Local variable 'loss' referenced before assignment

it gives me this error; local variable ‘loss’ referenced before assignment

def train_epoch(models, loss_module, criterion, optimizers_backbone, optimizers_module, dataloaders, epoch, epoch_loss):

    global iters
    for data in tqdm(dataloaders['train'], leave=False, total=len(dataloaders['train'])):
        with torch.cuda.device(CUDA_VISIBLE_DEVICES):
            inputs = data[0].cuda()
            labels = data[1].cuda()

        iters += 1


        scores, _, features = models(inputs) 
        target_loss = criterion(scores, labels)
        if epoch > epoch_loss:
            features[0] = features[0].detach()
            features[1] = features[1].detach()
            features[2] = features[2].detach()
            features[3] = features[3].detach()

        pred_loss = loss_module(features)
        pred_loss = pred_loss.view(pred_loss.size(0))
        m_module_loss   = LossPredLoss(pred_loss, target_loss, margin=MARGIN)
        m_backbone_loss = torch.sum(target_loss) / target_loss.size(0)        
        loss = m_backbone_loss + WEIGHT * m_module_loss 
    return loss

Which line occurs the error?
If it is return loss, then check out the for loop iterate at least once.

I don’t think so for-loop is running at least once.

It seems dataloaders is invalid.
print len(dataloaders['train']) then it may returns 0

in Train() where I am called train_epoch(), I have printed the data loaders

DataLoader {‘train’: <torch.utils.data.dataloader.DataLoader object at 0x7f4d756dbc10>, ‘test’: <torch.utils.data.dataloader.DataLoader object at 0x7f4d756dbb20>}
DataLoader {‘train’: <torch.utils.data.dataloader.DataLoader object at 0x7f4d756dbc10>, ‘test’: <torch.utils.data.dataloader.DataLoader object at 0x7f4d756dbb20>}

def train(models, loss_module, criterion, optimizers_backbone, optimizers_module, scheduler_backbone, scheduler_module, dataloaders, num_epochs, epoch_loss):
    print('>> Train a Model.')
    best_acc = 0.
    for epoch in range(num_epochs):

        best_loss = torch.tensor([0.5]).cuda()
        print("DataLoader", dataloaders)
        loss = train_epoch(models, loss_module, criterion, optimizers_backbone, optimizers_module, dataloaders, epoch, epoch_loss)


        if False and epoch % 20  == 7:
            acc = test(models, epoch, method, dataloaders, mode='test')
            # acc = test(models, dataloaders, mc, 'test')
            if best_acc < acc:
                best_acc = acc
                print('Val Acc: {:.3f} \t Best Acc: {:.3f}'.format(acc, best_acc))
    print('>> Finished.')

This code works absolutely fine for CIFAR-100 when I tried this for ImageNet, it started giving this error. and now even not working for CIFAR100

Hi, the dataloader you have printed is fine, these are reference to the dataloader objects. Please check the length of dataloaders object, easy way can be

# OR 
for i, data in enumerate(dataloaders['train']):
print("Length of dataloader:", i)

The training code looks perfect, but your loss variable is defined only into the loop. And the loop runs number of batches present in dataloader.
The loop is not running for certain dataloader, and loss variable is left unassigned.

You can initialize the loss variable outside the for loop, but that wont solve the issue. It will just stop throwing the error.

Hope it helps. :slight_smile: