import numpy as np
from cleverhans.utils_tf import clip_eta
from cleverhans.devtools.checks import CleverHansTest
import tensorflow as tf
class TestUtils(CleverHansTest):
def setUp(self):
super(TestUtils, self).setUp()
self.sess = tf.Session()
def test_clip_eta_norm_0(self):
# Test that `clip_eta` still works when the norm of `eta` is
# zero. This used to cause a divide by zero for ord 1 and ord
# 2.
eta = tf.zeros((5, 3))
assert eta.dtype == tf.float32, eta.dtype
eps = .25
for ord_arg in [np.inf, 1, 2]:
clipped = clip_eta(eta, ord_arg, eps)
clipped = self.sess.run(clipped)
assert not np.any(np.isinf(clipped))
assert not np.any(np.isnan(clipped)), (ord_arg, clipped)
def test_clip_eta_goldilocks(self):
# Test that the clipping handles perturbations that are
# too small, just right, and too big correctly
eta = tf.constant([[2.], [3.], [4.]])
assert eta.dtype == tf.float32, eta.dtype
eps = 3.
for ord_arg in [np.inf, 1, 2]:
for sign in [-1., 1.]:
clipped = clip_eta(eta * sign, ord_arg, eps)
clipped_value = self.sess.run(clipped)
gold = sign * np.array([[2.], [3.], [3.]])
self.assertClose(clipped_value, gold)
grad, = tf.gradients(clipped, eta)
grad_value = self.sess.run(grad)
# Note: the second 1. is debatable (the left-sided derivative
# and the right-sided derivative do not match, so formally
# the derivative is not defined). This test makes sure that
# we at least handle this oddity consistently across all the
# argument values we test
gold = sign * np.array([[1.], [1.], [0.]])
assert np.allclose(grad_value, gold)
