# Copyright 2016 Google Inc. 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.ops.check_ops."""

from __future__ import absolute_import

from __future__ import division

from __future__ import print_function

import tensorflow as tf

class AssertLessTest(tf.test.TestCase):

def test_raises_when_equal(self):

with self.test_session():

small = tf.constant([1, 2], name="small")

with tf.control_dependencies([tf.assert_less(small, small)]):

out = tf.identity(small)

with self.assertRaisesOpError("small.*small"):

out.eval()

def test_raises_when_greater(self):

with self.test_session():

small = tf.constant([1, 2], name="small")

big = tf.constant([3, 4], name="big")

with tf.control_dependencies([tf.assert_less(big, small)]):

out = tf.identity(small)

with self.assertRaisesOpError("big.*small"):

out.eval()

def test_doesnt_raise_when_less(self):

with self.test_session():

small = tf.constant([3, 1], name="small")

big = tf.constant([4, 2], name="big")

with tf.control_dependencies([tf.assert_less(small, big)]):

out = tf.identity(small)

out.eval()

def test_doesnt_raise_when_less_and_broadcastable_shapes(self):

with self.test_session():

small = tf.constant([1], name="small")

big = tf.constant([3, 2], name="big")

with tf.control_dependencies([tf.assert_less(small, big)]):

out = tf.identity(small)

out.eval()

def test_raises_when_less_but_non_broadcastable_shapes(self):

with self.test_session():

small = tf.constant([1, 1, 1], name="small")

big = tf.constant([3, 2], name="big")

with self.assertRaisesRegexp(ValueError, "broadcast"):

with tf.control_dependencies([tf.assert_less(small, big)]):

out = tf.identity(small)

out.eval()

def test_doesnt_raise_when_both_empty(self):

with self.test_session():

larry = tf.constant([])

curly = tf.constant([])

with tf.control_dependencies([tf.assert_less(larry, curly)]):

out = tf.identity(larry)

out.eval()

class AssertLessEqualTest(tf.test.TestCase):

def test_doesnt_raise_when_equal(self):

with self.test_session():

small = tf.constant([1, 2], name="small")

with tf.control_dependencies([tf.assert_less_equal(small, small)]):

out = tf.identity(small)

out.eval()

def test_raises_when_greater(self):

with self.test_session():

small = tf.constant([1, 2], name="small")

big = tf.constant([3, 4], name="big")

with tf.control_dependencies([tf.assert_less_equal(big, small)]):

out = tf.identity(small)

with self.assertRaisesOpError("big.*small"):

out.eval()

def test_doesnt_raise_when_less_equal(self):

with self.test_session():

small = tf.constant([1, 2], name="small")

big = tf.constant([3, 2], name="big")

with tf.control_dependencies([tf.assert_less_equal(small, big)]):

out = tf.identity(small)

out.eval()

def test_doesnt_raise_when_less_equal_and_broadcastable_shapes(self):

with self.test_session():

small = tf.constant([1], name="small")

big = tf.constant([3, 1], name="big")

with tf.control_dependencies([tf.assert_less_equal(small, big)]):

out = tf.identity(small)

out.eval()

def test_raises_when_less_equal_but_non_broadcastable_shapes(self):

with self.test_session():

small = tf.constant([1, 1, 1], name="small")

big = tf.constant([3, 1], name="big")

with self.assertRaisesRegexp(ValueError, "broadcast"):

with tf.control_dependencies([tf.assert_less_equal(small, big)]):

out = tf.identity(small)

out.eval()

def test_doesnt_raise_when_both_empty(self):

with self.test_session():

larry = tf.constant([])

curly = tf.constant([])

with tf.control_dependencies([tf.assert_less_equal(larry, curly)]):

out = tf.identity(larry)

out.eval()

class AssertNegativeTest(tf.test.TestCase):

def test_doesnt_raise_when_negative(self):

with self.test_session():

frank = tf.constant([-1, -2], name="frank")

with tf.control_dependencies([tf.assert_negative(frank)]):

out = tf.identity(frank)

out.eval()

def test_raises_when_positive(self):

with self.test_session():

doug = tf.constant([1, 2], name="doug")

with tf.control_dependencies([tf.assert_negative(doug)]):

out = tf.identity(doug)

with self.assertRaisesOpError("doug"):

out.eval()

def test_raises_when_zero(self):

with self.test_session():

claire = tf.constant([0], name="claire")

with tf.control_dependencies([tf.assert_negative(claire)]):

out = tf.identity(claire)

with self.assertRaisesOpError("claire"):

out.eval()

def test_empty_tensor_doesnt_raise(self):

