how to change the labels in a datafolder of pytorch?
I first load an unlabeled dataset as following:
unlabeled_set = DatasetFolder(&quot;food-11/training/unlabeled&quot;, loader=lambda x: Image.open(x), extensions=&quot;jpg&quot;, transform=train_tfm)
and now since I'm trying to conduct semi-supervised learning: I'm trying to define the following function. The input &quot;dataset&quot; is the unlabeled_set I just loaded.
As I want to change the label of the dataset to be the one I predicted, not the original labels(all of the original labels were 1's), how can I do?
I have tried using dataset.targets to change the labels, but it doesn't work at all.
the following is my function:
import torch
def get_pseudo_labels(dataset, model, threshold=0.07):
    # This functions generates pseudo-labels of a dataset using given model.
    # It returns an instance of DatasetFolder containing images whose prediction confidences exceed a given threshold.
    # You are NOT allowed to use any models trained on external data for pseudo-labeling.
    device = &quot;cuda&quot; if torch.cuda.is_available() else &quot;cpu&quot;
    x = []
    y = []
  
    # print(dataset.targets[0])

    # Construct a data loader.
    data_loader = DataLoader(dataset, batch_size=batch_size, shuffle=False)

    # Make sure the model is in eval mode.
    model.eval()
    # Define softmax function.
    softmax = nn.Softmax()
    counter = 0
    # Iterate over the dataset by batches.
    for batch in tqdm(data_loader):
        img, _ = batch

        # Forward the data
        # Using torch.no_grad() accelerates the forward process.
        with torch.no_grad():
            logits = model(img.to(device))

        # Obtain the probability distributions by applying softmax on logits.
        probs = softmax(logits)
        count = 0
        # ---------- TODO ----------
        # Filter the data and construct a new dataset.
        dataset.targets = torch.tensor(dataset.targets)
        for p in probs:
          if torch.max(p) &gt;= threshold:
            if not(counter in x):
              x.append(counter)
            dataset.targets[counter] = torch.argmax(p)
            
          counter += 1

    
    # Turn off the eval mode.
    model.train()
    # dat = DataLoader(ImgDataset(x,y), batch_size=batch_size, shuffle=False)
    print(dataset.targets[10])
    new = torch.utils.data.Subset(dataset, x)
    
    return new```


PyTorch DataSets can return tuples of values, but they have no inherent &quot;features&quot;/&quot;target&quot; distinction. You can create your modified DataSet like so:
labeled_data = [*zip(dataset, labels)]
data_loader = DataLoader(labeled_dataset, batch_size=batch_size, shuffle=False)

for imgs, labels in data_loader: # per batch
    ...

