def test_tolerates_identity(self):
conv = snt.Conv2D(output_channels=5, kernel_shape=3, padding='VALID')
linear = snt.Linear(23)
module = snt.Sequential([
tf.identity,
conv,
tf.identity,
tf.nn.relu,
tf.identity,
snt.BatchFlatten(),
tf.identity,
linear,
tf.identity,
])
network = ibp.VerifiableModelWrapper(module)
network(self._inputs)
v_layers = auto_verifier.VerifiableLayerBuilder(network).build_layers()
self.assertLen(v_layers, 3)
self.assertIsInstance(v_layers[0], layers.Conv)
self.assertIs(conv, v_layers[0].module)
self.assertIsInstance(v_layers[1], layers.Activation)
self.assertEqual('relu', v_layers[1].activation)
self.assertIsInstance(v_layers[2], layers.Linear)
self.assertIs(linear, v_layers[2].module)
def test_stack_with_tf_activation(self, activation_fn):
conv = snt.Conv2D(output_channels=5, kernel_shape=3, padding='VALID')
linear = snt.Linear(23)
module = snt.Sequential(
[conv, activation_fn,
snt.BatchFlatten(), linear])
network = ibp.VerifiableModelWrapper(module)
network(self._inputs)
v_layers = auto_verifier.VerifiableLayerBuilder(network).build_layers()
self.assertLen(v_layers, 3)
self.assertIsInstance(v_layers[0], layers.Conv)
self.assertIs(conv, v_layers[0].module)
self.assertIsInstance(v_layers[0].input_node, ibp.ModelInputWrapper)
self.assertIsInstance(v_layers[1], layers.Activation)
self.assertEqual(activation_fn.__name__, v_layers[1].activation)
self.assertIs(v_layers[0].output_node, v_layers[1].input_node)
self.assertIsInstance(v_layers[2], layers.Linear)
self.assertIs(linear, v_layers[2].module)
self.assertIs(v_layers[2].output_node, network.output_module)
def test_empty_network(self):
module = snt.Sequential([])
network = ibp.VerifiableModelWrapper(module)
network(self._inputs)
v_layers = auto_verifier.VerifiableLayerBuilder(network).build_layers()
self.assertEmpty(v_layers)
def test_leading_batchnorm_rejected(self):
module = snt.Sequential([
_BatchNorm(),
snt.Conv2D(output_channels=5, kernel_shape=3, padding='VALID'),
])
network = ibp.VerifiableModelWrapper(module)
network(self._inputs)
with self.assertRaises(auto_verifier.NotVerifiableError):
_ = auto_verifier.VerifiableLayerBuilder(network).build_layers()
def test_avgpool_global(self):
def module(inputs):
return tf.reduce_mean(inputs, axis=(1, 2))
network = ibp.VerifiableModelWrapper(module)
network(self._inputs)
v_layers = auto_verifier.VerifiableLayerBuilder(network).build_layers()
self.assertLen(v_layers, 1)
self.assertIsInstance(v_layers[0], layers.AvgPool)
self.assertIs(None, v_layers[0].kernel_shape)
def test_leaky_relu(self):
module = lambda x: tf.nn.leaky_relu(x, alpha=0.3)
network = ibp.VerifiableModelWrapper(module)
network(self._inputs)
v_layers = auto_verifier.VerifiableLayerBuilder(network).build_layers()
self.assertLen(v_layers, 1)
self.assertIsInstance(v_layers[0], layers.Activation)
self.assertEqual('leaky_relu', v_layers[0].activation)
self.assertLen(v_layers[0].parameters, 1)
self.assertAllClose(0.3, v_layers[0].parameters['alpha'])
def test_standalone_conv_module(self):
module = snt.Conv2D(output_channels=5, kernel_shape=3, padding='VALID')
network = ibp.VerifiableModelWrapper(module)
network(self._inputs)
v_layers = auto_verifier.VerifiableLayerBuilder(network).build_layers()
self.assertLen(v_layers, 1)
self.assertIsInstance(v_layers[0], layers.Conv)
