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# 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."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import functools
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 math_ops
from tensorflow.python.platform import googletest
class SegmentReductionOpsTest(xla_test.XLATestCase):
"""Test cases for segment reduction ops."""
def _segmentReduction(self, op, data, indices, num_segments):
with self.cached_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 _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 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()
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