def testConv2dBackwardBounds(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.)
m.propagate_bounds(input_bounds) # Create IBP bounds.
crown_init_bounds = _generate_identity_spec([m], shape=(1, 1, 1, 1, 1))
output_bounds = m.propagate_bounds(crown_init_bounds)
concrete_bounds = output_bounds.concretize()
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
l, u = sess.run([concrete_bounds.lower, concrete_bounds.upper])
l = l.item()
u = u.item()
self.assertAlmostEqual(8., l)
self.assertAlmostEqual(16., u)
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 = input_bounds.propagate_through(m)
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 add_layer(net, module, inputs, flatten=False, batch_norm=None):
if flatten:
reshape_module = snt.BatchFlatten()
outputs = reshape_module(inputs)
net.append(
ibp.BatchReshapeWrapper(reshape_module,
outputs.shape[1:].as_list()), outputs,
inputs)
inputs = outputs
outputs = module(inputs)
if isinstance(module, AvgPool):
module.__name__ = 'avg_pool'
parameters = {
'ksize': [1] + module.kernel_shape + [1],
'padding': module.padding,
'strides': [1] + module.strides + [1]
}
net.append(ibp.IncreasingMonotonicWrapper(module, **parameters),
outputs, inputs)
elif isinstance(module, snt.Conv2D):
net.append(ibp.LinearConv2dWrapper(module), outputs, inputs)
elif isinstance(module, snt.Conv1D):
net.append(ibp.LinearConv1dWrapper(module), outputs, inputs)
elif isinstance(module, snt.Linear):
net.append(ibp.LinearFCWrapper(module), outputs, inputs)
else:
net.append(ibp.IncreasingMonotonicWrapper(module), outputs, inputs)
if batch_norm is not None:
inputs = outputs
outputs = batch_norm(inputs, is_training=False, test_local_stats=False)
net.append(ibp.BatchNormWrapper(batch_norm), outputs, inputs)
return outputs