def testRaiseValueErrorWithInvalidDepthMultiplier(self):
batch_size = 5
height, width = 299, 299
num_classes = 1000
inputs = tf.random_uniform((batch_size, height, width, 3))
with self.assertRaises(ValueError):
_ = inception.inception_v3(inputs, num_classes, depth_multiplier=-0.1)
with self.assertRaises(ValueError):
_ = inception.inception_v3(inputs, num_classes, depth_multiplier=0.0)
def testTrainEvalWithReuse(self):
train_batch_size = 5
eval_batch_size = 2
height, width = 150, 150
num_classes = 1000
train_inputs = tf.random_uniform((train_batch_size, height, width, 3))
inception.inception_v3(train_inputs, num_classes)
eval_inputs = tf.random_uniform((eval_batch_size, height, width, 3))
logits, _ = inception.inception_v3(eval_inputs, num_classes, is_training=False, reuse=True)
predictions = tf.argmax(logits, 1)
with self.test_session() as sess:
sess.run(tf.initialize_all_variables())
output = sess.run(predictions)
self.assertEquals(output.shape, (eval_batch_size,))
def main(_):
batch_shape = [FLAGS.batch_size, FLAGS.image_height, FLAGS.image_width, 3]
num_classes = 1001
tf.logging.set_verbosity(tf.logging.INFO)
with tf.Graph().as_default():
# Prepare graph
x_input = tf.placeholder(tf.float32, shape=batch_shape)
with slim.arg_scope(inception.inception_v3_arg_scope()):
_, end_points = inception.inception_v3(
x_input, num_classes=num_classes, is_training=False)
predicted_labels = tf.argmax(end_points['Predictions'], 1)
# Run computation
saver = tf.train.Saver(slim.get_model_variables())
session_creator = tf.train.ChiefSessionCreator(
scaffold=tf.train.Scaffold(saver=saver),
checkpoint_filename_with_path=FLAGS.checkpoint_path,
master=FLAGS.master)
with tf.train.MonitoredSession(session_creator=session_creator) as sess:
with tf.gfile.Open(FLAGS.output_file, 'w') as out_file:
for filenames, images in load_images(FLAGS.input_dir, batch_shape):
labels = sess.run(predicted_labels, feed_dict={x_input: images})
for filename, label in zip(filenames, labels):
out_file.write('{0},{1}\n'.format(filename, label))
def build_single_inceptionv3(train_tfdata, is_train, dropout_keep_prob, reduce_dim = False):
train_tfdata_resize = tf.image.resize_images(train_tfdata, (299, 299))
with slim.arg_scope(inception.inception_v3_arg_scope()):
identity, end_points = inception.inception_v3(train_tfdata_resize, dropout_keep_prob = dropout_keep_prob, is_training=is_train)
feature = slim.flatten(end_points['Mixed_7c'])
if reduce_dim:
feature = slim.fully_connected(feature, 256, scope='feat')
return identity, feature
def testLogitsNotSqueezed(self):
num_classes = 25
images = tf.random_uniform([1, 299, 299, 3])
logits, _ = inception.inception_v3(images, num_classes=num_classes, spatial_squeeze=False)
with self.test_session() as sess:
tf.initialize_all_variables().run()
logits_out = sess.run(logits)
self.assertListEqual(list(logits_out.shape), [1, 1, 1, num_classes])
def testBuildEndPointsWithDepthMultiplierGreaterThanOne(self):
batch_size = 5
height, width = 299, 299
num_classes = 1000
inputs = tf.random_uniform((batch_size, height, width, 3))
_, end_points = inception.inception_v3(inputs, num_classes)
endpoint_keys = [key for key in end_points.keys() if key.startswith("Mixed") or key.startswith("Conv")]
_, end_points_with_multiplier = inception.inception_v3(
inputs, num_classes, scope="depth_multiplied_net", depth_multiplier=2.0
)
for key in endpoint_keys:
original_depth = end_points[key].get_shape().as_list()[3]
new_depth = end_points_with_multiplier[key].get_shape().as_list()[3]
self.assertEqual(2.0 * original_depth, new_depth)
def testHalfSizeImages(self):
batch_size = 5
height, width = 150, 150
num_classes = 1000
inputs = tf.random_uniform((batch_size, height, width, 3))
logits, end_points = inception.inception_v3(inputs, num_classes)
self.assertTrue(logits.op.name.startswith("InceptionV3/Logits"))
self.assertListEqual(logits.get_shape().as_list(), [batch_size, num_classes])
pre_pool = end_points["Mixed_7c"]
self.assertListEqual(pre_pool.get_shape().as_list(), [batch_size, 3, 3, 2048])
def testBuildClassificationNetwork(self):
batch_size = 5
height, width = 299, 299
num_classes = 1000
inputs = tf.random_uniform((batch_size, height, width, 3))
logits, end_points = inception.inception_v3(inputs, num_classes)
self.assertTrue(logits.op.name.startswith("InceptionV3/Logits"))
self.assertListEqual(logits.get_shape().as_list(), [batch_size, num_classes])
self.assertTrue("Predictions" in end_points)
self.assertListEqual(end_points["Predictions"].get_shape().as_list(), [batch_size, num_classes])
def run(name, image_size, num_classes):
with tf.Graph().as_default():
image = tf.placeholder("float", [1, image_size, image_size, 3], name="input")
with slim.arg_scope(inception.inception_v3_arg_scope()):
logits, _ = inception.inception_v3(image, num_classes, is_training=False, spatial_squeeze=False)
probabilities = tf.nn.softmax(logits)
init_fn = slim.assign_from_checkpoint_fn('inception_v3.ckpt', slim.get_model_variables('InceptionV3'))
with tf.Session() as sess:
init_fn(sess)
saver = tf.train.Saver(tf.global_variables())
saver.save(sess, "output/"+name)
def testEvaluation(self):
batch_size = 2
height, width = 299, 299
num_classes = 1000
eval_inputs = tf.random_uniform((batch_size, height, width, 3))
logits, _ = inception.inception_v3(eval_inputs, num_classes, is_training=False)
predictions = tf.argmax(logits, 1)
with self.test_session() as sess:
sess.run(tf.initialize_all_variables())
output = sess.run(predictions)
self.assertEquals(output.shape, (batch_size,))
def predict(self,preprocessed_inputs):
"""
Runs inference on model
Args:
preprocessed_inputs: processed batch to go to model
Returns:
pre_logits: layer right before the logits
"""
with slim.arg_scope(inception.inception_v3_arg_scope()):
_,end_points = inception.inception_v3(
preprocessed_inputs,num_classes=1001,is_training=self._is_training,reuse=self._reuse)
return end_points['PreLogits']
def main(_):
# Images for inception classifier are normalized to be in [-1, 1] interval,
# eps is a difference between pixels so it should be in [0, 2] interval.
# Renormalizing epsilon from [0, 255] to [0, 2].
eps = 2.0 * FLAGS.max_epsilon / 255.0
batch_shape = [FLAGS.batch_size, FLAGS.image_height, FLAGS.image_width, 3]
num_classes = 1001
tf.logging.set_verbosity(tf.logging.INFO)
all_images_taget_class = load_target_class(FLAGS.input_dir)
with tf.Graph().as_default():
# Prepare graph
x_input = tf.placeholder(tf.float32, shape=batch_shape)
with slim.arg_scope(inception.inception_v3_arg_scope()):
logits, end_points = inception.inception_v3(
x_input, num_classes=num_classes, is_training=False)
target_class_input = tf.placeholder(tf.int32, shape=[FLAGS.batch_size])
one_hot_target_class = tf.one_hot(target_class_input, num_classes)
cross_entropy = tf.losses.softmax_cross_entropy(one_hot_target_class,
logits,
label_smoothing=0.1,
weights=1.0)
cross_entropy += tf.losses.softmax_cross_entropy(one_hot_target_class,
end_points['AuxLogits'],
label_smoothing=0.1,
weights=0.4)
x_adv = x_input - eps * tf.sign(tf.gradients(cross_entropy, x_input)[0])
x_adv = tf.clip_by_value(x_adv, -1.0, 1.0)
# Run computation
saver = tf.train.Saver(slim.get_model_variables())
session_creator = tf.train.ChiefSessionCreator(
scaffold=tf.train.Scaffold(saver=saver),
checkpoint_filename_with_path=FLAGS.checkpoint_path,
master=FLAGS.master)
with tf.train.MonitoredSession(session_creator=session_creator) as sess:
for filenames, images in load_images(FLAGS.input_dir, batch_shape):
target_class_for_batch = (
[all_images_taget_class[n] for n in filenames]
+ [0] * (FLAGS.batch_size - len(filenames)))
adv_images = sess.run(x_adv,
feed_dict={
x_input: images,
target_class_input: target_class_for_batch
})
save_images(adv_images, filenames, FLAGS.output_dir)
def testUnknowBatchSize(self):
batch_size = 1
height, width = 299, 299
num_classes = 1000
inputs = tf.placeholder(tf.float32, (None, height, width, 3))
logits, _ = inception.inception_v3(inputs, num_classes)
self.assertTrue(logits.op.name.startswith("InceptionV3/Logits"))
self.assertListEqual(logits.get_shape().as_list(), [None, num_classes])
images = tf.random_uniform((batch_size, height, width, 3))
with self.test_session() as sess:
sess.run(tf.initialize_all_variables())
output = sess.run(logits, {inputs: images.eval()})
self.assertEquals(output.shape, (batch_size, num_classes))
def testUnknownImageShape(self):
tf.reset_default_graph()
batch_size = 2
height, width = 299, 299
num_classes = 1000
input_np = np.random.uniform(0, 1, (batch_size, height, width, 3))
with self.test_session() as sess:
inputs = tf.placeholder(tf.float32, shape=(batch_size, None, None, 3))
logits, end_points = inception.inception_v3(inputs, num_classes)
self.assertListEqual(logits.get_shape().as_list(), [batch_size, num_classes])
pre_pool = end_points["Mixed_7c"]
feed_dict = {inputs: input_np}
tf.initialize_all_variables().run()
pre_pool_out = sess.run(pre_pool, feed_dict=feed_dict)
self.assertListEqual(list(pre_pool_out.shape), [batch_size, 8, 8, 2048])
def __call__(self, x_input, return_logits=False):
"""Constructs model and return probabilities for given input."""
