| # 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. |
| # ============================================================================== |
| """Test cases for segment reduction ops.""" |
| |
| import functools |
| |
| import numpy as np |
| |
| from tensorflow.compiler.tests import xla_test |
| from tensorflow.python.client import device_lib |
| from tensorflow.python.framework import dtypes |
| from tensorflow.python.ops import array_ops |
| from tensorflow.python.ops import math_ops |
| from tensorflow.python.platform import googletest |
| |
| |
| class SegmentReductionOpsTest(xla_test.XLATestCase): |
| """Test cases for segment reduction ops.""" |
| |
| def _findDevice(self, device_name): |
| devices = device_lib.list_local_devices() |
| for d in devices: |
| if d.device_type == device_name: |
| return True |
| return False |
| |
| def _segmentReduction(self, op, data, indices, num_segments): |
| with self.session() as sess, self.test_scope(): |
| d = array_ops.placeholder(data.dtype, shape=data.shape) |
| if isinstance(indices, int): |
| i = array_ops.placeholder(np.int32, shape=[]) |
| else: |
| i = array_ops.placeholder(indices.dtype, shape=indices.shape) |
| return sess.run(op(d, i, num_segments), {d: data, i: indices}) |
| |
| def _unsortedSegmentSum(self, data, indices, num_segments): |
| return self._segmentReduction(math_ops.unsorted_segment_sum, data, indices, |
| num_segments) |
| |
| def _segmentSumV2(self, data, indices, num_segments): |
| return self._segmentReduction(math_ops.segment_sum_v2, data, indices, |
| num_segments) |
| |
| def _segmentProdV2(self, data, indices, num_segments): |
| return self._segmentReduction(math_ops.segment_prod_v2, data, indices, |
| num_segments) |
| |
| def _segmentMinV2(self, data, indices, num_segments): |
| return self._segmentReduction(math_ops.segment_min_v2, data, indices, |
| num_segments) |
| |
| def _segmentMaxV2(self, data, indices, num_segments): |
| return self._segmentReduction(math_ops.segment_max_v2, data, indices, |
| num_segments) |
| |
| def _unsortedSegmentProd(self, data, indices, num_segments): |
| return self._segmentReduction(math_ops.unsorted_segment_prod, data, indices, |
| num_segments) |
| |
| def _unsortedSegmentMin(self, data, indices, num_segments): |
| return self._segmentReduction(math_ops.unsorted_segment_min, data, indices, |
| num_segments) |
| |
| def _unsortedSegmentMax(self, data, indices, num_segments): |
| return self._segmentReduction(math_ops.unsorted_segment_max, data, indices, |
| num_segments) |
| |
| def testSegmentSum(self): |
| for dtype in self.numeric_types: |
| self.assertAllClose( |
| np.array([1, 0, 2, 12], dtype=dtype), |
| self._segmentSumV2( |
| np.array([0, 1, 2, 3, 4, 5], dtype=dtype), |
| np.array([0, 0, 2, 3, 3, 3], dtype=np.int32), 4)) |
| |
| def testSegmentProd(self): |
| for dtype in self.numeric_types: |
| self.assertAllClose( |
| np.array([0, 1, 2, 60], dtype=dtype), |
| self._segmentProdV2( |
| np.array([0, 1, 2, 3, 4, 5], dtype=dtype), |
| np.array([0, 0, 2, 3, 3, 3], dtype=np.int32), 4)) |
| |
| def testSegmentProdNumSegmentsLess(self): |
| for dtype in self.numeric_types: |
| self.assertAllClose( |
| np.array([0, 1, 2], dtype=dtype), |
| self._segmentProdV2( |
| np.array([0, 1, 2, 3, 4, 5], dtype=dtype), |
| np.array([0, 0, 2, 3, 3, 3], dtype=np.int32), 3)) |
| |
| def testSegmentProdNumSegmentsMore(self): |
| for dtype in self.numeric_types: |
| self.assertAllClose( |
| np.array([0, 1, 2, 60, 1], dtype=dtype), |
| self._segmentProdV2( |
| np.array([0, 1, 2, 3, 4, 5], dtype=dtype), |
| np.array([0, 0, 2, 3, 3, 3], dtype=np.int32), 5)) |
| |
