Thanks!
I have another question to make sure I understand this correctly.
In the #statistics section I currently have this which is giving me an accuracy value of 2.3 which doesn’t make sense.
            total_train += labels.nelement()

            correct_train += preds.eq(labels.data).sum().item()

            train_accuracy = 100 * correct_train / total_train

so then, should I replace the three lines above with the line below?:
train_accuracy =  torch.sum(preds == labels).float() / labels.nelement()
The full code of the train_model function is below, with the old accuracy calculation commented in the statistics section. Would this be the correct way then, or am I still missing something?
def train_model(model, dataloaders, criterion, optimizer, num_epochs=25, is_inception=False):

    since = time.time()

    val_acc_history = []

    best_model_wts = copy.deepcopy(model.state_dict())

    best_acc = 0.0

    for epoch in range(num_epochs):

        print('Epoch {}/{}'.format(epoch, num_epochs - 1))

        print('-' * 10)

        # Each epoch has a training and validation phase

        for phase in ['train', 'val']:

            if phase == 'train':

                model.train()  # Set model to training mode

            else:

                model.eval()   # Set model to evaluate mode


            running_loss = 0

            total_train = 0

            correct_train = 0

            # Iterate over data.

            for inputs, labels in dataloaders[phase]:

                inputs = inputs.to(device) #OriginalImage

                labels = labels.to(device) #Masks


                # zero the parameter gradients

                optimizer.zero_grad()

                # forward

                # track history if only in train

                with torch.set_grad_enabled(phase == 'train'):

                    # Get model outputs and calculate loss

                    # Special case for inception because in training it has an auxiliary output. In train

                    #   mode we calculate the loss by summing the final output and the auxiliary output

                    #   but in testing we only consider the final output.

                    if is_inception and phase == 'train':

                        # From https://discuss.pytorch.org/t/how-to-optimize-inception-model-with-auxiliary-classifiers/7958

                        outputs, aux_outputs = model(inputs)

                        loss1 = criterion(outputs, labels)

                        loss2 = criterion(aux_outputs, labels)

                        loss = loss1 + 0.4*loss2

                    else:

                        outputs = model(inputs)['out']

                        labels = labels.long()


                        outputs = outputs.squeeze(1)

                        loss = criterion(outputs, labels)

                    _, preds = torch.max(outputs, 1)

                    # backward + optimize only if in training phase

                    if phase == 'train':

                        loss.backward()

                        optimizer.step()

                # statistics

                running_loss += loss.item() #* inputs.size(0)

                ##total_train += labels.nelement()

                ##correct_train += preds.eq(labels.data).sum().item()

                ##train_accuracy = 100 * correct_train / total_train
                train_accuracy =  torch.sum(preds == labels).float() / labels.nelement()


            epoch_loss = running_loss / len(dataloaders[phase].dataset)

            ##epoch_acc = running_corrects.double() / len(dataloaders[phase].dataset)

            epoch_acc = train_accuracy/len(dataloaders[phase].dataset)

            print('{} Loss: {:.4f} Acc: {:.4f}'.format(phase, epoch_loss, epoch_acc))

            # deep copy the model

            if phase == 'val' and epoch_acc > best_acc:

                best_acc = epoch_acc

                best_model_wts = copy.deepcopy(model.state_dict())

            if phase == 'val':

                val_acc_history.append(epoch_acc)

        print()

    time_elapsed = time.time() - since

    print('Training complete in {:.0f}m {:.0f}s'.format(time_elapsed // 60, time_elapsed % 60))

    print('Best val Acc: {:4f}'.format(best_acc))

    # load best model weights

    model.load_state_dict(best_model_wts)

    return model, val_acc_history