reuse = True if self.built else None
with slim.arg_scope(inception.inception_v3_arg_scope()):
# Inception preprocessing uses [-1, 1]-scaled input.
x_input = x_input * 2.0 - 1.0
_, end_points = inception.inception_v3(
x_input, num_classes=self.num_classes, is_training=False,
reuse=reuse)
self.built = True
self.logits = end_points['Logits']
# Strip off the extra reshape op at the output
self.probs = end_points['Predictions'].op.inputs[0]
if return_logits:
return self.logits
else:
return self.probs
def testBuildEndPoints(self):
batch_size = 5
height, width = 299, 299
num_classes = 1000
inputs = tf.random_uniform((batch_size, height, width, 3))
_, end_points = inception.inception_v3(inputs, num_classes)
self.assertTrue("Logits" in end_points)
logits = end_points["Logits"]
self.assertListEqual(logits.get_shape().as_list(), [batch_size, num_classes])
self.assertTrue("AuxLogits" in end_points)
aux_logits = end_points["AuxLogits"]
self.assertListEqual(aux_logits.get_shape().as_list(), [batch_size, num_classes])
self.assertTrue("Mixed_7c" in end_points)
pre_pool = end_points["Mixed_7c"]
self.assertListEqual(pre_pool.get_shape().as_list(), [batch_size, 8, 8, 2048])
self.assertTrue("PreLogits" in end_points)
pre_logits = end_points["PreLogits"]
self.assertListEqual(pre_logits.get_shape().as_list(), [batch_size, 1, 1, 2048])
def __call__(self, x_input,return_logits=False):
"""Constructs model and return probabilities for given input."""
reuse = True if self.built else None
with slim.arg_scope(inception.inception_v3_arg_scope()):
_, end_points = inception.inception_v3(
x_input, num_classes=self.nb_classes, is_training=False,
reuse=reuse)
self.built = True
self.logits = end_points['Logits']
self.probs = end_points['Predictions'].op.inputs[0]
if return_logits:
return self.logits
else:
return self.probs
def main(_):
# eps = FLAGS.max_epsilon / 255.0
eps = 2.0 / 255.0
batch_shape = [FLAGS.batch_size, FLAGS.image_height, FLAGS.image_width, 3]
num_classes = 1001
tf.logging.set_verbosity(tf.logging.INFO)
with tf.Graph().as_default():
# Prepare graph
x_input = tf.placeholder(tf.float32, shape=batch_shape)
x_max = tf.clip_by_value(x_input + eps, -1.0, 1.0)
x_min = tf.clip_by_value(x_input - eps, -1.0, 1.0)
# 2-step FGSM attack:
noisy_images = x_input + eps * tf.sign(tf.random_normal(batch_shape))
x_noisy = tf.clip_by_value(noisy_images, x_min, x_max)
with slim.arg_scope(inception.inception_v3_arg_scope()):
_, end_points = inception.inception_v3(x_noisy,
num_classes=num_classes,
is_training=False)
predictions = end_points['Predictions']
# Run computation
saver = tf.train.Saver(slim.get_model_variables())
session_creator = tf.train.ChiefSessionCreator(
scaffold=tf.train.Scaffold(saver=saver),
checkpoint_filename_with_path=FLAGS.checkpoint_path,
master=FLAGS.master)
with tf.train.MonitoredSession(
session_creator=session_creator) as sess:
with tf.gfile.Open(FLAGS.output_file, 'w') as out_file:
for filenames, images in load_images(FLAGS.input_dir,
batch_shape):
pred = sess.run(predictions, feed_dict={x_input: images})
for filename, p in zip(filenames, list(pred)):
p_str = " ".join([str(i) for i in list(p)])
out_file.write('{0},{1}\n'.format(filename, p_str))
def get_inception_v3(self, images, reuse=None, scope=None):
arg_scope = inception.inception_v3_arg_scope()
with slim.arg_scope(arg_scope):
with tf.variable_scope(scope or 'image_feature_extractor',
reuse=reuse):
images = tf.image.resize_bilinear(images,
size=[299, 299],
align_corners=False)
scaled_input_tensor = tf.scalar_mul((1.0 / 255), images)
scaled_input_tensor = tf.subtract(scaled_input_tensor, 0.5)
scaled_input_tensor = tf.multiply(scaled_input_tensor, 2.0)
logits, end_points = inception.inception_v3(
scaled_input_tensor,
is_training=False,
num_classes=1001,
reuse=reuse)
h = end_points['PreLogits']
h = slim.flatten(h)
return h
def main(_):
batch_shape = [FLAGS.batch_size, FLAGS.image_height, FLAGS.image_width, 3]
num_classes = 1001
node_lookup = ReadableLabel(
"data/inception-v3/imagenet1000_clsid_to_human.txt")
tf.logging.set_verbosity(tf.logging.INFO)
with tf.Graph().as_default():
# Prepare graph
x_input = tf.placeholder(tf.float32, shape=batch_shape)
with slim.arg_scope(inception.inception_v3_arg_scope()):
_, end_points = inception.inception_v3(x_input,
num_classes=num_classes,
is_training=False)
predicted_labels = tf.argmax(end_points['Predictions'], 1)
inception_feature = end_points['PreLogits']
# Run computation
saver = tf.train.Saver(slim.get_model_variables())
session_creator = tf.train.ChiefSessionCreator(
scaffold=tf.train.Scaffold(saver=saver),
checkpoint_filename_with_path=FLAGS.checkpoint_path,
master=FLAGS.master)
with tf.train.MonitoredSession(
session_creator=session_creator) as sess:
# with tf.gfile.Open(FLAGS.output_file, 'w') as out_file:
for filenames, images in load_images(FLAGS.input_dir, batch_shape):
labels, features = sess.run(
[predicted_labels, inception_feature],
feed_dict={x_input: images})
print(features.shape)
for filename, label in zip(filenames, labels):
# out_file.write('{0},{1}\n'.format(filename, label))
print(filename, label,
node_lookup.get_human_readable(label))
def __init__(self, model_path=INCEPTION_V3_PATH, image_size=299):
cache_dir = os.sep.join(model_path.split(os.sep)[:-1])
get_file(fname=model_path.split(os.sep)[-1],
cache_dir=cache_dir,
cache_subdir='',
origin=INCEPTION_V3_URL,
untar=True)
self._get_session()
self.checkpoint_file = model_path
self.image_size = image_size
self._get_inception_preprocessing(inception_size=299)
self.arg_scope = inception.inception_v3_arg_scope()
with slim.arg_scope(self.arg_scope):
self.logits, self.end_points = inception.inception_v3(
self.scaled_input_tensor,
is_training=False,
num_classes=1001,
reuse=False)
self._restore()
def logits_and_labels(self, xs_ph):
xs_ph = xs_ph * 2.0 - 1.0
batch_size = xs_ph.get_shape().as_list()[0]
xs_ph_tile = tf.tile(tf.expand_dims(xs_ph, 1), [1, self.num_ensemble, 1, 1, 1])
xs_ph_tile = tf.reshape(xs_ph_tile, (-1,) + self.x_shape)
with slim.arg_scope(inception.inception_v3_arg_scope()):
with tf.variable_scope("RandDisc"):
xs_ph_tile = self.iterative_clustering_layer(source=xs_ph_tile, n_clusters=self.n_clusters,
sigma=10, alpha=10, noise_level_1=self.noise_level,
noise_level_2=self.noise_level)
logits, end_points = inception.inception_v3(
xs_ph_tile,
num_classes=self.n_class,
is_training=False,
reuse=tf.AUTO_REUSE)
logits = tf.reshape(logits, [batch_size, self.num_ensemble, -1])
logits = tf.reduce_mean(logits, axis = 1)
predicted_labels = tf.argmax(logits, 1)
return logits, predicted_labels
def exercise8():
CLASS_NAME_REGEX = re.compile(r'^n\d+\s+(.*)\s*$', re.M | re.U)
def prepare_image(fname, show=False):
channels = 3
test_image = mpimg.imread(os.path.join(fname))[:, :, :channels]
test_image = test_image.astype(np.float64)
test_image_shape = test_image.shape
if show:
plt.imshow(test_image)
plt.axis('off')
plt.show()
test_image = test_image.reshape(-1, 3)
scaler = StandardScaler()
test_image = scaler.fit_transform(test_image)
return test_image.reshape(test_image_shape)
def load_class_names():
with open(os.path.join('datasets', 'inception', 'imagenet_class_names.txt'), 'rb') as f:
content = f.read().decode('utf-8')
return CLASS_NAME_REGEX.findall(content)
test_img = prepare_image('cat.jpg', False)
# fetch_pretrained_inception_v3()
class_names = ['background'] + load_class_names()
X = tf.placeholder(tf.float32, shape=[None, 299, 299, 3])
with slim.arg_scope(inception.inception_v3_arg_scope()):
logits, end_points = inception.inception_v3(X, num_classes=1001, is_training=False)
predictions = end_points['Predictions']
saver = tf.train.Saver()
X_test = test_img.reshape((-1,) + test_img.shape)
with tf.Session() as sess:
saver.restore(sess, INCEPTION_V3_CHECKPOINT_PATH)
predictions_val = sess.run(predictions, feed_dict={X: X_test})
top5 = np.argpartition(predictions_val[0], -5)[-5:]
top5 = reversed(top5[np.argsort(predictions_val[0][top5])])
for i in top5:
print('{0}: {1:.2f}%'.format(class_names[i], 100 * predictions_val[0][i]))
def __call__(self, x_input):
"""Constructs model and return probabilities for given input."""
