tensorflow/python/keras/layers/preprocessing/preprocessing_stage_functional_test.py - platform/external/tensorflow - Git at Google

 # Copyright 2020 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.
 # ==============================================================================
 """Functional preprocessing stage tests."""
 # pylint: disable=g-classes-have-attributes

 import time
 import numpy as np

 from tensorflow.python.data.ops import dataset_ops
 from tensorflow.python.keras import keras_parameterized
 from tensorflow.python.keras.engine import base_preprocessing_layer
 from tensorflow.python.keras.engine.input_layer import Input
 from tensorflow.python.keras.layers import convolutional
 from tensorflow.python.keras.layers import core
 from tensorflow.python.keras.layers import merge
 from tensorflow.python.keras.layers.preprocessing import image_preprocessing
 from tensorflow.python.keras.layers.preprocessing import normalization
 from tensorflow.python.keras.layers.preprocessing import preprocessing_stage
 from tensorflow.python.keras.layers.preprocessing import preprocessing_test_utils
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.platform import test


 class PL(base_preprocessing_layer.PreprocessingLayer):

   def __init__(self, **kwargs):
     self.adapt_time = None
     self.adapt_count = 0
     super(PL, self).__init__(**kwargs)

   def adapt(self, data, reset_state=True):
     self.adapt_time = time.time()
     self.adapt_count += 1

   def call(self, inputs):
     return inputs + 1


 class PLMerge(PL):

   def call(self, inputs):
     return inputs[0] + inputs[1]


 class PLSplit(PL):

   def call(self, inputs):
     return inputs + 1, inputs - 1


 @keras_parameterized.run_all_keras_modes(always_skip_v1=True)
 class PreprocessingStageTest(keras_parameterized.TestCase,
                              preprocessing_test_utils.PreprocessingLayerTest):

   def test_adapt_preprocessing_stage_with_single_input_output(self):

     x = Input(shape=(3,))

     l0 = PL()
     y = l0(x)

     l1 = PL()
     z = l1(y)

     stage = preprocessing_stage.FunctionalPreprocessingStage(x, z)
     stage.compile()

     # Test with NumPy array
     one_array = np.ones((4, 3), dtype='float32')
     stage.adapt(one_array)
     self.assertEqual(l0.adapt_count, 1)
     self.assertEqual(l1.adapt_count, 1)
     self.assertLessEqual(l0.adapt_time, l1.adapt_time)

     # Check call
     z = stage(array_ops.ones((4, 3), dtype='float32'))
     self.assertAllClose(z, np.ones((4, 3), dtype='float32') + 2.)

     # Test with dataset
     adapt_data = dataset_ops.Dataset.from_tensor_slices(one_array)
     adapt_data = adapt_data.batch(2)  # 5 batches of 2 samples

     stage.adapt(adapt_data)
     self.assertEqual(l0.adapt_count, 2)
     self.assertEqual(l1.adapt_count, 2)
     self.assertLessEqual(l0.adapt_time, l1.adapt_time)

     # Test error with bad data
     with self.assertRaisesRegex(ValueError, 'requires a '):
       stage.adapt(None)

     # Disallow calling fit
     with self.assertRaisesRegex(ValueError, 'Preprocessing stage'):
       stage.fit(None)

   def test_adapt_preprocessing_stage_with_list_input(self):

     x0 = Input(shape=(3,))
     x1 = Input(shape=(3,))
     x2 = Input(shape=(3,))

     l0 = PLMerge()
     y = l0([x0, x1])

     l1 = PLMerge()
     y = l1([y, x2])

     l2 = PLSplit()
     z, y = l2(y)

     stage = preprocessing_stage.FunctionalPreprocessingStage([x0, x1, x2],
                                                              [y, z])
     stage.compile()

     # Test with NumPy array
     one_array = np.ones((4, 3), dtype='float32')
     stage.adapt([one_array, one_array, one_array])
     self.assertEqual(l0.adapt_count, 1)
     self.assertEqual(l1.adapt_count, 1)
     self.assertEqual(l2.adapt_count, 1)
     self.assertLessEqual(l0.adapt_time, l1.adapt_time)
     self.assertLessEqual(l1.adapt_time, l2.adapt_time)

     # Check call
     y, z = stage([
         array_ops.ones((4, 3), dtype='float32'),
         array_ops.ones((4, 3), dtype='float32'),
         array_ops.ones((4, 3), dtype='float32')
     ])
     self.assertAllClose(y, np.ones((4, 3), dtype='float32') + 1.)
     self.assertAllClose(z, np.ones((4, 3), dtype='float32') + 3.)

