66c48046f1
This is mostly the result of an internal cleanup and formatting pass. PiperOrigin-RevId: 286318018 Change-Id: I8f9e2f7519070035da73f9f24d2fc90864abc51b
193 lines
7.6 KiB
Python
193 lines
7.6 KiB
Python
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Test cases for segment reduction ops."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import functools
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import numpy as np
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from tensorflow.compiler.tests import xla_test
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from tensorflow.python.framework import dtypes
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import math_ops
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from tensorflow.python.platform import googletest
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class SegmentReductionOpsTest(xla_test.XLATestCase):
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"""Test cases for segment reduction ops."""
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def _segmentReduction(self, op, data, indices, num_segments):
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with self.session() as sess, self.test_scope():
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d = array_ops.placeholder(data.dtype, shape=data.shape)
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if isinstance(indices, int):
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i = array_ops.placeholder(np.int32, shape=[])
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else:
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i = array_ops.placeholder(indices.dtype, shape=indices.shape)
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return sess.run(op(d, i, num_segments), {d: data, i: indices})
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def _unsortedSegmentSum(self, data, indices, num_segments):
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return self._segmentReduction(math_ops.unsorted_segment_sum, data, indices,
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num_segments)
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def _unsortedSegmentProd(self, data, indices, num_segments):
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return self._segmentReduction(math_ops.unsorted_segment_prod, data, indices,
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num_segments)
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def _unsortedSegmentMin(self, data, indices, num_segments):
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return self._segmentReduction(math_ops.unsorted_segment_min, data, indices,
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num_segments)
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def _unsortedSegmentMax(self, data, indices, num_segments):
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return self._segmentReduction(math_ops.unsorted_segment_max, data, indices,
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num_segments)
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def testUnsortedSegmentSum0DIndices1DData(self):
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for dtype in self.numeric_types:
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self.assertAllClose(
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np.array(
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[[0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 1, 2, 3, 4, 5],
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[0, 0, 0, 0, 0, 0]],
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dtype=dtype),
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self._unsortedSegmentSum(
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np.array([0, 1, 2, 3, 4, 5], dtype=dtype), 2, 4))
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def testUnsortedSegmentSum1DIndices1DData(self):
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for dtype in self.numeric_types:
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self.assertAllClose(
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np.array([1, 3, 2, 9], dtype=dtype),
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self._unsortedSegmentSum(
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np.array([0, 1, 2, 3, 4, 5], dtype=dtype),
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np.array([3, 0, 2, 1, 3, 3], dtype=np.int32), 4))
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def testUnsortedSegmentSum1DIndices1DDataNegativeIndices(self):
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for dtype in self.numeric_types:
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self.assertAllClose(
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np.array([6, 3, 0, 6], dtype=dtype),
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self._unsortedSegmentSum(
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np.array([0, 1, 2, 3, 4, 5, 6], dtype=dtype),
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np.array([3, -1, 0, 1, 0, -1, 3], dtype=np.int32), 4))
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def testUnsortedSegmentSum1DIndices2DDataDisjoint(self):
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for dtype in self.numeric_types:
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data = np.array(
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[[0, 1, 2, 3], [20, 21, 22, 23], [30, 31, 32, 33], [40, 41, 42, 43],
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[50, 51, 52, 53]],
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dtype=dtype)
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indices = np.array([8, 1, 0, 3, 7], dtype=np.int32)
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num_segments = 10
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y = self._unsortedSegmentSum(data, indices, num_segments)
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self.assertAllClose(
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np.array(
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[[30, 31, 32, 33], [20, 21, 22, 23], [0, 0, 0, 0],
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[40, 41, 42, 43], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0],
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[50, 51, 52, 53], [0, 1, 2, 3], [0, 0, 0, 0]],
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dtype=dtype), y)
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def testUnsortedSegmentSum1DIndices2DDataNonDisjoint(self):
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for dtype in self.numeric_types:
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data = np.array(
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[[0, 1, 2, 3], [20, 21, 22, 23], [30, 31, 32, 33], [40, 41, 42, 43],
