Python tf2.enabled函数代码示例

  def test_run_all_keras_modes_with_all_model_types_annotate_class_2(self):
    l = []

    @keras_parameterized.run_with_all_model_types
    class ExampleTest(keras_parameterized.TestCase):

      def runTest(self):
        pass

      @keras_parameterized.run_all_keras_modes
      @parameterized.named_parameters(dict(testcase_name="_arg",
                                           arg=True))
      def testBody(self, arg):
        mode = "eager" if context.executing_eagerly() else "graph"
        should_run_eagerly = testing_utils.should_run_eagerly()
        l.append((mode, should_run_eagerly, testing_utils.get_model_type()))

    e = ExampleTest()
    e.testBody_arg_v2_eager_functional()
    e.testBody_arg_v2_function_functional()
    e.testBody_arg_v2_eager_sequential()
    e.testBody_arg_v2_function_sequential()
    e.testBody_arg_v2_eager_subclass()
    e.testBody_arg_v2_function_subclass()

    if not tf2.enabled():
      e.testBody_arg_v1_graph_functional()
      e.testBody_arg_v1_graph_sequential()
      e.testBody_arg_v1_graph_subclass()

    expected_combinations = {
        ("eager", True, "functional"),
        ("eager", False, "functional"),
        ("eager", True, "sequential"),
        ("eager", False, "sequential"),
        ("eager", True, "subclass"),
        ("eager", False, "subclass"),
    }

    if not tf2.enabled():
      expected_combinations = expected_combinations.union({
          ("graph", False, "functional"),
          ("graph", False, "sequential"),
          ("graph", False, "subclass"),
      })

    self.assertLen(l, len(expected_combinations))
    self.assertEqual(set(l), expected_combinations)

    ts = unittest.makeSuite(ExampleTest)
    res = unittest.TestResult()
    ts.run(res)

    self.assertLen(l, len(expected_combinations) * 2)

  def test_run_all_keras_modes_extra_params(self):
    l = []

    class ExampleTest(keras_parameterized.TestCase):

      def runTest(self):
        pass

      @keras_parameterized.run_all_keras_modes
      @parameterized.named_parameters(
          [dict(testcase_name="_0", with_brackets=True),
           dict(testcase_name="_1", with_brackets=False)])
      def testBody(self, with_brackets):
        mode = "eager" if context.executing_eagerly() else "graph"
        with_brackets = "with_brackets" if with_brackets else "without_brackets"
        should_run_eagerly = testing_utils.should_run_eagerly()
        l.append((with_brackets, mode, should_run_eagerly))

    e = ExampleTest()
    if not tf2.enabled():
      e.testBody_0_v1_graph()
      e.testBody_1_v1_graph()

    e.testBody_0_v2_eager()
    e.testBody_0_v2_function()
    e.testBody_1_v2_eager()
    e.testBody_1_v2_function()

    expected_combinations = {
        ("with_brackets", "eager", True),
        ("with_brackets", "eager", False),
        ("without_brackets", "eager", True),
        ("without_brackets", "eager", False),
    }

    if not tf2.enabled():
      expected_combinations = expected_combinations.union({
          ("with_brackets", "graph", False),
          ("without_brackets", "graph", False),
      })

    self.assertLen(l, len(expected_combinations))
    self.assertEqual(set(l), expected_combinations)

    ts = unittest.makeSuite(ExampleTest)
    res = unittest.TestResult()
    ts.run(res)

    self.assertLen(l, len(expected_combinations) * 2)

  def testBatchSplitting(self, input_type, api_type, iteration_type,
                         split_batch_by, distribution,
                         enable_get_next_as_optional):
    worker_device_pairs = [("", ["/device:GPU:0", "/device:CPU:0"])]
    batch_size = 10
    if tf2.enabled():
      dataset_fn = lambda _: dataset_ops.DatasetV2.range(100).batch(batch_size)
    else:
      dataset_fn = lambda _: dataset_ops.Dataset.range(100).batch(batch_size)

    updated_batch_size = (
        batch_size // split_batch_by if split_batch_by else batch_size)
    expected_values = [[range(i, i+updated_batch_size),
                        range(i+updated_batch_size, i+2*updated_batch_size)]
                       for i in range(0, 100, updated_batch_size*2)]

    self._test_input_iteration(
        input_type,
        api_type,
        iteration_type,
        dataset_fn,
        worker_device_pairs,
        expected_values,
        distribution,
        sess=None,
        split_batch_by=split_batch_by,
        enable_get_next_as_optional=True)

  def test_optimizer_errors(self):
    opt = 1
    if tf2.enabled():
      expected_regex = ('"opt" must be an instance of a '
                        'tf.keras.optimizers.Optimizer, but got')
    else:
      expected_regex = ('"opt" must be an instance of a tf.train.Optimizer or '
                        'a tf.keras.optimizers.Optimizer, but got')
    with self.assertRaisesRegexp(ValueError, expected_regex):
      enable_mixed_precision_graph_rewrite(opt)
    self.assertFalse(config.get_optimizer_experimental_options()
                     .get('auto_mixed_precision', False))

