# coding=utf-8
# Copyright 2019 The Interval Bound Propagation Authors.
#
# 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 symbolic bounds."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from absl.testing import parameterized
import interval_bound_propagation as ibp
import numpy as np
import sonnet as snt
import tensorflow.compat.v1 as tf
class SymbolicBoundsTest(parameterized.TestCase, tf.test.TestCase):
def testConvertSymbolicBounds(self):
z = tf.constant([[1, 2, 3, 4]], dtype=tf.float32)
z = tf.reshape(z, [1, 2, 2])
b = ibp.SymbolicBounds.convert(z)
for l in (b.lower, b.upper):
self.assertEqual([1, 4, 2, 2], l.w.shape.as_list())
self.assertEqual([1, 2, 2], l.b.shape.as_list())
self.assertEqual([1, 4], l.lower.shape.as_list())
self.assertEqual([1, 4], l.upper.shape.as_list())
def testFCSymbolicBounds(self):
m = snt.Linear(1, initializers={
'w': tf.constant_initializer(1.),
'b': tf.constant_initializer(2.),
})
z = tf.constant([[1, 2, 3]], dtype=tf.float32)
m(z) # Connect to create weights.
m = ibp.LinearFCWrapper(m)
input_bounds = ibp.IntervalBounds(z - 1., z + 1.)
input_bounds = ibp.SymbolicBounds.convert(input_bounds)
output_bounds = m.propagate_bounds(input_bounds)
concrete_bounds = ibp.IntervalBounds.convert(output_bounds)
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
l, u, cl, cu = sess.run([output_bounds.lower, output_bounds.upper,
concrete_bounds.lower, concrete_bounds.upper])
self.assertTrue(np.all(l.w == 1.))
self.assertTrue(np.all(l.b == 2.))
self.assertAlmostEqual([[0, 1, 2]], l.lower.tolist())
self.assertAlmostEqual([[2, 3, 4]], l.upper.tolist())
self.assertTrue(np.all(u.w == 1.))
self.assertTrue(np.all(u.b == 2.))
self.assertAlmostEqual([[0, 1, 2]], u.lower.tolist())
self.assertAlmostEqual([[2, 3, 4]], u.upper.tolist())
cl = cl.item()
cu = cu.item()
self.assertAlmostEqual(5., cl)
self.assertAlmostEqual(11., cu)
def testConv2dSymbolicBounds(self):
m = snt.Conv2D(
output_channels=1,
kernel_shape=(2, 2),
padding='VALID',
stride=1,
use_bias=True,
initializers={
'w': tf.constant_initializer(1.),
'b': tf.constant_initializer(2.),
})
z = tf.constant([1, 2, 3, 4], dtype=tf.float32)
z = tf.reshape(z, [1, 2, 2, 1])
m(z) # Connect to create weights.
m = ibp.LinearConv2dWrapper(m)
input_bounds = ibp.IntervalBounds(z - 1., z + 1.)
input_bounds = ibp.SymbolicBounds.convert(input_bounds)
output_bounds = m.propagate_bounds(input_bounds)
output_bounds = ibp.IntervalBounds.convert(output_bounds)
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
l, u = sess.run([output_bounds.lower, output_bounds.upper])
l = l.item()
u = u.item()
self.assertAlmostEqual(8., l)
self.assertAlmostEqual(16., u)
def testConv1dSymbolicBounds(self):
m = snt.Conv1D(
output_channels=1,
kernel_shape=(2),
padding='VALID',
stride=1,
use_bias=True,
initializers={
'w': tf.constant_initializer(1.),
'b': tf.constant_initializer(3.),
})
z = tf.constant([3, 4], dtype=tf.float32)
z = tf.reshape(z, [1, 2, 1])
m(z) # Connect to create weights.
m = ibp.LinearConv1dWrapper(m)
input_bounds = ibp.IntervalBounds(z - 1., z + 1.)
input_bounds = ibp.SymbolicBounds.convert(input_bounds)
output_bounds = m.propagate_bounds(input_bounds)
output_bounds = ibp.IntervalBounds.convert(output_bounds)
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
l, u = sess.run([output_bounds.lower, output_bounds.upper])
l = l.item()
u = u.item()
self.assertAlmostEqual(8., l)
self.assertAlmostEqual(12., u)
def testReluSymbolicBounds(self):
m = tf.nn.relu
z = tf.constant([[-2, 3]], dtype=tf.float32)
m = ibp.IncreasingMonotonicWrapper(m)
input_bounds = ibp.IntervalBounds(z - 1., z + 1.)
input_bounds = ibp.SymbolicBounds.convert(input_bounds)
output_bounds = m.propagate_bounds(input_bounds)
output_bounds = ibp.IntervalBounds.convert(output_bounds)
with self.test_session() as sess:
l, u = sess.run([output_bounds.lower, output_bounds.upper])
self.assertAlmostEqual([[0., 2.]], l.tolist())
self.assertAlmostEqual([[0., 4.]], u.tolist())
if __name__ == '__main__':
tf.test.main()
