Python 如何正确地重塑张量？

我试图使用pytorch geometric实现经典GCN模型的规范化邻接矩阵，如下所示，代码取自文档

import torch
from torch_geometric.nn import MessagePassing
from torch_geometric.utils import add_self_loops, degree
import torch
from torch_geometric.data import Data
from torch_geometric.utils import erdos_renyi_graph
edge_index = erdos_renyi_graph(50, edge_prob=0.2)
x = torch.eye(50, 50)
data = Data(edge_index=edge_index, x=x,)

edge_index, _ = add_self_loops(edge_index, num_nodes=data.x.size(0))
row, col = edge_index
deg = degree(col, x.size(0), dtype=x.dtype)
deg_inv_sqrt = deg.pow(-0.5)
norm = deg_inv_sqrt[row] * deg_inv_sqrt[col]
print(norm.size()

这个张量的输出是

torch.Size（[500]）

如何获得（50,50）的输出？

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任何帮助都将不胜感激

我想您会感到困惑，因为PyTorch Geometric使用了邻接矩阵的压缩或稀疏表示。 我是PyTorch的新手，但以下内容将满足您的需求：

import torch
from torch_geometric.nn import MessagePassing
from torch_geometric.utils import add_self_loops, degree
from torch_geometric.data import Data
from torch_geometric.utils import erdos_renyi_graph
from torch_geometric.utils import to_dense_adj

edge_index = erdos_renyi_graph(5, edge_prob=0.3)
x = torch.eye(5, 5)
data = Data(edge_index=edge_index, x=x)

edge_index, _ = add_self_loops(edge_index, num_nodes=data.x.size(0))
row, col = edge_index
# build adjacency matrix
# from sparse to dense representation
adj = to_dense_adj(edge_index)[0]
deg = degree(col, x.size(0), dtype=x.dtype)
deg_inv_sqrt = deg.pow(-0.5)
norm = deg_inv_sqrt[row] * deg_inv_sqrt[col]
# build "normalized" adjacency matrix
normalized_adj = adj * torch.ger(deg_inv_sqrt,deg_inv_sqrt)
print(normalized_adj)