old_contents: # Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  import os
  import cv2
  import math
  import random
  import numpy as np
  
  from paddle.vision.datasets import DatasetFolder
  from paddle.vision.transforms import transforms
  from paddle import fluid
  
  
  class ImageNetDataset(DatasetFolder):
      def __init__(self,
                   path,
                   mode='train',
                   image_size=224,
                   resize_short_size=256):
          super(ImageNetDataset, self).__init__(path)
          self.mode = mode
  
          normalize = transforms.Normalize(
              mean=[123.675, 116.28, 103.53], std=[58.395, 57.120, 57.375])
          if self.mode == 'train':
              self.transform = transforms.Compose([
                  transforms.RandomResizedCrop(image_size),
                  transforms.RandomHorizontalFlip(),
                  transforms.Permute(mode='CHW'), normalize
              ])
          else:
              self.transform = transforms.Compose([
                  transforms.Resize(resize_short_size),
                  transforms.CenterCrop(image_size),
                  transforms.Permute(mode='CHW'), normalize
              ])
  
      def __getitem__(self, idx):
          img_path, label = self.samples[idx]
          img = cv2.imread(img_path).astype(np.float32)
          label = np.array([label]).astype(np.int64)
          return self.transform(img), label
  
      def __len__(self):
          return len(self.samples)
  
new_contents: # Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  import os
  import cv2
  import math
  import random
  import numpy as np
  
  from paddle.vision.datasets import DatasetFolder
  from paddle.vision.transforms import transforms
  from paddle import fluid
  
  
  class ImageNetDataset(DatasetFolder):
      def __init__(self,
                   path,
                   mode='train',
                   image_size=224,
                   resize_short_size=256):
          super(ImageNetDataset, self).__init__(path)
          self.mode = mode
  
          normalize = transforms.Normalize(
              mean=[123.675, 116.28, 103.53], std=[58.395, 57.120, 57.375])
          if self.mode == 'train':
              self.transform = transforms.Compose([
                  transforms.RandomResizedCrop(image_size),
                  transforms.RandomHorizontalFlip(),
                  transforms.Transpose(order=(2, 0, 1)), normalize
              ])
          else:
              self.transform = transforms.Compose([
                  transforms.Resize(resize_short_size),
                  transforms.CenterCrop(image_size),
                  transforms.Transpose(order=(2, 0, 1)), normalize
              ])
  
      def __getitem__(self, idx):
          img_path, label = self.samples[idx]
          img = cv2.imread(img_path).astype(np.float32)
          label = np.array([label]).astype(np.int64)
          return self.transform(img), label
  
      def __len__(self):
          return len(self.samples)
  
subject: fix permute api to transpose (#4913)