reuse = True if self.built else None
if not False:
with slim.arg_scope(inception.inception_v3_arg_scope()):
_, end_points = inception.inception_v3(
x_input, num_classes=self.num_classes, is_training=False,
reuse=reuse)
else:
import inception_resnet_v2
with slim.arg_scope(inception_resnet_v2.inception_resnet_v2_arg_scope()):
_, end_points = inception_resnet_v2.inception_resnet_v2(
x_input, num_classes=self.num_classes, is_training=False,
reuse=reuse)
self.built = True
output = end_points['Predictions']
# Strip off the extra reshape op at the output
probs = output.op.inputs[0]
return probs
def create_inception_network(image_tensor,
cnn_output_num=1024,
target_num=512,
is_training=True,
space_name="embedding_net",
reuse=False):
with tf.variable_scope(space_name, reuse=reuse): # 変数を共有
with slim.arg_scope(inception.inception_v3_arg_scope()):
outputs_cnn, endpoints = inception.inception_v3(
image_tensor,
num_classes=cnn_output_num,
is_training=is_training)
normalizer_params = {'is_training': is_training}
with slim.arg_scope([slim.layers.fully_connected],
activation_fn=tf.nn.relu,
normalizer_fn=slim.batch_norm,
normalizer_params=normalizer_params):
hidden = slim.layers.flatten(outputs_cnn)
outputs = slim.layers.fully_connected(hidden, target_num)
return outputs
def __init__(self):
self.x_input = tf.placeholder('float32', [batch_size, 299, 299, 3],
name='x_input_input_to_facenet')
# self.r_input = tf.placeholder('float32', [batch_size, 299, 299, 3], name='reference_input_input_to_facenet')
self.y_input = tf.placeholder(tf.int64, [batch_size],
name='true_label')
# self.phase_train_placeholder = tf.placeholder(tf.bool, name='phase_train')
# input_v3 = self.r_input * 2-1
# out_160 = set_loader.prewhitenfacenet(self.x_input)
# t_target_image_160 = set_loader.prewhitenfacenet(self.reference_input)
#resgn
# self.net_g = SRGAN_g(self.x_input, is_train=False, reuse=tf.AUTO_REUSE)
with slim.arg_scope(inception.inception_v3_arg_scope()):
_, end_points = inception.inception_v3(self.x_input,
num_classes=1001,
is_training=False,
reuse=tf.AUTO_REUSE)
# with slim.arg_scope(inception.inception_v3_arg_scope()):
# _, end_points = inception.inception_v3(
# self.net_g.outputs, num_classes=1001, is_training=False, reuse=tf.AUTO_REUSE)
self.pre_softmax = end_points['Predictions']
#calculate the predict label
# self.pre_softmax = tf.transpose(set_loader.softmax(distance1)) #
y_xent = tf.nn.sparse_softmax_cross_entropy_with_logits(
labels=self.y_input, logits=self.pre_softmax)
self.xent = tf.reduce_sum(y_xent) #
self.y_pred = tf.argmax(self.pre_softmax, 1)
self.correct_prediction = tf.equal(self.y_pred, self.y_input)
self.num_correct = tf.reduce_sum(
tf.cast(self.correct_prediction, tf.int64))
self.accuracy = tf.reduce_mean(
tf.cast(self.correct_prediction, tf.float32))
def google_model_make_prediction(filename):
image = os.path.join('{0}{1}'.format(c.image_dir, filename))
verify_image(image)
tf.reset_default_graph()
X = tf.placeholder(tf.float32, shape=[None, 299, 299, 3], name="X")
with slim.arg_scope(inception.inception_v3_arg_scope()):
logits, end_points = inception.inception_v3(X, num_classes=1001, is_training=False)
with tf.name_scope("ouput"):
flower_logits = tf.layers.dense(tf.squeeze(end_points["PreLogits"], axis=[1, 2]), 5, name="flower_logits")
Y_proba = tf.nn.softmax(flower_logits, name="Y_proba")
saver = tf.train.Saver()
init = tf.global_variables_initializer()
X_input = prepare_image(mpimg.imread(image), 299, 299, .01)
X_input.shape
with tf.Session() as sess:
init.run()
saver.restore(sess, c.model_dir + c.model_name)
probs = sess.run(Y_proba, feed_dict={X: X_input.reshape(-1,299,299,3)})
return [round(f * 100, 2) for f in probs[0]]
def testBuildEndPoints(self):
batch_size = 5
height, width = 299, 299
num_classes = 1000
inputs = tf.random_uniform((batch_size, height, width, 3))
_, end_points = inception.inception_v3(inputs, num_classes)
self.assertTrue('Logits' in end_points)
logits = end_points['Logits']
self.assertListEqual(logits.get_shape().as_list(),
[batch_size, num_classes])
self.assertTrue('AuxLogits' in end_points)
aux_logits = end_points['AuxLogits']
self.assertListEqual(aux_logits.get_shape().as_list(),
[batch_size, num_classes])
self.assertTrue('Mixed_7c' in end_points)
pre_pool = end_points['Mixed_7c']
self.assertListEqual(pre_pool.get_shape().as_list(),
[batch_size, 8, 8, 2048])
self.assertTrue('PreLogits' in end_points)
pre_logits = end_points['PreLogits']
self.assertListEqual(pre_logits.get_shape().as_list(),
[batch_size, 1, 1, 2048])
def main(_):
batch_shape = [FLAGS.batch_size, FLAGS.image_height, FLAGS.image_width, 3]
num_classes = 1001
itr = 30
tf.logging.set_verbosity(tf.logging.INFO)
#print("input dir = {0}".format(FLAGS.input_dir))
with tf.Graph().as_default():
# Prepare graph
x_input = tf.placeholder(tf.float32, shape=batch_shape)
#finished input
with slim.arg_scope(inception.inception_v3_arg_scope()):
_, end_points = inception.inception_v3(x_input,
num_classes=num_classes,
is_training=False)
predicted_labels = tf.argmax(end_points['Predictions'], 1)
# Run computation
saver = tf.train.Saver(slim.get_model_variables())
session_creator = tf.train.ChiefSessionCreator(
scaffold=tf.train.Scaffold(saver=saver),
checkpoint_filename_with_path=FLAGS.checkpoint_path,
master=FLAGS.master)
with tf.train.MonitoredSession(
session_creator=session_creator) as sess:
with tf.gfile.Open(FLAGS.output_file, 'w') as out_file:
for filenames, images in load_images(FLAGS.input_dir,
batch_shape):
for idxx in range(batch_shape[0]):
images[idxx, :, :, :] = defend_tv(
images[idxx]) # tv defense
labels = sess.run(predicted_labels,
feed_dict={x_input: images})
for filename, label in zip(filenames, labels):
out_file.write('{0},{1}\n'.format(filename, label))
def construct_inception():
X = tf.placeholder(tf.float32, shape=[None, 299, 299, 3], name="X")
y = tf.placeholder(tf.int32, shape=[None])
training = tf.placeholder_with_default(False, shape=[])
with slim.arg_scope(inception.inception_v3_arg_scope()):
logits, end_points = inception.inception_v3(X,
num_classes=1001,
is_training=training)
inception_saver = tf.train.Saver()
prelogits = tf.squeeze(end_points["PreLogits"], axis=[1, 2])
n_outputs = 10
# connect logits to n_outputs
with tf.name_scope("new_output_layer"):
new_logits = tf.layers.dense(prelogits, n_outputs, name="new_logits")
Y_proba = tf.nn.softmax(new_logits, name="Y_proba")
with tf.name_scope("train"):
xentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=new_logits, labels=y)
loss = tf.reduce_mean(xentropy)
optimizer = tf.train.AdamOptimizer()
#new_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope="new_logits") ###### only train 'new_logits'
#training_op = optimizer.minimize(loss, var_list=new_vars) ###### only train 'new_logits'
training_op = optimizer.minimize(loss)
with tf.name_scope("eval"):
correct = tf.nn.in_top_k(new_logits, y, 1)
accuracy = tf.reduce_mean(tf.cast(correct, tf.float32))
with tf.name_scope("init_and_save"):
init = tf.global_variables_initializer()
saver = tf.train.Saver()
def create_model(x, reuse=None):
"""Create model graph.
Args:
x: input images
reuse: reuse parameter which will be passed to underlying variable scopes.