# A tensor is negative when it satisfies:

# For every element x_i in x, x_i < 0

# and an empty tensor has no elements, so this is trivially satisfied.

# This is standard set theory.

with self.test_session():

empty = tf.constant([], name="empty")

with tf.control_dependencies([tf.assert_negative(empty)]):

out = tf.identity(empty)

out.eval()

class AssertPositiveTest(tf.test.TestCase):

def test_raises_when_negative(self):

with self.test_session():

freddie = tf.constant([-1, -2], name="freddie")

with tf.control_dependencies([tf.assert_positive(freddie)]):

out = tf.identity(freddie)

with self.assertRaisesOpError("freddie"):

out.eval()

def test_doesnt_raise_when_positive(self):

with self.test_session():

remmy = tf.constant([1, 2], name="remmy")

with tf.control_dependencies([tf.assert_positive(remmy)]):

out = tf.identity(remmy)

out.eval()

def test_raises_when_zero(self):

with self.test_session():

meechum = tf.constant([0], name="meechum")

with tf.control_dependencies([tf.assert_positive(meechum)]):

out = tf.identity(meechum)

with self.assertRaisesOpError("meechum"):

out.eval()

def test_empty_tensor_doesnt_raise(self):

# A tensor is positive when it satisfies:

# For every element x_i in x, x_i > 0

# and an empty tensor has no elements, so this is trivially satisfied.

# This is standard set theory.

with self.test_session():

empty = tf.constant([], name="empty")

with tf.control_dependencies([tf.assert_positive(empty)]):

out = tf.identity(empty)

out.eval()

class AssertRankTest(tf.test.TestCase):

def test_rank_zero_tensor_raises_if_rank_too_small_static_rank(self):

with self.test_session():

tensor = tf.constant(1, name="my_tensor")

desired_rank = 1

with self.assertRaisesRegexp(ValueError, "my_tensor.*rank"):

with tf.control_dependencies([tf.assert_rank(tensor, desired_rank)]):

tf.identity(tensor).eval()

def test_rank_zero_tensor_raises_if_rank_too_small_dynamic_rank(self):

with self.test_session():

tensor = tf.placeholder(tf.float32, name="my_tensor")

desired_rank = 1

with tf.control_dependencies([tf.assert_rank(tensor, desired_rank)]):

with self.assertRaisesOpError("my_tensor.*rank"):

tf.identity(tensor).eval(feed_dict={tensor: 0})

def test_rank_zero_tensor_doesnt_raise_if_rank_just_right_static_rank(self):

with self.test_session():

tensor = tf.constant(1, name="my_tensor")

desired_rank = 0

with tf.control_dependencies([tf.assert_rank(tensor, desired_rank)]):

tf.identity(tensor).eval()

def test_rank_zero_tensor_doesnt_raise_if_rank_just_right_dynamic_rank(self):

with self.test_session():

tensor = tf.placeholder(tf.float32, name="my_tensor")

desired_rank = 0

with tf.control_dependencies([tf.assert_rank(tensor, desired_rank)]):

tf.identity(tensor).eval(feed_dict={tensor: 0})

def test_rank_one_tensor_raises_if_rank_too_large_static_rank(self):

with self.test_session():

tensor = tf.constant([1, 2], name="my_tensor")

desired_rank = 0

with self.assertRaisesRegexp(ValueError, "my_tensor.*rank"):

with tf.control_dependencies([tf.assert_rank(tensor, desired_rank)]):

tf.identity(tensor).eval()

def test_rank_one_tensor_raises_if_rank_too_large_dynamic_rank(self):

with self.test_session():

tensor = tf.placeholder(tf.float32, name="my_tensor")

desired_rank = 0

with tf.control_dependencies([tf.assert_rank(tensor, desired_rank)]):

with self.assertRaisesOpError("my_tensor.*rank"):

tf.identity(tensor).eval(feed_dict={tensor: [1, 2]})

def test_rank_one_tensor_doesnt_raise_if_rank_just_right_static_rank(self):

with self.test_session():

tensor = tf.constant([1, 2], name="my_tensor")

desired_rank = 1

with tf.control_dependencies([tf.assert_rank(tensor, desired_rank)]):

tf.identity(tensor).eval()

def test_rank_one_tensor_doesnt_raise_if_rank_just_right_dynamic_rank(self):

with self.test_session():

tensor = tf.placeholder(tf.float32, name="my_tensor")

desired_rank = 1

with tf.control_dependencies([tf.assert_rank(tensor, desired_rank)]):

tf.identity(tensor).eval(feed_dict={tensor: [1, 2]})

