I do not know if it can. I want to train a network in two process, the former sub network and  the latter sub network.The latter receives a tensor from the former.First I train the former network to a stable state.
Instead of freezing the paramters by requires_grad=False or specify the trainale parameters, I’d like to save the former’s output tensor ,such as .pth, .h5, .npy, so that I can train the latter without a long and redundant forward\backward tensor flow.
I came across the situation that my former network consists of too many nn.Sequential ,and I do not put them into one class ,such as class FormetNet(nn.Module),I want to train the latter one but it accept a tensor from former’s output,so I must let the data flows to former network first.Is there any idea about how to train the latter without too much change?