Should be None first call and True every subsequent call.
Returns:
(logits, end_points) - tuple of model logits and enpoints
Raises:
ValueError: if model type specified by --model_name flag is invalid.
"""
if FLAGS.model_name == 'inception_v3':
with slim.arg_scope(inception.inception_v3_arg_scope()):
return inception.inception_v3(
x, num_classes=NUM_CLASSES, is_training=False, reuse=reuse)
elif FLAGS.model_name == 'inception_resnet_v2':
with slim.arg_scope(inception_resnet_v2.inception_resnet_v2_arg_scope()):
return inception_resnet_v2.inception_resnet_v2(
x, num_classes=NUM_CLASSES, is_training=False, reuse=reuse)
else:
raise ValueError('Invalid model name: %s' % (FLAGS.model_name))
def create_model(x, reuse=None):
"""Create model graph.
Args:
x: input images
reuse: reuse parameter which will be passed to underlying variable scopes.
Should be None first call and True every subsequent call.
Returns:
(logits, end_points) - tuple of model logits and enpoints
Raises:
ValueError: if model type specified by --model_name flag is invalid.
"""
if FLAGS.model_name == 'inception_v3':
with slim.arg_scope(inception.inception_v3_arg_scope()):
return inception.inception_v3(
x, num_classes=NUM_CLASSES, is_training=False, reuse=reuse)
elif FLAGS.model_name == 'inception_resnet_v2':
with slim.arg_scope(inception_resnet_v2.inception_resnet_v2_arg_scope()):
return inception_resnet_v2.inception_resnet_v2(
x, num_classes=NUM_CLASSES, is_training=False, reuse=reuse)
else:
raise ValueError('Invalid model name: %s' % (FLAGS.model_name))
def inception_model_fn(features, labels, mode, params):
"""Inception v3 model using Estimator API."""
del params
num_classes = FLAGS.num_classes
training_active = (mode == tf.estimator.ModeKeys.TRAIN)
eval_active = (mode == tf.estimator.ModeKeys.EVAL)
if training_active:
size = FLAGS.train_batch_size // FLAGS.num_shards
else:
size = FLAGS.eval_batch_size
input_transform_fn = TensorTranspose(size, is_input=True)
features = input_transform_fn(features)
with slim.arg_scope(inception.inception_v3_arg_scope(
use_fused_batchnorm=FLAGS.use_fused_batchnorm)):
logits, end_points = inception.inception_v3(
features,
num_classes,
is_training=training_active,
depth_multiplier=FLAGS.depth_multiplier)
predictions = {
'classes': tf.argmax(input=logits, axis=1),
'probabilities': tf.nn.softmax(logits, name='softmax_tensor')
}
if mode == tf.estimator.ModeKeys.PREDICT:
return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)
if 'AuxLogits' in end_points:
aux_loss = tf.losses.softmax_cross_entropy(
onehot_labels=labels,
logits=end_points['AuxLogits'],
weights=0.4,
label_smoothing=0.1,
scope='aux_loss')
tf.losses.add_loss(aux_loss)
prediction_loss = tf.losses.softmax_cross_entropy(
onehot_labels=labels,
logits=logits,
weights=1.0,
label_smoothing=0.1)
tf.losses.add_loss(prediction_loss)
loss = tf.losses.get_total_loss(add_regularization_losses=True)
initial_learning_rate = FLAGS.learning_rate * FLAGS.train_batch_size / 256
final_learning_rate = 0.01 * initial_learning_rate
train_op = None
if training_active:
# Multiply the learning rate by 0.1 every 30 epochs.
training_set_len = imagenet.get_split_size('train')
batches_per_epoch = training_set_len // FLAGS.train_batch_size
learning_rate = tf.train.exponential_decay(
learning_rate=initial_learning_rate,
global_step=tf.train.get_global_step(),
decay_steps=_LEARNING_RATE_DECAY_EPOCHS * batches_per_epoch,
decay_rate=_LEARNING_RATE_DECAY,
staircase=True)
# Set a minimum boundary for the learning rate.
learning_rate = tf.maximum(
learning_rate, final_learning_rate, name='learning_rate')
# tf.summary.scalar('learning_rate', learning_rate)
if FLAGS.optimizer == 'sgd':
tf.logging.info('Using SGD optimizer')
optimizer = tf.train.GradientDescentOptimizer(
learning_rate=FLAGS.learning_rate)
elif FLAGS.optimizer == 'momentum':
tf.logging.info('Using Momentum optimizer')
optimizer = tf.train.MomentumOptimizer(
learning_rate=FLAGS.learning_rate, momentum=0.9)
else:
tf.logging.fatal('Unknown optimizer:', FLAGS.optimizer)
if FLAGS.use_tpu:
optimizer = tpu_optimizer.CrossShardOptimizer(optimizer)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
train_op = optimizer.minimize(
loss, global_step=tf.train.get_or_create_global_step())
eval_metrics = None
if eval_active:
def metric_fn(labels, logits):
predictions = tf.argmax(input=logits, axis=1)
accuracy = tf.metrics.accuracy(tf.argmax(input=labels, axis=1),
predictions)
return {'accuracy': accuracy}
eval_metrics = (metric_fn, [labels, logits])
return tpu_estimator.TPUEstimatorSpec(
mode=mode, loss=loss, train_op=train_op, eval_metrics=eval_metrics)
# inception_v3网络的图片输入尺寸，检查点文件
img_size = inception.inception_v3.default_image_size
checkpoint_file = os.path.join("inception_v3", "inception_v3.ckpt")
tf.reset_default_graph()
slim = tf.contrib.slim
# 设置输入
input_img = tf.placeholder("float", [None, img_size, img_size, 3])
# 载入inception v3模型
with tf.Session() as sess:
arg_scope = inception.inception_v3_arg_scope()
with slim.arg_scope(arg_scope):
_, end_points = inception.inception_v3(input_img, is_training=False)
saver = tf.train.Saver()
saver.restore(sess, checkpoint_file)
for video_data in data.data:
# 获取此视频时间序列特征的存放路径
seq_path = os.path.join('data', 'sequences', video_data[2] + '-' + str(seq_length) +
'-features')
# 检查是否已经存在该视频的特征
if os.path.isfile(seq_path + '.npy'):
pbar.update(1)
continue
def main(_):
# Images for inception classifier are normalized to be in [-1, 1] interval,
# eps is a difference between pixels so it should be in [0, 2] interval.
# Renormalizing epsilon from [0, 255] to [0, 2].
eps = 2.0 * FLAGS.max_epsilon / 255.0
alpha = 2.0 * FLAGS.iter_alpha / 255.0
num_iter = FLAGS.num_iter
batch_shape = [FLAGS.batch_size, FLAGS.image_height, FLAGS.image_width, 3]
num_classes = 1001
tf.logging.set_verbosity(tf.logging.INFO)
all_images_taget_class = load_target_class(FLAGS.input_dir)
with tf.Graph().as_default():
# Prepare graph
x_input = tf.placeholder(tf.float32, shape=batch_shape)
x_adv = x_input
target_class_input = tf.placeholder(tf.int32, shape=[FLAGS.batch_size])
one_hot_target_class = tf.one_hot(target_class_input, num_classes)
with slim.arg_scope(inception.inception_v3_arg_scope()):
logits, end_points = inception.inception_v3(
x_input,
num_classes=num_classes,
is_training=False,
reuse=False)
cross_entropy = tf.losses.softmax_cross_entropy(
one_hot_target_class, logits, label_smoothing=0.1, weights=1.0)
grad = tf.gradients(cross_entropy, x_adv)[0]
# Run computation
saver = tf.train.Saver(slim.get_model_variables())
session_creator = tf.train.ChiefSessionCreator(
scaffold=tf.train.Scaffold(saver=saver),
checkpoint_filename_with_path=FLAGS.checkpoint_path,
master=FLAGS.master)
with tf.train.MonitoredSession(
session_creator=session_creator) as sess:
for filenames, images_, values in load_images(
FLAGS.input_dir, batch_shape):
max_epsilon = FLAGS.max_epsilon * 2 / 255.0
target_class_for_batch = ([
all_images_taget_class[filename] for filename in filenames
] + [0] * (FLAGS.batch_size - len(filenames)))
max_clip = np.clip(images_ + eps, -1, 1.0)
min_clip = np.clip(images_ - eps, -1, 1.0)
grad_value = sess.run(grad,
feed_dict={
x_input:
values,
target_class_input:
target_class_for_batch
})
grad_value = grad_value / (
np.std(grad_value, axis=(1, 2, 3), keepdims=True) + 1e-15)
values = values - 0.2 * float(
float(FLAGS.step_n) - float(FLAGS.step)) / float(
FLAGS.step_n) * 0.5 * grad_value * max_epsilon
values = np.maximum(values, min_clip)
values = np.minimum(values, max_clip)
save_images(values, filenames, FLAGS.output_dir)
min_after_dequeue = min_queue_size
)
tf.summary.image('images',image_batch)
#reshape to labels to vector
return image_batch,tf.reshape(label_batch,[BATCH_SIZE])
#preprocessing and training
with tf.device('/cpu:0'):
print('starting to load data. please wait...')
key,image,label = parse_data([FLOWERS_DIR])
scaled,label = format_input(image,label)
image_batch,label_batch = gen_batch(scaled,label)
training = tf.placeholder_with_default(False, shape=[])
with slim.arg_scope(inception.inception_v3_arg_scope()):
old_logits,end_points = inception.inception_v3(
image_batch,num_classes=1001,is_training=is_training)
#saver = tf.train.Saver()
#reuse_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES)
#reuse_vars_dict = {var.name: var for var in reuse_vars}
#original_saver = tf.train.Saver(reuse_vars_dict)
original_saver = tf.train.Saver()
with tf.name_scope('flower_output'):
pre_logits = end_points['PreLogits']
print(pre_logits.op.name)
#logits = tf.layers.conv2d(pre_logits,kernel_initializer=tf.contrib.layers.variance_scaling_initializer(),
# kernel_size=1,strides=1,filters=5,padding='SAME',name='flower_logits')
#prediction = tf.nn.softmax(logits,name='flower_softmax')
#logits_full = tf.reshape(logits,shape=[-1,NUM_OUT])
#logits_full = tf.squeeze(logits)
pool = tf.squeeze(pre_logits)
#
# The below code runs a Tensorflow session to generate targeted adversarial images. In each case, there's a specific target class that we're trying to trick the image classifier to output.
