testing_utils.py

# Copyright 2016 The TensorFlow 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.
# ==============================================================================
"""Utilities for unit-testing Keras."""

import collections
import contextlib
import functools
import itertools
import threading

import numpy as np

from tensorflow.python import tf2
from tensorflow.python.eager import context
from tensorflow.python.framework import config
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor_shape
from tensorflow.python.framework import tensor_spec
from tensorflow.python.framework import test_util
from tensorflow.python.keras import backend
from tensorflow.python.keras import layers
from tensorflow.python.keras import models
from tensorflow.python.keras.engine import base_layer_utils
from tensorflow.python.keras.optimizer_v2 import adadelta as adadelta_v2
from tensorflow.python.keras.optimizer_v2 import adagrad as adagrad_v2
from tensorflow.python.keras.optimizer_v2 import adam as adam_v2
from tensorflow.python.keras.optimizer_v2 import adamax as adamax_v2
from tensorflow.python.keras.optimizer_v2 import gradient_descent as gradient_descent_v2
from tensorflow.python.keras.optimizer_v2 import nadam as nadam_v2
from tensorflow.python.keras.optimizer_v2 import rmsprop as rmsprop_v2
from tensorflow.python.keras.utils import tf_contextlib
from tensorflow.python.keras.utils import tf_inspect
from tensorflow.python.util import tf_decorator


def string_test(actual, expected):
  np.testing.assert_array_equal(actual, expected)


def numeric_test(actual, expected):
  np.testing.assert_allclose(actual, expected, rtol=1e-3, atol=1e-6)


def get_test_data(train_samples,
                  test_samples,
                  input_shape,
                  num_classes,
                  random_seed=None):
  """Generates test data to train a model on.

  Args:
    train_samples: Integer, how many training samples to generate.
    test_samples: Integer, how many test samples to generate.
    input_shape: Tuple of integers, shape of the inputs.
    num_classes: Integer, number of classes for the data and targets.
    random_seed: Integer, random seed used by numpy to generate data.

  Returns:
    A tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`.
  """
  if random_seed is not None:
    np.random.seed(random_seed)
  num_sample = train_samples + test_samples
  templates = 2 * num_classes * np.random.random((num_classes,) + input_shape)
  y = np.random.randint(0, num_classes, size=(num_sample,))
  x = np.zeros((num_sample,) + input_shape, dtype=np.float32)
  for i in range(num_sample):
    x[i] = templates[y[i]] + np.random.normal(loc=0, scale=1., size=input_shape)
  return ((x[:train_samples], y[:train_samples]),
          (x[train_samples:], y[train_samples:]))


@test_util.disable_cudnn_autotune
def layer_test(layer_cls,
               kwargs=None,
               input_shape=None,
               input_dtype=None,
               input_data=None,
               expected_output=None,
               expected_output_dtype=None,
               expected_output_shape=None,
               validate_training=True,
               adapt_data=None,
               custom_objects=None,
               test_harness=None,
               supports_masking=None):
  """Test routine for a layer with a single input and single output.

  Args:
    layer_cls: Layer class object.
    kwargs: Optional dictionary of keyword arguments for instantiating the
      layer.
    input_shape: Input shape tuple.
    input_dtype: Data type of the input data.
    input_data: Numpy array of input data.
    expected_output: Numpy array of the expected output.
    expected_output_dtype: Data type expected for the output.
    expected_output_shape: Shape tuple for the expected shape of the output.
    validate_training: Whether to attempt to validate training on this layer.
      This might be set to False for non-differentiable layers that output
      string or integer values.
    adapt_data: Optional data for an 'adapt' call. If None, adapt() will not
      be tested for this layer. This is only relevant for PreprocessingLayers.
    custom_objects: Optional dictionary mapping name strings to custom objects
      in the layer class. This is helpful for testing custom layers.
    test_harness: The Tensorflow test, if any, that this function is being
      called in.
    supports_masking: Optional boolean to check the `supports_masking` property
      of the layer. If None, the check will not be performed.

  Returns:
    The output data (Numpy array) returned by the layer, for additional
    checks to be done by the calling code.

