interval-bound-propagation
interval-bound-propagation copied to clipboard
deepmind
Execution error
TensorFlow version 2.15.0 Sonnet version 2.0.2
predictor(test_data.image, override=True, is_training=False) TypeError: 'VerifiableModelWrapper' object is not callable
# 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.
"""Evaluates a verifiable model on Mnist or CIFAR-10."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from absl import logging
import interval_bound_propagation as ibp
import tensorflow.compat.v1 as tf
def layers(model_size):
"""Returns the layer specification for a given model name."""
if model_size == "tiny":
return (("linear", 100), ("activation", "relu"))
elif model_size == "small":
return (
("conv2d", (4, 4), 16, "VALID", 2),
("activation", "relu"),
("conv2d", (4, 4), 32, "VALID", 1),
("activation", "relu"),
("linear", 100),
("activation", "relu"),
)
elif model_size == "medium":
return (
("conv2d", (3, 3), 32, "VALID", 1),
("activation", "relu"),
("conv2d", (4, 4), 32, "VALID", 2),
("activation", "relu"),
("conv2d", (3, 3), 64, "VALID", 1),
("activation", "relu"),
("conv2d", (4, 4), 64, "VALID", 2),
("activation", "relu"),
("linear", 512),
("activation", "relu"),
("linear", 512),
("activation", "relu"),
)
elif model_size == "large_200":
# Some old large checkpoints have 200 hidden neurons in the last linear
# layer.
return (
("conv2d", (3, 3), 64, "SAME", 1),
("activation", "relu"),
("conv2d", (3, 3), 64, "SAME", 1),
("activation", "relu"),
("conv2d", (3, 3), 128, "SAME", 2),
("activation", "relu"),
("conv2d", (3, 3), 128, "SAME", 1),
("activation", "relu"),
("conv2d", (3, 3), 128, "SAME", 1),
("activation", "relu"),
("linear", 200),
("activation", "relu"),
)
elif model_size == "large":
return (
("conv2d", (3, 3), 64, "SAME", 1),
("activation", "relu"),
("conv2d", (3, 3), 64, "SAME", 1),
("activation", "relu"),
("conv2d", (3, 3), 128, "SAME", 2),
("activation", "relu"),
("conv2d", (3, 3), 128, "SAME", 1),
("activation", "relu"),
("conv2d", (3, 3), 128, "SAME", 1),
("activation", "relu"),
("linear", 512),
("activation", "relu"),
)
else:
raise ValueError('Unknown model: "{}"'.format(model_size))
def show_metrics(metric_values, bound_method="ibp"):
if bound_method == "crown-ibp":
verified_accuracy = metric_values.crown_ibp_verified_accuracy
else:
verified_accuracy = metric_values.verified_accuracy
print(
"nominal accuracy = {:.2f}%, "
"verified accuracy = {:.2f}%, "
"accuracy under PGD attack = {:.2f}%".format(
metric_values.nominal_accuracy * 100.0,
verified_accuracy * 100.0,
metric_values.attack_accuracy * 100.0,
)
)
# Test using while loop.
def get_test_metrics(batch_size, attack_builder=ibp.UntargetedPGDAttack):
"""Returns the test metrics."""
num_test_batches = len(data_test[0]) // batch_size
assert (
len(data_test[0]) % batch_size == 0
), "Test data is not a multiple of batch size."
def cond(i, *unused_args):
return i < num_test_batches
def body(i, metrics):
"""Compute the sum of all metrics."""