#
# *Note - this currently isn't working - it's generating adversarial images, but they aren't correctly targeted*
# In[ ]:
all_images_target_class = {image_metadata["ImageId"][i]+".png": image_metadata["TargetClass"][i]
for i in image_metadata.index}
with tf.Graph().as_default():
x_input = tf.placeholder(tf.float32, shape=batch_shape)
with slim.arg_scope(inception.inception_v3_arg_scope()):
logits, end_points = inception.inception_v3(
x_input, num_classes=num_classes, is_training=False)
target_class_input = tf.placeholder(tf.int32, shape=[batch_size])
one_hot_target_class = tf.one_hot(target_class_input, num_classes)
cross_entropy = tf.losses.softmax_cross_entropy(one_hot_target_class,
logits,
label_smoothing=0.1,
weights=1.0)
cross_entropy += tf.losses.softmax_cross_entropy(one_hot_target_class,
end_points['AuxLogits'],
label_smoothing=0.1,
weights=0.4)
x_adv = x_input - eps * tf.sign(tf.gradients(cross_entropy, x_input)[0])
x_adv = tf.clip_by_value(x_adv, -1.0, 1.0)
saver = tf.train.Saver(slim.get_model_variables())
def main(model,
content_weights,
style_weights,
learning_rate,
alpha,
beta,
cycles=float("inf"),
training_time=float("inf"),
start_jittering=0.,
stop_jittering=float("inf"),
jitter_freq=50,
display_image_freq=300,
max_image_dim=512,
pre_calc_style_grams=0,
content_targets=0,
random_initializer=False,
use_wass=False):
# Content weights - Dictionary with the tensor names as the keys and the weights as the values
# Style weights - Same as content weights but for style
# Alpha, Beta - Total weighting of the content vs style respectively
# Cycles - how many iterations to perform on each image. Set to float("inf") to remove (must specify time limit instead)
# Training time - time limit for optimization. Set to float("inf") to remove (must specify training time instead)
# Stop jittering - Float between 0 and 1. Fraction of the total allowed cycles or training time to jitter the image during optimization. Set to 0 for no image jittering
# Jitter frequency - Number of training cycles between image shifts
# Display image frequency - Number of training cycles between displaying the result image
# Maximum image dimension - Scale down the largest dimension of the content image to match this
if model in ['Inception_V1', 'Inception_V3']:
from tensorflow.contrib.slim.nets import inception as model_module
elif model == 'VGG_19':
from tensorflow.contrib.slim.nets import vgg as model_module
if (cycles == float("inf")) and (training_time == float("inf")):
print("Error: Must specify time or cycle limit")
return False
jitter_stop_cycle = float("inf")
jitter_stop_time = float("inf")
jitter_start_cycle = 0
jitter_start_time = 0
if cycles < float("inf"):
jitter_start_cycle = start_jittering * cycles
if stop_jittering < float("inf"):
jitter_stop_cycle = stop_jittering * cycles
if training_time < float("inf"):
jitter_start_time = start_jittering * training_time
if stop_jittering < float("inf"):
jitter_stop_time = stop_jittering * training_time
slim = tf.contrib.slim
content_image = load_images("./contentpicture", max_image_dim)
print("Content Image: ", content_image.shape)
style_image = load_images("./stylepicture",
target_shape=content_image.shape)
print("Style Image: ", style_image.shape)
g = tf.Graph()
with g.as_default():
# Prepare graph
var_input = tf.placeholder(shape=(None, content_image.shape[1],
content_image.shape[2], 3),
dtype=tf.float32,
name='var_input')
batch, h, w, ch = content_image.shape
init_val, scale, corr_matrix = lucid_ops.get_fourier_features(
content_image)
decorrelate_matrix = tf.constant(corr_matrix, shape=[3, 3])
var_decor = tf.reshape(
tf.matmul(tf.reshape(var_input, [-1, 3]),
tf.matrix_inverse(decorrelate_matrix)),
[1, content_image.shape[1], content_image.shape[2], 3]) * 4.0
four_space_complex = tf.spectral.rfft2d(
tf.transpose(var_decor, perm=[0, 3, 1, 2]))
four_space_complex = four_space_complex / scale
four_space = tf.concat(
[tf.real(four_space_complex),
tf.imag(four_space_complex)], axis=0)
four_input = tf.Variable(init_val)
four_to_complex = tf.complex(four_input[0], four_input[1])
four_to_complex = scale * four_to_complex
rgb_space = tf.expand_dims(tf.transpose(
tf.spectral.irfft2d(four_to_complex), perm=[1, 2, 0]),
axis=0)
rgb_space = rgb_space[:, :h, :w, :ch] / 4.0
recorr_img = tf.reshape(
tf.matmul(tf.reshape(rgb_space, [-1, 3]), decorrelate_matrix),
[1, content_image.shape[1], content_image.shape[2], 3])
input_img = (recorr_img + 1.0) / 2.0
VGG_MEANS = np.array([[[[0.485, 0.456, 0.406]]]]).astype('float32')
VGG_MEANS = tf.constant(VGG_MEANS, shape=[1, 1, 1, 3])
vgg_input = (input_img - VGG_MEANS) * 255.0
bgr_input = tf.stack([
vgg_input[:, :, :, 2], vgg_input[:, :, :, 1], vgg_input[:, :, :, 0]
],
axis=-1)
with g.gradient_override_map({'Relu': 'Custom1', 'Relu6': 'Custom2'}):
if model == 'Inception_V1':
with slim.arg_scope(model_module.inception_v1_arg_scope()):
_, end_points = model_module.inception_v1(
input_img,
num_classes=1001,
spatial_squeeze=False,
is_training=False)
elif model == 'Inception_V3':
with slim.arg_scope(model_module.inception_v3_arg_scope()):
_, end_points = model_module.inception_v3(
input_img,
num_classes=1001,
spatial_squeeze=False,
is_training=False)
elif model == 'VGG_19':
with slim.arg_scope(model_module.vgg_arg_scope()):
_, end_points = model_module.vgg_19(bgr_input,
num_classes=1000,
spatial_squeeze=False,
is_training=False)
content_placeholders = {}
content_losses = {}
total_content_loss = 0
style_losses = {}
total_style_loss = 0
input_grams = {}
style_gram_placeholders = {}
mean_placeholders = {}
tr_cov_placeholders = {}
root_cov_placeholders = {}
means = {}
tr_covs = {}
root_covs = {}
for layer in content_weights.keys():
# Creates the placeholder for importing the content targets and creates the operations to compute the loss at each content layer
_, h, w, d = g.get_tensor_by_name(layer).get_shape()
content_placeholders[layer] = tf.placeholder(tf.float32,
shape=[None, h, w, d])
content_losses[layer] = tf.reduce_mean(
tf.abs(content_placeholders[layer] -
g.get_tensor_by_name(layer)))
total_content_loss += content_losses[layer] * content_weights[layer]
for layer in style_weights.keys():
# Creates the placeholder for importing the pre-calculated style grams and creates the operations to compute the loss at each style layer
_, h, w, d = g.get_tensor_by_name(layer).get_shape()
N = h.value * w.value
M = d.value
if use_wass:
means[layer], cov = wass_style_ops.calc_2_moments(
g.get_tensor_by_name(layer))
eigvals, eigvects = tf.self_adjoint_eig(cov)
eigroot_mat = tf.diag(tf.sqrt(tf.maximum(eigvals, 0)))
root_covs[layer] = tf.matmul(tf.matmul(eigvects, eigroot_mat),
eigvects,
transpose_b=True)
tr_covs[layer] = tf.reduce_sum(tf.maximum(eigvals, 0))
mean_placeholders[layer] = tf.placeholder(
tf.float32, shape=means[layer].get_shape())
tr_cov_placeholders[layer] = tf.placeholder(
tf.float32, shape=tr_covs[layer].get_shape())
root_cov_placeholders[layer] = tf.placeholder(
tf.float32, shape=root_covs[layer].get_shape())
style_losses[layer] = wass_style_ops.calc_l2wass_dist(
mean_placeholders[layer], tr_cov_placeholders[layer],
root_cov_placeholders[layer], means[layer], cov)
else:
input_grams[layer] = gram(g.get_tensor_by_name(layer))
style_gram_placeholders[layer] = tf.placeholder(
tf.float32, shape=input_grams[layer].get_shape())
style_losses[layer] = tf.reduce_mean(
