RuntimeError: Trying to backward through the graph a second time, but the saved intermediate results have already been freed. Specify retain_graph=True when calling backward the first time.

[XieZixiUSTC]

i encounter this problem,please help me ,thanks a lot

the code is :

compute output

    output = model(images)
    # loss = criterion(output, target)
    # SAM
    # first forward-backward pass
    loss = criterion(output, target)  # use this loss for any training statistics
    loss.backward()
    optimizer.first_step(zero_grad=True)

    # second forward-backward pass
    criterion(output, target).backward()  # make sure to do a full forward pass
    optimizer.second_step(zero_grad=True)

    # measure accuracy and record loss
    acc1, acc5 = accuracy(output, target, topk=(1, 5))
    losses.update(loss.item(), images.size(0))
    top1.update(acc1[0], images.size(0))
    top5.update(acc5[0], images.size(0))

the problen is :
Traceback (most recent call last):
File "C:/softWare/SAM/main.py", line 557, in
main()
File "C:/softWare/SAM/main.py", line 156, in main
main_worker(args.gpu, ngpus_per_node, args)
File "C:/softWare/SAM/main.py", line 340, in main_worker
acc2, loss2=train(train_loader, model, criterion, optimizer, epoch, args, f)
File "C:/softWare/SAM/main.py", line 413, in train
criterion(output, target).backward() # make sure to do a full forward pass
File "C:\softWare\Python37\lib\site-packages\torch\tensor.py", line 221, in backward
torch.autograd.backward(self, gradient, retain_graph, create_graph)
File "C:\softWare\Python37\lib\site-packages\torch\autograd_init_.py", line 132, in backward
allow_unreachable=True) # allow_unreachable flag
RuntimeError: Trying to backward through the graph a second time, but the saved intermediate results have already been freed. Specify retain_graph=True when calling backward the first time.

[davda54]

Hi, you should call the forward pass output = model(images) again before criterion(output, target).backward()

[XieZixiUSTC]

@davda54 thanks,it work

[1215481871]

Sorry, I got the similar question. I actually did the full second forward pass but also reported "Trying to backward through the graph a second time (or directly access saved variables 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 variables after calling backward."

Here is my code:
output = backbone(imgs)
loss = self.criterion(output,labs)
backbone_opt.zero_grad()
loss.backward()
backbone_opt.first_step(zero_grad=True)

        output2 = backbone(imgs)
        loss2 = self.criterion(output2,labs)
        loss2.backward()
        backbone_opt.second_step(zero_grad=True)

But if I substitute "output2 = backbone(imgs)" with "output2 = backbone(imgs.detach())", it works.
Does this implementation make sense or I also made some mistakes here?