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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.
# ==============================================================================
"""Tests for tensorflow.contrib.reduce_slice_ops."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import unittest
from tensorflow.contrib.reduce_slice_ops.python.ops import reduce_slice_ops
from tensorflow.python.framework.test_util import TensorFlowTestCase
from tensorflow.python.platform import googletest
class ReduceSliceTest(TensorFlowTestCase):
def testReduceSliceSum1D(self):
x = np.array([1, 40, 700], dtype=np.int32)
indices = np.array([[0, 1], [0, 3], [1, 2], [1, 3], [0, 2]], dtype=np.int32)
result = np.array([1, 741, 40, 740, 41], dtype=np.int32)
with self.test_session(use_gpu=True):
y_tf = reduce_slice_ops.reduce_slice_sum(x, indices, 0).eval()
self.assertAllEqual(y_tf, result)
def testReduceSliceSum2D(self):
x = np.array([[1, 2, 3], [40, 50, 60], [700, 800, 900]], dtype=np.int32)
indices = np.array([[0, 1], [0, 3], [1, 2], [1, 3], [0, 2]], dtype=np.int32)
result = np.array(
[[1, 2, 3], [741, 852, 963], [40, 50, 60], [740, 850, 960],
[41, 52, 63]],
dtype=np.int32)
with self.test_session(use_gpu=True):
y_tf = reduce_slice_ops.reduce_slice_sum(x, indices, 0).eval()
self.assertAllEqual(y_tf, result)
def testReduceSliceSum3D(self):
x = np.array(
[[[1, 2], [3, 4]], [[50, 60], [70, 80]], [[600, 700], [800, 900]]],
dtype=np.int32)
indices = np.array([[0, 1], [0, 3], [1, 2], [1, 3], [0, 2]], dtype=np.int32)
result = np.array(
[[[1, 2], [3, 4]], [[651, 762], [873, 984]], [[50, 60], [70, 80]],
[[650, 760], [870, 980]], [[51, 62], [73, 84]]],
dtype=np.int32)
with self.test_session(use_gpu=True):
y_tf = reduce_slice_ops.reduce_slice_sum(x, indices, 0).eval()
self.assertAllEqual(y_tf, result)
def testReduceSliceSumAxis1(self):
x = np.transpose(
np.array([[1, 2, 3], [40, 50, 60], [700, 800, 900]], dtype=np.int32))
indices = np.array([[0, 1], [0, 3], [1, 2], [1, 3], [0, 2]], dtype=np.int32)
result = np.transpose(
np.array(
[[1, 2, 3], [741, 852, 963], [40, 50, 60], [740, 850, 960],
[41, 52, 63]],
dtype=np.int32))
with self.test_session(use_gpu=True):
y_tf = reduce_slice_ops.reduce_slice_sum(x, indices, 1).eval()
self.assertAllEqual(y_tf, result)
def testReduceSliceSum1DIndices(self):
x = np.array(
[[1, 2, 3], [40, 50, 60], [700, 800, 900], [1000, 2000, 3000],
[40000, 50000, 60000]],
dtype=np.int32)
indices = np.array([0, 0, 2, 5], dtype=np.int32)
result = np.array(
[[0, 0, 0], [41, 52, 63], [41700, 52800, 63900]], dtype=np.int32)
with self.test_session(use_gpu=True):
y_tf = reduce_slice_ops.reduce_slice_sum(x, indices, 0).eval()
self.assertAllEqual(y_tf, result)
def testReduceSliceProd(self):
x = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=np.int32)
indices = np.array([[0, 1], [0, 3], [1, 2], [1, 3], [0, 2]], dtype=np.int32)
result = np.array(
[[1, 2, 3], [28, 80, 162], [4, 5, 6], [28, 40, 54], [4, 10, 18]],
dtype=np.int32)
with self.test_session(use_gpu=True):
y_tf = reduce_slice_ops.reduce_slice_prod(x, indices, 0).eval()
self.assertAllEqual(y_tf, result)
def testReduceSliceMax(self):
x = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=np.int32)
indices = np.array([[0, 1], [0, 3], [1, 2], [1, 3], [0, 2]], dtype=np.int32)
result = np.array(
[[1, 2, 3], [7, 8, 9], [4, 5, 6], [7, 8, 9], [4, 5, 6]], dtype=np.int32)
with self.test_session(use_gpu=True):
y_tf = reduce_slice_ops.reduce_slice_max(x, indices, 0).eval()
self.assertAllEqual(y_tf, result)
def testReduceSliceMin(self):
x = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=np.int32)
indices = np.array([[0, 1], [0, 3], [1, 2], [1, 3], [0, 2]], dtype=np.int32)
result = np.array(
[[1, 2, 3], [1, 2, 3], [4, 5, 6], [4, 5, 6], [1, 2, 3]], dtype=np.int32)
with self.test_session(use_gpu=True):
y_tf = reduce_slice_ops.reduce_slice_min(x, indices, 0).eval()
self.assertAllEqual(y_tf, result)
def testReduceSliceEmptyDataRows(self):
x = np.empty((0, 1, 2, 3, 4, 5, 6), dtype=np.int32)
indices = np.array([[0, 1], [0, 3], [1, 2], [1, 3], [0, 2]], dtype=np.int32)
result = np.zeros((5, 1, 2, 3, 4, 5, 6), dtype=np.int32)
with self.test_session(use_gpu=True):
y_tf = reduce_slice_ops.reduce_slice_sum(x, indices, 0).eval()
self.assertAllEqual(y_tf, result)
def testReduceSliceEmptyDataCols(self):
x = np.empty((100, 0, 2, 3, 4, 5, 6), dtype=np.int32)
indices = np.array([[0, 1], [0, 3], [1, 2], [1, 3], [0, 2]], dtype=np.int32)
result = np.empty((5, 0, 2, 3, 4, 5, 6), dtype=np.int32)
with self.test_session(use_gpu=True):
y_tf = reduce_slice_ops.reduce_slice_sum(x, indices, 0).eval()
self.assertAllEqual(y_tf, result)
def testReduceSliceEmptyIndicesRows(self):
x = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=np.int32)
indices = np.empty((0, 2), dtype=np.int32)
result = np.empty((0, 3), dtype=np.int32)
with self.test_session(use_gpu=True):
y_tf = reduce_slice_ops.reduce_slice_sum(x, indices, 0).eval()
self.assertAllEqual(y_tf, result)
def testReduceSliceEmpty0Indices1D(self):
x = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=np.int32)
indices = np.empty((0,), dtype=np.int32)
result = np.empty((0, 3), dtype=np.int32)
with self.test_session(use_gpu=True):
y_tf = reduce_slice_ops.reduce_slice_sum(x, indices, 0).eval()
self.assertAllEqual(y_tf, result)
def testReduceSliceEmpty1Indices1D(self):
x = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=np.int32)
indices = np.array([0], dtype=np.int32)
result = np.empty((0, 3), dtype=np.int32)
with self.test_session(use_gpu=True):
y_tf = reduce_slice_ops.reduce_slice_sum(x, indices, 0).eval()
self.assertAllEqual(y_tf, result)
if __name__ == "__main__":
googletest.main()
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