  Raises:
    ValueError: if `input_shape is None`.
  """
  if input_data is None:
    if input_shape is None:
      raise ValueError('input_shape is None')
    if not input_dtype:
      input_dtype = 'float32'
    input_data_shape = list(input_shape)
    for i, e in enumerate(input_data_shape):
      if e is None:
        input_data_shape[i] = np.random.randint(1, 4)
    input_data = 10 * np.random.random(input_data_shape)
    if input_dtype[:5] == 'float':
      input_data -= 0.5
    input_data = input_data.astype(input_dtype)
  elif input_shape is None:
    input_shape = input_data.shape
  if input_dtype is None:
    input_dtype = input_data.dtype
  if expected_output_dtype is None:
    expected_output_dtype = input_dtype

  if dtypes.as_dtype(expected_output_dtype) == dtypes.string:
    if test_harness:
      assert_equal = test_harness.assertAllEqual
    else:
      assert_equal = string_test
  else:
    if test_harness:
      assert_equal = test_harness.assertAllClose
    else:
      assert_equal = numeric_test

  # instantiation
  kwargs = kwargs or {}
  layer = layer_cls(**kwargs)

  if (supports_masking is not None
      and layer.supports_masking != supports_masking):
    raise AssertionError(
        'When testing layer %s, the `supports_masking` property is %r'
        'but expected to be %r.\nFull kwargs: %s' %
        (layer_cls.__name__, layer.supports_masking, supports_masking, kwargs))

  # Test adapt, if data was passed.
  if adapt_data is not None:
    layer.adapt(adapt_data)

  # test get_weights , set_weights at layer level
  weights = layer.get_weights()
  layer.set_weights(weights)

  # test and instantiation from weights
  if 'weights' in tf_inspect.getargspec(layer_cls.__init__):
    kwargs['weights'] = weights
    layer = layer_cls(**kwargs)

  # test in functional API
  x = layers.Input(shape=input_shape[1:], dtype=input_dtype)
  y = layer(x)
  if backend.dtype(y) != expected_output_dtype:
    raise AssertionError('When testing layer %s, for input %s, found output '
                         'dtype=%s but expected to find %s.\nFull kwargs: %s' %
                         (layer_cls.__name__, x, backend.dtype(y),
                          expected_output_dtype, kwargs))

  def assert_shapes_equal(expected, actual):
    """Asserts that the output shape from the layer matches the actual shape."""
    if len(expected) != len(actual):
      raise AssertionError(
          'When testing layer %s, for input %s, found output_shape='
          '%s but expected to find %s.\nFull kwargs: %s' %
          (layer_cls.__name__, x, actual, expected, kwargs))

    for expected_dim, actual_dim in zip(expected, actual):
      if isinstance(expected_dim, tensor_shape.Dimension):
        expected_dim = expected_dim.value
      if isinstance(actual_dim, tensor_shape.Dimension):
        actual_dim = actual_dim.value
      if expected_dim is not None and expected_dim != actual_dim:
        raise AssertionError(
            'When testing layer %s, for input %s, found output_shape='
            '%s but expected to find %s.\nFull kwargs: %s' %
            (layer_cls.__name__, x, actual, expected, kwargs))

  if expected_output_shape is not None:
    assert_shapes_equal(tensor_shape.TensorShape(expected_output_shape),
                        y.shape)

  # check shape inference
  model = models.Model(x, y)
  computed_output_shape = tuple(
      layer.compute_output_shape(
          tensor_shape.TensorShape(input_shape)).as_list())
  computed_output_signature = layer.compute_output_signature(
      tensor_spec.TensorSpec(shape=input_shape, dtype=input_dtype))
  actual_output = model.predict(input_data)
  actual_output_shape = actual_output.shape
  assert_shapes_equal(computed_output_shape, actual_output_shape)
  assert_shapes_equal(computed_output_signature.shape, actual_output_shape)
  if computed_output_signature.dtype != actual_output.dtype:
    raise AssertionError(
        'When testing layer %s, for input %s, found output_dtype='
        '%s but expected to find %s.\nFull kwargs: %s' %
        (layer_cls.__name__, x, actual_output.dtype,
         computed_output_signature.dtype, kwargs))
  if expected_output is not None:
    assert_equal(actual_output, expected_output)

  # test serialization, weight setting at model level
  model_config = model.get_config()
  recovered_model = models.Model.from_config(model_config, custom_objects)
  if model.weights:
    weights = model.get_weights()
    recovered_model.set_weights(weights)
    output = recovered_model.predict(input_data)
    assert_equal(output, actual_output)

