Made the code compatible with Python 2.

.gitignore

@@ -0,0 +1,104 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+env/
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# dotenv
+.env
+
+# virtualenv
+.venv
+venv/
+ENV/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+
+# MacOS
+*.DS_Store

requirement.txt

@@ -0,0 +1,7 @@
+keras
+tensorflow
+numpy
+scipy
+matplotlib
+future
+h5py

setup_cifar.py

@@ -0,0 +1,123 @@
+## setup_cifar.py -- cifar data and model loading code
+##
+## Copyright (C) 2016, Nicholas Carlini <nicholas@carlini.com>.
+##
+## This program is licenced under the BSD 2-Clause licence,
+## contained in the LICENCE file in this directory.
+
+
+import tensorflow as tf
+import numpy as np
+import os
+import pickle
+import gzip
+import pickle
+import urllib.request
+
+from keras.models import Sequential
+from keras.layers import Dense, Dropout, Activation, Flatten
+from keras.layers import Conv2D, MaxPooling2D
+from keras.utils import np_utils
+from keras.models import load_model
+
+def load_batch(fpath, label_key='labels'):
+    f = open(fpath, 'rb')
+    d = pickle.load(f, encoding="bytes")
+    for k, v in d.items():
+        del(d[k])
+        d[k.decode("utf8")] = v
+    f.close()
+    data = d["data"]
+    labels = d[label_key]
+
+    data = data.reshape(data.shape[0], 3, 32, 32)
+    final = np.zeros((data.shape[0], 32, 32, 3),dtype=np.float32)
+    final[:,:,:,0] = data[:,0,:,:]
+    final[:,:,:,1] = data[:,1,:,:]
+    final[:,:,:,2] = data[:,2,:,:]
+
+    final /= 255
+    labels2 = np.zeros((len(labels), 10))
+    labels2[np.arange(len(labels2)), labels] = 1
+
+    return final, labels
+
+def load_batch(fpath):
+    f = open(fpath,"rb").read()
+    size = 32*32*3+1
+    labels = []
+    images = []
+    for i in range(10000):
+        arr = np.fromstring(f[i*size:(i+1)*size],dtype=np.uint8)
+        lab = np.identity(10)[arr[0]]
+        img = arr[1:].reshape((3,32,32)).transpose((1,2,0))
+
+        labels.append(lab)
+        images.append(img/255)
+    return np.array(images),np.array(labels)
+
+
+class CIFAR:
+    def __init__(self):
+        train_data = []
+        train_labels = []
+
+        if not os.path.exists("cifar-10-batches-bin"):
+            urllib.request.urlretrieve("https://www.cs.toronto.edu/~kriz/cifar-10-binary.tar.gz",
+                                       "cifar-data.tar.gz")
+            os.popen("tar -xzf cifar-data.tar.gz").read()
+
+
+        for i in range(5):
+            r,s = load_batch("cifar-10-batches-bin/data_batch_"+str(i+1)+".bin")
+            train_data.extend(r)
+            train_labels.extend(s)
+
+        train_data = np.array(train_data,dtype=np.float32)
+        train_labels = np.array(train_labels)
+
+        self.test_data, self.test_labels = load_batch("cifar-10-batches-bin/test_batch.bin")
+
+        VALIDATION_SIZE = 5000
+
+        self.validation_data = train_data[:VALIDATION_SIZE, :, :, :]
+        self.validation_labels = train_labels[:VALIDATION_SIZE]
+        self.train_data = train_data[VALIDATION_SIZE:, :, :, :]
+        self.train_labels = train_labels[VALIDATION_SIZE:]
+
+class CIFARModel:
+    def __init__(self, restore, session=None):
+        self.num_channels = 3
+        self.image_size = 32
+        self.num_labels = 10
+
+        model = Sequential()
+
+        model.add(Conv2D(64, (3, 3),
+                                input_shape=(32, 32, 3)))
+        model.add(Activation('relu'))
+        model.add(Conv2D(64, (3, 3)))
+        model.add(Activation('relu'))
+        model.add(MaxPooling2D(pool_size=(2, 2)))
+
+        model.add(Conv2D(128, (3, 3)))
+        model.add(Activation('relu'))
+        model.add(Conv2D(128, (3, 3)))
+        model.add(Activation('relu'))
+        model.add(MaxPooling2D(pool_size=(2, 2)))
+
+        model.add(Flatten())
+        model.add(Dense(256))
+        model.add(Activation('relu'))
+        model.add(Dense(256))
+        model.add(Activation('relu'))
+        model.add(Dense(10))
+
+        model.load_weights(restore)
+
+        self.model = model
+
+    def predict(self, data):
+        return self.model(data)
+
+

setup_mnist.py

@@ -7,6 +7,10 @@
 
 ## Modified for MagNet's use.
 
+from __future__ import print_function
+from future.standard_library import install_aliases
+install_aliases()
+
 import numpy as np
 import os
 import gzip

worker.py

@@ -5,6 +5,8 @@
 ## This program is licenced under the BSD 2-Clause licence,
 ## contained in the LICENCE file in this directory.
 
+from __future__ import print_function
+
 import matplotlib
 matplotlib.use('Agg')
 from scipy.stats import entropy
@@ -19,6 +21,7 @@
 from utils import prepare_data
 import utils
 import matplotlib.pyplot as plt
+import six
 
 
 class AEDetector:
@@ -218,7 +221,8 @@ def __init__(self, examples, labels, name=""):
         examples: Path or object of input examples.
         labels: Ground truth labels.
         """
-        if isinstance(examples, str): self.data = utils.load_obj(examples)
+
+        if isinstance(examples, six.string_types): self.data = utils.load_obj(examples)
         else: self.data = examples
         self.labels = labels
         self.name = name
@@ -332,7 +336,7 @@ def plot_various_confidences(self, graph_name, drop_rate,
             none.append(none_acc)
 
         size = 2.5
-        plt.plot(confs, none, c="green", label="No fefense", marker="x", markersize=size)
+        plt.plot(confs, none, c="green", label="No defense", marker="x", markersize=size)
         plt.plot(confs, det_only, c="orange", label="With detector", marker="o", markersize=size)
         plt.plot(confs, ref_only, c="blue", label="With reformer", marker="^", markersize=size)
         plt.plot(confs, both, c="red", label="With detector & reformer", marker="s", markersize=size)