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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 util.py."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from tensorflow.python.data.ops import dataset_ops
from tensorflow.python.estimator import util
from tensorflow.python.framework import constant_op
from tensorflow.python.platform import test
from tensorflow.python.training import training
class UtilTest(test.TestCase):
"""Tests for miscellaneous Estimator utils."""
def test_parse_input_fn_result_tuple(self):
def _input_fn():
features = constant_op.constant(np.arange(100))
labels = constant_op.constant(np.arange(100, 200))
return features, labels
features, labels, hooks = util.parse_input_fn_result(_input_fn())
with self.cached_session() as sess:
vals = sess.run([features, labels])
self.assertAllEqual(vals[0], np.arange(100))
self.assertAllEqual(vals[1], np.arange(100, 200))
self.assertEqual(hooks, [])
def test_parse_input_fn_result_dataset(self):
def _input_fn():
features = np.expand_dims(np.arange(100), 0)
labels = np.expand_dims(np.arange(100, 200), 0)
return dataset_ops.Dataset.from_tensor_slices((features, labels))
features, labels, hooks = util.parse_input_fn_result(_input_fn())
with training.MonitoredSession(hooks=hooks) as sess:
vals = sess.run([features, labels])
self.assertAllEqual(vals[0], np.arange(100))
self.assertAllEqual(vals[1], np.arange(100, 200))
self.assertIsInstance(hooks[0], util._DatasetInitializerHook)
def test_parse_input_fn_result_features_only(self):
def _input_fn():
return constant_op.constant(np.arange(100))
features, labels, hooks = util.parse_input_fn_result(_input_fn())
with self.cached_session() as sess:
vals = sess.run([features])
self.assertAllEqual(vals[0], np.arange(100))
self.assertEqual(labels, None)
self.assertEqual(hooks, [])
def test_parse_input_fn_result_features_only_dataset(self):
def _input_fn():
features = np.expand_dims(np.arange(100), 0)
return dataset_ops.Dataset.from_tensor_slices(features)
features, labels, hooks = util.parse_input_fn_result(_input_fn())
with training.MonitoredSession(hooks=hooks) as sess:
vals = sess.run([features])
self.assertAllEqual(vals[0], np.arange(100))
self.assertEqual(labels, None)
self.assertIsInstance(hooks[0], util._DatasetInitializerHook)
def test_parse_input_fn_result_invalid(self):
def _input_fn():
features = np.expand_dims(np.arange(100), 0)
labels = np.expand_dims(np.arange(100, 200), 0)
return dataset_ops.Dataset.from_tensor_slices((features, labels, labels))
with self.assertRaisesRegexp(ValueError, 'input_fn should return'):
util.parse_input_fn_result(_input_fn())
if __name__ == '__main__':
test.main()
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