tf.abs(input_grams[layer] -
style_gram_placeholders[layer]))
total_style_loss += style_weights[layer] * style_losses[layer]
total_loss = alpha * total_content_loss + beta * total_style_loss
update = tf.train.AdamOptimizer(learning_rate).minimize(
total_loss, var_list=[four_input])
saver = tf.train.Saver(slim.get_model_variables())
with tf.Session() as sess:
tf.global_variables_initializer().run()
restore_model(saver, model, sess)
if display_image_freq < float("inf"):
display_image(content_image)
display_image(style_image)
style_four_trans = sess.run(
four_space, feed_dict={var_input: preprocess(style_image)})
copy_style_four_to_input_op = four_input.assign(style_four_trans)
sess.run(copy_style_four_to_input_op)
# Calculates the style grams for each style layer and saves them to feed to their placeholders
pre_calc_style_grams = {}
pre_calc_mean_placeholders = {}
pre_calc_tr_cov_placeholders = {}
pre_calc_root_cov_placeholders = {}
for layer in style_weights.keys():
print(layer)
if use_wass:
pre_calc_mean_placeholders[layer] = sess.run(means[layer])
pre_calc_tr_cov_placeholders[layer] = sess.run(
tr_covs[layer])
pre_calc_root_cov_placeholders[layer] = sess.run(
root_covs[layer])
else:
pre_calc_style_grams[layer] = sess.run(input_grams[layer])
content_four_trans = sess.run(
four_space, feed_dict={var_input: preprocess(content_image)})
copy_content_four_to_input_op = four_input.assign(
content_four_trans)
sess.run(copy_content_four_to_input_op)
# Allows content targets to be used if they have already been calculated from a previous iteration
content_targets = {}
for layer in content_weights.keys():
print(layer)
content_targets[layer] = sess.run(g.get_tensor_by_name(layer))
if random_initializer:
reassign_random = four_input.assign(
np.random.normal(size=(2, 3, content_image.shape[1],
(content_image.shape[2] + 2) // 2),
scale=0.01))
sess.run(reassign_random)
assign_jitter = four_input.assign(four_space)
# Generates the feed dictionary for session update
feed_dict = {}
for layer in content_weights.keys():
feed_dict[content_placeholders[layer]] = content_targets[layer]
for layer in style_weights.keys():
if use_wass:
feed_dict[mean_placeholders[
layer]] = pre_calc_mean_placeholders[layer]
feed_dict[tr_cov_placeholders[
layer]] = pre_calc_tr_cov_placeholders[layer]
feed_dict[root_cov_placeholders[
layer]] = pre_calc_root_cov_placeholders[layer]
else:
feed_dict[style_gram_placeholders[
layer]] = pre_calc_style_grams[layer]
start_time = time.time()
i = 0
_, h, w, d = content_image.shape
while ((i < cycles)
and (time.time() - start_time < training_time)):
# Perform update step
loss, _, temp_image, tcl, tsl = sess.run([
total_loss, update, recorr_img, total_content_loss,
total_style_loss
],
feed_dict=feed_dict)
if (i % jitter_freq == 0 and i < jitter_stop_cycle
and (i > jitter_start_cycle
or time.time() - start_time > jitter_start_time)
and time.time() - start_time < jitter_stop_time):
temp_image = np.roll(temp_image,
shift=randint(-1, 1),
axis=randint(1, 2))
sess.run(assign_jitter, feed_dict={var_input: temp_image})
# Print loss updates every 10 iterations
if (i % 10 == 0):
print(loss, i, tsl, tcl)
# Display image
if display_image_freq < float("inf"):
if i % display_image_freq == 0:
#image_out = un_preprocess(np.clip(sess.run(recorr_img), -1., 1.))
display_image(
un_preprocess(np.clip(temp_image, -1., 1.)), True,
i)
i += 1
# Display the final image and save it to the folder
image_out = un_preprocess(sess.run(recorr_img))
display_image(image_out, save=True, name='final')
if i >= cycles:
print("Reached Cycle Limit: ", cycles)
if (time.time() - start_time > training_time):
print("Reached Time Limit: ", time.time() - start_time)
"data/inception-v3/imagenet1000_clsid_to_human.txt")
inception_graph = tf.Graph()
inception_sess = tf.Session(graph=inception_graph)
slim = tf.contrib.slim
batch_shape = [1, 299, 299, 3]
num_classes = 1001
latest_checkpoint = tf.train.latest_checkpoint(checkpoint_path)
with inception_graph.as_default(), inception_sess.as_default() as sess:
# Prepare graph
x_input = tf.placeholder(tf.float32, shape=batch_shape)
with slim.arg_scope(inception.inception_v3_arg_scope()):
_, end_points = inception.inception_v3(x_input,
num_classes=num_classes,
is_training=False,
dropout_keep_prob=1.0)
inception_feature = end_points['PreLogits']
predicted_labels = tf.argmax(end_points['Predictions'], 1)
# Run computation
print("Restoring Saved Variables from Checkpoint: {}".format(
latest_checkpoint))
saver = tf.train.Saver(slim.get_model_variables())
saver.restore(sess, latest_checkpoint)
z = 0
n = 82
data = bson.decode_file_iter(open(input_bson_filename, 'rb'))
def main(_):
# Images for inception classifier are normalized to be in [-1, 1] interval,
# eps is a difference between pixels so it should be in [0, 2] interval.
# Renormalizing epsilon from [0, 255] to [0, 2].
eps = 2.0 * FLAGS.max_epsilon / 255.0
alpha = 2.0 * FLAGS.iter_alpha / 255.0
num_iter = FLAGS.num_iter
batch_shape = [FLAGS.batch_size, FLAGS.image_height, FLAGS.image_width, 3]
num_classes = 1001
tf.logging.set_verbosity(tf.logging.INFO)
label_smoothing = 0.1
coeff_mul_alpha = 0.019
AUX_ENS_V2 = 2.4
AUX_INC_V3 = 0.87
aux_weight = AUX_INC_V3
model_mode = 0
with tf.Graph().as_default():
# Prepare graph
x_input = tf.placeholder(tf.float32, shape=batch_shape)
x_max = tf.clip_by_value(x_input + eps, -1.0, 1.0)
x_min = tf.clip_by_value(x_input - eps, -1.0, 1.0)
with tf.variable_scope('model_a'):
with slim.arg_scope(inception.inception_v3_arg_scope()):
inception.inception_v3(x_input,
num_classes=num_classes,
is_training=False)
with tf.variable_scope('model_b'):
with slim.arg_scope(inception.inception_v3_arg_scope()):
inception.inception_v3(x_input,
num_classes=num_classes,
is_training=False)
with tf.variable_scope('model_c'):
with slim.arg_scope(
inception_resnet_v2.inception_resnet_v2_arg_scope()):
original_logits, _ = inception_resnet_v2.inception_resnet_v2(
x_input, num_classes=num_classes, is_training=False)
x_adv = x_input
def wrap_guess_target_class(logits, arr_kth_largest):
arr_logits = np.array(logits)
kth_largest = int(arr_kth_largest)
return np.array(
[arr.argsort()[-kth_largest] for arr in arr_logits],
dtype=np.int32)
target_class_input = tf.py_func(
wrap_guess_target_class,
[original_logits, np.array(5)], tf.int32)
one_hot_target_class = tf.one_hot(target_class_input, num_classes)
for i_iter in range(num_iter):
model_mode = i_iter % 4
if i_iter >= 16:
model_mode = 3
if i_iter == 0:
label_smoothing = 0.1
coeff_mul_alpha = 0.019
elif i_iter == 10:
label_smoothing = 0
coeff_mul_alpha = 0.031
if model_mode == 1:
with tf.variable_scope('model_a'):
with slim.arg_scope(inception.inception_v3_arg_scope()):
logits, end_points = inception.inception_v3(
x_adv,
num_classes=num_classes,
is_training=False,
reuse=True)
elif model_mode == 0:
with tf.variable_scope('model_b'):
with slim.arg_scope(inception.inception_v3_arg_scope()):
logits, end_points = inception.inception_v3(
x_adv,
num_classes=num_classes,
is_training=False,
reuse=True)
elif model_mode == 2:
with tf.variable_scope('model_a'):
with slim.arg_scope(inception.inception_v3_arg_scope()):
logits, end_points = inception.inception_v3(
x_adv,
num_classes=num_classes,
is_training=False,
reuse=True)
else:
with tf.variable_scope('model_c'):
with slim.arg_scope(inception_resnet_v2.