     # Test with dataset
     adapt_data = dataset_ops.Dataset.from_tensor_slices(
         (one_array, one_array, one_array))
     adapt_data = adapt_data.batch(2)  # 5 batches of 2 samples

     stage.adapt(adapt_data)
     self.assertEqual(l0.adapt_count, 2)
     self.assertEqual(l1.adapt_count, 2)
     self.assertEqual(l2.adapt_count, 2)
     self.assertLessEqual(l0.adapt_time, l1.adapt_time)
     self.assertLessEqual(l1.adapt_time, l2.adapt_time)

     # Test error with bad data
     with self.assertRaisesRegex(ValueError, 'requires a '):
       stage.adapt(None)

   def test_adapt_preprocessing_stage_with_dict_input(self):
     x0 = Input(shape=(3,), name='x0')
     x1 = Input(shape=(4,), name='x1')
     x2 = Input(shape=(3, 5), name='x2')

     # dimension will mismatch if x1 incorrectly placed.
     x1_sum = core.Lambda(
         lambda x: math_ops.reduce_sum(x, axis=-1, keepdims=True))(
             x1)
     x2_sum = core.Lambda(lambda x: math_ops.reduce_sum(x, axis=-1))(x2)

     l0 = PLMerge()
     y = l0([x0, x1_sum])

     l1 = PLMerge()
     y = l1([y, x2_sum])

     l2 = PLSplit()
     z, y = l2(y)
     stage = preprocessing_stage.FunctionalPreprocessingStage(
         {
             'x2': x2,
             'x0': x0,
             'x1': x1
         }, [y, z])
     stage.compile()

     # Test with dict of NumPy array
     one_array0 = np.ones((4, 3), dtype='float32')
     one_array1 = np.ones((4, 4), dtype='float32')
     one_array2 = np.ones((4, 3, 5), dtype='float32')
     adapt_data = {'x1': one_array1, 'x0': one_array0, 'x2': one_array2}
     stage.adapt(adapt_data)
     self.assertEqual(l0.adapt_count, 1)
     self.assertEqual(l1.adapt_count, 1)
     self.assertEqual(l2.adapt_count, 1)
     self.assertLessEqual(l0.adapt_time, l1.adapt_time)
     self.assertLessEqual(l1.adapt_time, l2.adapt_time)

     # Check call
     y, z = stage({
         'x1': array_ops.constant(one_array1),
         'x2': array_ops.constant(one_array2),
         'x0': array_ops.constant(one_array0)
     })
     self.assertAllClose(y, np.zeros((4, 3), dtype='float32') + 9.)
     self.assertAllClose(z, np.zeros((4, 3), dtype='float32') + 11.)

     # Test with list of NumPy array
     adapt_data = [one_array0, one_array1, one_array2]
     stage.adapt(adapt_data)
     self.assertEqual(l0.adapt_count, 2)
     self.assertEqual(l1.adapt_count, 2)
     self.assertEqual(l2.adapt_count, 2)
     self.assertLessEqual(l0.adapt_time, l1.adapt_time)
     self.assertLessEqual(l1.adapt_time, l2.adapt_time)

     # Test with flattened dataset
     adapt_data = dataset_ops.Dataset.from_tensor_slices(
         (one_array0, one_array1, one_array2))
     adapt_data = adapt_data.batch(2)  # 5 batches of 2 samples

     stage.adapt(adapt_data)
     self.assertEqual(l0.adapt_count, 3)
     self.assertEqual(l1.adapt_count, 3)
     self.assertEqual(l2.adapt_count, 3)
     self.assertLessEqual(l0.adapt_time, l1.adapt_time)
     self.assertLessEqual(l1.adapt_time, l2.adapt_time)

