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# Copyright 2015 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.
# ==============================================================================
"""Functional tests for reduction ops."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from tensorflow.python.framework import ops
from tensorflow.python.ops import math_ops
from tensorflow.python.platform import test
class BaseReductionTest(test.TestCase):
def _tf_reduce(self, x, reduction_axes, keepdims):
raise NotImplementedError()
class BigReductionTest(BaseReductionTest):
"""Test reductions for sum and boolean all over a wide range of shapes."""
def _tf_reduce_max(self, x, reduction_axes, keepdims):
return math_ops.reduce_max(x, reduction_axes, keepdims)
def _tf_reduce_all(self, x, reduction_axes, keepdims):
return math_ops.reduce_all(x, reduction_axes, keepdims)
def _tf_reduce_mean(self, x, reduction_axes, keepdims):
return math_ops.reduce_mean(x, reduction_axes, keepdims)
def _tf_reduce_sum(self, x, reduction_axes, keepdims):
return math_ops.reduce_sum(x, reduction_axes, keepdims)
def testFloat32Sum(self):
# make sure we test all possible kernel invocations
# logic is the same for all ops, test just float32 for brevity
arr_ = np.ones([4097, 4097], dtype=np.float32)
for size_x in [
1, 2, 3, 4, 16, 17, 32, 33, 64, 65, 128, 131, 256, 263, 1024, 1025,
4096, 4097
]:
for size_y in [
1, 2, 3, 4, 16, 17, 32, 33, 64, 65, 128, 131, 256, 263, 1024, 1025,
4096, 4097
]:
arr = arr_[0:size_x, 0:size_y]
col_sum = np.ones([size_y], dtype=np.float32) * size_x
row_sum = np.ones([size_x], dtype=np.float32) * size_y
full_sum = np.ones([], dtype=np.float32) * size_x * size_y
with self.session(graph=ops.Graph(), use_gpu=True) as sess:
tf_row_sum = self._tf_reduce_sum(arr, 1, False)
tf_col_sum = self._tf_reduce_sum(arr, 0, False)
tf_full_sum = self._tf_reduce_sum(arr, [0, 1], False)
tf_out_row, tf_out_col, tf_out_full = sess.run(
[tf_row_sum, tf_col_sum, tf_full_sum])
self.assertAllClose(col_sum, tf_out_col)
self.assertAllClose(row_sum, tf_out_row)
self.assertAllClose(full_sum, tf_out_full)
arr_ = np.ones([130, 130, 130], dtype=np.float32)
for size_x in range(1, 130, 13):
for size_y in range(1, 130, 13):
for size_z in range(1, 130, 13):
arr = arr_[0:size_x, 0:size_y, 0:size_z]
sum_y = np.ones([size_x, size_z], dtype=np.float32)
sum_xz = np.ones([size_y], dtype=np.float32)
with self.session(graph=ops.Graph(), use_gpu=True) as sess:
tf_sum_xz = self._tf_reduce_mean(arr, [0, 2], False)
tf_sum_y = self._tf_reduce_mean(arr, 1, False)
tf_out_sum_xz, tf_out_sum_y = sess.run([tf_sum_xz, tf_sum_y])
self.assertAllClose(sum_y, tf_out_sum_y)
self.assertAllClose(sum_xz, tf_out_sum_xz)
def testFloat32Max(self):
# make sure we test all possible kernel invocations
# logic is the same for all ops, test just float32 for brevity
arr_ = np.random.uniform(
low=-3, high=-1, size=[4105, 4105]).astype(np.float32)
for size_x in [
1, 2, 3, 4, 16, 17, 32, 33, 64, 65, 128, 131, 256, 263, 1024, 1025,
4096, 4097
]:
for size_y in [
1, 2, 3, 4, 16, 17, 32, 33, 64, 65, 128, 131, 256, 263, 1024, 1025,
4096, 4097
]:
arr = arr_[0:size_x, 0:size_y]
col_max = np.max(arr, axis=0)
row_max = np.max(arr, axis=1)
full_max = np.max(col_max)
with self.session(graph=ops.Graph(), use_gpu=True) as sess:
tf_row_max = self._tf_reduce_max(arr, 1, False)
tf_col_max = self._tf_reduce_max(arr, 0, False)
tf_full_max = self._tf_reduce_max(arr, [0, 1], False)
tf_out_row, tf_out_col, tf_out_full = sess.run(
[tf_row_max, tf_col_max, tf_full_max])
self.assertAllClose(col_max, tf_out_col)
self.assertAllClose(row_max, tf_out_row)
self.assertAllClose(full_max, tf_out_full)
arr_ = np.random.uniform(
low=-3, high=-1, size=[130, 130, 130]).astype(np.float32)
for size_x in range(1, 130, 13):
for size_y in range(1, 130, 13):
for size_z in range(1, 130, 13):
arr = arr_[0:size_x, 0:size_y, 0:size_z]
sum_y = np.max(arr, axis=1)
sum_xz = np.max(arr, axis=(0, 2))
with self.session(graph=ops.Graph(), use_gpu=True) as sess:
tf_sum_xz = self._tf_reduce_max(arr, [0, 2], False)
tf_sum_y = self._tf_reduce_max(arr, 1, False)
tf_out_sum_xz, tf_out_sum_y = sess.run([tf_sum_xz, tf_sum_y])
self.assertAllClose(sum_y, tf_out_sum_y)
self.assertAllClose(sum_xz, tf_out_sum_xz)
def testBooleanAll(self):
# make sure we test all possible kernel invocations
# test operation where T(0) is not the identity
arr_ = np.ones([4097, 4097], dtype=np.bool)
for size_x in [
1, 2, 3, 4, 16, 17, 32, 33, 64, 65, 128, 131, 256, 263, 1024, 1025,
4096, 4097
]:
for size_y in [
1, 2, 3, 4, 16, 17, 32, 33, 64, 65, 128, 131, 256, 263, 1024, 1025,
4096, 4097
]:
arr = arr_[0:size_x, 0:size_y]
col_sum = np.ones([size_y], dtype=np.bool)
row_sum = np.ones([size_x], dtype=np.bool)
full_sum = np.ones([1], dtype=np.bool).reshape([])
with self.session(graph=ops.Graph(), use_gpu=True) as sess:
tf_row_sum = self._tf_reduce_all(arr, 1, False)
tf_col_sum = self._tf_reduce_all(arr, 0, False)
tf_full_sum = self._tf_reduce_all(arr, [0, 1], False)
tf_out_row, tf_out_col, tf_out_full = sess.run(
[tf_row_sum, tf_col_sum, tf_full_sum])
self.assertAllClose(col_sum, tf_out_col)
self.assertAllClose(row_sum, tf_out_row)
self.assertAllClose(full_sum, tf_out_full)
arr_ = np.ones([130, 130, 130], dtype=np.bool)
for size_x in range(1, 130, 13):
for size_y in range(1, 130, 13):
for size_z in range(1, 130, 13):
arr = arr_[0:size_x, 0:size_y, 0:size_z]
sum_y = np.ones([size_x, size_z], dtype=np.bool)
sum_xz = np.ones([size_y], dtype=np.bool)
with self.session(graph=ops.Graph(), use_gpu=True) as sess:
tf_sum_xz = self._tf_reduce_all(arr, [0, 2], False)
tf_sum_y = self._tf_reduce_all(arr, 1, False)
tf_out_sum_xz, tf_out_sum_y = sess.run([tf_sum_xz, tf_sum_y])
self.assertAllClose(sum_y, tf_out_sum_y)
self.assertAllClose(sum_xz, tf_out_sum_xz)
if __name__ == "__main__":
test.main()
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