Thanks for your suggestion. I guess what I did is similar to what you suggested.
Let me explain what I want to do in more detail.
I have CIFAR10 and ordered all samples based on a hardness measure (Curriculum). Then I want to load every 100 from the dataset, in order.
This is what I did:

change the dataset of (60000, 32, 32, 2) to (600, 100, 32, 32, 3)
write my custom dataloader
load every 100 one time and change the batch size for the loader to 1.

class MyDataset(Dataset):
    def __init__(self, data, targets, transform=None):
        self.data = data
        self.targets = torch.LongTensor(targets)
        self.transform = transform

    def __getitem__(self, index):
        x = self.data[index]
        y = self.targets[index]

        if self.transform:
            x = np.zeros((100, 3, 32, 32))
            for k in range(self.data[index].shape[0]):
                x[k] = self.transform((255.0 * self.data[index][k]).astype(np.uint8))
        return x, y

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

normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                 std=[255.0* 0.229, 255.0* 0.224, 255.0*0.225])

transform = transforms.Compose([transforms.ToPILImage(),
                                transforms.RandomHorizontalFlip(),
                                transforms.RandomCrop(32, 4),
                                transforms.ToTensor(),
                                normalize])

features_list = []
labels_list = []
for i in range(500):
    a = [features[i * 100:((i + 1) * 100)]]
    features_list.append(a[0])
    b = [labels[i * 100:((i + 1) * 100)]]
    labels_list.append(b[0])

dataset = MyDataset(np.asarray(features_list), np.asarray(labels_list), transform=transform)
train_loader = DataLoader(dataset, batch_size=1, shuffle=True)



features_list = []
labels_list = []
for i in range(100):
    a = [features[50000 + (i * 100):50000 + ((i + 1) * 100)]]
    features_list.append(a[0])
    b = [labels[i * 100:((i + 1) * 100)]]
    labels_list.append(b[0])

dataset = MyDataset(np.asarray(features_list), np.asarray(labels_list), transform=transform)
val_loader = DataLoader(dataset, batch_size=1, shuffle=True)

What do you think of this?