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[50, 51, 52, 53]],
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dtype=dtype)
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indices = np.array([0, 1, 2, 0, 1], dtype=np.int32)
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num_segments = 4
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y = self._unsortedSegmentSum(data, indices, num_segments)
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self.assertAllClose(
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np.array(
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[[40, 42, 44, 46], [70, 72, 74, 76], [30, 31, 32, 33],
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[0, 0, 0, 0]],
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dtype=dtype), y)
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def testUnsortedSegmentSum2DIndices3DData(self):
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for dtype in self.numeric_types:
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data = np.array(
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[[[0, 1, 2], [10, 11, 12]], [[100, 101, 102], [110, 111, 112]], [[
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200, 201, 202
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], [210, 211, 212]], [[300, 301, 302], [310, 311, 312]]],
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dtype=dtype)
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indices = np.array([[3, 5], [3, 1], [5, 0], [6, 2]], dtype=np.int32)
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num_segments = 8
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y = self._unsortedSegmentSum(data, indices, num_segments)
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self.assertAllClose(
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np.array(
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[[210, 211, 212], [110, 111, 112], [310, 311, 312], [
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100, 102, 104
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], [0, 0, 0.], [210, 212, 214], [300, 301, 302], [0, 0, 0]],
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dtype=dtype), y)
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def testUnsortedSegmentSum1DIndices3DData(self):
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for dtype in self.numeric_types:
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data = np.array(
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[[[0, 1, 2], [10, 11, 12]], [[100, 101, 102], [110, 111, 112]], [[
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200, 201, 202
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], [210, 211, 212]], [[300, 301, 302], [310, 311, 312]]],
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dtype=dtype)
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indices = np.array([3, 0, 2, 5], dtype=np.int32)
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num_segments = 6
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y = self._unsortedSegmentSum(data, indices, num_segments)
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self.assertAllClose(
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np.array(
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[[[100, 101, 102.], [110, 111, 112]], [[0, 0, 0], [0, 0, 0]],
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[[200, 201, 202], [210, 211, 212]], [[0, 1, 2.], [10, 11, 12]],
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[[0, 0, 0], [0, 0, 0]], [[300, 301, 302], [310, 311, 312]]],
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dtype=dtype), y)
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def testUnsortedSegmentSumShapeError(self):
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for dtype in self.numeric_types:
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data = np.ones((4, 8, 7), dtype=dtype)
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indices = np.ones((3, 2), dtype=np.int32)
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num_segments = 4
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self.assertRaises(
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ValueError,
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functools.partial(self._segmentReduction,
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math_ops.unsorted_segment_sum, data, indices,
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num_segments))
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def testUnsortedSegmentOps1DIndices1DDataNegativeIndices(self):
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"""Tests for min, max, and prod ops.
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These share most of their implementation with sum, so we only test basic
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functionality.
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"""
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for dtype in self.numeric_types:
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self.assertAllClose(
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np.array([8, 3, 1, 0], dtype=dtype),
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self._unsortedSegmentProd(
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np.array([0, 1, 2, 3, 4, 5, 6], dtype=dtype),
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np.array([3, -1, 0, 1, 0, -1, 3], dtype=np.int32), 4))
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for dtype in self.int_types | self.float_types:
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minval = dtypes.as_dtype(dtype).min
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maxval = dtypes.as_dtype(dtype).max
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self.assertAllClose(
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np.array([2, 3, maxval, 0], dtype=dtype),
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self._unsortedSegmentMin(
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np.array([0, 1, 2, 3, 4, 5, 6], dtype=dtype),
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np.array([3, -1, 0, 1, 0, -1, 3], dtype=np.int32), 4))
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self.assertAllClose(
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np.array([4, 3, minval, 6], dtype=dtype),
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self._unsortedSegmentMax(
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np.array([0, 1, 2, 3, 4, 5, 6], dtype=dtype),
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np.array([3, -1, 0, 1, 0, -1, 3], dtype=np.int32), 4))
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if __name__ == "__main__":
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googletest.main()
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