Python data.Dataset类代码示例

  def testSaveRestoreMultipleIterator(self):
    checkpoint_directory = self.get_temp_dir()
    checkpoint_prefix = os.path.join(checkpoint_directory, 'ckpt')
    dataset = Dataset.from_tensor_slices([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11])
    dataset = dataset.map(math_ops.square).batch(2)
    iterator_1 = datasets.Iterator(dataset)
    iterator_2 = datasets.Iterator(dataset)
    dataset_2 = Dataset.range(10)
    iterator_3 = datasets.Iterator(dataset_2)

    checkpoint = checkpointable_utils.Checkpoint(
        iterator_1=iterator_1, iterator_2=iterator_2, iterator_3=iterator_3)
    self.assertAllEqual([1, 4], iterator_1.get_next().numpy())
    self.assertEqual(0, iterator_3.get_next().numpy())
    self.assertEqual(1, iterator_3.get_next().numpy())
    self.assertEqual(2, iterator_3.get_next().numpy())

    save_path = checkpoint.save(checkpoint_prefix)
    self.assertAllEqual([1, 4], iterator_2.get_next().numpy())
    self.assertAllEqual([9, 16], iterator_2.get_next().numpy())
    self.assertEqual(3, iterator_3.get_next().numpy())
    checkpoint.restore(save_path)
    self.assertAllEqual([9, 16], iterator_1.get_next().numpy())
    self.assertAllEqual([1, 4], iterator_2.get_next().numpy())
    self.assertEqual(3, iterator_3.get_next().numpy())

 def testNestedOutputs(self):
   ds = Dataset.zip((Dataset.range(4), Dataset.zip((Dataset.range(4),
                                                    Dataset.range(4)))))
   total = 0
   # The Iterator will return a nested structure of Tensor objects.
   # Some funkiness to compare against simple integers.
   for (i, x) in enumerate(datasets.Iterator(ds)):
     want = (i, (i, i))
     got = (x[0].numpy(), (x[1][0].numpy(), x[1][1].numpy()))
     self.assertEqual(got, want)
     total += 1
   self.assertEqual(4, total)

  def benchmarkSliceBatchCacheRepeatCallable(self):
    input_size = 10000
    batch_size = 100
    num_epochs = 100

    input_data = np.random.randn(input_size)

    dataset = (
        Dataset.from_tensor_slices(input_data).batch(batch_size).cache()
        .repeat(num_epochs))
    iterator = datasets.Iterator(dataset)

    ends = [time.time()]
    for _ in iterator:
      ends.append(time.time())

    deltas = np.ediff1d(ends)
    median_wall_time = np.median(deltas)
    print(
        'Slice/batch/cache/repeat eager input size: %d batch size: %d Median '
        'wall time per element: %f'
        % (input_size, batch_size, median_wall_time))
    self.report_benchmark(
        iters=len(deltas),
        wall_time=median_wall_time,
        name='benchmark_slice_batch_cache_repeat_eager_input_%d_batch_%d' %
        (input_size, batch_size))

def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):
    """ Trains a linear regression model of one feature.
    Args:
        :param features: pandas DataFrame of features
        :param targets: pandas DataFrame of targets
        :param batch_size: size of batches to be passed to the model
        :param shuffle: weather to shuffle the data
        :param num_epochs: number of epochs for which data should be repeated. None = repeat indefinitely
    :return:
        Tuple of (features, labels) for next data batch
    """
    # Convert pandas data into a dict of np arrays.
    features = {key: np.array(value) for key, value in dict(features).items()}

    # Construct a dataset, and configure batching/repeating.
    ds = Dataset.from_tensor_slices((features, targets))
    ds = ds.batch(batch_size).repeat(num_epochs)

    # Shuffle the data, if specified.
    if shuffle:
        ds.shuffle(buffer_size=10000)

    # Return the next batch of data.
    features, labels = ds.make_one_shot_iterator().get_next()
    return features, labels

  def testMapAndFilter(self):
    def even(x):
      return math_ops.equal(math_ops.mod(x, 2), 0)

    it = datasets.Iterator(Dataset.range(8).map(math_ops.square).filter(even))
    got = [x.numpy() for x in it]
    self.assertAllEqual([0, 4, 16, 36], got)

  def testOverrideThreadPool(self):

    def get_thread_id(_):
      # Python creates a dummy thread object to represent the current
      # thread when called from an "alien" thread (such as a
      # `PrivateThreadPool` thread in this case). It does not include
      # the TensorFlow-given display name, but it has a unique
      # identifier that maps one-to-one with the underlying OS thread.
      return np.array(threading.current_thread().ident).astype(np.int64)

    for num_threads in [1, 2, 4, 8, 16]:

      dataset = (
          Dataset.range(1000).map(
              lambda x: script_ops.py_func(get_thread_id, [x], dtypes.int64),
              num_parallel_calls=32).apply(unique.unique()))

      dataset = threadpool.override_threadpool(
          dataset,
          threadpool.PrivateThreadPool(
              num_threads, display_name='private_thread_pool_%d' % num_threads))

