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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 MapDefunOp."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.contrib.data.python.ops import map_defun
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import errors
from tensorflow.python.framework import function
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import check_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.platform import test
class MapDefunTest(test.TestCase):
def testMapDefun_Simple(self):
@function.Defun(dtypes.int32)
def simple_fn(x):
return x * 2 + 3
with self.test_session():
nums = [[1, 2], [3, 4], [5, 6]]
elems = constant_op.constant(nums, dtype=dtypes.int32, name="data")
r = map_defun.map_defun(simple_fn, [elems], [dtypes.int32], [(2,)])[0]
expected = elems * 2 + 3
self.assertAllEqual(self.evaluate(r), self.evaluate(expected))
def testMapDefun_MismatchedTypes(self):
@function.Defun(dtypes.int32)
def fn(x):
return math_ops.cast(x, dtypes.float64)
with self.test_session():
nums = [1, 2, 3, 4, 5, 6]
elems = constant_op.constant(nums, dtype=dtypes.int32, name="data")
r = map_defun.map_defun(fn, [elems], [dtypes.int32], [()])[0]
with self.assertRaises(errors.InvalidArgumentError):
self.evaluate(r)
def testMapDefun_MultipleOutputs(self):
@function.Defun(dtypes.int32)
def fn(x):
return (x, math_ops.cast(x * 2 + 3, dtypes.float64))
with self.test_session():
nums = [[1, 2], [3, 4], [5, 6]]
elems = constant_op.constant(nums, dtype=dtypes.int32, name="data")
r = map_defun.map_defun(fn, [elems], [dtypes.int32, dtypes.float64],
[(2,), (2,)])
expected = [elems, elems * 2 + 3]
self.assertAllEqual(self.evaluate(r), self.evaluate(expected))
def testMapDefun_ShapeInference(self):
@function.Defun(dtypes.int32)
def fn(x):
return x
nums = [[1, 2], [3, 4], [5, 6]]
elems = constant_op.constant(nums, dtype=dtypes.int32, name="data")
result = map_defun.map_defun(fn, [elems], [dtypes.int32], [(2,)])[0]
self.assertEqual(result.get_shape(), (3, 2))
def testMapDefun_PartialShapeInference(self):
@function.Defun(dtypes.int32)
def fn(x):
return x
elems = array_ops.placeholder(dtypes.int64, (None, 2))
result = map_defun.map_defun(fn, [elems], [dtypes.int32], [(2,)])
self.assertEqual(result[0].get_shape().as_list(), [None, 2])
def testMapDefun_RaisesErrorOnRuntimeShapeMismatch(self):
@function.Defun(dtypes.int32, dtypes.int32)
def fn(x, y):
return x, y
elems1 = array_ops.placeholder(dtypes.int32)
elems2 = array_ops.placeholder(dtypes.int32)
result = map_defun.map_defun(fn, [elems1, elems2],
[dtypes.int32, dtypes.int32], [(), ()])
with self.test_session() as sess:
with self.assertRaisesWithPredicateMatch(
errors.InvalidArgumentError,
"All inputs must have the same dimension 0."):
sess.run(result, feed_dict={elems1: [1, 2, 3, 4, 5], elems2: [1, 2, 3]})
def testMapDefun_RaisesDefunError(self):
@function.Defun(dtypes.int32)
def fn(x):
with ops.control_dependencies([check_ops.assert_equal(x, 0)]):
return array_ops.identity(x)
elems = constant_op.constant([0, 0, 0, 37, 0])
result = map_defun.map_defun(fn, [elems], [dtypes.int32], [()])
with self.test_session():
with self.assertRaises(errors.InvalidArgumentError):
self.evaluate(result)
if __name__ == "__main__":
test.main()
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