Let us assume we want to copy a model and then fine-tune a model as normal after that. Let us also assume that we don’t need to resume the optimizer that was used to train the model before. We just create a totally new new optimizer and start training as in normal fine-tuning.
With that I am very curious what is wrong with deepcopy and why we need to use load_state_dict instead?
I read some discussion on pytorch.org and some well-known people (e.g. @ptrblck, @albanD, @apaszke, to name a few) suggest to use copy.deepcopy. But I heard some concern about that as in this post. Could someone elaborate more on this? Thanks!