  # test training mode (e.g. useful for dropout tests)
  # Rebuild the model to avoid the graph being reused between predict() and
  # See b/120160788 for more details. This should be mitigated after 2.0.
  layer_weights = layer.get_weights()  # Get the layer weights BEFORE training.
  if validate_training:
    model = models.Model(x, layer(x))
    if _thread_local_data.run_eagerly is not None:
      model.compile(
          'rmsprop',
          'mse',
          weighted_metrics=['acc'],
          run_eagerly=should_run_eagerly())
    else:
      model.compile('rmsprop', 'mse', weighted_metrics=['acc'])
    model.train_on_batch(input_data, actual_output)

  # test as first layer in Sequential API
  layer_config = layer.get_config()
  layer_config['batch_input_shape'] = input_shape
  layer = layer.__class__.from_config(layer_config)

  # Test adapt, if data was passed.
  if adapt_data is not None:
    layer.adapt(adapt_data)

  model = models.Sequential()
  model.add(layers.Input(shape=input_shape[1:], dtype=input_dtype))
  model.add(layer)

  layer.set_weights(layer_weights)
  actual_output = model.predict(input_data)
  actual_output_shape = actual_output.shape
  for expected_dim, actual_dim in zip(computed_output_shape,
                                      actual_output_shape):
    if expected_dim is not None:
      if expected_dim != actual_dim:
        raise AssertionError(
            'When testing layer %s **after deserialization**, '
            'for input %s, found output_shape='
            '%s but expected to find inferred shape %s.\nFull kwargs: %s' %
            (layer_cls.__name__,
             x,
             actual_output_shape,
             computed_output_shape,
             kwargs))
  if expected_output is not None:
    assert_equal(actual_output, expected_output)

  # test serialization, weight setting at model level
  model_config = model.get_config()
  recovered_model = models.Sequential.from_config(model_config, custom_objects)
  if model.weights:
    weights = model.get_weights()
    recovered_model.set_weights(weights)
    output = recovered_model.predict(input_data)
    assert_equal(output, actual_output)

  # for further checks in the caller function
  return actual_output


_thread_local_data = threading.local()
_thread_local_data.model_type = None
_thread_local_data.run_eagerly = None
_thread_local_data.saved_model_format = None
_thread_local_data.save_kwargs = None


@tf_contextlib.contextmanager
def model_type_scope(value):
  """Provides a scope within which the model type to test is equal to `value`.

  The model type gets restored to its original value upon exiting the scope.

  Args:
     value: model type value

  Yields:
    The provided value.
  """
  previous_value = _thread_local_data.model_type
  try:
    _thread_local_data.model_type = value
    yield value
  finally:
    # Restore model type to initial value.
    _thread_local_data.model_type = previous_value


@tf_contextlib.contextmanager
def run_eagerly_scope(value):
  """Provides a scope within which we compile models to run eagerly or not.

  The boolean gets restored to its original value upon exiting the scope.

  Args:
     value: Bool specifying if we should run models eagerly in the active test.
     Should be True or False.

  Yields:
    The provided value.
  """
  previous_value = _thread_local_data.run_eagerly
  try:
    _thread_local_data.run_eagerly = value
    yield value
  finally:
    # Restore model type to initial value.
    _thread_local_data.run_eagerly = previous_value


def should_run_eagerly():
  """Returns whether the models we are testing should be run eagerly."""
  if _thread_local_data.run_eagerly is None:
    raise ValueError('Cannot call `should_run_eagerly()` outside of a '
                     '`run_eagerly_scope()` or `run_all_keras_modes` '
                     'decorator.')

  return _thread_local_data.run_eagerly and context.executing_eagerly()


@tf_contextlib.contextmanager
def saved_model_format_scope(value, **kwargs):
  """Provides a scope within which the savde model format to test is `value`.

  The saved model format gets restored to its original value upon exiting the
  scope.

  Args:
     value: saved model format value
     **kwargs: optional kwargs to pass to the save function.

