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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 reduction operators."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from tensorflow.compiler.tests.xla_test import XLATestCase
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import errors_impl
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.platform import googletest
class ReduceOpsTest(XLATestCase):
def _testReduction(self, tf_reduce_fn, np_reduce_fn, dtype, test_inputs,
rtol=1e-4, atol=1e-4):
"""Tests that the output of 'tf_reduce_fn' matches numpy's output."""
for test_input in test_inputs:
with self.test_session() as sess:
with self.test_scope():
a = array_ops.placeholder(dtype)
index = array_ops.placeholder(dtypes.int32)
out = tf_reduce_fn(a, index)
result = sess.run(out, {a: test_input, index: [0]})
self.assertAllClose(result, np_reduce_fn(test_input, axis=0),
rtol=rtol, atol=atol)
result = sess.run(out, {a: test_input, index: [1]})
self.assertAllClose(result, np_reduce_fn(test_input, axis=1),
rtol=rtol, atol=atol)
result = sess.run(out, {a: test_input, index: [-1]})
self.assertAllClose(result, np_reduce_fn(test_input, axis=1),
rtol=rtol, atol=atol)
with self.assertRaisesWithPredicateMatch(
errors_impl.InvalidArgumentError, 'Invalid reduction dim'):
sess.run(out, {a: test_input, index: [-33]})
with self.assertRaisesWithPredicateMatch(
errors_impl.InvalidArgumentError, 'Invalid reduction dim'):
sess.run(out, {a: test_input, index: [2]})
FLOAT_DATA = [
np.zeros(shape=(2, 0)),
np.zeros(shape=(0, 30)),
np.arange(1, 7).reshape(2, 3),
np.arange(-10, -4).reshape(2, 3),
np.arange(-4, 2).reshape(2, 3),
]
NONEMPTY_FLOAT_DATA = [
np.arange(1, 7).reshape(2, 3),
np.arange(-10, -4).reshape(2, 3),
np.arange(-4, 2).reshape(2, 3),
]
BOOL_DATA = [
np.array([], dtype=np.bool).reshape(2, 0),
np.array([], dtype=np.bool).reshape(0, 3),
np.array([[False, True, False], [True, True, False]]),
]
def testReduceSum(self):
self._testReduction(math_ops.reduce_sum, np.sum, np.float32,
self.FLOAT_DATA)
def testReduceProd(self):
self._testReduction(math_ops.reduce_prod, np.prod, np.float32,
self.FLOAT_DATA)
def testReduceMin(self):
def reference_min(inp, axis):
"""Wrapper around np.amin that returns +infinity for an empty input."""
if inp.shape[axis] == 0:
return np.full(inp.shape[0:axis] + inp.shape[axis + 1:], float('inf'))
return np.amin(inp, axis)
self._testReduction(math_ops.reduce_min, reference_min, np.float32,
self.FLOAT_DATA)
def testReduceMax(self):
def reference_max(inp, axis):
"""Wrapper around np.amax that returns -infinity for an empty input."""
if inp.shape[axis] == 0:
return np.full(inp.shape[0:axis] + inp.shape[axis + 1:], float('-inf'))
return np.amax(inp, axis)
self._testReduction(math_ops.reduce_max, reference_max, np.float32,
self.FLOAT_DATA)
def testReduceMean(self):
# TODO(phawkins): mean on XLA currently returns 0 instead of NaN when
# reducing across zero inputs.
self._testReduction(math_ops.reduce_mean, np.mean, np.float32,
self.NONEMPTY_FLOAT_DATA)
def testReduceAll(self):
self._testReduction(math_ops.reduce_all, np.all, np.bool, self.BOOL_DATA)
def testReduceAny(self):
self._testReduction(math_ops.reduce_any, np.any, np.bool, self.BOOL_DATA)
if __name__ == '__main__':
googletest.main()
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