inception_resnet_v2_arg_scope()):
logits, end_points = inception_resnet_v2.inception_resnet_v2(
x_adv,
num_classes=num_classes,
is_training=False,
reuse=True)
if model_mode == 3:
aux_weight = AUX_ENS_V2
else:
aux_weight = AUX_INC_V3
cross_entropy = tf.losses.softmax_cross_entropy(
one_hot_target_class,
logits,
label_smoothing=label_smoothing,
weights=1.0)
cross_entropy += tf.losses.softmax_cross_entropy(
one_hot_target_class,
end_points['AuxLogits'],
label_smoothing=label_smoothing,
weights=aux_weight)
compute_gradient = tf.gradients(cross_entropy, x_adv)[0]
if model_mode == 2:
with tf.variable_scope('model_b'):
with slim.arg_scope(inception.inception_v3_arg_scope()):
logits_2, end_points_2 = inception.inception_v3(
x_adv,
num_classes=num_classes,
is_training=False,
reuse=True)
cross_entropy_2 = tf.losses.softmax_cross_entropy(
one_hot_target_class,
logits_2,
label_smoothing=label_smoothing,
weights=1.0)
cross_entropy_2 += tf.losses.softmax_cross_entropy(
one_hot_target_class,
end_points_2['AuxLogits'],
label_smoothing=label_smoothing,
weights=aux_weight)
compute_gradient_2 = tf.gradients(cross_entropy_2, x_adv)[0]
equal_gradient_sign = tf.cast(
tf.equal(tf.sign(compute_gradient),
tf.sign(compute_gradient_2)), tf.float32)
compute_gradient = tf.multiply(
tf.add(compute_gradient, compute_gradient_2),
equal_gradient_sign)
gradient_clip = tf.clip_by_value(compute_gradient, -0.0001, 0.0001)
multiplier = 1.0
if model_mode == 3:
if i_iter < 13:
multiplier = (1 - 0.0025 * i_iter) * 2.5 * alpha
else:
multiplier = (1 - coeff_mul_alpha * i_iter) * 2.5 * alpha
elif model_mode == 2:
multiplier = (1 - coeff_mul_alpha * i_iter) * 3.6 * alpha
else:
multiplier = (1 - coeff_mul_alpha * i_iter) * 1.2 * alpha
x_next = x_adv - multiplier * tf.add(
0.5 * tf.sign(compute_gradient), 0.5 * gradient_clip * 10000)
x_next = tf.clip_by_value(x_next, x_min, x_max)
x_adv = x_next
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
all_vars = tf.global_variables()
model_a_vars = [
k for k in all_vars if k.name.startswith('model_a')
] # FLAGS.model_a_scope
model_b_vars = [
k for k in all_vars if k.name.startswith('model_b')
] # FLAGS.model_b_scope
model_c_vars = [
k for k in all_vars if k.name.startswith('model_c')
] # FLAGS.model_c_scope
model_a_checkpoint = 'inception_v3.ckpt'
model_b_checkpoint = 'adv_inception_v3.ckpt'
model_c_checkpoint = 'ens_adv_inception_resnet_v2.ckpt'
tf.train.Saver(convert_dict(model_a_vars,
model_a_checkpoint)).restore(
sess, model_a_checkpoint)
tf.train.Saver(convert_dict(model_b_vars,
model_b_checkpoint)).restore(
sess, model_b_checkpoint)
tf.train.Saver(convert_dict(model_c_vars,
model_c_checkpoint)).restore(
sess, model_c_checkpoint)
for filenames, images in load_images(FLAGS.input_dir, batch_shape):
adv_images = sess.run(x_adv, feed_dict={x_input: images})
save_images(adv_images, filenames, FLAGS.output_dir)
def generator_loss(gan_model, *args, **kwargs):
# Calculate the original loss from GAN
wass_loss = tfgan.losses.wasserstein_generator_loss(
gan_model, *args, **kwargs)
# Synthesis input images
adv_perbs = gan_model.generated_data * 128.0 + 128.0
adv_perbs_mask = tf.cast((adv_perbs[:, :, :, 0] < FLAGS.adv_filter) |
(adv_perbs[:, :, :, 1] < FLAGS.adv_filter) |
(adv_perbs[:, :, :, 2] < FLAGS.adv_filter),
tf.float32)
adv_perbs_mask = tf.tile(tf.expand_dims(adv_perbs_mask, 3),
[1, 1, 1, 3])
adv_perbs_large = tf.pad(
adv_perbs * adv_perbs_mask, [[0, 0]] +
[[int(math.floor(image_pad)),
int(math.ceil(image_pad))]] * 2 + [[0, 0]])
adv_transform_rot = tf.Variable(np.zeros((FLAGS.batch_size, 3, 3)),
dtype=tf.float32)
adv_transform_rot = tf.scatter_nd_update(
adv_transform_rot, [[x, 0, 0] for x in xrange(FLAGS.batch_size)],
tf.math.cos(adv_theta))
adv_transform_rot = tf.scatter_nd_update(
adv_transform_rot, [[x, 0, 1] for x in xrange(FLAGS.batch_size)],
-tf.math.sin(adv_theta))
adv_transform_rot = tf.scatter_nd_update(
adv_transform_rot, [[x, 0, 2] for x in xrange(FLAGS.batch_size)],
(1 - tf.math.cos(adv_theta) + tf.math.sin(adv_theta)) * 299.0 / 2)
adv_transform_rot = tf.scatter_nd_update(
adv_transform_rot, [[x, 1, 0] for x in xrange(FLAGS.batch_size)],
tf.math.sin(adv_theta))
adv_transform_rot = tf.scatter_nd_update(
adv_transform_rot, [[x, 1, 1] for x in xrange(FLAGS.batch_size)],
tf.math.cos(adv_theta))
adv_transform_rot = tf.scatter_nd_update(
adv_transform_rot, [[x, 1, 2] for x in xrange(FLAGS.batch_size)],
(1 - tf.math.cos(adv_theta) - tf.math.sin(adv_theta)) * 299.0 / 2)
adv_transform_rot = tf.scatter_nd_update(
adv_transform_rot, [[x, 2, 2] for x in xrange(FLAGS.batch_size)],
tf.ones(shape=(FLAGS.batch_size, )))
adv_transform_move = tf.Variable(np.zeros((FLAGS.batch_size, 3, 3)),
dtype=tf.float32)
adv_transform_move = tf.scatter_nd_update(
adv_transform_move, [[x, 0, 0] for x in xrange(FLAGS.batch_size)],
tf.ones(shape=(FLAGS.batch_size, )))
adv_transform_move = tf.scatter_nd_update(
adv_transform_move, [[x, 0, 2] for x in xrange(FLAGS.batch_size)],
adv_dx)
adv_transform_move = tf.scatter_nd_update(
adv_transform_move, [[x, 1, 1] for x in xrange(FLAGS.batch_size)],
tf.ones(shape=(FLAGS.batch_size, )))
adv_transform_move = tf.scatter_nd_update(
adv_transform_move, [[x, 1, 2] for x in xrange(FLAGS.batch_size)],
adv_dy)
adv_transform_move = tf.scatter_nd_update(
adv_transform_move, [[x, 2, 2] for x in xrange(FLAGS.batch_size)],
tf.ones(shape=(FLAGS.batch_size, )))
adv_transform = tf.reshape(
tf.matmul(adv_transform_rot, adv_transform_move), [-1, 9])[:, :8]
adv_perbs_trans = tf.contrib.image.transform(adv_perbs_large,
adv_transform)
adv_perbs_trans_mask = tf.cast((adv_perbs_trans[:, :, :, 0] > 0) &
(adv_perbs_trans[:, :, :, 1] > 0) &
(adv_perbs_trans[:, :, :, 2] > 0),
tf.float32)
adv_perbs_trans_mask = tf.tile(tf.expand_dims(adv_perbs_trans_mask, 3),
[1, 1, 1, 3])
adv_images = tf.tile(
tf.expand_dims(tf.cast(image_orig, tf.float32), 0),
[FLAGS.batch_size, 1, 1, 1])
adv_images = adv_perbs_trans * adv_perbs_trans_mask + adv_images * (
1 - adv_perbs_trans_mask)
adv_images_to_save = tf.identity(tf.map_fn(tf.image.encode_png,
tf.cast(
adv_images, tf.uint8),
dtype=tf.string),
name='adv_images_to_save')
tf.summary.image('adv_images',
tf.cast(adv_images, tf.uint8),
max_outputs=FLAGS.batch_size)
adv_inputs = adv_images / 255.0
with tf.variable_scope('Inception'):
with slim.arg_scope(inception.inception_v3_arg_scope()):
adv_logits, _ = inception.inception_v3(adv_inputs,
num_classes=1001,
is_training=False)
adv_logits_to_save = tf.identity(adv_logits, name='adv_logits_to_save')
tf.summary.histogram('generator_adv_classes',
tf.argmax(adv_logits, axis=1))
# Add the adversarial loss
with tf.name_scope('generator_loss'):
# Came from: https://github.com/carlini/nn_robust_attacks/blob/master/l2_attack.py
adv_targets = tf.one_hot(
tf.constant([FLAGS.adv_target] * FLAGS.batch_size,
dtype=tf.int32), 1001)
adv_target_logit = tf.reduce_sum(adv_targets * adv_logits, 1)
adv_others_logit = tf.reduce_max(
(1 - adv_targets) * adv_logits - (adv_targets * 10000), 1)
adv_loss = tf.reduce_mean(
tf.maximum(
0.0, adv_others_logit - adv_target_logit +
FLAGS.adv_confidence))
tf.summary.scalar('generator_adv_loss', adv_loss)
total_loss = wass_loss + FLAGS.adv_lambda * adv_loss
tf.summary.scalar('generator_total_loss', total_loss)
return total_loss
def main():
params = {
'batch_size': 16,
'checkpoint_path':
"test_models/inception_resnet_v2/ens/ens_adv_inception_resnet_v2.ckpt", # "test_models/inception_v3/ens3/ens3_adv_inception_v3.ckpt",
'images_benign_dir':
'/home/yantao/datasets/imagenet_100image/original',
'images_adv_dir': "images_adv",
'model_name': 'inception_resnet_v2',
'IMAGE_SIZE': 299,
"NUM_CLASSES": 1001,
}
sess = tf.Session()
images_benign, _ = load_images(dir_path=params['images_benign_dir'],