     # Test with dataset in dict shape
     adapt_data = dataset_ops.Dataset.from_tensor_slices({
         'x0': one_array0,
         'x2': one_array2,
         'x1': one_array1
     })
     adapt_data = adapt_data.batch(2)  # 5 batches of 2 samples
     stage.adapt(adapt_data)
     self.assertEqual(l0.adapt_count, 4)
     self.assertEqual(l1.adapt_count, 4)
     self.assertEqual(l2.adapt_count, 4)
     self.assertLessEqual(l0.adapt_time, l1.adapt_time)
     self.assertLessEqual(l1.adapt_time, l2.adapt_time)

     # Test error with bad data
     with self.assertRaisesRegex(ValueError, 'requires a '):
       stage.adapt(None)

   def test_adapt_preprocessing_stage_with_dict_output(self):
     x = Input(shape=(3,), name='x')

     l0 = PLSplit()
     y0, y1 = l0(x)

     l1 = PLSplit()
     z0, z1 = l1(y0)
     stage = preprocessing_stage.FunctionalPreprocessingStage({'x': x}, {
         'y1': y1,
         'z1': z1,
         'y0': y0,
         'z0': z0
     })
     stage.compile()

     # Test with NumPy array
     one_array = np.ones((4, 3), dtype='float32')
     adapt_data = {'x': one_array}
     stage.adapt(adapt_data)
     self.assertEqual(l0.adapt_count, 1)
     self.assertEqual(l1.adapt_count, 1)
     self.assertLessEqual(l0.adapt_time, l1.adapt_time)

     # Check call
     outputs = stage({'x': array_ops.constant(one_array)})
     self.assertEqual(set(outputs.keys()), {'y0', 'y1', 'z0', 'z1'})
     self.assertAllClose(outputs['y0'], np.ones((4, 3), dtype='float32') + 1.)
     self.assertAllClose(outputs['y1'], np.ones((4, 3), dtype='float32') - 1.)
     self.assertAllClose(outputs['z0'], np.ones((4, 3), dtype='float32') + 2.)
     self.assertAllClose(outputs['z1'], np.ones((4, 3), dtype='float32'))

   def test_preprocessing_stage_with_nested_input(self):
     # Test with NumPy array
     x0 = Input(shape=(3,))
     x1 = Input(shape=(3,))
     x2 = Input(shape=(3,))

     l0 = PLMerge()
     y = l0([x0, x1])

     l1 = PLMerge()
     y = l1([y, x2])

     l2 = PLSplit()
     z, y = l2(y)

     stage = preprocessing_stage.FunctionalPreprocessingStage([x0, [x1, x2]],
                                                              [y, z])
     stage.compile()
     one_array = np.ones((4, 3), dtype='float32')
     stage.adapt([one_array, [one_array, one_array]])
     self.assertEqual(l0.adapt_count, 1)
     self.assertEqual(l1.adapt_count, 1)
     self.assertEqual(l2.adapt_count, 1)
     self.assertLessEqual(l0.adapt_time, l1.adapt_time)
     self.assertLessEqual(l1.adapt_time, l2.adapt_time)

     # Check call
     y, z = stage([
         array_ops.ones((4, 3), dtype='float32'),
         [
             array_ops.ones((4, 3), dtype='float32'),
             array_ops.ones((4, 3), dtype='float32')
         ]
     ])
     self.assertAllClose(y, np.ones((4, 3), dtype='float32') + 1.)
     self.assertAllClose(z, np.ones((4, 3), dtype='float32') + 3.)

     # Test with dataset
     adapt_data = dataset_ops.Dataset.from_tensor_slices(
         (one_array, (one_array, one_array)))
     adapt_data = adapt_data.batch(2)  # 5 batches of 2 samples

     stage.adapt(adapt_data)
     self.assertEqual(l0.adapt_count, 2)
     self.assertEqual(l1.adapt_count, 2)
     self.assertEqual(l2.adapt_count, 2)
     self.assertLessEqual(l0.adapt_time, l1.adapt_time)
     self.assertLessEqual(l1.adapt_time, l2.adapt_time)

