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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 sparse ops."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import sparse_tensor
from tensorflow.python.framework import test_util
from tensorflow.python.ops import sparse_ops
from tensorflow.python.platform import googletest
@test_util.run_all_in_graph_and_eager_modes
class SparseOpsTest(test_util.TensorFlowTestCase):
def testSparseEye(self):
def test_one(n, m, as_tensors):
expected = np.eye(n, m)
if as_tensors:
m = constant_op.constant(m)
n = constant_op.constant(n)
s = sparse_ops.sparse_eye(n, m)
d = sparse_ops.sparse_to_dense(s.indices, s.dense_shape, s.values)
self.assertAllEqual(self.evaluate(d), expected)
for n in range(2, 10, 2):
for m in range(2, 10, 2):
# Test with n and m as both constants and tensors.
test_one(n, m, True)
test_one(n, m, False)
def testSparseExpandDims(self):
for rank in range(1, 4):
# Create a dummy input. When rank=3, shape=[2, 4, 6].
shape = np.arange(1, rank + 1) * 2
before = np.arange(np.prod(shape)).reshape(shape)
# Make entries sparse.
before *= np.random.binomial(1, .2, before.shape)
dense_shape = before.shape
indices = np.array(np.where(before)).T
values = before[before != 0]
# Try every possible valid value of axis.
for axis in range(-rank - 1, rank):
expected_after = np.expand_dims(before, axis)
for axis_as_tensor in [False, True]:
dense_shape_t = constant_op.constant(dense_shape, dtype=dtypes.int64)
indices_t = constant_op.constant(indices)
values_t = constant_op.constant(values)
before_t = sparse_tensor.SparseTensor(
indices=indices_t, values=values_t, dense_shape=dense_shape_t)
if axis_as_tensor:
axis = constant_op.constant(axis)
s = sparse_ops.sparse_expand_dims(before_t, axis)
d = sparse_ops.sparse_to_dense(s.indices, s.dense_shape, s.values)
self.assertAllEqual(self.evaluate(d), expected_after)
if __name__ == '__main__':
googletest.main()
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