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# Copyright 2015 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 tensorflow.kernels.sparse_op."""
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 dtypes
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import sparse_ops
from tensorflow.python.platform import test
def _SparseToDense(sparse_indices,
output_size,
sparse_values,
default_value,
validate_indices=True):
feed_sparse_indices = array_ops.placeholder(dtypes.int32)
feed_dict = {feed_sparse_indices: sparse_indices}
return sparse_ops.sparse_to_dense(
feed_sparse_indices,
output_size,
sparse_values,
default_value=default_value,
validate_indices=validate_indices).eval(feed_dict=feed_dict)
class SparseToDenseTest(xla_test.XLATestCase):
def testInt(self):
with self.cached_session(), self.test_scope():
tf_ans = _SparseToDense([1, 3], [5], 1, 0)
np_ans = np.array([0, 1, 0, 1, 0]).astype(np.int32)
self.assertAllClose(np_ans, tf_ans)
def testFloat(self):
with self.cached_session(), self.test_scope():
tf_ans = _SparseToDense([1, 3], [5], 1.0, 0.0)
np_ans = np.array([0, 1, 0, 1, 0]).astype(np.float32)
self.assertAllClose(np_ans, tf_ans)
def testSetValue(self):
with self.cached_session(), self.test_scope():
tf_ans = _SparseToDense([1, 3], [5], [1, 2], -1)
np_ans = np.array([-1, 1, -1, 2, -1]).astype(np.int32)
self.assertAllClose(np_ans, tf_ans)
def testSetSingleValue(self):
with self.cached_session(), self.test_scope():
tf_ans = _SparseToDense([1, 3], [5], 1, -1)
np_ans = np.array([-1, 1, -1, 1, -1]).astype(np.int32)
self.assertAllClose(np_ans, tf_ans)
def test2d(self):
# pylint: disable=bad-whitespace
with self.cached_session(), self.test_scope():
tf_ans = _SparseToDense([[1, 3], [2, 0]], [3, 4], 1, -1)
np_ans = np.array([[-1, -1, -1, -1],
[-1, -1, -1, 1],
[ 1, -1, -1, -1]]).astype(np.int32)
self.assertAllClose(np_ans, tf_ans)
def testZeroDefault(self):
with self.cached_session():
x = sparse_ops.sparse_to_dense(2, [4], 7).eval()
self.assertAllEqual(x, [0, 0, 7, 0])
def test3d(self):
with self.cached_session(), self.test_scope():
tf_ans = _SparseToDense([[1, 3, 0], [2, 0, 1]], [3, 4, 2], 1, -1)
np_ans = np.ones((3, 4, 2), dtype=np.int32) * -1
np_ans[1, 3, 0] = 1
np_ans[2, 0, 1] = 1
self.assertAllClose(np_ans, tf_ans)
def testBadShape(self):
with self.cached_session(), self.test_scope():
with self.assertRaisesWithPredicateMatch(ValueError, "must be rank 1"):
_SparseToDense([1, 3], [[5], [3]], 1, -1)
def testBadValue(self):
with self.cached_session(), self.test_scope():
with self.assertRaisesOpError(
r"sparse_values has incorrect shape \[2,1\], "
r"should be \[\] or \[2\]"):
_SparseToDense([1, 3], [5], [[5], [3]], -1)
def testBadNumValues(self):
with self.cached_session(), self.test_scope():
with self.assertRaisesOpError(
r"sparse_values has incorrect shape \[3\], should be \[\] or \[2\]"):
_SparseToDense([1, 3], [5], [1, 2, 3], -1)
def testBadDefault(self):
with self.cached_session(), self.test_scope():
with self.assertRaisesOpError("default_value should be a scalar"):
_SparseToDense([1, 3], [5], [1, 2], [0])
if __name__ == "__main__":
test.main()
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