      thread_ids = []
      for next_element in datasets.Iterator(dataset):
        thread_ids.append(next_element)
      self.assertEqual(len(thread_ids), len(set(thread_ids)))
      self.assertGreater(len(thread_ids), 0)
      # NOTE(mrry): We don't control the thread pool scheduling, and
      # so cannot guarantee that all of the threads in the pool will
      # perform work.
      self.assertLessEqual(len(thread_ids), num_threads)

  def testMultipleIteratorsOnADatasetThatUsesFunctions(self):
    ds = Dataset.from_tensor_slices([1, 2, 3, 4, 5, 6]).map(math_ops.square)

    got1 = [x.numpy() for x in datasets.Iterator(ds)]
    self.assertAllEqual([1, 4, 9, 16, 25, 36], got1)
    got2 = [x.numpy() for x in datasets.Iterator(ds)]
    self.assertAllEqual(got1, got2)

 def testGetNextOneShotIterator(self):
   iterator = Dataset.range(4).make_one_shot_iterator()
   self.assertEqual(0, iterator.get_next().numpy())
   self.assertEqual(1, iterator.get_next().numpy())
   self.assertEqual(2, iterator.get_next().numpy())
   self.assertEqual(3, iterator.get_next().numpy())
   with self.assertRaises(errors.OutOfRangeError):
     iterator.get_next()

 def testGetNext(self):
   iterator = datasets.Iterator(Dataset.range(4))
   self.assertEqual(0, iterator.get_next().numpy())
   self.assertEqual(1, iterator.get_next().numpy())
   self.assertEqual(2, iterator.get_next().numpy())
   self.assertEqual(3, iterator.get_next().numpy())
   with self.assertRaises(errors.OutOfRangeError):
     iterator.get_next()

  def testPyFunc(self):

    def my_map(inp):
      return [[x + 1 for x in inp]]

    ds = Dataset.range(4).map(
        lambda x: script_ops.py_func(my_map, [[x]], dtypes.int64))
    got = [x.numpy() for x in datasets.Iterator(ds)]
    self.assertAllEqual([[1], [2], [3], [4]], got)

  def testMultipleIteratorsOnTheSameDataset(self):
    ds = Dataset.range(4)
    it1 = datasets.Iterator(ds)
    it2 = datasets.Iterator(ds)
    got = [x.numpy() for x in it1]
    self.assertAllEqual([0, 1, 2, 3], got)

    got = [x.numpy() for x in it2]
    self.assertAllEqual([0, 1, 2, 3], got)

  def testTensorsExplicitPrefetchToDevice(self):
    ds = Dataset.from_tensor_slices([0., 1.])
    ds = ds.apply(prefetching_ops.prefetch_to_device(test.gpu_device_name()))

    with self.assertRaisesRegexp(TypeError, 'prefetch_to_device'):
      datasets.Iterator(ds)

    for i, x in enumerate(ds):
      with ops.device(test.gpu_device_name()):
        x = math_ops.add(x, x)
        self.assertEqual(float(i) + float(i), x.numpy())

 def testMapCaptureLookupTable(self):
   default_val = -1
   keys = constant_op.constant(['brain', 'salad', 'surgery'])
   values = constant_op.constant([0, 1, 2], dtypes.int64)
   table = lookup.HashTable(
       lookup.KeyValueTensorInitializer(keys, values), default_val)
   dataset = Dataset.from_tensor_slices(['brain', 'salad', 'surgery'])
   dataset = dataset.map(table.lookup)
   it = datasets.Iterator(dataset)
   got = [x.numpy() for x in it]
   self.assertAllEqual([0, 1, 2], got)

 def testRestoreInReconstructedIterator(self):
   checkpoint_directory = self.get_temp_dir()
   checkpoint_prefix = os.path.join(checkpoint_directory, 'ckpt')
   dataset = Dataset.range(10)
   for i in range(5):
     iterator = datasets.Iterator(dataset)
     checkpoint = checkpointable_utils.Checkpoint(iterator=iterator)
     checkpoint.restore(checkpoint_management.latest_checkpoint(
         checkpoint_directory))
     for j in range(2):
       self.assertEqual(i * 2 + j, iterator.get_next().numpy())
     checkpoint.save(file_prefix=checkpoint_prefix)

  def testRestoreExhaustedIterator(self):
    checkpoint_directory = self.get_temp_dir()
    checkpoint_prefix = os.path.join(checkpoint_directory, 'ckpt')
    dataset = Dataset.range(3)
    iterator = datasets.Iterator(dataset)

    checkpoint = checkpointable_utils.Checkpoint(iterator=iterator)
    self.assertEqual(0, iterator.get_next().numpy())
    self.assertEqual(1, iterator.get_next().numpy())
    save_path = checkpoint.save(checkpoint_prefix)
    self.assertEqual(2, iterator.get_next().numpy())
    checkpoint.restore(save_path)
    self.assertEqual(2, iterator.get_next().numpy())