def input_bounds(inputs,
delta,
lower_bound=0.,
upper_bound=1.,
preprocess_fn=None):
"""Calculates interval bounds on the network inputs.
Args:
inputs: 2D tensor of shape (batch_size, input_size), or 4D tensor of
shape (batch_size, height, width, channels), of input examples.
delta: Permitted perturbation on each input.
lower_bound: Scalar - smallest permissible input (pixel) value.
upper_bound: Scalar - largest permissible input (pixel) value.
preprocess_fn: Optional function mapping tensor to tensor
performing pre-processing on the raw inputs.
Returns:
`IntervalBounds` for the inputs, relative to `inputs`.
"""
# Input range, according to permitted perturbation radius.
if preprocess_fn:
lb = preprocess_fn(tf.maximum(inputs - delta, lower_bound)) - inputs
ub = preprocess_fn(tf.minimum(inputs + delta, upper_bound)) - inputs
else:
lb = tf.maximum(-delta, lower_bound - inputs)
ub = tf.minimum(delta, upper_bound - inputs)
return ibp.RelativeIntervalBounds(lb, ub, inputs)
def test_batchnorm_bounds(self, batchnorm_class, dtype, tol, is_training):
batch_size = 11
input_size = 7
output_size = 5
lb_in = tf.random_normal(dtype=dtype, shape=(batch_size, input_size))
ub_in = tf.random_normal(dtype=dtype, shape=(batch_size, input_size))
lb_in, ub_in = tf.minimum(lb_in, ub_in), tf.maximum(lb_in, ub_in)
nominal = tf.random_normal(dtype=dtype, shape=(batch_size, input_size))
# Linear layer.
w = tf.random_normal(dtype=dtype, shape=(input_size, output_size))
b = tf.random_normal(dtype=dtype, shape=(output_size, ))
# Batch norm layer.
epsilon = 1.e-2
bn_initializers = {
'beta': tf.random_normal_initializer(),
'gamma': tf.random_uniform_initializer(.1, 3.),
'moving_mean': tf.random_normal_initializer(),
'moving_variance': tf.random_uniform_initializer(.1, 3.)
}
batchnorm_module = batchnorm_class(offset=True,
scale=True,
eps=epsilon,
initializers=bn_initializers)
# Connect the batchnorm module to the graph.
batchnorm_module(tf.random_normal(dtype=dtype,
shape=(batch_size, output_size)),
is_training=is_training)
bounds_in = ibp.RelativeIntervalBounds(lb_in - nominal,
ub_in - nominal, nominal)
bounds_out = bounds_in.apply_linear(None, w, b)
bounds_out = bounds_out.apply_batch_norm(
batchnorm_module, batchnorm_module.mean if is_training else
batchnorm_module.moving_mean, batchnorm_module.variance
if is_training else batchnorm_module.moving_variance,
batchnorm_module.gamma, batchnorm_module.beta, epsilon)
lb_out, ub_out = bounds_out.lower, bounds_out.upper
# Separately, calculate dual objective by adjusting the linear layer.
wn, bn = layer_utils.combine_with_batchnorm(w, b, batchnorm_module)
bounds_out_lin = bounds_in.apply_linear(None, wn, bn)
lb_out_lin, ub_out_lin = bounds_out_lin.lower, bounds_out_lin.upper
init_op = tf.global_variables_initializer()
with self.test_session() as session:
session.run(init_op)
(lb_out_val, ub_out_val, lb_out_lin_val,
ub_out_lin_val) = session.run(
(lb_out, ub_out, lb_out_lin, ub_out_lin))
self.assertAllClose(lb_out_val, lb_out_lin_val, atol=tol, rtol=tol)
self.assertAllClose(ub_out_val, ub_out_lin_val, atol=tol, rtol=tol)
def test_linear_bounds(self, dtype, tol):
w = tf.constant([[1.0, 2.0, 3.0], [4.0, -5.0, 6.0]], dtype=dtype)
b = tf.constant([0.1, 0.2, 0.3], dtype=dtype)
lb_in = tf.constant([[-1.0, -1.0]], dtype=dtype)
ub_in = tf.constant([[2.0, 2.0]], dtype=dtype)
nominal = tf.constant([[3.1, 4.2]], dtype=dtype)
bounds_in = ibp.RelativeIntervalBounds(lb_in - nominal,
ub_in - nominal, nominal)
bounds_out = bounds_in.apply_linear(None, w, b)
lb_out, ub_out = bounds_out.lower, bounds_out.upper
lb_out_exp = np.array([[-4.9, -11.8, -8.7]])
ub_out_exp = np.array([[10.1, 9.2, 18.3]])
