| # Copyright 2023 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 unique ops.""" |
| |
| 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.ops import array_ops |
| from tensorflow.python.ops import gen_array_ops |
| from tensorflow.python.platform import googletest |
| |
| |
| class UniqueTest(xla_test.XLATestCase): |
| |
| def testNegativeAxis(self): |
| """Verifies that an axis with negative index is converted to positive.""" |
| with self.session() as session: |
| with self.test_scope(): |
| px = array_ops.placeholder(dtypes.float32, [2, 1, 1], name="x") |
| axis = constant_op.constant([-1], dtype=dtypes.int32) |
| output = gen_array_ops.unique_v2(px, axis) |
| result = session.run( |
| output, {px: np.array([[[-2.0]], [[10.0]]], dtype=np.float32)} |
| ) |
| self.assertAllEqual( |
| result.y, np.array([[[-2.0]], [[10.0]]], dtype=np.float32) |
| ) |
| self.assertAllEqual(result.idx, np.array([0], dtype=np.int32)) |
| |
| |
| if __name__ == "__main__": |
| googletest.main() |