I try to use dgl.HeteroGraphConv as a feature extractor in reinforcement learning.
So, when I sample replay-buffer, it will return a batch of hetero graph.
Each graph has 9000 nodes, 26000 edges, and 1900*1 features for each nodes.
These graphs are come from a source graph with add or delete some edges (less than 5%), all graph has the same feats.
I try to use dgl.batch() but I find it is too slow.
So I try to using for-loop, but I find this approach does not make efficient use of GPU resources.
What should I do? should I reduce the batch size and keep using dgl.batch()?