sorry about the vagueness.
In the example of capsule net:
a example of these tensors in capsule net implementation 14


there tensors are used for computing coefficients assigning to feature maps which produce these coefficients under torch.no_grad().


these tensors have nothing to do with training or learning. Just some values computed by a certain procedure (dynamic routing in capsule net).


can simply seen as computing the cosine similarity of certain layers’ feature maps.


And as you mentioned:

model.to()  transfers all “states” to the specified device.

It seems model.to() only cares about the “states”. Maybe some_tensor.to(feature_map.device) is the best we can get.