tmp_coo = sp.coo_matrix(adj_matrix) values = tmp_coo.data indices = 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=tensor([[0, 0, 1, 1, 2, 2, 3], # [0, 1, 1, 3, 2, 3, 0]]), # values=tensor([1, 1, 1, 1, 1, 1, 1]), # size=(4, 4), nnz=7, layout=torch.sparse_coo)

torch 矩阵转numpy 矩阵

A = torch.load('adj1.pt')
A = A.numpy()
numpy 矩阵转 scipy 稀疏矩阵
A = sp.coo_matrix(A)
scipy 稀疏矩阵转numpy 矩阵
A.toarray()
将 Scipy Sparse 矩阵转换成 torch sparse 矩阵
def sparse_mx_to_torch_sparse_tensor(sparse_mx):
    """Convert a scipy sparse matrix to a torch 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(sparse_mx.data)
    shape = torch.Size(sparse_mx.shape)
    return torch.sparse.FloatTensor(indices, values, shape)
torch sparse矩阵转 torch 稠密矩阵
sparse_adj.to_dense()