'MyDataset' object has no attribute 'get_labels'

When I try to use my own Dataset class, I get the error 'MyDataset' object has no attribute 'get_labels' and cannot proceed.

The content of the Dataloader is as follows, and there is nothing strange about it. It processes the image data and label data in .npz format.

class MyDataset(data.Dataset):
    def __init__(self, images, labels, transform=None):
        self.images = images
        self.labels = labels
        self.transform = transform

    def __len__(self):
        return len(self.images)

    def __getitem__(self, index):
        image = self.images[index]
        label = self.labels[index]

        if self.transform is not None:
            image = self.transform(image=image)["image"]

        return image, label

train_dataset = MyDataset(train_imgs, train_labels, transform=transform)
train_dataloader = torch.utils.data.DataLoader(train_dataset,
                                               sampler=ImbalancedDatasetSampler(train_dataset),
                                               batch_size= batch_size,
                                               shuffle=True,
                                               num_workers=2)

Is there something wrong with the code? I don’t think it’s a typo.

How can I fix it so that it works correctly?

[jaebbb]

add this in your dataset code, and turn off the shuffle

def get_labels(self): return self.labels

[Waterkin]

You need to include a function to get the label in that class. As you can see in imbalanced-dataset-sampler/torchsampler/imbalanced.py, the code to get the labels looks like this Hi, Should we do shuffle=False for training, validating and testing ?