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# Copyright 2018 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.
# ==============================================================================
"""Tests for training utility functions."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from tensorflow.python.eager import context
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor_util
from tensorflow.python.framework import test_util
from tensorflow.python.keras.engine import base_layer
from tensorflow.python.keras.engine import training_utils
from tensorflow.python.platform import test
class TrainingUtilTest(test.TestCase):
@test_util.run_in_graph_and_eager_modes
def test_convert_to_iterator_single_numpy(self):
batch_size = 2
a = np.ones([10, 10])
iterator, steps_per_epoch = training_utils.convert_to_iterator(
x=a, batch_size=batch_size)
self.assertEquals(steps_per_epoch, 5)
expected_batch = a[:batch_size, :]
actual_batch, = iterator.get_next()
self.assertAllEqual(expected_batch, actual_batch)
@test_util.run_in_graph_and_eager_modes
def test_convert_to_iterator_single_tensor(self):
batch_size = 2
a = ops.convert_to_tensor(np.ones([10, 10]))
iterator, steps_per_epoch = training_utils.convert_to_iterator(
x=a, batch_size=batch_size)
self.assertEquals(steps_per_epoch, 5)
expected_batch = a[:batch_size, :]
actual_batch, = iterator.get_next()
self.assertAllEqual(expected_batch, actual_batch)
@test_util.run_in_graph_and_eager_modes
def test_convert_to_iterator_y(self):
batch_size = 2
a = np.ones([10, 100])
b = np.ones([10, 10])
iterator, steps_per_epoch = training_utils.convert_to_iterator(
x=a, y=b, batch_size=batch_size)
self.assertEquals(steps_per_epoch, 5)
expected_x = a[:batch_size, :]
expected_y = b[:batch_size, :]
actual_x, actual_y = iterator.get_next()
self.assertAllEqual(expected_x, actual_x)
self.assertAllEqual(expected_y, actual_y)
@test_util.run_in_graph_and_eager_modes
def test_convert_to_iterator_sample_weights(self):
batch_size = 2
a = ops.convert_to_tensor(np.ones([10, 100]))
b = ops.convert_to_tensor(np.ones([10, 10]))
sw = ops.convert_to_tensor(np.ones([10]))
iterator, steps_per_epoch = training_utils.convert_to_iterator(
x=a, y=b, sample_weights=sw, batch_size=batch_size)
self.assertEquals(steps_per_epoch, 5)
expected_x = a[:batch_size, :]
expected_y = b[:batch_size, :]
expected_sw = sw[:batch_size]
actual_x, actual_y, actual_sw = iterator.get_next()
self.assertAllEqual(expected_x, actual_x)
self.assertAllEqual(expected_y, actual_y)
self.assertAllEqual(expected_sw, actual_sw)
@test_util.run_in_graph_and_eager_modes
def test_convert_to_iterator_nested(self):
batch_size = 2
x = {'1': np.ones([10, 100]), '2': [np.zeros([10, 10]), np.ones([10, 20])]}
iterator, steps_per_epoch = training_utils.convert_to_iterator(
x=x, batch_size=batch_size)
self.assertEquals(steps_per_epoch, 5)
expected_x1 = x['1'][:batch_size, :]
expected_x2_0 = x['2'][0][:batch_size, :]
expected_x2_1 = x['2'][1][:batch_size, :]
actual_x, = iterator.get_next()
actual_x1 = actual_x['1'][:batch_size, :]
actual_x2_0 = actual_x['2'][0][:batch_size, :]
actual_x2_1 = actual_x['2'][1][:batch_size, :]
self.assertAllEqual(expected_x1, actual_x1)
self.assertAllEqual(expected_x2_0, actual_x2_0)
self.assertAllEqual(expected_x2_1, actual_x2_1)
@test_util.run_in_graph_and_eager_modes
def test_convert_to_iterator_epochs(self):
batch_size = 2
a = np.ones([10, 10])
iterator, steps_per_epoch = training_utils.convert_to_iterator(
x=a, batch_size=batch_size, epochs=2)
self.assertEquals(steps_per_epoch, 5)
expected_batch = a[:batch_size, :]
# loop through one whole epoch
for _ in range(6):
actual_batch, = iterator.get_next()
self.assertAllEqual(expected_batch, actual_batch)
@test_util.run_in_graph_and_eager_modes
def test_convert_to_iterator_insufficient_info(self):
# with batch_size and steps_per_epoch not set
with self.assertRaises(ValueError):
a = np.ones([10, 10])
_ = training_utils.convert_to_iterator(x=a)
def test_nested_all(self):
nested_data = {'a': True, 'b': [True, True, (False, True)]}
all_true = training_utils._nested_all(nested_data, lambda x: x)
