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I tried running the code with batch size 1 on a dataset where number of items are in order of 100-1000 across all sets in a datapoint. It fails with CUDA out of mem error. Specifically the code fails at:
https://github.com/yule-BUAA/DNNTSP/blob/master/model/weighted_graph_conv.py#L35

Is there any way to optimize the size of the edge_weights considering the fact that if we have say 600 nodes then size of edge weight comes around 30x360000 where 30 is the number of sets in a single datapoint.

Do you suggest optimization at a different step?

HI there,

Would you love to contribute this to the library?

We would love to see this and of course it would be cited in the deisgn paper.

Bests,

Benedek