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# Copyright 2017 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 utilities working with user input."""
from absl.testing import parameterized
from tensorflow.python.data.kernel_tests import test_base
from tensorflow.python.data.util import convert
from tensorflow.python.framework import combinations
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import tensor_shape
from tensorflow.python.platform import test
from tensorflow.python.util import compat
class ConvertTest(test_base.DatasetTestBase, parameterized.TestCase):
@combinations.generate(test_base.default_test_combinations())
def testInteger(self):
resp = convert.optional_param_to_tensor("foo", 3)
self.assertEqual(3, self.evaluate(resp))
@combinations.generate(test_base.default_test_combinations())
def testIntegerDefault(self):
resp = convert.optional_param_to_tensor("foo", None)
self.assertEqual(0, self.evaluate(resp))
@combinations.generate(test_base.default_test_combinations())
def testStringDefault(self):
resp = convert.optional_param_to_tensor("bar", None, "default",
dtypes.string)
self.assertEqual(compat.as_bytes("default"), self.evaluate(resp))
@combinations.generate(test_base.default_test_combinations())
def testString(self):
resp = convert.optional_param_to_tensor("bar", "value", "default",
dtypes.string)
self.assertEqual(compat.as_bytes("value"), self.evaluate(resp))
@combinations.generate(test_base.default_test_combinations())
def testPartialShapeToTensorKnownDimension(self):
self.assertAllEqual([1],
self.evaluate(
convert.partial_shape_to_tensor(
tensor_shape.TensorShape([1]))))
self.assertAllEqual([1], self.evaluate(
convert.partial_shape_to_tensor((1,))))
self.assertAllEqual([1], self.evaluate(
convert.partial_shape_to_tensor([1])))
self.assertAllEqual([1],
self.evaluate(
convert.partial_shape_to_tensor(
constant_op.constant([1], dtype=dtypes.int64))))
@combinations.generate(test_base.graph_only_combinations())
def testPartialShapeToTensorUnknownDimension(self):
self.assertAllEqual([-1],
self.evaluate(
convert.partial_shape_to_tensor(
tensor_shape.TensorShape([None]))))
self.assertAllEqual([-1],
self.evaluate(convert.partial_shape_to_tensor((None,))))
self.assertAllEqual([-1],
self.evaluate(convert.partial_shape_to_tensor([None])))
self.assertAllEqual([-1],
self.evaluate(convert.partial_shape_to_tensor([-1])))
self.assertAllEqual([-1],
self.evaluate(
convert.partial_shape_to_tensor(
constant_op.constant([-1],
dtype=dtypes.int64))))
with self.assertRaisesRegex(
ValueError, r"The given shape .* must be a 1-D tensor of `tf.int64` "
r"values, but the shape was \(2, 2\)."):
convert.partial_shape_to_tensor(constant_op.constant(
[[1, 1], [1, 1]], dtype=dtypes.int64))
with self.assertRaisesRegex(
TypeError, r"The given shape .* must be a 1-D tensor of `tf.int64` "
r"values, but the element type was float32."):
convert.partial_shape_to_tensor(constant_op.constant([1., 1.]))
@combinations.generate(test_base.default_test_combinations())
def testPartialShapeToTensorMultipleDimensions(self):
self.assertAllEqual([3, 6],
self.evaluate(
convert.partial_shape_to_tensor(
tensor_shape.TensorShape([3, 6]))))
self.assertAllEqual([3, 6],
self.evaluate(convert.partial_shape_to_tensor((3, 6))))
self.assertAllEqual([3, 6],
self.evaluate(convert.partial_shape_to_tensor([3, 6])))
self.assertAllEqual([3, 6],
self.evaluate(
convert.partial_shape_to_tensor(
constant_op.constant([3, 6],
dtype=dtypes.int64))))
self.assertAllEqual([3, -1],
self.evaluate(
convert.partial_shape_to_tensor(
tensor_shape.TensorShape([3, None]))))
self.assertAllEqual([3, -1],
self.evaluate(
convert.partial_shape_to_tensor((3, None))))
self.assertAllEqual([3, -1],
self.evaluate(
convert.partial_shape_to_tensor([3, None])))
self.assertAllEqual([3, -1],
self.evaluate(
convert.partial_shape_to_tensor(
constant_op.constant([3, -1],
dtype=dtypes.int64))))
self.assertAllEqual([-1, -1],
self.evaluate(
convert.partial_shape_to_tensor(
tensor_shape.TensorShape([None, None]))))
self.assertAllEqual([-1, -1],
self.evaluate(
convert.partial_shape_to_tensor((None, None))))
self.assertAllEqual([-1, -1],
self.evaluate(
convert.partial_shape_to_tensor([None, None])))
self.assertAllEqual([-1, -1],
self.evaluate(
convert.partial_shape_to_tensor(
constant_op.constant([-1, -1],
dtype=dtypes.int64))))
@combinations.generate(test_base.default_test_combinations())
def testPartialShapeToTensorScalar(self):
self.assertAllEqual([],
self.evaluate(
convert.partial_shape_to_tensor(
tensor_shape.TensorShape([]))))
self.assertAllEqual([], self.evaluate(convert.partial_shape_to_tensor(())))
self.assertAllEqual([], self.evaluate(convert.partial_shape_to_tensor([])))
self.assertAllEqual([],
self.evaluate(
convert.partial_shape_to_tensor(
constant_op.constant([], dtype=dtypes.int64))))
if __name__ == "__main__":
test.main()