def test_rank_one_tensor_raises_if_rank_too_small_static_rank(self):

with self.test_session():

tensor = tf.constant([1, 2], name="my_tensor")

desired_rank = 2

with self.assertRaisesRegexp(ValueError, "my_tensor.*rank"):

with tf.control_dependencies([tf.assert_rank(tensor, desired_rank)]):

tf.identity(tensor).eval()

def test_rank_one_tensor_raises_if_rank_too_small_dynamic_rank(self):

with self.test_session():

tensor = tf.placeholder(tf.float32, name="my_tensor")

desired_rank = 2

with tf.control_dependencies([tf.assert_rank(tensor, desired_rank)]):

with self.assertRaisesOpError("my_tensor.*rank"):

tf.identity(tensor).eval(feed_dict={tensor: [1, 2]})

class AssertRankAtLeastTest(tf.test.TestCase):

def test_rank_zero_tensor_raises_if_rank_too_small_static_rank(self):

with self.test_session():

tensor = tf.constant(1, name="my_tensor")

desired_rank = 1

with self.assertRaisesRegexp(ValueError, "my_tensor.*rank"):

with tf.control_dependencies([tf.assert_rank_at_least(tensor,

desired_rank)]):

tf.identity(tensor).eval()

def test_rank_zero_tensor_raises_if_rank_too_small_dynamic_rank(self):

with self.test_session():

tensor = tf.placeholder(tf.float32, name="my_tensor")

desired_rank = 1

with tf.control_dependencies([tf.assert_rank_at_least(tensor,

desired_rank)]):

with self.assertRaisesOpError("my_tensor.*rank"):

tf.identity(tensor).eval(feed_dict={tensor: 0})

def test_rank_zero_tensor_doesnt_raise_if_rank_just_right_static_rank(self):

with self.test_session():

tensor = tf.constant(1, name="my_tensor")

desired_rank = 0

with tf.control_dependencies([tf.assert_rank_at_least(tensor,

desired_rank)]):

tf.identity(tensor).eval()

def test_rank_zero_tensor_doesnt_raise_if_rank_just_right_dynamic_rank(self):

with self.test_session():

tensor = tf.placeholder(tf.float32, name="my_tensor")

desired_rank = 0

with tf.control_dependencies([tf.assert_rank_at_least(tensor,

desired_rank)]):

tf.identity(tensor).eval(feed_dict={tensor: 0})

def test_rank_one_ten_doesnt_raise_raise_if_rank_too_large_static_rank(self):

with self.test_session():

tensor = tf.constant([1, 2], name="my_tensor")

desired_rank = 0

with tf.control_dependencies([tf.assert_rank_at_least(tensor,

desired_rank)]):

tf.identity(tensor).eval()

def test_rank_one_ten_doesnt_raise_if_rank_too_large_dynamic_rank(self):

with self.test_session():

tensor = tf.placeholder(tf.float32, name="my_tensor")

desired_rank = 0

with tf.control_dependencies([tf.assert_rank_at_least(tensor,

desired_rank)]):

tf.identity(tensor).eval(feed_dict={tensor: [1, 2]})

def test_rank_one_tensor_doesnt_raise_if_rank_just_right_static_rank(self):

with self.test_session():

tensor = tf.constant([1, 2], name="my_tensor")

desired_rank = 1

with tf.control_dependencies([tf.assert_rank_at_least(tensor,

desired_rank)]):

tf.identity(tensor).eval()

def test_rank_one_tensor_doesnt_raise_if_rank_just_right_dynamic_rank(self):

with self.test_session():

tensor = tf.placeholder(tf.float32, name="my_tensor")

desired_rank = 1

with tf.control_dependencies([tf.assert_rank_at_least(tensor,

desired_rank)]):

tf.identity(tensor).eval(feed_dict={tensor: [1, 2]})

def test_rank_one_tensor_raises_if_rank_too_small_static_rank(self):

with self.test_session():

tensor = tf.constant([1, 2], name="my_tensor")

desired_rank = 2

with self.assertRaisesRegexp(ValueError, "my_tensor.*rank"):

with tf.control_dependencies([tf.assert_rank_at_least(tensor,

desired_rank)]):

tf.identity(tensor).eval()

def test_rank_one_tensor_raises_if_rank_too_small_dynamic_rank(self):

with self.test_session():

tensor = tf.placeholder(tf.float32, name="my_tensor")

desired_rank = 2

with tf.control_dependencies([tf.assert_rank_at_least(tensor,

desired_rank)]):

with self.assertRaisesOpError("my_tensor.*rank"):