self.assertIs(module, v_layers[0].module)
self.assertIsInstance(v_layers[0].input_node, ibp.ModelInputWrapper)
self.assertIs(v_layers[0].output_node, network.output_module)
def test_unrecognised_trailing_calculation_rejected(self):
class InvalidModule(snt.AbstractModule):
def _build(self, inputs):
module = snt.Conv2D(output_channels=5,
kernel_shape=3,
padding='VALID')
return 2 * module(inputs)
module = InvalidModule()
network = ibp.VerifiableModelWrapper(module)
network(self._inputs)
with self.assertRaises(auto_verifier.NotVerifiableError):
_ = auto_verifier.VerifiableLayerBuilder(network).build_layers()
def test_maxpool_global(self, with_relu):
def module(inputs):
outputs = tf.reduce_max(inputs, axis=(1, 2))
if with_relu:
outputs = tf.nn.relu(outputs)
return outputs
network = ibp.VerifiableModelWrapper(module)
network(self._inputs)
v_layers = auto_verifier.VerifiableLayerBuilder(network).build_layers()
self.assertLen(v_layers, 1)
self.assertIsInstance(v_layers[0], layers.MaxPool)
self.assertIs(None, v_layers[0].kernel_shape)
self.assertEqual(with_relu, v_layers[0].with_relu)
def test_avgpool(self):
def module(inputs):
return tf.nn.avg_pool(inputs,
ksize=(3, 3),
padding='VALID',
strides=(2, 2))
network = ibp.VerifiableModelWrapper(module)
network(self._inputs)
v_layers = auto_verifier.VerifiableLayerBuilder(network).build_layers()
self.assertLen(v_layers, 1)
self.assertIsInstance(v_layers[0], layers.AvgPool)
self.assertEqual([3, 3], v_layers[0].kernel_shape)
self.assertEqual([2, 2], v_layers[0].strides)
def test_flatten_and_linear(self):
linear = snt.Linear(23)
module = snt.Sequential([
snt.BatchFlatten(),
linear,
])
network = ibp.VerifiableModelWrapper(module)
network(self._inputs)
v_layers = auto_verifier.VerifiableLayerBuilder(network).build_layers()
self.assertLen(v_layers, 1)
self.assertIsInstance(v_layers[0], layers.Linear)
self.assertIs(linear, v_layers[0].module)
self.assertEqual([2352], v_layers[0].input_node.shape)
self.assertIs(v_layers[0].output_node, network.output_module)
def test_maxpool(self, with_relu):
def module(inputs):
outputs = tf.nn.max_pool(inputs,
ksize=(3, 3),
padding='VALID',
strides=(2, 2))
if with_relu:
outputs = tf.nn.relu(outputs)
return outputs
network = ibp.VerifiableModelWrapper(module)
network(self._inputs)
v_layers = auto_verifier.VerifiableLayerBuilder(network).build_layers()
self.assertLen(v_layers, 1)
self.assertIsInstance(v_layers[0], layers.MaxPool)
self.assertEqual([3, 3], v_layers[0].kernel_shape)
self.assertEqual([2, 2], v_layers[0].strides)
self.assertEqual(with_relu, v_layers[0].with_relu)
def test_batchnorm(self):
conv = snt.Conv2D(output_channels=5, kernel_shape=3, padding='VALID')
batchnorm = _BatchNorm()
module = snt.Sequential([
conv,
batchnorm,
])
network = ibp.VerifiableModelWrapper(module)
network(self._inputs)
v_layers = auto_verifier.VerifiableLayerBuilder(network).build_layers()
self.assertLen(v_layers, 1)
self.assertIsInstance(v_layers[0], layers.Conv)
self.assertIs(conv, v_layers[0].module)
self.assertIs(batchnorm, v_layers[0].batch_norm)
self.assertIsInstance(v_layers[0].input_node, ibp.ModelInputWrapper)
self.assertIs(v_layers[0].output_node, network.output_module)
def _verifiable_layer_builder(self, net):
return auto_verifier.VerifiableLayerBuilder(net)