| def testSegmentMin(self): |
| for dtype in self.int_types | self.float_types: |
| maxval = dtypes.as_dtype(dtype).max |
| if dtype == np.float64 and self._findDevice("TPU"): |
| maxval = np.Inf |
| self.assertAllClose( |
| np.array([0, maxval, 2, 3], dtype=dtype), |
| self._segmentMinV2( |
| np.array([0, 1, 2, 3, 4, 5], dtype=dtype), |
| np.array([0, 0, 2, 3, 3, 3], dtype=np.int32), 4)) |
| |
| def testSegmentMinNumSegmentsLess(self): |
| for dtype in self.int_types | self.float_types: |
| maxval = dtypes.as_dtype(dtype).max |
| if dtype == np.float64 and self._findDevice("TPU"): |
| maxval = np.Inf |
| self.assertAllClose( |
| np.array([0, maxval, 2], dtype=dtype), |
| self._segmentMinV2( |
| np.array([0, 1, 2, 3, 4, 5], dtype=dtype), |
| np.array([0, 0, 2, 3, 3, 3], dtype=np.int32), 3)) |
| |
| def testSegmentMinNumSegmentsMore(self): |
| for dtype in self.int_types | self.float_types: |
| maxval = dtypes.as_dtype(dtype).max |
| if dtype == np.float64 and self._findDevice("TPU"): |
| maxval = np.Inf |
| self.assertAllClose( |
| np.array([0, maxval, 2, 3, maxval], dtype=dtype), |
| self._segmentMinV2( |
| np.array([0, 1, 2, 3, 4, 5], dtype=dtype), |
| np.array([0, 0, 2, 3, 3, 3], dtype=np.int32), 5)) |
| |
| def testSegmentMax(self): |
| for dtype in self.int_types | self.float_types: |
| minval = dtypes.as_dtype(dtype).min |
| if dtype == np.float64 and self._findDevice("TPU"): |
| minval = -np.Inf |
| self.assertAllClose( |
| np.array([1, minval, 2, 5], dtype=dtype), |
| self._segmentMaxV2( |
| np.array([0, 1, 2, 3, 4, 5], dtype=dtype), |
| np.array([0, 0, 2, 3, 3, 3], dtype=np.int32), 4)) |
| |
| def testSegmentMaxNumSegmentsLess(self): |
| for dtype in self.int_types | self.float_types: |
| minval = dtypes.as_dtype(dtype).min |
| if dtype == np.float64 and self._findDevice("TPU"): |
| minval = -np.Inf |
| self.assertAllClose( |
| np.array([1, minval, 2], dtype=dtype), |
| self._segmentMaxV2( |
| np.array([0, 1, 2, 3, 4, 5], dtype=dtype), |
| np.array([0, 0, 2, 3, 3, 3], dtype=np.int32), 3)) |
| |
| def testSegmentMaxNumSegmentsMore(self): |
| for dtype in self.int_types | self.float_types: |
| minval = dtypes.as_dtype(dtype).min |
| if dtype == np.float64 and self._findDevice("TPU"): |
| minval = -np.Inf |
| self.assertAllClose( |
| np.array([1, minval, 2, 5, minval], dtype=dtype), |
| self._segmentMaxV2( |
| np.array([0, 1, 2, 3, 4, 5], dtype=dtype), |
| np.array([0, 0, 2, 3, 3, 3], dtype=np.int32), 5)) |
| |
| def testUnsortedSegmentSum0DIndices1DData(self): |
| for dtype in self.numeric_types: |
| self.assertAllClose( |
| np.array( |
| [[0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 1, 2, 3, 4, 5], |
| [0, 0, 0, 0, 0, 0]], |
| dtype=dtype), |
| self._unsortedSegmentSum( |
| np.array([0, 1, 2, 3, 4, 5], dtype=dtype), 2, 4)) |
| |
| def testUnsortedSegmentSum1DIndices1DData(self): |
| for dtype in self.numeric_types: |
| self.assertAllClose( |
| np.array([1, 3, 2, 9], dtype=dtype), |
| self._unsortedSegmentSum( |
| np.array([0, 1, 2, 3, 4, 5], dtype=dtype), |
| np.array([3, 0, 2, 1, 3, 3], dtype=np.int32), 4)) |
| |
| def testUnsortedSegmentSum1DIndices1DDataNegativeIndices(self): |
| for dtype in self.numeric_types: |
| self.assertAllClose( |
| np.array([6, 3, 0, 6], dtype=dtype), |
| self._unsortedSegmentSum( |
| np.array([0, 1, 2, 3, 4, 5, 6], dtype=dtype), |
| np.array([3, -1, 0, 1, 0, -1, 3], dtype=np.int32), 4)) |
| |
| def testUnsortedSegmentSum1DIndices2DDataDisjoint(self): |
| for dtype in self.numeric_types: |
| data = np.array( |
| [[0, 1, 2, 3], [20, 21, 22, 23], [30, 31, 32, 33], [40, 41, 42, 43], |
| [50, 51, 52, 53]], |
| dtype=dtype) |
