287cfc0730dfaa73d868a42b0715d5d1cbcc94c0,tensorflow_datasets/core/dataset_builder_test.py,DatasetBuilderTest,test_determinism,#DatasetBuilderTest#,122

Before Change

  @testing.run_in_graph_and_eager_modes()
  def test_determinism(self):
    with testing.tmp_dir(self.get_temp_dir()) as tmp_dir:
      ds = registered.load(
          name="dummy_dataset_shared_generator",
          data_dir=tmp_dir,
          split=splits_lib.Split.TRAIN,
          shuffle_files=False)
      ds_values = list(dataset_utils.as_numpy(ds))

      // Ensure determinism. If this test fail, this mean that numpy random
      // module isn"t always determinist (maybe between version, architecture,
      // ...), and so our datasets aren"t guaranteed either.
      l = list(range(20))
      np.random.RandomState(42).shuffle(l)
      self.assertEqual(l, [
          0, 17, 15, 1, 8, 5, 11, 3, 18, 16, 13, 2, 9, 19, 4, 12, 7, 10, 14, 6
      ])

      // Ensure determinism. If this test fails, this mean the dataset are not
      // deterministically generated.
      self.assertEqual(
          [e["x"] for e in ds_values],
          [6, 16, 19, 12, 14, 18, 5, 13, 15, 4, 10, 17, 0, 8, 3, 1, 9, 7, 11,
           2],
      )

  @testing.run_in_graph_and_eager_modes()
  def test_load_from_gcs(self):
    from tensorflow_datasets.image_classification import mnist  // pylint:disable=import-outside-toplevel,g-import-not-at-top
    with testing.tmp_dir(self.get_temp_dir()) as tmp_dir:

After Change

  @testing.run_in_graph_and_eager_modes()
  def test_determinism(self):
    ds = self.builder.as_dataset(
        split=splits_lib.Split.TRAIN, shuffle_files=False)
    ds_values = list(dataset_utils.as_numpy(ds))

    // Ensure determinism. If this test fail, this mean that numpy random
    // module isn"t always determinist (maybe between version, architecture,

Instances