| # 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 tensorflow.ops.tf.gather_nd.""" |
| |
| import numpy as np |
| |
| from tensorflow.compiler.tests import xla_test |
| from tensorflow.python.framework import errors |
| from tensorflow.python.framework import test_util |
| from tensorflow.python.ops import array_ops |
| from tensorflow.python.platform import test |
| |
| |
| class GatherNdTest(xla_test.XLATestCase): |
| |
| def _runGather(self, params, indices): |
| with self.session(): |
| paramsp = array_ops.placeholder(params.dtype) |
| indicesp = array_ops.placeholder(indices.dtype) |
| with self.test_scope(): |
| gather_nd_t = array_ops.gather_nd(paramsp, indicesp) |
| feed_dict = {paramsp: params, indicesp: indices} |
| return gather_nd_t.eval(feed_dict=feed_dict) |
| |
| def testSimpleDtype(self): |
| for dtype in self.numeric_types: |
| self.assertAllEqual( |
| np.array([7, 7, 8], dtype=dtype), |
| self._runGather( |
| np.array([8, 1, 2, 3, 7, 5], dtype=dtype), |
| np.array([[4], [4], [0]], np.int32))) |
| |
| @test_util.disable_mlir_bridge("Error handling") |
| def testEmptyIndicesAndParamsOKButJustEmptyParamsFails(self): |
| with self.session(): |
| params = np.ones((3, 3), dtype=np.float32) |
| |
| indices_empty = np.empty((0, 2), dtype=np.int32) |
| gather_nd_ok_val = self._runGather(params, indices_empty) |
| self.assertAllClose(np.empty((0,), dtype=np.float32), gather_nd_ok_val) |
| |
| indices_empty = np.empty((0, 1), dtype=np.int32) |
| gather_nd_ok_val = self._runGather(params, indices_empty) |
| self.assertAllClose(np.empty((0, 3), dtype=np.float32), gather_nd_ok_val) |
| |
| params_empty = np.empty((0, 3), dtype=np.float32) |
| indices_empty = np.empty((0, 2), dtype=np.int32) |
| gather_nd_ok_val = self._runGather(params_empty, indices_empty) |
| self.assertAllClose(np.empty((0,), dtype=np.float32), gather_nd_ok_val) |
| |
| params_empty = np.empty((0, 3), dtype=np.float32) |
| indices_nonempty = np.zeros((1, 2), dtype=np.int32) |
| with self.assertRaisesWithPredicateMatch( |
| errors.InvalidArgumentError, r"Gather dimension 0 is of size zero"): |
| self._runGather(params_empty, indices_nonempty) |
| |
| def testIndexScalar(self): |
| params = np.array( |
| [[-8, -1, -2, -3, -7, -5], [8, 1, 2, 3, 7, 5]], dtype=np.float32).T |
| indices = np.array([4, 1], dtype=np.int32) |
| gather_nd_val = self._runGather(params, indices) |
| self.assertAllEqual(np.array(7), gather_nd_val) |
| |
| def testParamsRankLargerThanIndexIndexScalarSlices(self): |
| params = np.array( |
| [[-8, -1, -2, -3, -7, -5], [8, 1, 2, 3, 7, 5]], dtype=np.float32).T |
| indices = np.array( |
| [ |
| 4, |
| ], dtype=np.int32) |
| gather_nd_val = self._runGather(params, indices) |
| self.assertAllEqual(np.array([-7, 7]), gather_nd_val) |
| |
| def testParamsRankLargerThanIndexSlices(self): |
| params = np.array( |
| [[-8, -1, -2, -3, -7, -5], [8, 1, 2, 3, 7, 5]], dtype=np.float32).T |
| indices = np.array([[4], [4], [0]], np.int32) |
| gather_nd_val = self._runGather(params, indices) |
| self.assertAllEqual(np.array([[-7, 7], [-7, 7], [-8, 8]]), gather_nd_val) |
| |
| def testHigherRankParamsLargerThanIndexSlices(self): |
| params = np.array( |
| [[[-8, -1, -2, -3, -7, -5], [8, 1, 2, 3, 7, 5]], |
| [[-80, -10, -20, -30, -70, -50], [80, 10, 20, 30, 70, 50]]], |
| dtype=np.float32).T |
| indices = np.array([[4], [4], [0]], np.int32) |
| gather_nd_val = self._runGather(params, indices) |
| self.assertAllEqual(params[[4, 4, 0]], gather_nd_val) |
| |
| def testEmptyIndicesLastRankMeansCopyEntireTensor(self): |
| params = np.array( |
| [[[-8, -1, -2, -3, -7, -5], [8, 1, 2, 3, 7, 5]], |
| [[-80, -10, -20, -30, -70, -50], [80, 10, 20, 30, 70, 50]]], |
| dtype=np.float32).T |
| indices = np.array([[], []], dtype=np.int32) # Size (2, 0) |
| gather_nd_val = self._runGather(params, indices) |
| self.assertAllEqual( |
| np.vstack((params[np.newaxis, :], params[np.newaxis, :])), |
| gather_nd_val) |
| |
| def testHigherRankParamsAndIndicesLargerThanIndexSlices(self): |
| params = np.array( |
| [[[-8, -1, -2, -3, -7, -5], [8, 1, 2, 3, 7, 5]], |
| [[-80, -10, -20, -30, -70, -50], [80, 10, 20, 30, 70, 50]]], |
| dtype=np.float32).T |
| indices = np.array([[[3], [2], [1]], [[4], [4], [0]]], np.int32) |
| gather_nd_val = self._runGather(params, indices) |
| self.assertAllEqual(params[[3, 2, 1, 4, 4, 0]].reshape(2, 3, 2, 2), |
| gather_nd_val) |
| |
| def testHigherRankParams(self): |
| shape = (10, 20, 5, 1, 17) |
| params = np.random.rand(*shape).astype(np.float32) |
| indices = np.vstack( |
| [np.random.randint(0, s, size=2000, dtype=np.int32) for s in shape]).T |
| gather_nd_val = self._runGather(params, indices) |
| |
| expected = params[tuple(indices.T)] |
| self.assertAllEqual(expected, gather_nd_val) |
| |
| def testHigherRankParamsAndIndices(self): |
| shape = (10, 20, 5, 1, 17) |
| params = np.random.rand(*shape).astype(np.float32) |
| indices = np.vstack( |
| [np.random.randint(0, s, size=2000, dtype=np.int32) for s in shape]).T |
| indices_reshaped = indices.reshape([10, 10, 20, 5]) |
| gather_nd_val = self._runGather(params, indices_reshaped) |
| expected = params[tuple(indices.T)] |
| self.assertAllEqual(expected.reshape([10, 10, 20]), gather_nd_val) |
| |
| |
| if __name__ == "__main__": |
| test.main() |