But I got the following error:
RuntimeError: Trying to backward through the graph a second time (or directly access saved tensors after they have already been freed). Saved intermediate values of the graph are freed when you call .backward() or autograd.grad(). Specify retain_graph=True if you need to backward through the graph a second time or if you need to access saved tensors after calling backward.
Basically, what I want to do is, freeze “model” but train “fc_new”. But “fc_new” needs to consume the output of “model” as input.
Can someone provide me some instructions on how to fix this problem? Thanks.
Note, in the code you posted, you never actually usefc_new – you don’t
have fc_new consuming all_preds.
Based on what I think you want, you could do this:
fc_new = nn.linear(256, 10)
opt_new = torch.optim.Adam(fc_new.parameters(), lr=0.1)
with torch.no_grad: # won't construct computation graph and pred1 and pred2 won't carry requires_grad = True
all_preds = torch.concat([pred1, pred2]).flatten()
new_preds = fc_new (all_preds) # which I think you want
loss_new = my_loss_func (new_preds, labels)
This approach to “freezing” model is possible because model comes before fc_new – that is, you are not feeding the output of fc_new into the part you
want frozen – and because you have easy access to model and fc_new
separately (rather than them both being part of some larger model that you
would have to dig down into).