    opt = gradient_descent_v1.GradientDescentOptimizer(1.0)
    opt = loss_scale_optimizer_v1.MixedPrecisionLossScaleOptimizer(opt,
                                                                   'dynamic')
    with self.assertRaisesRegexp(ValueError,
                                 '"opt" must not already be an instance of a '
                                 'MixedPrecisionLossScaleOptimizer.'):
      enable_mixed_precision_graph_rewrite(opt)
    self.assertFalse(config.get_optimizer_experimental_options()
                     .get('auto_mixed_precision', False))

    opt = gradient_descent_v2.SGD(1.0)
    opt = loss_scale_optimizer_v2.LossScaleOptimizer(opt, 'dynamic')
    with self.assertRaisesRegexp(ValueError,
                                 '"opt" must not already be an instance of a '
                                 'LossScaleOptimizer.'):
      enable_mixed_precision_graph_rewrite(opt)
    self.assertFalse(config.get_optimizer_experimental_options()
                     .get('auto_mixed_precision', False))

def deserialize(config, custom_objects=None):
  """Instantiates a layer from a config dictionary.

  Arguments:
      config: dict of the form {'class_name': str, 'config': dict}
      custom_objects: dict mapping class names (or function names)
          of custom (non-Keras) objects to class/functions

  Returns:
      Layer instance (may be Model, Sequential, Network, Layer...)
  """
  from tensorflow.python.keras import models  # pylint: disable=g-import-not-at-top
  globs = globals()  # All layers.
  globs['Network'] = models.Network
  globs['Model'] = models.Model
  globs['Sequential'] = models.Sequential
  layer_class_name = config['class_name']
  if layer_class_name in _DESERIALIZATION_TABLE:
    version = 'v2' if tf2.enabled() else 'v1'
    config['class_name'] = _DESERIALIZATION_TABLE[layer_class_name][version]

  return deserialize_keras_object(
      config,
      module_objects=globs,
      custom_objects=custom_objects,
      printable_module_name='layer')

def get_expected_metric_variable_names(var_names, name_suffix=''):
  """Returns expected metric variable names given names and prefix/suffix."""
  if tf2.enabled() or context.executing_eagerly():
    # In V1 eager mode and V2 variable names are not made unique.
    return [n + ':0' for n in var_names]
  # In V1 graph mode variable names are made unique using a suffix.
  return [n + name_suffix + ':0' for n in var_names]

 def testUnevenDatasetBatches(self, input_type, api_type, iteration_type):
   strategy = mirrored_strategy.MirroredStrategy(
       devices=(self._cpu_and_one_gpu_devices()[0][1] +
                self._cpu_and_one_gpu_devices()[1][1]),
       cross_device_ops=cross_device_ops_lib.MultiWorkerAllReduce(
           ["/job:worker/task:0", "/job:worker/task:1"], 2))
   worker_devices = self._cpu_and_one_gpu_devices()
   with context.graph_mode(), strategy.scope(), self.cached_session() as sess:
     if tf2.enabled():
       dataset_fn = lambda _: dataset_ops.DatasetV2.range(9).batch(2)
     else:
       dataset_fn = lambda _: dataset_ops.Dataset.range(9).batch(2)
     if input_type == "dataset":
       # Autosharded
       expected_values = [[[0, 1], [4, 5], [2, 3], [6, 7]], [[8], [], [], []]]
     else:
       expected_values = [[[0, 1], [2, 3], [0, 1], [2, 3]],
                          [[4, 5], [6, 7], [4, 5], [6, 7]], [[8], [], [8], []]]
     self._test_input_iteration(
         input_type,
         api_type,
         iteration_type,
         dataset_fn,
         worker_devices,
         expected_values,
         strategy,
         sess=sess,
         enable_get_next_as_optional=True)

  def testOneDevicePerWorker(self, input_type, api_type, iteration_type,
                             enable_get_next_as_optional):
    strategy = mirrored_strategy.MirroredStrategy(
        devices=(self._cpu_devices()[0][1] + self._cpu_devices()[1][1]),
        cross_device_ops=cross_device_ops_lib.MultiWorkerAllReduce(
            ["/job:worker/task:0", "/job:worker/task:1"], 1))
    worker_devices = self._cpu_devices()
    with context.graph_mode(), strategy.scope(), self.cached_session() as sess:
      if tf2.enabled():
        dataset_fn = lambda _: dataset_ops.DatasetV2.range(4)
      else:
        dataset_fn = lambda _: dataset_ops.Dataset.range(4)

      if input_type == "dataset":
        # Autosharded
        expected_values = [[0, 1], [2, 3]]
      else:
        expected_values = [[0, 0], [1, 1], [2, 2], [3, 3]]
      self._test_input_iteration(
          input_type,
          api_type,
          iteration_type,
          dataset_fn,
          worker_devices,
          expected_values,
          strategy,
          sess=sess,
          enable_get_next_as_optional=enable_get_next_as_optional)

  def testGradientFloat16(self):

    def grad(x):
      with backprop.GradientTape() as tape:
        tape.watch(x)
        y = nn_ops.l2_loss(nn_ops.relu(x))
      return tape.gradient(y, x)

    def f():
      with test_util.use_gpu():
        # Randomly construct a 1D shape from [1, 40)
        shape = random_ops.random_uniform([1],
                                          minval=1,
                                          maxval=40,
                                          dtype=dtypes.int32)
        x32 = random_ops.random_uniform(shape, minval=-1, maxval=1)
        x16 = math_ops.cast(x32, dtype=dtypes.float16)
        return grad(x32), grad(x16)