with self.test_session() as session:
lb_out_act, ub_out_act = session.run((lb_out, ub_out))
self.assertAllClose(lb_out_exp, lb_out_act, atol=tol, rtol=tol)
self.assertAllClose(ub_out_exp, ub_out_act, atol=tol, rtol=tol)
def test_linear_bounds_shape(self, dtype):
batch_size = 11
input_size = 7
output_size = 5
w = tf.placeholder(dtype=dtype, shape=(input_size, output_size))
b = tf.placeholder(dtype=dtype, shape=(output_size, ))
lb_rel_in = tf.placeholder(dtype=dtype, shape=(batch_size, input_size))
ub_rel_in = tf.placeholder(dtype=dtype, shape=(batch_size, input_size))
nominal = tf.placeholder(dtype=dtype, shape=(batch_size, input_size))
bounds_in = ibp.RelativeIntervalBounds(lb_rel_in, ub_rel_in, nominal)
bounds_out = bounds_in.apply_linear(None, w, b)
lb_out, ub_out = bounds_out.lower, bounds_out.upper
self.assertEqual(dtype, lb_out.dtype)
self.assertEqual(dtype, ub_out.dtype)
self.assertEqual((batch_size, output_size), lb_out.shape)
self.assertEqual((batch_size, output_size), ub_out.shape)
def test_conv2d_bounds(self, dtype, tol):
batch_size = 53
input_height = 17
input_width = 7
kernel_height = 3
kernel_width = 4
input_channels = 3
output_channels = 2
padding = 'VALID'
strides = (2, 1)
w = tf.random_normal(dtype=dtype,
shape=(kernel_height, kernel_width,
input_channels, output_channels))
b = tf.random_normal(dtype=dtype, shape=(output_channels, ))
lb_in = tf.random_normal(dtype=dtype,
shape=(batch_size, input_height, input_width,
input_channels))
ub_in = tf.random_normal(dtype=dtype,
shape=(batch_size, input_height, input_width,
input_channels))
lb_in, ub_in = tf.minimum(lb_in, ub_in), tf.maximum(lb_in, ub_in)
nominal = tf.random_normal(dtype=dtype,
shape=(batch_size, input_height,
input_width, input_channels))
bounds_in = ibp.RelativeIntervalBounds(lb_in - nominal,
ub_in - nominal, nominal)
bounds_out = bounds_in.apply_conv2d(None, w, b, padding, strides)
lb_out, ub_out = bounds_out.lower, bounds_out.upper
# Compare against equivalent linear layer.
bounds_out_lin = _materialised_conv_bounds(w, b, padding, strides,
bounds_in)
lb_out_lin, ub_out_lin = bounds_out_lin.lower, bounds_out_lin.upper
with self.test_session() as session:
(lb_out_val, ub_out_val, lb_out_lin_val,
ub_out_lin_val) = session.run(
(lb_out, ub_out, lb_out_lin, ub_out_lin))
self.assertAllClose(lb_out_val, lb_out_lin_val, atol=tol, rtol=tol)
self.assertAllClose(ub_out_val, ub_out_lin_val, atol=tol, rtol=tol)
def test_conv2d_bounds_shape(self, dtype):
batch_size = 23
input_height = 17
input_width = 7
kernel_height = 3
kernel_width = 4
input_channels = 3
output_channels = 5
padding = 'VALID'
strides = (2, 1)
# Expected output dimensions, based on convolution settings.
output_height = 8
output_width = 4
w = tf.placeholder(dtype=dtype,
shape=(kernel_height, kernel_width, input_channels,
output_channels))
b = tf.placeholder(dtype=dtype, shape=(output_channels, ))
lb_rel_in = tf.placeholder(dtype=dtype,
shape=(batch_size, input_height,
input_width, input_channels))
ub_rel_in = tf.placeholder(dtype=dtype,
shape=(batch_size, input_height,
input_width, input_channels))
nominal = tf.placeholder(dtype=dtype,
shape=(batch_size, input_height, input_width,
input_channels))
bounds_in = ibp.RelativeIntervalBounds(lb_rel_in, ub_rel_in, nominal)
bounds_out = bounds_in.apply_conv2d(None, w, b, padding, strides)
lb_out, ub_out = bounds_out.lower, bounds_out.upper
self.assertEqual(dtype, lb_out.dtype)
self.assertEqual(dtype, ub_out.dtype)
self.assertEqual(
(batch_size, output_height, output_width, output_channels),
lb_out.shape)
self.assertEqual(
(batch_size, output_height, output_width, output_channels),
ub_out.shape)
