4888cb7a5299f7eed9214ddc70871dc05ea0f4d2,model/ft_ResNet50/train.py,,train_model,#Any#Any#Any#Any#Any#,151

Before Change

                else:
                    inputs, labels = Variable(inputs), Variable(labels)
                // if we use low precision, input also need to be fp16
                if fp16:
                    inputs = inputs.half()
 
                // zero the parameter gradients
                optimizer.zero_grad()

                // forward
                if phase == "val":

After Change

                // backward + optimize only if in training phase
                if phase == "train":
                    if fp16: // we use optimier to backward loss
                        with amp.scale_loss(loss, optimizer) as scaled_loss:
                            scaled_loss.backward()
                    else:
                        loss.backward()
                    optimizer.step()

Instances