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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.
# ==============================================================================
"""Functional tests for XLA Reverse Ops."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import itertools
import numpy as np
from tensorflow.compiler.tests import xla_test
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.ops import array_ops
from tensorflow.python.platform import googletest
class ReverseOpsTest(xla_test.XLATestCase):
def testReverseOneDim(self):
shape = (7, 5, 9, 11)
for revdim in range(-len(shape), len(shape)):
self._AssertReverseEqual([revdim], shape)
def testReverseMoreThanOneDim(self):
shape = (7, 5, 9, 11)
# The offset is used to test various (but not all) combinations of negative
# and positive axis indices that are guaranteed to not collide at the same
# index.
for revdims in itertools.chain.from_iterable(
itertools.combinations(range(-offset,
len(shape) - offset), k)
for k in range(2,
len(shape) + 1)
for offset in range(0, len(shape))):
self._AssertReverseEqual(revdims, shape)
def _AssertReverseEqual(self, revdims, shape):
np.random.seed(120)
pval = np.random.randint(0, 100, size=shape).astype(float)
with self.cached_session():
with self.test_scope():
p = array_ops.placeholder(dtypes.int32, shape=shape)
axis = constant_op.constant(
np.array(revdims, dtype=np.int32),
shape=(len(revdims),),
dtype=dtypes.int32)
rval = array_ops.reverse(p, axis).eval({p: pval})
slices = [
slice(-1, None, -1)
if d in revdims or d - len(shape) in revdims else slice(None)
for d in range(len(shape))
]
self.assertEqual(pval[slices].flatten().tolist(), rval.flatten().tolist())
if __name__ == '__main__':
googletest.main()
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