| # Copyright 2020 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. |
| # ============================================================================== |
| """Test for XLA implementation of tf.searchsorted.""" |
| |
| import numpy as np |
| |
| from tensorflow.compiler.tests import xla_test |
| from tensorflow.python.ops import array_ops |
| from tensorflow.python.platform import test |
| |
| |
| class SearchSorteddOpTest(xla_test.XLATestCase): |
| |
| def test1D(self): |
| # Test against NumPy implementation (which is 1D only). |
| np.random.seed(1) |
| for side in ['left', 'right']: |
| for dtype in [np.float32, np.int32]: |
| values = np.random.uniform( |
| low=-1000, high=1000, size=(10,)).astype(dtype) |
| unsorted = np.random.uniform( |
| low=-1000, high=1000, size=(20,)).astype(dtype) |
| |
| sorted_sequence = np.sort(unsorted) |
| np_ans = np.searchsorted(sorted_sequence, values, side=side) |
| |
| with self.session() as session: |
| with self.test_scope(): |
| tf_ans = array_ops.searchsorted(sorted_sequence, values, side=side) |
| tf_out = session.run(tf_ans) |
| self.assertAllEqual(np_ans, tf_out) |
| |
| def _test2DExample(self, dtype, side, sorted_sequence, values, correct_ans): |
| |
| with self.session() as session: |
| with self.test_scope(): |
| tf_ans = array_ops.searchsorted(sorted_sequence, values, side=side) |
| tf_out = session.run(tf_ans) |
| self.assertAllEqual(correct_ans, tf_out) |
| |
| def testLowerBound2DExample(self): |
| # 2D TensorFlow documentation example. |
| for dtype in self.float_types | self.int_types: |
| sorted_sequence = np.array([[0, 3, 9, 9, 10], [1, 2, 3, 4, 5]], dtype) |
| values = np.array([[2, 4, 9], [0, 2, 6]], dtype) |
| correct_ans = np.array([[1, 2, 2], [0, 1, 5]], dtype) |
| self._test2DExample(dtype, 'left', sorted_sequence, values, correct_ans) |
| |
| def testUpperBound2DExample(self): |
| # 2D TensorFlow documentation example. |
| for dtype in self.float_types | self.int_types: |
| sorted_sequence = np.array([[0, 3, 9, 9, 10], [1, 2, 3, 4, 5]], dtype) |
| values = np.array([[2, 4, 9], [0, 2, 6]], dtype) |
| correct_ans = np.array([[1, 2, 4], [0, 2, 5]], dtype) |
| self._test2DExample(dtype, 'right', sorted_sequence, values, correct_ans) |
| |
| |
| if __name__ == '__main__': |
| test.main() |