NotImplemented Error while running ImbalancedDatasetSampler

I followed the steps exactly according to the readme file. Yet I am getting a notimplemented error. There’s no explanation for the error as well.

Here’s my code: `from torchvision import transforms from torchsampler import ImbalancedDatasetSampler

batch_size = 128 val_split = 0.2 shuffle_dataset=True random_seed=42

dataset_size = len(melanoma_dataset) indices = list(range(dataset_size)) split = int(np.floor(val_split * dataset_size)) if shuffle_dataset : np.random.seed(random_seed) np.random.shuffle(indices) train_indices, test_indices = indices[split:], indices[:split]

train_loader = torch.utils.data.DataLoader(melanoma_dataset,batch_size=batch_size,sampler=ImbalancedDatasetSampler(melanoma_dataset)) test_loader = torch.utils.data.DataLoader(melanoma_dataset,batch_size=batch_size,sampler=test_sampler)`

[SorenJ89]

If anyone reads this, this worked for me:

def callback_get_label(dataset, idx):
    #callback function used in imbalanced dataset loader.
    input, target = dataset[idx]
    return np.argwhere(target.numpy()).item()

Edit: I suspect it would be faster to not cast the tensor to numpy, so the following change should do the same within the tensor framework:

def callback_get_label(dataset, idx):
    #callback function used in imbalanced dataset loader.
    input, target = dataset[idx]
    return target.nonzero().item()

[PaleNeutron]

If you have an int label, try use this:

def callback_get_label(dataset, idx):
    #callback function used in imbalanced dataset loader.
    i, target = dataset[idx]
    return int(target)