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我想试试这里演示的链接预测功能。这是我的版本：

PyTorch Geometric v2.0.2
PyTorch v1.9.0+cu111

我很困惑为什么cuda:0要为每个张量打印，但是当我通过RandomLinkSplit.

import torch

import torch_geometric.transforms as T
from torch_geometric.nn import GCNConv
from torch_geometric.datasets import Planetoid
from torch_geometric.utils import negative_sampling

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
transform = T.Compose([
    T.NormalizeFeatures(),
    T.ToDevice(device),
])

dataset = Planetoid(root='/tmp/Planetoid', name='Cora', transform=transform)

data = dataset[0]
print(data.to_dict())
print(data.keys)

transform = T.RandomLinkSplit(num_val=0.05, num_test=0.1, is_undirected=True,)
train_data, val_data, test_data = transform(data)

输出：

{'x': tensor([[0., 0., 0.,  ..., 0., 0., 0.],
        [0., 0., 0.,  ..., 0., 0., 0.],
        [0., 0., 0.,  ..., 0., 0., 0.],
        ...,
        [0., 0., 0.,  ..., 0., 0., 0.],
        [0., 0., 0.,  ..., 0., 0., 0.],
        [0., 0., 0.,  ..., 0., 0., 0.]], device='cuda:0'), 'edge_index': tensor([[   0,    0,    0,  ..., 2707, 2707, 2707],
        [ 633, 1862, 2582,  ...,  598, 1473, 2706]], device='cuda:0'), 'y': tensor([3, 4, 4,  ..., 3, 3, 3], device='cuda:0'), 'train_mask': tensor([ True,  True,  True,  ..., False, False, False], device='cuda:0'), 'val_mask': tensor([False, False, False,  ..., False, False, False], device='cuda:0'), 'test_mask': tensor([False, False, False,  ...,  True,  True,  True], device='cuda:0')}
['val_mask', 'test_mask', 'edge_index', 'train_mask', 'x', 'y']

---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
/tmp/ipykernel_72/414574324.py in <module>
     20 
     21 transform = T.RandomLinkSplit(num_val=0.05, num_test=0.1, is_undirected=True,)
---> 22 train_data, val_data, test_data = transform(data)

/usr/local/lib/python3.7/dist-packages/torch_geometric/transforms/random_link_split.py in __call__(self, data)
    204                 train_edges,
    205                 neg_edge_index[:, num_neg_val + num_neg_test:],
--> 206                 out=train_store,
    207             )
    208             self._create_label(

/usr/local/lib/python3.7/dist-packages/torch_geometric/transforms/random_link_split.py in _create_label(self, store, index, neg_edge_index, out)
    284             if neg_edge_index.numel() > 0:
    285                 edge_label = torch.cat([edge_label, neg_edge_label], dim=0)
--> 286                 edge_index = torch.cat([edge_index, neg_edge_index], dim=-1)
    287             out[self.key] = edge_label
    288             out[f'{self.key}_index'] = edge_index

RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu! (when checking arugment for argument tensors in method wrapper__cat)
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1 回答 1

0

这个问题确实是一个错误。感谢您报告它。

于 2021-12-07T13:05:33.127 回答