CODEWITHSUNDEEP
pythondeep-learningpytorch
Hammad Younas
Hammad Younas

Reputation: 73

ValueError: not enough values to unpack (expected 3, got 2) in Pytorch

this is my Define validate function
when I load the model and start prediction using this code I have received the error using PyTorch.and after this, I am iterating through the epoch loop and batch loop and I landed with this error.

def validate_epoch(net, val_loader,loss_type='CE'):
    net.train(False)
    running_loss = 0.0
    sm = nn.Softmax(dim=1)

    truth = []
    preds = []
    bar = tqdm(total=len(val_loader), desc='Processing', ncols=90)
    names_all = []
    n_batches = len(val_loader)
    for i, (batch, targets, names) in enumerate(val_loader):
        if loss_type == 'CE':
                labels = Variable(targets.float())
                inputs = Variable(batch)
        elif loss_type == 'MSE':
                labels = Variable(targets.float())
                inputs = Variable(batch)

        outputs = net(inputs)
        labels = labels.long()
        loss = criterion(outputs, labels)
        if loss_type =='CE':
            probs = sm(outputs).data.cpu().numpy()
        elif loss_type =='MSE':
            probs = outputs
            probs[probs < 0] = 0
            probs[probs > 4] = 4
            probs = probs.view(1,-1).squeeze(0).round().data.cpu().numpy()
        preds.append(probs)
        truth.append(targets.cpu().numpy())
        names_all.extend(names)
        running_loss += loss.item()
        bar.update(1)
        gc.collect()
    gc.collect()
    bar.close()
    if loss_type =='CE':
        preds = np.vstack(preds)
    else:
        preds = np.hstack(preds)
    truth = np.hstack(truth)
    return running_loss / n_batches, preds, truth, names_all

And this is the main function where I call validate function get the error when model is loaded and start prediction on the test loader

criterion = nn.CrossEntropyLoss()

model.eval()

test_losses = []
test_mse = []
test_kappa = []
test_acc = []


test_started = time.time()

test_loss, probs, truth, file_names = validate_epoch(model, test_iterator)

as you can see in traceback error it gives some Terminal shows error:

ValueError                                Traceback (most recent call last)
<ipython-input-27-d2b4a1ca3852> in <module>
     12 test_started = time.time()
     13 
---> 14 test_loss, probs, truth, file_names = validate_epoch(model, test_iterator)
     15 preds = probs.argmax(1)
     16 

<ipython-input-25-34e29e0ff6ed> in validate_epoch(net, val_loader, loss_type)
      9     names_all = []
     10     n_batches = len(val_loader)
---> 11     for i, (batch, targets, names) in enumerate(val_loader):
     12         if loss_type == 'CE':
     13                 labels = Variable(targets.float())

ValueError: not enough values to unpack (expected 3, got 2)

Upvotes: 5

Views: 3549

Answers (1)

DerekG
DerekG

Reputation: 3938

From torchvision.datasets.ImageFolder documentation:

"Returns: (sample, target) where target is class_index of the target class."

So, quite simply, the dataset object you're currently using returns a tuple with 2 items. You'll get an error if you try to store this tuple in 3 variables. The correct line would be:

for i, (batch, targets) in enumerate(val_loader):

If you really need the names (which I assume is the file path for each image) you can define a new dataset object that inherits from the ImageFolder dataset and overload the __getitem__ function to also return this information.

Upvotes: 1

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