test_data = ibp.build_dataset(data_test, batch_size=batch_size, sequential=True)
predictor(test_data.image, override=True, is_training=False)
input_interval_bounds = ibp.IntervalBounds(
tf.maximum(test_data.image - cEPSILON, input_bounds[0]),
tf.minimum(test_data.image + cEPSILON, input_bounds[1]),
)
predictor.propagate_bounds(input_interval_bounds)
test_specification = ibp.ClassificationSpecification(
test_data.label, num_classes
)
test_attack = attack_builder(
predictor,
test_specification,
cEPSILON,
input_bounds=input_bounds,
optimizer_builder=ibp.UnrolledAdam,
)
# Use CROWN-IBP bound or IBP bound.
if cBOUND_METHOD == "crown-ibp":
test_losses = ibp.crown.Losses(
predictor,
test_specification,
test_attack,
use_crown_ibp=True,
crown_bound_schedule=tf.constant(1.0),
)
else:
test_losses = ibp.Losses(predictor, test_specification, test_attack)
test_losses(test_data.label)
new_metrics = []
for m, n in zip(metrics, test_losses.scalar_metrics):
new_metrics.append(m + n)
return i + 1, new_metrics
if cBOUND_METHOD == "crown-ibp":
metrics = ibp.crown.ScalarMetrics
else:
metrics = ibp.ScalarMetrics
total_count = tf.constant(0, dtype=tf.int32)
total_metrics = [
tf.constant(0, dtype=tf.float32) for _ in range(len(metrics._fields))
]
total_count, total_metrics = tf.while_loop(
cond,
body,
loop_vars=[total_count, total_metrics],
back_prop=False,
parallel_iterations=1,
)
total_count = tf.cast(total_count, tf.float32)
test_metrics = []
for m in total_metrics:
test_metrics.append(m / total_count)
return metrics(*test_metrics)
def main():
test_metrics = get_test_metrics(200, ibp.UntargetedPGDAttack)
# Prepare to load the pretrained-model.
saver = tf.compat.v1.train.Saver(original_predictor.get_variables())
# Run everything.
tf_config = tf.ConfigProto()
tf_config.gpu_options.allow_growth = True
with tf.train.SingularMonitoredSession(config=tf_config) as sess:
logging.info('Restoring from checkpoint "%s".', checkpoint_path)
saver.restore(sess, checkpoint_path)
logging.info("Evaluating at epsilon = %f.", cEPSILON)
metric_values = sess.run(test_metrics)
show_metrics(metric_values, cBOUND_METHOD)
if __name__ == "__main__":
print("TensorFlow version {}".format(tf.__version__))
# print("Sonnet version {}".format(snt.__version__))
# python sg_eval.py --model_dir './Models/crown-ibp/cifar_2-255_large/' --epsilon 0.00784313725490196 --bound_method=crown-ibp
cDATASET = "cifar10"
cMODEL = "large"
cEPSILON = 0.00784313725490196
cBOUND_METHOD = "ibp"
cMODEL_DIR = ".//Models//ibp//cifar_2-255_large_200//"
checkpoint_path = tf.train.latest_checkpoint(cMODEL_DIR)
if checkpoint_path is None:
raise OSError("Cannot find a valid checkpoint in {}.".format(cMODEL_DIR))
# Dataset.
input_bounds = (0.0, 1.0)
num_classes = 10
if cDATASET == "mnist":
data_train, data_test = tf.keras.datasets.mnist.load_data()
else:
assert cDATASET == "cifar10", 'Unknown dataset "{}"'.format(cDATASET)
data_train, data_test = tf.keras.datasets.cifar10.load_data()
data_train = (data_train[0], data_train[1].flatten())
data_test = (data_test[0], data_test[1].flatten())
# Base predictor network.
original_predictor = ibp.DNN(num_classes, layers(cMODEL))
predictor = original_predictor
if cDATASET == "cifar10":
mean = (0.4914, 0.4822, 0.4465)
std = (0.2023, 0.1994, 0.2010)
predictor = ibp.add_image_normalization(original_predictor, mean, std)
if cBOUND_METHOD == "crown-ibp":
predictor = ibp.crown.VerifiableModelWrapper(predictor)
else:
predictor = ibp.VerifiableModelWrapper(predictor)
print(predictor)
main()