     # Test error with bad data
     with self.assertRaisesRegex(ValueError, 'requires a '):
       stage.adapt(None)

   def test_include_layers_with_dict_input(self):

     class PLMergeDict(PLMerge):

       def call(self, inputs):
         return inputs['a'] + inputs['b']

     x0 = Input(shape=(3,))
     x1 = Input(shape=(3,))

     l0 = PLMergeDict()
     y = l0({'a': x0, 'b': x1})

     l1 = PLSplit()
     z, y = l1(y)

     stage = preprocessing_stage.FunctionalPreprocessingStage([x0, x1], [y, z])
     stage.compile()

     one_array = np.ones((4, 3), dtype='float32')
     adapt_data = dataset_ops.Dataset.from_tensor_slices((one_array, one_array))
     stage.adapt(adapt_data)
     self.assertEqual(l0.adapt_count, 1)
     self.assertEqual(l1.adapt_count, 1)
     self.assertLessEqual(l0.adapt_time, l1.adapt_time)

     # Check call
     y, z = stage([
         array_ops.ones((4, 3), dtype='float32'),
         array_ops.ones((4, 3), dtype='float32')
     ])
     self.assertAllClose(y, np.ones((4, 3), dtype='float32'))
     self.assertAllClose(z, np.ones((4, 3), dtype='float32') + 2.)

   def test_include_layers_with_nested_input(self):

     class PLMergeNest(PLMerge):

       def call(self, inputs):
         a = inputs[0]
         b = inputs[1][0]
         c = inputs[1][1]
         return a + b + c

     x0 = Input(shape=(3,))
     x1 = Input(shape=(3,))
     x2 = Input(shape=(3,))

     l0 = PLMergeNest()
     y = l0([x0, [x1, x2]])

     stage = preprocessing_stage.FunctionalPreprocessingStage([x0, x1, x2], y)
     stage.compile()

     one_array = np.ones((4, 3), dtype='float32')
     adapt_data = dataset_ops.Dataset.from_tensor_slices((one_array,) * 3)
     stage.adapt(adapt_data)
     self.assertEqual(l0.adapt_count, 1)

     # Check call
     y = stage([
         array_ops.ones((4, 3), dtype='float32'),
         array_ops.ones((4, 3), dtype='float32'),
         array_ops.ones((4, 3), dtype='float32')
     ])
     self.assertAllClose(y, np.ones((4, 3), dtype='float32') + 2.)

   def test_mixing_preprocessing_and_regular_layers(self):
     x0 = Input(shape=(10, 10, 3))
     x1 = Input(shape=(10, 10, 3))
     x2 = Input(shape=(10, 10, 3))

     y0 = merge.Add()([x0, x1])
     y1 = image_preprocessing.CenterCrop(8, 8)(x2)
     y1 = convolutional.ZeroPadding2D(padding=1)(y1)

     z = merge.Add()([y0, y1])
     z = normalization.Normalization()(z)
     z = convolutional.Conv2D(4, 3)(z)

     stage = preprocessing_stage.FunctionalPreprocessingStage([x0, x1, x2], z)

     data = [
         np.ones((12, 10, 10, 3), dtype='float32'),
         np.ones((12, 10, 10, 3), dtype='float32'),
         np.ones((12, 10, 10, 3), dtype='float32')
     ]

     stage.adapt(data)
     _ = stage(data)
     stage.compile('rmsprop', 'mse')
     with self.assertRaisesRegex(ValueError, 'Preprocessing stage'):
       stage.fit(data, np.ones((12, 8, 8, 4)))

     ds_x0 = dataset_ops.Dataset.from_tensor_slices(np.ones((12, 10, 10, 3)))
     ds_x1 = dataset_ops.Dataset.from_tensor_slices(np.ones((12, 10, 10, 3)))
     ds_x2 = dataset_ops.Dataset.from_tensor_slices(np.ones((12, 10, 10, 3)))
     ds_x = dataset_ops.Dataset.zip((ds_x0, ds_x1, ds_x2))
     ds_y = dataset_ops.Dataset.from_tensor_slices(np.ones((12, 8, 8, 4)))
     dataset = dataset_ops.Dataset.zip((ds_x, ds_y)).batch(4)

     with self.assertRaisesRegex(ValueError, 'Preprocessing stage'):
       stage.fit(dataset)
     _ = stage.evaluate(data, np.ones((12, 8, 8, 4)))
     _ = stage.predict(data)


