text: import torch.utils.data 
  import torchvision.transforms as transforms
  from PIL import Image
  import numpy as np
  
  class Dataset( torch.utils.data.Dataset):
      def __init__( self , img_list , crop_size = (256,256) ):
          super(type(self),self).__init__()
          self.img_list = img_list
          self.crop_size = crop_size
          self.to_tensor = transforms.ToTensor()
      def crop_resize_totensor(self,img, crop_location ):
          img256 = img.crop( crop_location )
          img128 = img256.resize( (self.crop_size[0] // 2 , self.crop_size[1] // 2 ),  resample = Image.BILINEAR)
          img64 = img128.resize( ( self.crop_size[0] // 4 , self.crop_size[1] // 4 ) , resample = Image.BILINEAR)
          return self.to_tensor(img256) , self.to_tensor(img128) , self.to_tensor(img64)
  
  
      def __len__( self ):
          return len(self.img_list)
      def __getitem__( self , idx ):
          #filename processing
          blurry_img_name = self.img_list[idx].split(' ')[-2]
          clear_img_name = self.img_list[idx].split(' ')[-1]
  
          blurry_img = Image.open( blurry_img_name )
          clear_img = Image.open( clear_img_name )
          assert blurry_img.size == clear_img.size
          crop_left = int( np.floor( np.random.uniform( 0 , blurry_img.size[0] - self.crop_size[0] + 1 ) ))
          crop_top = int( np.floor( np.random.uniform( 0 , blurry_img.size[1] - self.crop_size[1] + 1) ))
          crop_location = ( crop_left , crop_top , crop_left + self.crop_size[0] , crop_top + self.crop_size[1] )
          
          img256 , img128 , img64 = self.crop_resize_totensor( blurry_img, crop_location )
          label256 , label128 , label64 = self.crop_resize_totensor( clear_img, crop_location ) 
          batch = { 'img256':img256 , 'img128':img128 , 'img64':img64 , 'label256':label256 , 'label128':label128 , 'label64':label64 }
          for k in batch:
              batch[k] = batch[k] * 2 - 1.0#in range [-1,1] 
          return batch
  
  class TestDataset( torch.utils.data.Dataset):
      def __init__( self , img_list  ):
          super(type(self),self).__init__()
          self.img_list = img_list
          self.to_tensor = transforms.ToTensor()
      def resize_totensor(self,img ):
          img_size = img.size
          img256 = img
          img128 = img256.resize( (img_size[0] // 2 , img_size[1] // 2 ),  resample = Image.BILINEAR)
          img64 = img128.resize( ( img_size[0] // 4 , img_size[1] // 4 ) , resample = Image.BILINEAR)
          return self.to_tensor(img256) , self.to_tensor(img128) , self.to_tensor(img64)
  
  
      def __len__( self ):
          return len(self.img_list)
      def __getitem__( self , idx ):
          #filename processing
          blurry_img_name = self.img_list[idx].split(' ')[-2]
          clear_img_name = self.img_list[idx].split(' ')[-1]
  
          blurry_img = Image.open( blurry_img_name )
          clear_img = Image.open( clear_img_name )
          assert blurry_img.size == clear_img.size
          
          img256 , img128 , img64 = self.resize_totensor( blurry_img )
          label256  = self.to_tensor( clear_img )
          batch = { 'img256':img256 , 'img128':img128 , 'img64':img64 , 'label256':label256 }
          for k in batch:
              batch[k] = batch[k] * 2 - 1.0#in range [-1,1] 
          return batch
  
  