text: #!/usr/bin/env python
  # coding=utf-8
  import argparse
  import os
  import random
  import shutil
  import time
  import warnings
  import numpy as np
  
  import torch
  import torch.nn as nn
  import torch.nn.parallel
  import torch.backends.cudnn as cudnn
  import torch.distributed as dist
  import torch.optim
  import torch.utils.data
  import torch.utils.data.distributed
  import torchvision.transforms as transforms
  import torchvision.datasets as datasets
  import torchvision.models as models
  from PIL import Image
  
  src_root_dir = '/data/LargeData/Large/ImageNet'
  des_root_dir = 'data/imagenet_resized_256/data/'
  if not os.path.exists(des_root_dir):
      os.makedirs(des_root_dir)
  
  phase_list = ['train', 'val']
  for phase in phase_list:
      if not os.path.exists(os.path.join(des_root_dir, phase)):
          os.mkdir(os.path.join(des_root_dir, phase))
      data_dir = os.path.join(src_root_dir, phase)
      tg_dataset = datasets.ImageFolder(data_dir)
      for cls_name in tg_dataset.classes:
          if not os.path.exists(os.path.join(des_root_dir, phase, cls_name)):
              os.mkdir(os.path.join(des_root_dir, phase, cls_name))
      cnt = 0
      for item in tg_dataset.imgs:
          img_path = item[0]
          img = Image.open(img_path)
          img = img.convert('RGB')
          #save_path = img_path.replace('imagenet', 'imagenet_resized_256')
          save_path = img_path.replace('/data/LargeData/Large/ImageNet', 'data/imagenet_resized_256/data/')
          resized_img = img.resize((256,256), Image.BILINEAR)
          resized_img.save(save_path)
          cnt = cnt+1
          if cnt % 1000 == 0:
              print(cnt, save_path)
  
  print("Hello World")
  
  