  Yields:
    The provided value.
  """
  previous_format = _thread_local_data.saved_model_format
  previous_kwargs = _thread_local_data.save_kwargs
  try:
    _thread_local_data.saved_model_format = value
    _thread_local_data.save_kwargs = kwargs
    yield
  finally:
    # Restore saved model format to initial value.
    _thread_local_data.saved_model_format = previous_format
    _thread_local_data.save_kwargs = previous_kwargs


def get_save_format():
  if _thread_local_data.saved_model_format is None:
    raise ValueError(
        'Cannot call `get_save_format()` outside of a '
        '`saved_model_format_scope()` or `run_with_all_saved_model_formats` '
        'decorator.')
  return _thread_local_data.saved_model_format


def get_save_kwargs():
  if _thread_local_data.save_kwargs is None:
    raise ValueError(
        'Cannot call `get_save_kwargs()` outside of a '
        '`saved_model_format_scope()` or `run_with_all_saved_model_formats` '
        'decorator.')
  return _thread_local_data.save_kwargs or {}


def get_model_type():
  """Gets the model type that should be tested."""
  if _thread_local_data.model_type is None:
    raise ValueError('Cannot call `get_model_type()` outside of a '
                     '`model_type_scope()` or `run_with_all_model_types` '
                     'decorator.')

  return _thread_local_data.model_type


def get_small_sequential_mlp(num_hidden, num_classes, input_dim=None):
  model = models.Sequential()
  if input_dim:
    model.add(layers.Dense(num_hidden, activation='relu', input_dim=input_dim))
  else:
    model.add(layers.Dense(num_hidden, activation='relu'))
  activation = 'sigmoid' if num_classes == 1 else 'softmax'
  model.add(layers.Dense(num_classes, activation=activation))
  return model


def get_small_functional_mlp(num_hidden, num_classes, input_dim):
  inputs = layers.Input(shape=(input_dim,))
  outputs = layers.Dense(num_hidden, activation='relu')(inputs)
  activation = 'sigmoid' if num_classes == 1 else 'softmax'
  outputs = layers.Dense(num_classes, activation=activation)(outputs)
  return models.Model(inputs, outputs)


class SmallSubclassMLP(models.Model):
  """A subclass model based small MLP."""

  def __init__(self,
               num_hidden,
               num_classes,
               use_bn=False,
               use_dp=False,
               **kwargs):
    super(SmallSubclassMLP, self).__init__(name='test_model', **kwargs)
    self.use_bn = use_bn
    self.use_dp = use_dp

    self.layer_a = layers.Dense(num_hidden, activation='relu')
    activation = 'sigmoid' if num_classes == 1 else 'softmax'
    self.layer_b = layers.Dense(num_classes, activation=activation)
    if self.use_dp:
      self.dp = layers.Dropout(0.5)
    if self.use_bn:
      self.bn = layers.BatchNormalization(axis=-1)

  def call(self, inputs, **kwargs):
    x = self.layer_a(inputs)
    if self.use_dp:
      x = self.dp(x)
    if self.use_bn:
      x = self.bn(x)
    return self.layer_b(x)


class _SmallSubclassMLPCustomBuild(models.Model):
  """A subclass model small MLP that uses a custom build method."""

  def __init__(self, num_hidden, num_classes):
    super(_SmallSubclassMLPCustomBuild, self).__init__()
    self.layer_a = None
    self.layer_b = None
    self.num_hidden = num_hidden
    self.num_classes = num_classes

  def build(self, input_shape):
    self.layer_a = layers.Dense(self.num_hidden, activation='relu')
    activation = 'sigmoid' if self.num_classes == 1 else 'softmax'
    self.layer_b = layers.Dense(self.num_classes, activation=activation)

  def call(self, inputs, **kwargs):
    x = self.layer_a(inputs)
    return self.layer_b(x)


def get_small_subclass_mlp(num_hidden, num_classes):
  return SmallSubclassMLP(num_hidden, num_classes)


def get_small_subclass_mlp_with_custom_build(num_hidden, num_classes):
  return _SmallSubclassMLPCustomBuild(num_hidden, num_classes)


def get_small_mlp(num_hidden, num_classes, input_dim):
  """Get a small mlp of the model type specified by `get_model_type`."""
  model_type = get_model_type()
  if model_type == 'subclass':
    return get_small_subclass_mlp(num_hidden, num_classes)
  if model_type == 'subclass_custom_build':
    return get_small_subclass_mlp_with_custom_build(num_hidden, num_classes)
  if model_type == 'sequential':
    return get_small_sequential_mlp(num_hidden, num_classes, input_dim)
  if model_type == 'functional':
    return get_small_functional_mlp(num_hidden, num_classes, input_dim)
  raise ValueError('Unknown model type {}'.format(model_type))


class _SubclassModel(models.Model):
  """A Keras subclass model."""

  def __init__(self, model_layers, *args, **kwargs):
    """Instantiate a model.

