| # Copyright 2017 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 tf.dynamic_stitch.""" |
| |
| 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 data_flow_ops |
| from tensorflow.python.platform import googletest |
| |
| |
| class DynamicStitchTest(xla_test.XLATestCase): |
| |
| def _AssertDynamicStitchResultIs(self, indices, data, expected): |
| with self.session() as session: |
| index_placeholders = [ |
| array_ops.placeholder(dtypes.as_dtype(arg.dtype)) for arg in indices |
| ] |
| data_placeholders = [ |
| array_ops.placeholder(dtypes.as_dtype(arg.dtype)) for arg in data |
| ] |
| with self.test_scope(): |
| output = data_flow_ops.dynamic_stitch(index_placeholders, |
| data_placeholders) |
| |
| feed_dict = {} |
| for placeholder, value in zip(index_placeholders, indices): |
| feed_dict[placeholder] = value |
| for placeholder, value in zip(data_placeholders, data): |
| feed_dict[placeholder] = value |
| result = session.run(output, feed_dict=feed_dict) |
| self.assertAllClose(expected, result, rtol=1e-3) |
| |
| def testSimpleEmpty(self): |
| idx1 = np.array([0, 2], dtype=np.int32) |
| idx2 = np.array([[1], [3]], dtype=np.int32) |
| val1 = np.array([[], []], dtype=np.int32) |
| val2 = np.array([[[]], [[]]], dtype=np.int32) |
| self._AssertDynamicStitchResultIs( |
| [idx1, idx2], [val1, val2], |
| expected=np.array([[], [], [], []], np.int32)) |
| |
| def testEmptyIndex(self): |
| idx1 = np.array([], dtype=np.int32) |
| idx2 = np.array([[], []], dtype=np.int32) |
| val1 = np.ndarray(shape=(0, 9), dtype=np.int32) |
| val2 = np.ndarray(shape=(2, 0, 9), dtype=np.int32) |
| self._AssertDynamicStitchResultIs([idx1, idx2], [val1, val2], |
| expected=np.ndarray( |
| shape=(0, 9), dtype=np.int32)) |
| |
| def testSimple1D(self): |
| val1 = np.array([0, 4, 7], dtype=np.int32) |
| val2 = np.array([1, 6, 2, 3, 5], dtype=np.int32) |
| val3 = np.array([0, 40, 70], dtype=np.float32) |
| val4 = np.array([10, 60, 20, 30, 50], dtype=np.float32) |
| expected = np.array([0, 10, 20, 30, 40, 50, 60, 70], dtype=np.float32) |
| self._AssertDynamicStitchResultIs( |
| [val1, val2], [val3, val4], expected=expected) |
| |
| def testSimple2D(self): |
| val1 = np.array([0, 4, 7], dtype=np.int32) |
| val2 = np.array([1, 6], dtype=np.int32) |
| val3 = np.array([2, 3, 5], dtype=np.int32) |
| val4 = np.array([[0, 1], [40, 41], [70, 71]], dtype=np.float32) |
| val5 = np.array([[10, 11], [60, 61]], dtype=np.float32) |
| val6 = np.array([[20, 21], [30, 31], [50, 51]], dtype=np.float32) |
| expected = np.array( |
| [[0, 1], [10, 11], [20, 21], [30, 31], [40, 41], [50, 51], [60, 61], |
| [70, 71]], |
| dtype=np.float32) |
| self._AssertDynamicStitchResultIs( |
| [val1, val2, val3], [val4, val5, val6], expected=expected) |
| |
| |
| if __name__ == "__main__": |
| googletest.main() |