code: import cv2
  import ast
  import torch
  import numpy as np
  import random
  from torch.utils.data import DataLoader, Dataset
  
  cv2.setNumThreads(1)
  device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
  class VimeoDataset(Dataset):
      def __init__(self, dataset_name, batch_size=32):
          self.batch_size = batch_size
          self.dataset_name = dataset_name
          self.load_data()
          self.h = 256
          self.w = 448
          self.data_root = 'vimeo_triplet'
          self.image_root = os.path.join(self.data_root, 'sequences')
          train_fn = os.path.join(self.data_root, 'tri_trainlist.txt')
          test_fn = os.path.join(self.data_root, 'tri_testlist.txt')
          with open(train_fn, 'r') as f:
              self.trainlist = f.read().splitlines()
          with open(test_fn, 'r') as f:
              self.testlist = f.read().splitlines()                                                    
  
      def __len__(self):
          return len(self.meta_data)
  
      def load_data(self):
          if self.dataset_name == 'train':
              self.meta_data = self.trainlist
          else:
              self.meta_data = self.testlist
  
      def aug(self, img0, gt, img1, h, w):
          ih, iw, _ = img0.shape
          x = np.random.randint(0, ih - h + 1)
          y = np.random.randint(0, iw - w + 1)
          img0 = img0[x:x+h, y:y+w, :]
          img1 = img1[x:x+h, y:y+w, :]
          gt = gt[x:x+h, y:y+w, :]
          return img0, gt, img1
  
      def getimg(self, index):
          imgpath = self.meta_data[index]
          imgpaths = [imgpath + '/im1.png', imgpath + '/im2.png', imgpath + '/im3.png']
  
          # Load images
          img0 = cv2.imread(imgpaths[0])
          gt = cv2.imread(imgpaths[1])
          img1 = cv2.imread(imgpaths[2])
          return img0, gt, img1
              
      def __getitem__(self, index):        
          img0, gt, img1 = self.getimg(index)
          if self.dataset_name == 'train':
              img0, gt, img1 = self.aug(img0, gt, img1, 224, 224)
              if random.uniform(0, 1) < 0.5:
                  img0 = img0[:, :, ::-1]
                  img1 = img1[:, :, ::-1]
                  gt = gt[:, :, ::-1]
              if random.uniform(0, 1) < 0.5:
                  img0 = img0[::-1]
                  img1 = img1[::-1]
                  gt = gt[::-1]
              if random.uniform(0, 1) < 0.5:
                  img0 = img0[:, ::-1]
                  img1 = img1[:, ::-1]
                  gt = gt[:, ::-1]
              if random.uniform(0, 1) < 0.5:
                  tmp = img1
                  img1 = img0
                  img0 = tmp
          img0 = torch.from_numpy(img0.copy()).permute(2, 0, 1)
          img1 = torch.from_numpy(img1.copy()).permute(2, 0, 1)
          gt = torch.from_numpy(gt.copy()).permute(2, 0, 1)
          return torch.cat((img0, img1, gt), 0)
  
apis: torch.cuda.is_available torch.cat numpy.random.randint