    Args:
      model_layers: a list of layers to be added to the model.
      *args: Model's args
      **kwargs: Model's keyword args, at most one of input_tensor -> the input
        tensor required for ragged/sparse input.
    """

    inputs = kwargs.pop('input_tensor', None)
    super(_SubclassModel, self).__init__(*args, **kwargs)
    # Note that clone and build doesn't support lists of layers in subclassed
    # models. Adding each layer directly here.
    for i, layer in enumerate(model_layers):
      setattr(self, self._layer_name_for_i(i), layer)

    self.num_layers = len(model_layers)

    if inputs is not None:
      self._set_inputs(inputs)

  def _layer_name_for_i(self, i):
    return 'layer{}'.format(i)

  def call(self, inputs, **kwargs):
    x = inputs
    for i in range(self.num_layers):
      layer = getattr(self, self._layer_name_for_i(i))
      x = layer(x)
    return x


class _SubclassModelCustomBuild(models.Model):
  """A Keras subclass model that uses a custom build method."""

  def __init__(self, layer_generating_func, *args, **kwargs):
    super(_SubclassModelCustomBuild, self).__init__(*args, **kwargs)
    self.all_layers = None
    self._layer_generating_func = layer_generating_func

  def build(self, input_shape):
    model_layers = []
    for layer in self._layer_generating_func():
      model_layers.append(layer)
    self.all_layers = model_layers

  def call(self, inputs, **kwargs):
    x = inputs
    for layer in self.all_layers:
      x = layer(x)
    return x


def get_model_from_layers(model_layers,
                          input_shape=None,
                          input_dtype=None,
                          name=None,
                          input_ragged=None,
                          input_sparse=None,
                          model_type=None):
  """Builds a model from a sequence of layers.

  Args:
    model_layers: The layers used to build the network.
    input_shape: Shape tuple of the input or 'TensorShape' instance.
    input_dtype: Datatype of the input.
    name: Name for the model.
    input_ragged: Boolean, whether the input data is a ragged tensor.
    input_sparse: Boolean, whether the input data is a sparse tensor.
    model_type: One of "subclass", "subclass_custom_build", "sequential", or
      "functional". When None, defaults to `get_model_type`.

  Returns:
    A Keras model.
  """
  if model_type is None:
    model_type = get_model_type()
  if model_type == 'subclass':
    inputs = None
    if input_ragged or input_sparse:
      inputs = layers.Input(
          shape=input_shape,
          dtype=input_dtype,
          ragged=input_ragged,
          sparse=input_sparse)
    return _SubclassModel(model_layers, name=name, input_tensor=inputs)

  if model_type == 'subclass_custom_build':
    layer_generating_func = lambda: model_layers
    return _SubclassModelCustomBuild(layer_generating_func, name=name)

  if model_type == 'sequential':
    model = models.Sequential(name=name)
    if input_shape:
      model.add(
          layers.InputLayer(
              input_shape=input_shape,
              dtype=input_dtype,
              ragged=input_ragged,
              sparse=input_sparse))
    for layer in model_layers:
      model.add(layer)
    return model

  if model_type == 'functional':
    if not input_shape:
      raise ValueError('Cannot create a functional model from layers with no '
                       'input shape.')
    inputs = layers.Input(
        shape=input_shape,
        dtype=input_dtype,
        ragged=input_ragged,
        sparse=input_sparse)
    outputs = inputs
    for layer in model_layers:
      outputs = layer(outputs)
    return models.Model(inputs, outputs, name=name)

  raise ValueError('Unknown model type {}'.format(model_type))


class Bias(layers.Layer):

  def build(self, input_shape):
    self.bias = self.add_variable('bias', (1,), initializer='zeros')

  def call(self, inputs):
    return inputs + self.bias


class _MultiIOSubclassModel(models.Model):
  """Multi IO Keras subclass model."""