 if __name__ == '__main__':
   test.main()
	# Copyright 2020 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.
	# ==============================================================================
	"""Functional preprocessing stage tests."""
	# pylint: disable=g-classes-have-attributes

	import time
	import numpy as np

	from tensorflow.python.data.ops import dataset_ops
	from tensorflow.python.keras import keras_parameterized
	from tensorflow.python.keras.engine import base_preprocessing_layer
	from tensorflow.python.keras.engine.input_layer import Input
	from tensorflow.python.keras.layers import convolutional
	from tensorflow.python.keras.layers import core
	from tensorflow.python.keras.layers import merge
	from tensorflow.python.keras.layers.preprocessing import image_preprocessing
	from tensorflow.python.keras.layers.preprocessing import normalization
	from tensorflow.python.keras.layers.preprocessing import preprocessing_stage
	from tensorflow.python.keras.layers.preprocessing import preprocessing_test_utils
	from tensorflow.python.ops import array_ops
	from tensorflow.python.ops import math_ops
	from tensorflow.python.platform import test


	class PL(base_preprocessing_layer.PreprocessingLayer):

	def __init__(self, **kwargs):
	self.adapt_time = None
	self.adapt_count = 0
	super(PL, self).__init__(**kwargs)

	def adapt(self, data, reset_state=True):
	self.adapt_time = time.time()
	self.adapt_count += 1

	def call(self, inputs):
	return inputs + 1


	class PLMerge(PL):

	def call(self, inputs):
	return inputs[0] + inputs[1]


	class PLSplit(PL):

	def call(self, inputs):
	return inputs + 1, inputs - 1


	@keras_parameterized.run_all_keras_modes(always_skip_v1=True)
	class PreprocessingStageTest(keras_parameterized.TestCase,
	preprocessing_test_utils.PreprocessingLayerTest):

	def test_adapt_preprocessing_stage_with_single_input_output(self):

	x = Input(shape=(3,))

	l0 = PL()
	y = l0(x)

	l1 = PL()
	z = l1(y)

	stage = preprocessing_stage.FunctionalPreprocessingStage(x, z)
	stage.compile()

	# Test with NumPy array
	one_array = np.ones((4, 3), dtype='float32')
	stage.adapt(one_array)
	self.assertEqual(l0.adapt_count, 1)
	self.assertEqual(l1.adapt_count, 1)
	self.assertLessEqual(l0.adapt_time, l1.adapt_time)

	# Check call
	z = stage(array_ops.ones((4, 3), dtype='float32'))
	self.assertAllClose(z, np.ones((4, 3), dtype='float32') + 2.)

	# Test with dataset
	adapt_data = dataset_ops.Dataset.from_tensor_slices(one_array)
	adapt_data = adapt_data.batch(2) # 5 batches of 2 samples

	stage.adapt(adapt_data)
	self.assertEqual(l0.adapt_count, 2)
	self.assertEqual(l1.adapt_count, 2)
	self.assertLessEqual(l0.adapt_time, l1.adapt_time)

	# Test error with bad data
	with self.assertRaisesRegex(ValueError, 'requires a '):
	stage.adapt(None)

	# Disallow calling fit
	with self.assertRaisesRegex(ValueError, 'Preprocessing stage'):
	stage.fit(None)

	def test_adapt_preprocessing_stage_with_list_input(self):

	x0 = Input(shape=(3,))
	x1 = Input(shape=(3,))
	x2 = Input(shape=(3,))

	l0 = PLMerge()
	y = l0([x0, x1])

	l1 = PLMerge()
	y = l1([y, x2])

	l2 = PLSplit()
	z, y = l2(y)

	stage = preprocessing_stage.FunctionalPreprocessingStage([x0, x1, x2],
	[y, z])
	stage.compile()

	# Test with NumPy array
	one_array = np.ones((4, 3), dtype='float32')
	stage.adapt([one_array, one_array, one_array])
	self.assertEqual(l0.adapt_count, 1)
	self.assertEqual(l1.adapt_count, 1)
	self.assertEqual(l2.adapt_count, 1)
	self.assertLessEqual(l0.adapt_time, l1.adapt_time)
	self.assertLessEqual(l1.adapt_time, l2.adapt_time)

	# Check call
	y, z = stage([
	array_ops.ones((4, 3), dtype='float32'),
	array_ops.ones((4, 3), dtype='float32'),
	array_ops.ones((4, 3), dtype='float32')
	])
	self.assertAllClose(y, np.ones((4, 3), dtype='float32') + 1.)
	self.assertAllClose(z, np.ones((4, 3), dtype='float32') + 3.)

