Comments (3)
In graph, 'u' is the destination node, 'v' is the source node, eg, if pre[i]['v'][j] = 3
, pre[i]['u'][j]=4
then node 3
is the i-step predecessor of node 4
. Hope can help you.
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Thank you for your answer!
It helps a lot :)
from lanegcn.
for k1 in ["left", "right"]:
graph[k1] = dict()
for k2 in ["u", "v"]:
temp = [graphs[i][k1][k2] + counts[i] for i in range(batch_size)]
temp = [
x if x.dim() > 0 else graph["pre"][0]["u"].new().resize_(0)
for x in temp
]
graph[k1][k2] = torch.cat(temp)
return graph
'left' and 'right' is error keys in graph which is not founded
from lanegcn.
Related Issues (20)
- Pretrain Model HOT 1
- Learning Rate Drop
- Meet error when installing mpi4py
- cannot run preprocess data(OSError: [Errno 24] Too many open files) HOT 5
- Does it really generate graph['node_idcs']? HOT 1
- AttributeError: 'RandomSampler' object has no attribute 'set_epoch' HOT 2
- train.py
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- visualization HOT 2
- Getting data Forbidden HOT 1
- IndexError: list index out of range HOT 2
- I used the pretrained model you provided, but the accuracy is much worse than that in Table 1 HOT 1
- ### Has anyone encountered the same issue below?
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