  def __init__(self, branch_a, branch_b, shared_input_branch=None,
               shared_output_branch=None, name=None):
    super(_MultiIOSubclassModel, self).__init__(name=name)
    self._shared_input_branch = shared_input_branch
    self._branch_a = branch_a
    self._branch_b = branch_b
    self._shared_output_branch = shared_output_branch

  def call(self, inputs, **kwargs):
    if self._shared_input_branch:
      for layer in self._shared_input_branch:
        inputs = layer(inputs)
      a = inputs
      b = inputs
    elif isinstance(inputs, dict):
      a = inputs['input_1']
      b = inputs['input_2']
    else:
      a, b = inputs

    for layer in self._branch_a:
      a = layer(a)
    for layer in self._branch_b:
      b = layer(b)
    outs = [a, b]

    if self._shared_output_branch:
      for layer in self._shared_output_branch:
        outs = layer(outs)

    return outs


class _MultiIOSubclassModelCustomBuild(models.Model):
  """Multi IO Keras subclass model that uses a custom build method."""

  def __init__(self, branch_a_func, branch_b_func,
               shared_input_branch_func=None,
               shared_output_branch_func=None):
    super(_MultiIOSubclassModelCustomBuild, self).__init__()
    self._shared_input_branch_func = shared_input_branch_func
    self._branch_a_func = branch_a_func
    self._branch_b_func = branch_b_func
    self._shared_output_branch_func = shared_output_branch_func

    self._shared_input_branch = None
    self._branch_a = None
    self._branch_b = None
    self._shared_output_branch = None

  def build(self, input_shape):
    if self._shared_input_branch_func():
      self._shared_input_branch = self._shared_input_branch_func()
    self._branch_a = self._branch_a_func()
    self._branch_b = self._branch_b_func()

    if self._shared_output_branch_func():
      self._shared_output_branch = self._shared_output_branch_func()

  def call(self, inputs, **kwargs):
    if self._shared_input_branch:
      for layer in self._shared_input_branch:
        inputs = layer(inputs)
      a = inputs
      b = inputs
    else:
      a, b = inputs

    for layer in self._branch_a:
      a = layer(a)
    for layer in self._branch_b:
      b = layer(b)
    outs = a, b

    if self._shared_output_branch:
      for layer in self._shared_output_branch:
        outs = layer(outs)

    return outs


def get_multi_io_model(
    branch_a,
    branch_b,
    shared_input_branch=None,
    shared_output_branch=None):
  """Builds a multi-io model that contains two branches.

  The produced model will be of the type specified by `get_model_type`.

  To build a two-input, two-output model:
    Specify a list of layers for branch a and branch b, but do not specify any
    shared input branch or shared output branch. The resulting model will apply
    each branch to a different input, to produce two outputs.

    The first value in branch_a must be the Keras 'Input' layer for branch a,
    and the first value in branch_b must be the Keras 'Input' layer for
    branch b.

    example usage:
    ```
    branch_a = [Input(shape=(2,), name='a'), Dense(), Dense()]
    branch_b = [Input(shape=(3,), name='b'), Dense(), Dense()]

    model = get_multi_io_model(branch_a, branch_b)
    ```

  To build a two-input, one-output model:
    Specify a list of layers for branch a and branch b, and specify a
    shared output branch. The resulting model will apply
    each branch to a different input. It will then apply the shared output
    branch to a tuple containing the intermediate outputs of each branch,
    to produce a single output. The first layer in the shared_output_branch
    must be able to merge a tuple of two tensors.

    The first value in branch_a must be the Keras 'Input' layer for branch a,
    and the first value in branch_b must be the Keras 'Input' layer for
    branch b.

    example usage:
    ```
    input_branch_a = [Input(shape=(2,), name='a'), Dense(), Dense()]
    input_branch_b = [Input(shape=(3,), name='b'), Dense(), Dense()]
    shared_output_branch = [Concatenate(), Dense(), Dense()]

    model = get_multi_io_model(input_branch_a, input_branch_b,
                               shared_output_branch=shared_output_branch)
    ```
  To build a one-input, two-output model:
    Specify a list of layers for branch a and branch b, and specify a
    shared input branch. The resulting model will take one input, and apply
    the shared input branch to it. It will then respectively apply each branch
    to that intermediate result in parallel, to produce two outputs.

    The first value in the shared_input_branch must be the Keras 'Input' layer
    for the whole model. Branch a and branch b should not contain any Input
    layers.

    example usage:
    ```
    shared_input_branch = [Input(shape=(2,), name='in'), Dense(), Dense()]
    output_branch_a = [Dense(), Dense()]
    output_branch_b = [Dense(), Dense()]


    model = get_multi_io_model(output__branch_a, output_branch_b,
                               shared_input_branch=shared_input_branch)
    ```

  Args:
    branch_a: A sequence of layers for branch a of the model.
    branch_b: A sequence of layers for branch b of the model.
    shared_input_branch: An optional sequence of layers to apply to a single
      input, before applying both branches to that intermediate result. If set,
      the model will take only one input instead of two. Defaults to None.
    shared_output_branch: An optional sequence of layers to merge the
      intermediate results produced by branch a and branch b. If set,
      the model will produce only one output instead of two. Defaults to None.