	# Test with dataset
	adapt_data = dataset_ops.Dataset.from_tensor_slices(
	(one_array, one_array, one_array))
	adapt_data = adapt_data.batch(2) # 5 batches of 2 samples

	stage.adapt(adapt_data)
	self.assertEqual(l0.adapt_count, 2)
	self.assertEqual(l1.adapt_count, 2)
	self.assertEqual(l2.adapt_count, 2)
	self.assertLessEqual(l0.adapt_time, l1.adapt_time)
	self.assertLessEqual(l1.adapt_time, l2.adapt_time)

	# Test error with bad data
	with self.assertRaisesRegex(ValueError, 'requires a '):
	stage.adapt(None)

	def test_adapt_preprocessing_stage_with_dict_input(self):
	x0 = Input(shape=(3,), name='x0')
	x1 = Input(shape=(4,), name='x1')
	x2 = Input(shape=(3, 5), name='x2')

	# dimension will mismatch if x1 incorrectly placed.
	x1_sum = core.Lambda(
	lambda x: math_ops.reduce_sum(x, axis=-1, keepdims=True))(
	x1)
	x2_sum = core.Lambda(lambda x: math_ops.reduce_sum(x, axis=-1))(x2)

	l0 = PLMerge()
	y = l0([x0, x1_sum])

	l1 = PLMerge()
	y = l1([y, x2_sum])

	l2 = PLSplit()
	z, y = l2(y)
	stage = preprocessing_stage.FunctionalPreprocessingStage(
	{
	'x2': x2,
	'x0': x0,
	'x1': x1
	}, [y, z])
	stage.compile()

	# Test with dict of NumPy array
	one_array0 = np.ones((4, 3), dtype='float32')
	one_array1 = np.ones((4, 4), dtype='float32')
	one_array2 = np.ones((4, 3, 5), dtype='float32')
	adapt_data = {'x1': one_array1, 'x0': one_array0, 'x2': one_array2}
	stage.adapt(adapt_data)
	self.assertEqual(l0.adapt_count, 1)
	self.assertEqual(l1.adapt_count, 1)
	self.assertEqual(l2.adapt_count, 1)
	self.assertLessEqual(l0.adapt_time, l1.adapt_time)
	self.assertLessEqual(l1.adapt_time, l2.adapt_time)

	# Check call
	y, z = stage({
	'x1': array_ops.constant(one_array1),
	'x2': array_ops.constant(one_array2),
	'x0': array_ops.constant(one_array0)
	})
	self.assertAllClose(y, np.zeros((4, 3), dtype='float32') + 9.)
	self.assertAllClose(z, np.zeros((4, 3), dtype='float32') + 11.)

	# Test with list of NumPy array
	adapt_data = [one_array0, one_array1, one_array2]
	stage.adapt(adapt_data)
	self.assertEqual(l0.adapt_count, 2)
	self.assertEqual(l1.adapt_count, 2)
	self.assertEqual(l2.adapt_count, 2)
	self.assertLessEqual(l0.adapt_time, l1.adapt_time)
	self.assertLessEqual(l1.adapt_time, l2.adapt_time)

	# Test with flattened dataset
	adapt_data = dataset_ops.Dataset.from_tensor_slices(
	(one_array0, one_array1, one_array2))
	adapt_data = adapt_data.batch(2) # 5 batches of 2 samples

	stage.adapt(adapt_data)
	self.assertEqual(l0.adapt_count, 3)
	self.assertEqual(l1.adapt_count, 3)
	self.assertEqual(l2.adapt_count, 3)
	self.assertLessEqual(l0.adapt_time, l1.adapt_time)
	self.assertLessEqual(l1.adapt_time, l2.adapt_time)

