main4.py

import numpy as np
from foolbox2.criteria import TargetClass
from foolbox2.models.wrappers2 import CompositeModel
# from fmodel3 import create_fmodel_combo
from fmodel import create_fmodel as create_fmodel_18
from fmodel2 import create_fmodel as create_fmodel_ALP
from fmodel5 import create_fmodel as create_fmodel_ALP1000
from foolbox2.attacks.iterative_projected_gradient import MomentumIterativeAttack
from foolbox2.distances import MeanSquaredMedianDistance
from foolbox2.adversarial import Adversarial
from adversarial_vision_challenge import load_model
from adversarial_vision_challenge import read_images
from adversarial_vision_challenge import store_adversarial
from adversarial_vision_challenge import attack_complete
from smiterative2 import SAIterativeAttack, RMSIterativeAttack, \
                        AdamIterativeAttack, AdagradIterativeAttack
import os
import sys
# from contextlib import contextmanager
# import numpy as np
# import tensorflow as tf
# import tensorflow.contrib.slim as slim
# from tensorflow.contrib.slim.nets import resnet_v1
# import tensorpack as tp
# import tensorpack.utils.viz as viz
# from skimage.transform import resize
# IMAGE_SIZE = 224
# @contextmanager
# def guided_relu():
#     """
#     Returns:
#         A context where the gradient of :meth:`tf.nn.relu` is replaced by
#         guided back-propagation, as described in the paper:
#         `Striving for Simplicity: The All Convolutional Net
#         <https://arxiv.org/abs/1412.6806>`_
#     """
#     from tensorflow.python.ops import gen_nn_ops   # noqa

#     @tf.RegisterGradient("GuidedReLU")
#     def GuidedReluGrad(op, grad):
#         return tf.where(0. < grad,
#                         gen_nn_ops.relu_grad(grad, op.outputs[0]),
#                         tf.zeros(grad.get_shape()))

#     g = tf.get_default_graph()
#     with g.gradient_override_map({'Relu': 'GuidedReLU'}):
#         yield


# def saliency_map(output, input, name="saliency_map"):
#     """
#     Produce a saliency map as described in the paper:
#     `Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
#     <https://arxiv.org/abs/1312.6034>`_.
#     The saliency map is the gradient of the max element in output w.r.t input.
#     Returns:
#         tf.Tensor: the saliency map. Has the same shape as input.
#     """
#     max_outp = tf.reduce_max(output, 1)
#     saliency_op = tf.gradients(max_outp, input)[:][0]
#     return tf.identity(saliency_op, name=name)

# class SaliencyModel(tp.ModelDescBase):
#     def inputs(self):
#         return [tf.placeholder(tf.float32, (IMAGE_SIZE, IMAGE_SIZE, 3), 'image')]

#     def build_graph(self, orig_image):
#         mean = tf.get_variable('resnet_v1_50/mean_rgb', shape=[3])
#         with guided_relu():
#             with slim.arg_scope(resnet_v1.resnet_arg_scope()):
#                 image = tf.expand_dims(orig_image - mean, 0)
#                 logits, _ = resnet_v1.resnet_v1_50(image, 1000)
#             saliency_map(logits, orig_image, name="saliency")

# def find_salience(predictor, im):
#     # resnet expect RGB inputs of 224x224x3
#     im = resize(im, (IMAGE_SIZE, IMAGE_SIZE))
#     im = im.astype(np.float32)[:, :, ::-1]
#     # print(type(im))
#     saliency_images = predictor(im)[0]
#     # print(saliency_images)
#     # print(type(saliency_images))
#     pos_saliency = np.maximum(0, saliency_images)
#     resized_pos_saliency = resize(pos_saliency, (64,64))
#     # print(resized_pos_saliency.shape)
#     return resized_pos_saliency

def run_attack(model, image, target_class, pos_salience):
    criterion = TargetClass(target_class)
    # model == Composite model
    # Backward model = substitute model (resnet vgg alex) used to calculate gradients
    # Forward model = black-box model
    distance = MeanSquaredMedianDistance
    attack = AdamIterativeAttack()
    # attack = foolbox.attacks.annealer(model, criterion)
    # prediction of our black box model on the original image
    original_label = np.argmax(model.predictions(image))
    adv = Adversarial(model, criterion, image, original_label, distance=distance)
    return attack(adv, pos_salience = pos_salience)

def main():
    # tf.logging.set_verbosity(tf.logging.INFO)
    # instantiate blackbox and substitute model
    # instantiate blackbox and substitute model
    forward_model = load_model()
    # backward_model1 = create_fmodel_18()
    backward_model2 = create_fmodel_ALP()
    backward_model3 = create_fmodel_ALP1000()
    # print(backward_model1[0])
    # instantiate differntiable composite model
    # (predictions from blackbox, gradients from substitute)
    model = CompositeModel(
        forward_model=forward_model,
        backward_models=[backward_model2, backward_model3],
        weights = [0.5, 0.5])
    # predictor = tp.OfflinePredictor(tp.PredictConfig(
    #     model=SaliencyModel(),
    #     session_init=tp.get_model_loader("resnet_v1_50.ckpt"),
    #     input_names=['image'],
    #     output_names=['saliency']))
    for (file_name, image, label) in read_images():
        # pos_salience = find_salience(predictor, image)
        adversarial = run_attack(model, image, label, None)
        store_adversarial(file_name, adversarial)
    attack_complete()

if __name__ == '__main__':
    main()