| indices = np.array([8, 1, 0, 3, 7], dtype=np.int32) |
| num_segments = 10 |
| y = self._unsortedSegmentSum(data, indices, num_segments) |
| self.assertAllClose( |
| np.array( |
| [[30, 31, 32, 33], [20, 21, 22, 23], [0, 0, 0, 0], |
| [40, 41, 42, 43], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], |
| [50, 51, 52, 53], [0, 1, 2, 3], [0, 0, 0, 0]], |
| dtype=dtype), y) |
| |
| def testUnsortedSegmentSum1DIndices2DDataNonDisjoint(self): |
| for dtype in self.numeric_types: |
| data = np.array( |
| [[0, 1, 2, 3], [20, 21, 22, 23], [30, 31, 32, 33], [40, 41, 42, 43], |
| [50, 51, 52, 53]], |
| dtype=dtype) |
| indices = np.array([0, 1, 2, 0, 1], dtype=np.int32) |
| num_segments = 4 |
| y = self._unsortedSegmentSum(data, indices, num_segments) |
| self.assertAllClose( |
| np.array( |
| [[40, 42, 44, 46], [70, 72, 74, 76], [30, 31, 32, 33], |
| [0, 0, 0, 0]], |
| dtype=dtype), y) |
| |
| def testUnsortedSegmentSum2DIndices3DData(self): |
| for dtype in self.numeric_types: |
| data = np.array( |
| [[[0, 1, 2], [10, 11, 12]], [[100, 101, 102], [110, 111, 112]], [[ |
| 200, 201, 202 |
| ], [210, 211, 212]], [[300, 301, 302], [310, 311, 312]]], |
| dtype=dtype) |
| indices = np.array([[3, 5], [3, 1], [5, 0], [6, 2]], dtype=np.int32) |
| num_segments = 8 |
| y = self._unsortedSegmentSum(data, indices, num_segments) |
| self.assertAllClose( |
| np.array( |
| [[210, 211, 212], [110, 111, 112], [310, 311, 312], [ |
| 100, 102, 104 |
| ], [0, 0, 0.], [210, 212, 214], [300, 301, 302], [0, 0, 0]], |
| dtype=dtype), y) |
| |
| def testUnsortedSegmentSum1DIndices3DData(self): |
| for dtype in self.numeric_types: |
| data = np.array( |
| [[[0, 1, 2], [10, 11, 12]], [[100, 101, 102], [110, 111, 112]], [[ |
| 200, 201, 202 |
| ], [210, 211, 212]], [[300, 301, 302], [310, 311, 312]]], |
| dtype=dtype) |
| indices = np.array([3, 0, 2, 5], dtype=np.int32) |
| num_segments = 6 |
| y = self._unsortedSegmentSum(data, indices, num_segments) |
| self.assertAllClose( |
| np.array( |
| [[[100, 101, 102.], [110, 111, 112]], [[0, 0, 0], [0, 0, 0]], |
| [[200, 201, 202], [210, 211, 212]], [[0, 1, 2.], [10, 11, 12]], |
| [[0, 0, 0], [0, 0, 0]], [[300, 301, 302], [310, 311, 312]]], |
| dtype=dtype), y) |
| |
| def testUnsortedSegmentSumShapeError(self): |
| for dtype in self.numeric_types: |
| data = np.ones((4, 8, 7), dtype=dtype) |
| indices = np.ones((3, 2), dtype=np.int32) |
| num_segments = 4 |
| self.assertRaises( |
| ValueError, |
| functools.partial(self._segmentReduction, |
| math_ops.unsorted_segment_sum, data, indices, |
| num_segments)) |
| |
| def testUnsortedSegmentOps1DIndices1DDataNegativeIndices(self): |
| """Tests for min, max, and prod ops. |
| |
| These share most of their implementation with sum, so we only test basic |
| functionality. |
| """ |
| for dtype in self.numeric_types: |
| self.assertAllClose( |
| np.array([8, 3, 1, 0], dtype=dtype), |
| self._unsortedSegmentProd( |
| np.array([0, 1, 2, 3, 4, 5, 6], dtype=dtype), |
| np.array([3, -1, 0, 1, 0, -1, 3], dtype=np.int32), 4)) |
| |
| for dtype in self.int_types | self.float_types: |
| minval = dtypes.as_dtype(dtype).min |
| maxval = dtypes.as_dtype(dtype).max |
| |
| self.assertAllClose( |
| np.array([2, 3, maxval, 0], dtype=dtype), |
| self._unsortedSegmentMin( |
| np.array([0, 1, 2, 3, 4, 5, 6], dtype=dtype), |
| np.array([3, -1, 0, 1, 0, -1, 3], dtype=np.int32), 4)) |
| self.assertAllClose( |
| np.array([4, 3, minval, 6], dtype=dtype), |
| self._unsortedSegmentMax( |
| np.array([0, 1, 2, 3, 4, 5, 6], dtype=dtype), |
| np.array([3, -1, 0, 1, 0, -1, 3], dtype=np.int32), 4)) |
| |
| |
| if __name__ == "__main__": |
| googletest.main() |