	# Test with dataset in dict shape
	adapt_data = dataset_ops.Dataset.from_tensor_slices({
	'x0': one_array0,
	'x2': one_array2,
	'x1': one_array1
	})
	adapt_data = adapt_data.batch(2) # 5 batches of 2 samples
	stage.adapt(adapt_data)
	self.assertEqual(l0.adapt_count, 4)
	self.assertEqual(l1.adapt_count, 4)
	self.assertEqual(l2.adapt_count, 4)
	self.assertLessEqual(l0.adapt_time, l1.adapt_time)
	self.assertLessEqual(l1.adapt_time, l2.adapt_time)

	# Test error with bad data
	with self.assertRaisesRegex(ValueError, 'requires a '):
	stage.adapt(None)

	def test_adapt_preprocessing_stage_with_dict_output(self):
	x = Input(shape=(3,), name='x')

	l0 = PLSplit()
	y0, y1 = l0(x)

	l1 = PLSplit()
	z0, z1 = l1(y0)
	stage = preprocessing_stage.FunctionalPreprocessingStage({'x': x}, {
	'y1': y1,
	'z1': z1,
	'y0': y0,
	'z0': z0
	})
	stage.compile()

	# Test with NumPy array
	one_array = np.ones((4, 3), dtype='float32')
	adapt_data = {'x': one_array}
	stage.adapt(adapt_data)
	self.assertEqual(l0.adapt_count, 1)
	self.assertEqual(l1.adapt_count, 1)
	self.assertLessEqual(l0.adapt_time, l1.adapt_time)

	# Check call
	outputs = stage({'x': array_ops.constant(one_array)})
	self.assertEqual(set(outputs.keys()), {'y0', 'y1', 'z0', 'z1'})
	self.assertAllClose(outputs['y0'], np.ones((4, 3), dtype='float32') + 1.)
	self.assertAllClose(outputs['y1'], np.ones((4, 3), dtype='float32') - 1.)
	self.assertAllClose(outputs['z0'], np.ones((4, 3), dtype='float32') + 2.)
	self.assertAllClose(outputs['z1'], np.ones((4, 3), dtype='float32'))

	def test_preprocessing_stage_with_nested_input(self):
	# Test with NumPy array
	x0 = Input(shape=(3,))
	x1 = Input(shape=(3,))
	x2 = Input(shape=(3,))

	l0 = PLMerge()
	y = l0([x0, x1])

	l1 = PLMerge()
	y = l1([y, x2])

	l2 = PLSplit()
	z, y = l2(y)

	stage = preprocessing_stage.FunctionalPreprocessingStage([x0, [x1, x2]],
	[y, z])
	stage.compile()
	one_array = np.ones((4, 3), dtype='float32')
	stage.adapt([one_array, [one_array, one_array]])
	self.assertEqual(l0.adapt_count, 1)
	self.assertEqual(l1.adapt_count, 1)
	self.assertEqual(l2.adapt_count, 1)
	self.assertLessEqual(l0.adapt_time, l1.adapt_time)
	self.assertLessEqual(l1.adapt_time, l2.adapt_time)

	# Check call
	y, z = stage([
	array_ops.ones((4, 3), dtype='float32'),
	[
	array_ops.ones((4, 3), dtype='float32'),
	array_ops.ones((4, 3), dtype='float32')
	]
	])
	self.assertAllClose(y, np.ones((4, 3), dtype='float32') + 1.)
	self.assertAllClose(z, np.ones((4, 3), dtype='float32') + 3.)

	# Test with dataset
	adapt_data = dataset_ops.Dataset.from_tensor_slices(
	(one_array, (one_array, one_array)))
	adapt_data = adapt_data.batch(2) # 5 batches of 2 samples

	stage.adapt(adapt_data)
	self.assertEqual(l0.adapt_count, 2)
	self.assertEqual(l1.adapt_count, 2)
	self.assertEqual(l2.adapt_count, 2)
	self.assertLessEqual(l0.adapt_time, l1.adapt_time)
	self.assertLessEqual(l1.adapt_time, l2.adapt_time)

