text: """
  Copyright 2020 The OneFlow 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 unittest
  
  import cv2
  import numpy as np
  
  import oneflow.experimental as flow
  
  
  @unittest.skipIf(
      not flow.unittest.env.eager_execution_enabled(),
      ".numpy() doesn't work in lazy mode",
  )
  class TestOFRecordModule(flow.unittest.TestCase):
      def test_record(test_case):
          batch_size = 1
          color_space = "RGB"
          height = 224
          width = 224
          output_layout = "NCHW"
          rgb_mean = [123.68, 116.779, 103.939]
          rgb_std = [58.393, 57.12, 57.375]
  
          record_reader = flow.nn.OfrecordReader(
              "/dataset/imagenette/ofrecord",
              batch_size=batch_size,
              data_part_num=1,
              part_name_suffix_length=5,
              shuffle_after_epoch=False,
          )
          record_image_decoder = flow.nn.OFRecordImageDecoder(
              "encoded", color_space=color_space
          )
          record_label_decoder = flow.nn.OfrecordRawDecoder(
              "class/label", shape=(), dtype=flow.int32
          )
          resize = flow.nn.image.Resize(
              resize_side="shorter", keep_aspect_ratio=True, target_size=256
          )
          crop_mirror_normal = flow.nn.CropMirrorNormalize(
              color_space=color_space,
              output_layout=output_layout,
              crop_h=height,
              crop_w=width,
              crop_pos_y=0.5,
              crop_pos_x=0.5,
              mean=rgb_mean,
              std=rgb_std,
              output_dtype=flow.float,
          )
  
          val_record = record_reader()
          label = record_label_decoder(val_record)
          image_raw_buffer = record_image_decoder(val_record)
          image_raw_buffer_nd = image_raw_buffer.numpy()[0]
  
          gt_np = cv2.imread("/dataset/imagenette/ofrecord/gt_tensor_buffer_image.png")
          test_case.assertTrue(np.array_equal(image_raw_buffer_nd, gt_np))
  
          image = resize(image_raw_buffer)
  
          resized_image_raw_buffer_nd = image.numpy()[0]
          gt_np = cv2.imread(
              "/dataset/imagenette/ofrecord/gt_tensor_buffer_resized_image.png"
          )
          test_case.assertTrue(np.array_equal(resized_image_raw_buffer_nd, gt_np))
  
          image = crop_mirror_normal(image)
  
          # recover image
          image_np = image.numpy()
          image_np = np.squeeze(image_np)
          image_np = np.transpose(image_np, (1, 2, 0))
          image_np = image_np * rgb_std + rgb_mean
          image_np = cv2.cvtColor(np.float32(image_np), cv2.COLOR_RGB2BGR)
          image_np = image_np.astype(np.uint8)
  
          # read gt
          gt_np = cv2.imread("/dataset/imagenette/ofrecord/gt_val_image.png")
  
          test_case.assertEqual(label.numpy()[0], 5)
          test_case.assertTrue(np.array_equal(image_np, gt_np))
  
  
  if __name__ == "__main__":
      unittest.main()
  