class ImageDataset(Dataset):
  def __init__(self, highres, transform=None):
    """
    highres(list): high resolution np image list
    transform(callable, optional): Optional transform to be applied to a sample
    """
    self.highres   = highres
    self.transform = transform

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

  def __getitem__(self, idx):
    image_hr = self.highres[idx]

    rc     = transforms.RandomCrop([41,41])
    toPIL  = transforms.ToPILImage()
    toTens = transforms.ToTensor()

    #don't convert to tensor then .numpy()
    image_hr = np.array(rc(toPIL(image_hr)))

    image_lr2 = cv2.resize(image_hr, (0, 0), fx=0.5, fy=0.5,   interpolation = cv2.INTER_CUBIC)
    image_lr2 = cv2.resize(image_lr2, (41, 41), interpolation = cv2.INTER_CUBIC)

    image_lr3 = cv2.resize(image_hr, (0, 0), fx=0.33, fy=0.33,   interpolation = cv2.INTER_CUBIC)
    image_lr3 = cv2.resize(image_lr3, (41, 41), interpolation = cv2.INTER_CUBIC)

    image_lr4 = cv2.resize(image_hr, (0, 0), fx=0.25, fy=0.25,   interpolation = cv2.INTER_CUBIC)
    image_lr4 = cv2.resize(image_lr4, (41, 41), interpolation = cv2.INTER_CUBIC)

    sample = {'hr': image_hr, 'lr2': image_lr2, 'lr3': image_lr3, 'lr4': image_lr4}

    if self.transform:
      sample['hr']  = self.transform(sample['hr'])
      sample['lr2'] = self.transform(sample['lr2'])
      sample['lr3'] = self.transform(sample['lr3'])
      sample['lr4'] = self.transform(sample['lr4'])

    return sample