# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Tests for tensorflow.kernels.sparse_op."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import numpy as np
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from tensorflow.compiler.tests import xla_test
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from tensorflow.python.framework import dtypes
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import sparse_ops
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from tensorflow.python.platform import test
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def _SparseToDense(sparse_indices,
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output_size,
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sparse_values,
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default_value,
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validate_indices=True):
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feed_sparse_indices = array_ops.placeholder(dtypes.int32)
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feed_dict = {feed_sparse_indices: sparse_indices}
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return sparse_ops.sparse_to_dense(
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feed_sparse_indices,
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output_size,
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sparse_values,
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default_value=default_value,
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validate_indices=validate_indices).eval(feed_dict=feed_dict)
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class SparseToDenseTest(xla_test.XLATestCase):
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def testInt(self):
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with self.cached_session(), self.test_scope():
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tf_ans = _SparseToDense([1, 3], [5], 1, 0)
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np_ans = np.array([0, 1, 0, 1, 0]).astype(np.int32)
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self.assertAllClose(np_ans, tf_ans)
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def testFloat(self):
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with self.cached_session(), self.test_scope():
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tf_ans = _SparseToDense([1, 3], [5], 1.0, 0.0)
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np_ans = np.array([0, 1, 0, 1, 0]).astype(np.float32)
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self.assertAllClose(np_ans, tf_ans)
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def testSetValue(self):
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with self.cached_session(), self.test_scope():
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tf_ans = _SparseToDense([1, 3], [5], [1, 2], -1)
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np_ans = np.array([-1, 1, -1, 2, -1]).astype(np.int32)
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self.assertAllClose(np_ans, tf_ans)
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def testSetSingleValue(self):
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with self.cached_session(), self.test_scope():
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tf_ans = _SparseToDense([1, 3], [5], 1, -1)
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np_ans = np.array([-1, 1, -1, 1, -1]).astype(np.int32)
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self.assertAllClose(np_ans, tf_ans)
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def test2d(self):
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# pylint: disable=bad-whitespace
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with self.cached_session(), self.test_scope():
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tf_ans = _SparseToDense([[1, 3], [2, 0]], [3, 4], 1, -1)
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np_ans = np.array([[-1, -1, -1, -1],
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[-1, -1, -1, 1],
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[ 1, -1, -1, -1]]).astype(np.int32)
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self.assertAllClose(np_ans, tf_ans)
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def testZeroDefault(self):
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with self.cached_session():
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x = sparse_ops.sparse_to_dense(2, [4], 7).eval()
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self.assertAllEqual(x, [0, 0, 7, 0])
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def test3d(self):
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with self.cached_session(), self.test_scope():
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tf_ans = _SparseToDense([[1, 3, 0], [2, 0, 1]], [3, 4, 2], 1, -1)
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np_ans = np.ones((3, 4, 2), dtype=np.int32) * -1
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np_ans[1, 3, 0] = 1
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np_ans[2, 0, 1] = 1
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self.assertAllClose(np_ans, tf_ans)
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def testBadShape(self):
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with self.cached_session(), self.test_scope():
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with self.assertRaisesWithPredicateMatch(ValueError, "must be rank 1"):
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_SparseToDense([1, 3], [[5], [3]], 1, -1)
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def testBadValue(self):
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with self.cached_session(), self.test_scope():
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with self.assertRaisesOpError(
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r"sparse_values has incorrect shape \[2,1\], "
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r"should be \[\] or \[2\]"):
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_SparseToDense([1, 3], [5], [[5], [3]], -1)
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def testBadNumValues(self):
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with self.cached_session(), self.test_scope():
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with self.assertRaisesOpError(
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r"sparse_values has incorrect shape \[3\], should be \[\] or \[2\]"):
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_SparseToDense([1, 3], [5], [1, 2, 3], -1)
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def testBadDefault(self):
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with self.cached_session(), self.test_scope():
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with self.assertRaisesOpError("default_value should be a scalar"):
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_SparseToDense([1, 3], [5], [1, 2], [0])
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if __name__ == "__main__":
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test.main()
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