    # We're going to ensure that the fp16 and fp32 gradients
    # are "close" to each other for ~100 random values.
    #
    # In TensorFlow 1.x, invoking f() (without eager execution enabled)
    # would construct a graph. Instead of construct a graph with O(100) nodes,
    # we construct a single graph to be executed ~100 times in a Session.
    if not tf2.enabled():
      d32_tensor, d16_tensor = f()
      with self.cached_session() as sess:
        f = lambda: sess.run([d32_tensor, d16_tensor])

    # Repeat the experiment for 100 times. All tensor shapes and its tensor
    # values are randomly generated for each run.
    for _ in xrange(100):
      d32, d16 = f()
      self.assertAllClose(d32, d16, atol=3e-4)

 def setUpClass(cls):
   if tf2.enabled():
     stats_aggregator._DEFAULT_MAX_QUEUE = 0  # pylint: disable=protected-access
     stats_aggregator.StatsAggregator = stats_aggregator.StatsAggregatorV2
     # TODO(b/116314787): add graph mode support for StatsAggregatorV2.
   else:
     stats_aggregator.StatsAggregator = stats_aggregator.StatsAggregatorV1
     return test_util.run_all_in_graph_and_eager_modes(cls)

 def dataset_fn(ctx):
   del ctx
   if tf2.enabled():
     dataset1 = dataset_ops.DatasetV2.range(4)
     dataset2 = dataset_ops.DatasetV2.range(4).map(lambda x: x**2)
     return dataset_ops.DatasetV2.zip((dataset1, dataset2))
   else:
     dataset1 = dataset_ops.Dataset.range(4)
     dataset2 = dataset_ops.Dataset.range(4).map(lambda x: x**2)
     return dataset_ops.Dataset.zip((dataset1, dataset2))

 def assertStatisticsHasSum(self,
                            handle,
                            tag,
                            expected_value,
                            num_events=-1,
                            offset=0):
   if tf2.enabled():
     self._assertEventHasSum(handle, tag, expected_value, num_events, offset)
   else:
     self._assertSummaryHasSum(handle, tag, expected_value)

  def test_run_all_keras_modes(self):
    l = []

    class ExampleTest(keras_parameterized.TestCase):

      def runTest(self):
        pass

      @keras_parameterized.run_all_keras_modes
      def testBody(self):
        mode = "eager" if context.executing_eagerly() else "graph"
        should_run_eagerly = testing_utils.should_run_eagerly()
        l.append((mode, should_run_eagerly))

    e = ExampleTest()
    if not tf2.enabled():
      e.testBody_v1_graph()
    e.testBody_v2_eager()
    e.testBody_v2_function()

    if not tf2.enabled():
      self.assertLen(l, 3)
      self.assertAllEqual(l, [
          ("graph", False),
          ("eager", True),
          ("eager", False),
      ])

      ts = unittest.makeSuite(ExampleTest)
      res = unittest.TestResult()
      ts.run(res)
      self.assertLen(l, 6)
    else:
      self.assertLen(l, 2)
      self.assertAllEqual(l, [
          ("eager", True),
          ("eager", False),
      ])

      ts = unittest.makeSuite(ExampleTest)
      res = unittest.TestResult()
      ts.run(res)
      self.assertLen(l, 4)

 def test_singleton_list(self):
   shape = tensor_shape.TensorShape([])
   fn_true = lambda: [constant_op.constant(1)]
   fn_false = lambda: [constant_op.constant(3)]
   # Non-strict cond is only available in v1
   if not tf2.enabled():
     self._testShape(fn_true, fn_false, shape)
     self._testReturnValues(fn_true, fn_false, 1, 3)
   self._testShape(fn_true, fn_false, [shape], strict=True)
   self._testReturnValues(fn_true, fn_false, [1], [3], strict=True)

 def test_serialize_deserialize_lstm(self, layer):
   lstm = layer(5, return_sequences=True)
   config = keras.layers.serialize(lstm)
   self.assertEqual(config['class_name'], 'LSTM')
   new_layer = keras.layers.deserialize(config)
   self.assertEqual(new_layer.units, 5)
   self.assertEqual(new_layer.return_sequences, True)
   if tf2.enabled():
     self.assertIsInstance(new_layer, keras.layers.UnifiedLSTM)
   else:
     self.assertIsInstance(new_layer, keras.layers.LSTM)