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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 ops which manipulate lists of tensors via bridge."""
# pylint: disable=g-bad-name
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
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.framework import errors
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import list_ops
from tensorflow.python.platform import test
def scalar_shape():
return ops.convert_to_tensor([], dtype=dtypes.int32)
class ListOpsTest(xla_test.XLATestCase):
def testElementShape(self):
with self.cached_session() as sess, self.test_scope():
dim = array_ops.placeholder(dtypes.int32)
l = list_ops.tensor_list_reserve(
element_shape=(dim, 15), num_elements=20,
element_dtype=dtypes.float32)
e32 = list_ops.tensor_list_element_shape(l, shape_type=dtypes.int32)
e64 = list_ops.tensor_list_element_shape(l, shape_type=dtypes.int64)
self.assertAllEqual(sess.run(e32, {dim: 10}), (10, 15))
self.assertAllEqual(sess.run(e64, {dim: 7}), (7, 15))
def testPushPop(self):
with self.cached_session() as sess, self.test_scope():
num = array_ops.placeholder(dtypes.int32)
l = list_ops.tensor_list_reserve(
element_shape=(7, 15), num_elements=num, element_dtype=dtypes.float32)
l = list_ops.tensor_list_push_back(
l, constant_op.constant(1.0, shape=(7, 15)))
l = list_ops.tensor_list_push_back(
l, constant_op.constant(2.0, shape=(7, 15)))
l, e2 = list_ops.tensor_list_pop_back(l, element_dtype=dtypes.float32)
_, e1 = list_ops.tensor_list_pop_back(l, element_dtype=dtypes.float32)
self.assertAllEqual(sess.run(e2, {num: 10}), 2.0 * np.ones((7, 15)))
self.assertAllEqual(sess.run(e1, {num: 10}), 1.0 * np.ones((7, 15)))
def testPushPopSeparateLists(self):
with self.cached_session() as sess, self.test_scope():
num = array_ops.placeholder(dtypes.int32)
l = list_ops.tensor_list_reserve(
element_shape=scalar_shape(),
num_elements=num,
element_dtype=dtypes.float32)
l = list_ops.tensor_list_push_back(l, constant_op.constant(1.0))
l2 = list_ops.tensor_list_push_back(l, constant_op.constant(2.0))
l3 = list_ops.tensor_list_push_back(l, constant_op.constant(3.0))
_, e11 = list_ops.tensor_list_pop_back(l, element_dtype=dtypes.float32)
l2, e21 = list_ops.tensor_list_pop_back(l2, element_dtype=dtypes.float32)
l2, e22 = list_ops.tensor_list_pop_back(l2, element_dtype=dtypes.float32)
l3, e31 = list_ops.tensor_list_pop_back(l3, element_dtype=dtypes.float32)
l3, e32 = list_ops.tensor_list_pop_back(l3, element_dtype=dtypes.float32)
result = sess.run([e11, [e21, e22], [e31, e32]], {num: 20})
self.assertEqual(result, [1.0, [2.0, 1.0], [3.0, 1.0]])
def testEmptyTensorList(self):
dim = 7
with self.cached_session() as sess, self.test_scope():
p = array_ops.placeholder(dtypes.int32)
l = list_ops.empty_tensor_list(
element_shape=(p, 15), element_dtype=dtypes.float32)
l = list_ops.tensor_list_push_back(
l, constant_op.constant(1.0, shape=(dim, 15)))
_, e = list_ops.tensor_list_pop_back(l, element_dtype=dtypes.float32)
with self.assertRaisesRegexp(errors.InvalidArgumentError,
"Use TensorListReserve instead"):
self.assertEqual(sess.run(e, {p: dim}), 1.0 * np.ones((dim, 15)))
if __name__ == "__main__":
test.main()
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