	# Test error with bad data
	with self.assertRaisesRegex(ValueError, 'requires a '):
	stage.adapt(None)

	def test_include_layers_with_dict_input(self):

	class PLMergeDict(PLMerge):

	def call(self, inputs):
	return inputs['a'] + inputs['b']

	x0 = Input(shape=(3,))
	x1 = Input(shape=(3,))

	l0 = PLMergeDict()
	y = l0({'a': x0, 'b': x1})

	l1 = PLSplit()
	z, y = l1(y)

	stage = preprocessing_stage.FunctionalPreprocessingStage([x0, x1], [y, z])
	stage.compile()

	one_array = np.ones((4, 3), dtype='float32')
	adapt_data = dataset_ops.Dataset.from_tensor_slices((one_array, one_array))
	stage.adapt(adapt_data)
	self.assertEqual(l0.adapt_count, 1)
	self.assertEqual(l1.adapt_count, 1)
	self.assertLessEqual(l0.adapt_time, l1.adapt_time)

	# Check call
	y, z = stage([
	array_ops.ones((4, 3), dtype='float32'),
	array_ops.ones((4, 3), dtype='float32')
	])
	self.assertAllClose(y, np.ones((4, 3), dtype='float32'))
	self.assertAllClose(z, np.ones((4, 3), dtype='float32') + 2.)

	def test_include_layers_with_nested_input(self):

	class PLMergeNest(PLMerge):

	def call(self, inputs):
	a = inputs[0]
	b = inputs[1][0]
	c = inputs[1][1]
	return a + b + c

	x0 = Input(shape=(3,))
	x1 = Input(shape=(3,))
	x2 = Input(shape=(3,))

	l0 = PLMergeNest()
	y = l0([x0, [x1, x2]])

	stage = preprocessing_stage.FunctionalPreprocessingStage([x0, x1, x2], y)
	stage.compile()

	one_array = np.ones((4, 3), dtype='float32')
	adapt_data = dataset_ops.Dataset.from_tensor_slices((one_array,) * 3)
	stage.adapt(adapt_data)
	self.assertEqual(l0.adapt_count, 1)

	# Check call
	y = stage([
	array_ops.ones((4, 3), dtype='float32'),
	array_ops.ones((4, 3), dtype='float32'),
	array_ops.ones((4, 3), dtype='float32')
	])
	self.assertAllClose(y, np.ones((4, 3), dtype='float32') + 2.)

	def test_mixing_preprocessing_and_regular_layers(self):
	x0 = Input(shape=(10, 10, 3))
	x1 = Input(shape=(10, 10, 3))
	x2 = Input(shape=(10, 10, 3))

	y0 = merge.Add()([x0, x1])
	y1 = image_preprocessing.CenterCrop(8, 8)(x2)
	y1 = convolutional.ZeroPadding2D(padding=1)(y1)

	z = merge.Add()([y0, y1])
	z = normalization.Normalization()(z)
	z = convolutional.Conv2D(4, 3)(z)

	stage = preprocessing_stage.FunctionalPreprocessingStage([x0, x1, x2], z)

	data = [
	np.ones((12, 10, 10, 3), dtype='float32'),
	np.ones((12, 10, 10, 3), dtype='float32'),
	np.ones((12, 10, 10, 3), dtype='float32')
	]

	stage.adapt(data)
	_ = stage(data)
	stage.compile('rmsprop', 'mse')
	with self.assertRaisesRegex(ValueError, 'Preprocessing stage'):
	stage.fit(data, np.ones((12, 8, 8, 4)))

	ds_x0 = dataset_ops.Dataset.from_tensor_slices(np.ones((12, 10, 10, 3)))
	ds_x1 = dataset_ops.Dataset.from_tensor_slices(np.ones((12, 10, 10, 3)))
	ds_x2 = dataset_ops.Dataset.from_tensor_slices(np.ones((12, 10, 10, 3)))
	ds_x = dataset_ops.Dataset.zip((ds_x0, ds_x1, ds_x2))
	ds_y = dataset_ops.Dataset.from_tensor_slices(np.ones((12, 8, 8, 4)))
	dataset = dataset_ops.Dataset.zip((ds_x, ds_y)).batch(4)

	with self.assertRaisesRegex(ValueError, 'Preprocessing stage'):
	stage.fit(dataset)
	_ = stage.evaluate(data, np.ones((12, 8, 8, 4)))
	_ = stage.predict(data)


	if __name__ == '__main__':
	test.main()