  Returns:
    A multi-io model of the type specified by `get_model_type`, specified
    by the different branches.
  """
  # Extract the functional inputs from the layer lists
  if shared_input_branch:
    inputs = shared_input_branch[0]
    shared_input_branch = shared_input_branch[1:]
  else:
    inputs = branch_a[0], branch_b[0]
    branch_a = branch_a[1:]
    branch_b = branch_b[1:]

  model_type = get_model_type()
  if model_type == 'subclass':
    return _MultiIOSubclassModel(branch_a, branch_b, shared_input_branch,
                                 shared_output_branch)

  if model_type == 'subclass_custom_build':
    return _MultiIOSubclassModelCustomBuild((lambda: branch_a),
                                            (lambda: branch_b),
                                            (lambda: shared_input_branch),
                                            (lambda: shared_output_branch))

  if model_type == 'sequential':
    raise ValueError('Cannot use `get_multi_io_model` to construct '
                     'sequential models')

  if model_type == 'functional':
    if shared_input_branch:
      a_and_b = inputs
      for layer in shared_input_branch:
        a_and_b = layer(a_and_b)
      a = a_and_b
      b = a_and_b
    else:
      a, b = inputs

    for layer in branch_a:
      a = layer(a)
    for layer in branch_b:
      b = layer(b)
    outputs = a, b

    if shared_output_branch:
      for layer in shared_output_branch:
        outputs = layer(outputs)

    return models.Model(inputs, outputs)

  raise ValueError('Unknown model type {}'.format(model_type))


_V2_OPTIMIZER_MAP = {
    'adadelta': adadelta_v2.Adadelta,
    'adagrad': adagrad_v2.Adagrad,
    'adam': adam_v2.Adam,
    'adamax': adamax_v2.Adamax,
    'nadam': nadam_v2.Nadam,
    'rmsprop': rmsprop_v2.RMSprop,
    'sgd': gradient_descent_v2.SGD
}


def get_v2_optimizer(name, **kwargs):
  """Get the v2 optimizer requested.

  This is only necessary until v2 are the default, as we are testing in Eager,
  and Eager + v1 optimizers fail tests. When we are in v2, the strings alone
  should be sufficient, and this mapping can theoretically be removed.

  Args:
    name: string name of Keras v2 optimizer.
    **kwargs: any kwargs to pass to the optimizer constructor.

  Returns:
    Initialized Keras v2 optimizer.

  Raises:
    ValueError: if an unknown name was passed.
  """
  try:
    return _V2_OPTIMIZER_MAP[name](**kwargs)
  except KeyError:
    raise ValueError(
        'Could not find requested v2 optimizer: {}\nValid choices: {}'.format(
            name, list(_V2_OPTIMIZER_MAP.keys())))


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 enable_v2_dtype_behavior(fn):
  """Decorator for enabling the layer V2 dtype behavior on a test."""
  return _set_v2_dtype_behavior(fn, True)


def disable_v2_dtype_behavior(fn):
  """Decorator for disabling the layer V2 dtype behavior on a test."""
  return _set_v2_dtype_behavior(fn, False)


def _set_v2_dtype_behavior(fn, enabled):
  """Returns version of 'fn' that runs with v2 dtype behavior on or off."""
  @functools.wraps(fn)
  def wrapper(*args, **kwargs):
    v2_dtype_behavior = base_layer_utils.V2_DTYPE_BEHAVIOR
    base_layer_utils.V2_DTYPE_BEHAVIOR = enabled
    try:
      return fn(*args, **kwargs)
    finally:
      base_layer_utils.V2_DTYPE_BEHAVIOR = v2_dtype_behavior

  return tf_decorator.make_decorator(fn, wrapper)


@contextlib.contextmanager
def device(should_use_gpu):
  """Uses gpu when requested and available."""
  if should_use_gpu and test_util.is_gpu_available():
    dev = '/device:GPU:0'
  else:
    dev = '/device:CPU:0'
  with ops.device(dev):
    yield


@contextlib.contextmanager
def use_gpu():
  """Uses gpu when requested and available."""
  with device(should_use_gpu=True):
    yield


def for_all_test_methods(decorator, *args, **kwargs):
  """Generate class-level decorator from given method-level decorator.