tf.identity(tensor).eval(feed_dict={tensor: [1, 2]})

class AssertNonNegativeTest(tf.test.TestCase):

def test_raises_when_negative(self):

with self.test_session():

zoe = tf.constant([-1, -2], name="zoe")

with tf.control_dependencies([tf.assert_non_negative(zoe)]):

out = tf.identity(zoe)

with self.assertRaisesOpError("zoe"):

out.eval()

def test_doesnt_raise_when_zero_and_positive(self):

with self.test_session():

lucas = tf.constant([0, 2], name="lucas")

with tf.control_dependencies([tf.assert_non_negative(lucas)]):

out = tf.identity(lucas)

out.eval()

def test_empty_tensor_doesnt_raise(self):

# A tensor is non-negative when it satisfies:

# For every element x_i in x, x_i >= 0

# and an empty tensor has no elements, so this is trivially satisfied.

# This is standard set theory.

with self.test_session():

empty = tf.constant([], name="empty")

with tf.control_dependencies([tf.assert_non_negative(empty)]):

out = tf.identity(empty)

out.eval()

class AssertNonPositiveTest(tf.test.TestCase):

def test_doesnt_raise_when_zero_and_negative(self):

with self.test_session():

tom = tf.constant([0, -2], name="tom")

with tf.control_dependencies([tf.assert_non_positive(tom)]):

out = tf.identity(tom)

out.eval()

def test_raises_when_positive(self):

with self.test_session():

rachel = tf.constant([0, 2], name="rachel")

with tf.control_dependencies([tf.assert_non_positive(rachel)]):

out = tf.identity(rachel)

with self.assertRaisesOpError("rachel"):

out.eval()

def test_empty_tensor_doesnt_raise(self):

# A tensor is non-positive when it satisfies:

# For every element x_i in x, x_i <= 0

# and an empty tensor has no elements, so this is trivially satisfied.

# This is standard set theory.

with self.test_session():

empty = tf.constant([], name="empty")

with tf.control_dependencies([tf.assert_non_positive(empty)]):

out = tf.identity(empty)

out.eval()

class IsStrictlyIncreasingTest(tf.test.TestCase):

def test_constant_tensor_is_not_strictly_increasing(self):

with self.test_session():

self.assertFalse(tf.is_strictly_increasing([1, 1, 1]).eval())

def test_decreasing_tensor_is_not_strictly_increasing(self):

with self.test_session():

self.assertFalse(tf.is_strictly_increasing([1, 0, -1]).eval())

def test_2d_decreasing_tensor_is_not_strictly_increasing(self):

with self.test_session():

self.assertFalse(tf.is_strictly_increasing([[1, 3], [2, 4]]).eval())

def test_increasing_tensor_is_increasing(self):

with self.test_session():

self.assertTrue(tf.is_strictly_increasing([1, 2, 3]).eval())

def test_increasing_rank_two_tensor(self):

with self.test_session():

self.assertTrue(tf.is_strictly_increasing([[-1, 2], [3, 4]]).eval())

def test_tensor_with_one_element_is_strictly_increasing(self):

with self.test_session():

self.assertTrue(tf.is_strictly_increasing([1]).eval())

def test_empty_tensor_is_strictly_increasing(self):

with self.test_session():

self.assertTrue(tf.is_strictly_increasing([]).eval())

class IsNonDecreasingTest(tf.test.TestCase):

def test_constant_tensor_is_non_decreasing(self):

with self.test_session():

self.assertTrue(tf.is_non_decreasing([1, 1, 1]).eval())

def test_decreasing_tensor_is_not_non_decreasing(self):

with self.test_session():

self.assertFalse(tf.is_non_decreasing([3, 2, 1]).eval())

def test_2d_decreasing_tensor_is_not_non_decreasing(self):

with self.test_session():

self.assertFalse(tf.is_non_decreasing([[1, 3], [2, 4]]).eval())

def test_increasing_rank_one_tensor_is_non_decreasing(self):

with self.test_session():

self.assertTrue(tf.is_non_decreasing([1, 2, 3]).eval())

def test_increasing_rank_two_tensor(self):

with self.test_session():

self.assertTrue(tf.is_non_decreasing([[-1, 2], [3, 3]]).eval())

def test_tensor_with_one_element_is_non_decreasing(self):

with self.test_session():

self.assertTrue(tf.is_non_decreasing([1]).eval())

def test_empty_tensor_is_non_decreasing(self):

with self.test_session():

self.assertTrue(tf.is_non_decreasing([]).eval())

if __name__ == "__main__":

tf.test.main()