dense() #torch.Size() 调用sp.coo_matrix(),构建方式同上,传入数据为np...
稀疏矩阵切片 稀疏矩阵切片不能使用coo_matrix(三元组),可以将矩阵转化为csr_matrix,转化方式很简单 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...
# adj_matrix 是邻接矩阵tmp_coo = sp.coo_matrix(adj_matrix)values = tmp_coo.dataindices = np.vstack((tmp_coo.row,tmp_coo.col))i = torch.LongTensor(indices)v = torch.LongTensor(values)edge_index=torch.sparse_coo_tensor(i,v,tmp_coo.shape)print(edge_index)# 输出:#tensor(indices=tenso...
我们可以通过torch.cuda.empty_cache()清空缓存来删掉这部分保留的内存: coo_matrix = sparse_matrix.to_sparse_coo() print('memory_allocated...: ', torch.cuda.memory_allocated()) print('memory_reserved: ', torch.cuda.memory_reserved()) torch.cuda.empty_cache 14010 PyTorch Trick集锦 with torch....
稀疏矩阵切片不能使用coo_matrix(三元组),可以将矩阵转化为csr_matrix,转化方式很简单 importscipy.sparse as sp row= [0, 0, 0, 1, 1, 1, 2, 2, 2]#行指标col = [0, 1, 2, 0, 1, 2, 0, 1, 2]#列指标data = [1, 0, 1, 0, 1, 1, 1, 1, 0]#在行指标列指标下的数字team =...
detach().numpy() val = val.detach().numpy() A = scipy.sparse.coo_matrix((val, edge_index), shape=(N, N)) svd = TruncatedSVD(n_components=self.in_channels, n_iter=128) svd.fit(A) x = svd.components_.T return torch.from_numpy(x).to(torch.float).to(pos_edge_index.device) ...
_sparse_coo_tensor_unsafe _sparse_log_softmax _sparse_log_softmax_backward_data _sparse_matrix_mask_helper _sparse_mm _sparse_softmax _sparse_softmax_backward_data _sparse_sparse_matmul _sparse_sum _stack _standard_gamma _standard_gamma_grad _std _storage_classes _string_classes _syevd_...
稀疏矩阵切⽚不能使⽤coo_matrix（三元组），可以将矩阵转化为csr_matrix,转化⽅式很简单 import scipy.sparse as sp row = [0, 0, 0, 1, 1, 1, 2, 2, 2] # ⾏指标 col = [0, 1, 2, 0, 1, 2, 0, 1, 2] # 列指标 data = [1, 0, 1, 0, 1, 1, 1, 1, 0] ...
TurakarcommentedApr 8, 2023 Seems like the problem also occurs on CPU. Sorry, something went wrong. Copy link Author TurakarcommentedApr 8, 2023 #14489is related, but seems to be about COO format. Sorry, something went wrong. zou3519added themodule: sparseRelated to torch.sparselabelApr 10...