I added following lines to imagenet example, using pretrained model of resnet18.
 for param in model.parameters():
      param.requires_grad = False

 # Replace the last fully-connected layer
 # Parameters of newly constructed modules have requires_grad=True by default
 model.fc = torch.nn.Linear(512, 3)

 optimizer = torch.optim.SGD(model.fc.parameters(), args.lr,
                            momentum=args.momentum,
                            weight_decay=args.weight_decay)

But then I have following error:
File "main.py", line 234, in train
    loss.backward()
File "/usr/local/lib/python2.7/dist-packages/torch/autograd/variable.py", line 146, in backward
    self._execution_engine.run_backward((self,), (gradient,), retain_variables)
RuntimeError: there are no graph nodes that require computing gradients

I would like to freeze all parameters of original ResNet18 and just learn the last layer with 3 classes. How I should do this correctly? Based on information from the forum, this should we the working version.