self.assertEquals(all_true, False)
nested_data = {'a': True, 'b': [True, True, (True, True)]}
all_true = training_utils._nested_all(nested_data, lambda x: x)
self.assertEquals(all_true, True)
def test_nested_any(self):
nested_data = [False, {'a': False, 'b': (False, True)}]
any_true = training_utils._nested_any(nested_data, lambda x: x)
self.assertEquals(any_true, True)
nested_data = [False, {'a': False, 'b': (False, False)}]
any_true = training_utils._nested_any(nested_data, lambda x: x)
self.assertEquals(any_true, False)
class ModelInputsTest(test.TestCase):
def test_single_thing(self):
a = np.ones(10)
model_inputs = training_utils.ModelInputs(a)
self.assertEquals(['input_1'], model_inputs.get_input_names())
vals = model_inputs.get_input_values()
self.assertAllEqual(np.ones(10), vals)
self.assertFalse(tensor_util.is_tensor(vals))
vals = model_inputs.get_symbolic_inputs()
self.assertTrue(tensor_util.is_tensor(vals))
vals = model_inputs.get_symbolic_inputs(return_single_as_list=True)
self.assertEquals(1, len(vals))
self.assertTrue(tensor_util.is_tensor(vals[0]))
def test_single_thing_eager(self):
with context.eager_mode():
a = np.ones(10)
model_inputs = training_utils.ModelInputs(a)
self.assertEquals(['input_1'], model_inputs.get_input_names())
vals = model_inputs.get_input_values()
self.assertAllEqual(np.ones(10), vals)
self.assertTrue(tensor_util.is_tensor(vals))
vals = model_inputs.get_symbolic_inputs()
self.assertTrue(isinstance(vals, base_layer.DeferredTensor))
vals = model_inputs.get_symbolic_inputs(return_single_as_list=True)
self.assertEquals(1, len(vals))
self.assertTrue(isinstance(vals[0], base_layer.DeferredTensor))
def test_list(self):
a = [np.ones(10), np.ones(20)]
model_inputs = training_utils.ModelInputs(a)
self.assertEquals(['input_1', 'input_2'], model_inputs.get_input_names())
vals = model_inputs.get_input_values()
self.assertEqual(2, len(vals))
self.assertAllEqual(np.ones(10), vals[0])
self.assertAllEqual(np.ones(20), vals[1])
self.assertFalse(tensor_util.is_tensor(vals[0]))
self.assertFalse(tensor_util.is_tensor(vals[1]))
vals = model_inputs.get_symbolic_inputs()
self.assertTrue(tensor_util.is_tensor(vals[0]))
self.assertTrue(tensor_util.is_tensor(vals[1]))
def test_list_eager(self):
with context.eager_mode():
a = [np.ones(10), np.ones(20)]
model_inputs = training_utils.ModelInputs(a)
self.assertEquals(['input_1', 'input_2'], model_inputs.get_input_names())
vals = model_inputs.get_input_values()
self.assertEqual(2, len(vals))
self.assertAllEqual(np.ones(10), vals[0])
self.assertAllEqual(np.ones(20), vals[1])
self.assertTrue(tensor_util.is_tensor(vals[0]))
self.assertTrue(tensor_util.is_tensor(vals[1]))
vals = model_inputs.get_symbolic_inputs()
self.assertTrue(isinstance(vals[0], base_layer.DeferredTensor))
self.assertTrue(isinstance(vals[1], base_layer.DeferredTensor))
def test_dict(self):
a = {'b': np.ones(10), 'a': np.ones(20)}
model_inputs = training_utils.ModelInputs(a)
self.assertEquals(['a', 'b'], model_inputs.get_input_names())
vals = model_inputs.get_input_values()
self.assertAllEqual(np.ones(20), vals['a'])
self.assertAllEqual(np.ones(10), vals['b'])
self.assertFalse(tensor_util.is_tensor(vals['a']))
self.assertFalse(tensor_util.is_tensor(vals['b']))
vals = model_inputs.get_symbolic_inputs()
self.assertTrue(tensor_util.is_tensor(vals['a']))
self.assertTrue(tensor_util.is_tensor(vals['b']))
def test_dict_eager(self):
with context.eager_mode():
a = {'b': np.ones(10), 'a': np.ones(20)}
model_inputs = training_utils.ModelInputs(a)
self.assertEquals(['a', 'b'], model_inputs.get_input_names())
vals = model_inputs.get_input_values()
self.assertAllEqual(np.ones(20), vals['a'])
self.assertAllEqual(np.ones(10), vals['b'])
self.assertTrue(tensor_util.is_tensor(vals['a']))
self.assertTrue(tensor_util.is_tensor(vals['b']))
vals = model_inputs.get_symbolic_inputs()
self.assertTrue(isinstance(vals['a'], base_layer.DeferredTensor))
self.assertTrue(isinstance(vals['b'], base_layer.DeferredTensor))
if __name__ == '__main__':
test.main()
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