  It is expected for the given decorator to take some arguments and return
  a method that is then called on the test method to produce a decorated
  method.

  Args:
    decorator: The decorator to apply.
    *args: Positional arguments
    **kwargs: Keyword arguments
  Returns: Function that will decorate a given classes test methods with the
    decorator.
  """

  def all_test_methods_impl(cls):
    """Apply decorator to all test methods in class."""
    for name in dir(cls):
      value = getattr(cls, name)
      if callable(value) and name.startswith('test') and (name !=
                                                          'test_session'):
        setattr(cls, name, decorator(*args, **kwargs)(value))
    return cls

  return all_test_methods_impl


# The description is just for documentation purposes.
def run_without_tensor_float_32(description):  # pylint: disable=unused-argument
  """Execute test with TensorFloat-32 disabled.

  While almost every real-world deep learning model runs fine with
  TensorFloat-32, many tests use assertAllClose or similar methods.
  TensorFloat-32 matmuls typically will cause such methods to fail with the
  default tolerances.

  Args:
    description: A description used for documentation purposes, describing why
      the test requires TensorFloat-32 to be disabled.

  Returns:
    Decorator which runs a test with TensorFloat-32 disabled.
  """

  def decorator(f):

    @functools.wraps(f)
    def decorated(self, *args, **kwargs):
      allowed = config.tensor_float_32_execution_enabled()
      try:
        config.enable_tensor_float_32_execution(False)
        f(self, *args, **kwargs)
      finally:
        config.enable_tensor_float_32_execution(allowed)

    return decorated

  return decorator


# The description is just for documentation purposes.
def run_all_without_tensor_float_32(description):  # pylint: disable=unused-argument
  """Execute all tests in a class with TensorFloat-32 disabled."""
  return for_all_test_methods(run_without_tensor_float_32, description)


def run_v2_only(func=None):
  """Execute the decorated test only if running in v2 mode.

  This function is intended to be applied to tests that exercise v2 only
  functionality. If the test is run in v1 mode it will simply be skipped.

  See go/tf-test-decorator-cheatsheet for the decorators to use in different
  v1/v2/eager/graph combinations.

  Args:
    func: function to be annotated. If `func` is None, this method returns a
      decorator the can be applied to a function. If `func` is not None this
      returns the decorator applied to `func`.

  Returns:
    Returns a decorator that will conditionally skip the decorated test method.
  """

  def decorator(f):
    if tf_inspect.isclass(f):
      raise ValueError('`run_v2_only` only supports test methods.')

    def decorated(self, *args, **kwargs):
      if not tf2.enabled():
        self.skipTest('Test is only compatible with v2')

      return f(self, *args, **kwargs)

    return decorated

  if func is not None:
    return decorator(func)

  return decorator


def generate_combinations_with_testcase_name(**kwargs):
  """Generate combinations based on its keyword arguments using combine().

  This function calls combine() and appends a testcase name to the list of
  dictionaries returned. The 'testcase_name' key is a required for named
  parameterized tests.

  Args:
    **kwargs: keyword arguments of form `option=[possibilities, ...]` or
      `option=the_only_possibility`.

  Returns:
    a list of dictionaries for each combination. Keys in the dictionaries are
    the keyword argument names.  Each key has one value - one of the
    corresponding keyword argument values.
  """
  sort_by_key = lambda k: k[0]
  combinations = []
  for key, values in sorted(kwargs.items(), key=sort_by_key):
    if not isinstance(values, list):
      values = [values]
    combinations.append([(key, value) for value in values])

  combinations = [collections.OrderedDict(result)
                  for result in itertools.product(*combinations)]
  named_combinations = []
  for combination in combinations:
    assert isinstance(combination, collections.OrderedDict)
    name = ''.join([
        '_{}_{}'.format(''.join(filter(str.isalnum, key)),
                        ''.join(filter(str.isalnum, str(value))))
        for key, value in combination.items()
    ])
    named_combinations.append(
        collections.OrderedDict(
            list(combination.items()) +
            [('testcase_name', '_test{}'.format(name))]))

  return named_combinations