size=[299, 299],
zero_one_bound=True)
images_adv, _ = load_images(dir_path=params['images_adv_dir'],
size=[299, 299],
zero_one_bound=True)
input_ph = tf.placeholder(
tf.float32,
shape=[None, params['IMAGE_SIZE'], params['IMAGE_SIZE'], 3])
if params['model_name'] == 'inception_v3':
with slim.arg_scope(inception.inception_v3_arg_scope()):
logits, _ = inception.inception_v3(
input_ph,
num_classes=params['NUM_CLASSES'],
is_training=False,
reuse=None)
elif params['model_name'] == 'inception_resnet_v2':
with slim.arg_scope(
inception_resnet_v2.inception_resnet_v2_arg_scope()):
logits, _ = inception_resnet_v2.inception_resnet_v2(
input_ph,
num_classes=params['NUM_CLASSES'],
is_training=False,
reuse=None)
else:
raise ValueError('Invalid model name: %s' % (params['model_name']))
saver = tf.train.Saver()
saver.restore(sess, params['checkpoint_path'])
idx = 0
pbar = tqdm(total=int(len(images_benign) / params['batch_size']) + 1)
pd_labels_benign = []
pd_labels_adv = []
while (idx * params['batch_size'] <= len(images_benign)):
if (idx + 1) * params['batch_size'] <= len(images_benign):
temp_images_benign = images_benign[idx *
params['batch_size']:(idx + 1) *
params['batch_size']]
temp_images_adv = images_adv[idx * params['batch_size']:(idx + 1) *
params['batch_size']]
else:
temp_images_benign = images_benign[idx * params['batch_size']:]
temp_images_adv = images_adv[idx * params['batch_size']:]
feed_dict = {
input_ph: temp_images_benign,
}
temp_outputs = sess.run(logits, feed_dict=feed_dict)
temp_labels = temp_outputs.argmax(axis=1).tolist()
pd_labels_benign += temp_labels
feed_dict = {
input_ph: temp_images_adv,
}
temp_outputs = sess.run(logits, feed_dict=feed_dict)
temp_labels = temp_outputs.argmax(axis=1).tolist()
pd_labels_adv += temp_labels
pbar.update()
idx += 1
pbar.close()
corrects = 0
for l_benign, l_adv in zip(pd_labels_benign, pd_labels_adv):
if l_adv == l_benign:
corrects += 1
acc = float(corrects) / len(pd_labels_benign)
print('labels benign: ', pd_labels_benign)
print('labels adv: ', pd_labels_adv)
print('acc: ', acc)
train_size = imagenet_size()
num_batches = int(float(train_size) / BATCH_SIZE)
x = tf.placeholder(dtype=tf.float32, shape=[None, 299, 299, 3])
y = tf.placeholder(dtype=tf.float32, shape=[None, 1000])
lr = tf.placeholder(tf.float32)
# keep_prob = tf.placeholder(tf.float32)
with tf.device('/cpu:0'):
q = tf.FIFOQueue(BATCH_SIZE, [tf.float32, tf.float32],
shapes=[[299, 299, 3], [1000]])
enqueue_op = q.enqueue_many([x, y])
x_b, y_b = q.dequeue_many(BATCH_SIZE / 4)
logits, end_points = inception.inception_v3(x_b, is_training=True)
with tf.name_scope('cross_entropy'):
cross_entropy = tf.nn.softmax_cross_entropy_with_logits(labels=y_b,
logits=logits)
with tf.name_scope('l2_loss'):
l2_loss = tf.reduce_sum(
5e-4 *
tf.stack([tf.nn.l2_loss(v) for v in tf.get_collection('weights')]))
tf.summary.scalar('l2_loss', l2_loss)
with tf.name_scope('loss'):
loss = cross_entropy + l2_loss
tf.summary.scalar('loss', loss)
def calculate_logits(self, inputs):
with slim.arg_scope(inception.inception_v3_arg_scope()):
logits, _ = inception.inception_v3(inputs, num_classes=self.n_classes, is_training=False)
return logits
def _iterative_fast_gradient_attack(ensemble,
eps,
alpha,
num_iter,
tic=time.time()):
""" Constructs the tensorflow graph that implements this attack.
Note this does not execute the attack; that is for the caller to do.
"""
# hardcode since we are in a hurry...
batch_shape = [FLAGS.batch_size, FLAGS.image_height, FLAGS.image_width, 3]
num_classes = 1001
# Network inputs
x_input = tf.placeholder(tf.float32, shape=batch_shape)
x_max = tf.clip_by_value(x_input + eps, -1.0, 1.0)
x_min = tf.clip_by_value(x_input - eps, -1.0, 1.0)
target_class_input = tf.placeholder(tf.int32, shape=[FLAGS.batch_size])
one_hot_target_class = tf.one_hot(
target_class_input,
num_classes) # note: labels are smoothed in loss function
# initially x_adv is the same as the input
x_adv = x_input
# initialize networks
if 'InceptionV3' in ensemble:
with slim.arg_scope(inception.inception_v3_arg_scope()):
inception.inception_v3(x_input,
num_classes=num_classes,
is_training=False,
scope='InceptionV3')
if 'resnet_v2_101' in ensemble:
with slim.arg_scope(resnet_v2.resnet_arg_scope()):
resnet_v2.resnet_v2_101(x_input,
num_classes=num_classes,
is_training=False,
scope='resnet_v2_101')
if 'InceptionResnetV2' in ensemble:
with slim.arg_scope(ir.inception_resnet_v2_arg_scope()):
ir.inception_resnet_v2(x_input,
num_classes=num_classes,
is_training=False,
scope='InceptionResnetV2')
if 'Adv-InceptionV3' in ensemble:
with slim.arg_scope(inception.inception_v3_arg_scope()):
inception.inception_v3(x_input,
num_classes=num_classes,
is_training=False,
scope='Adv-InceptionV3')
print('[INFO]: initial setup; net runtime: %0.2f seconds\n' %
(time.time() - tic))
print('[INFO]: ensemble contents:', ensemble)
for ts in range(num_iter):
# TODO: this code is gross; clean up if time permits...
#--------------------------------------------------
# contribution from InceptionV3
#--------------------------------------------------
if 'InceptionV3' in ensemble:
with slim.arg_scope(inception.inception_v3_arg_scope()):
logits, end_points = inception.inception_v3(
x_adv,
num_classes=num_classes,
is_training=False,
reuse=True,
scope='InceptionV3')
cross_entropy = tf.losses.softmax_cross_entropy(
one_hot_target_class, logits, label_smoothing=0.1, weights=1.0)
cross_entropy += tf.losses.softmax_cross_entropy(
one_hot_target_class,
end_points['AuxLogits'],
label_smoothing=0.1,
weights=0.4)
else:
cross_entropy = 0
#--------------------------------------------------
# contribution from resnet_v2
#--------------------------------------------------
if 'resnet_v2_101' in ensemble:
with slim.arg_scope(resnet_v2.resnet_arg_scope()):
logits_2, _ = resnet_v2.resnet_v2_101(x_adv,
num_classes=num_classes,
is_training=False,
reuse=True,
scope='resnet_v2_101')
# Note: ResnetV2 logits has shape (BATCH_SIZE, 1, 1, N_CLASSES)
# Hence the squeeze below.
cross_entropy_2 = tf.losses.softmax_cross_entropy(
one_hot_target_class,
tf.squeeze(logits_2),
label_smoothing=0.1,
weights=1.0)
else:
cross_entropy_2 = 0
#--------------------------------------------------
# contribution from ensemble {resnet_v2, inception_v3}
#--------------------------------------------------
if 'InceptionResnetV2' in ensemble:
with slim.arg_scope(ir.inception_resnet_v2_arg_scope()):
logits_3, _ = ir.inception_resnet_v2(x_adv,
num_classes=num_classes,
is_training=False,
reuse=True,
scope='InceptionResnetV2')
cross_entropy_3 = tf.losses.softmax_cross_entropy(
one_hot_target_class,
logits_3,
label_smoothing=0.1,
weights=1.0)
else:
cross_entropy_3 = 0
#--------------------------------------------------
# contribution from InceptionV3-adv
#--------------------------------------------------
if 'Adv-InceptionV3' in ensemble:
with slim.arg_scope(inception.inception_v3_arg_scope()):
logits_4, end_points_4 = inception.inception_v3(
x_adv,
num_classes=num_classes,
is_training=False,
reuse=True,
scope='Adv-InceptionV3')
cross_entropy_4 = tf.losses.softmax_cross_entropy(
one_hot_target_class,
logits_4,
label_smoothing=0.1,
weights=1.0)
cross_entropy_4 += tf.losses.softmax_cross_entropy(
one_hot_target_class,
end_points_4['AuxLogits'],
label_smoothing=0.1,
weights=0.4)
else:
cross_entropy_4 = 0
print(
'[INFO]: added %d models for timestep %d/%d; net runtime: %0.2f seconds'
% (len(ensemble), ts + 1, num_iter, time.time() - tic))
#--------------------------------------------------
# gradient step
#--------------------------------------------------
cross_entropy_avg = (cross_entropy + cross_entropy_2 +
cross_entropy_3 + cross_entropy_4) / len(ensemble)
nabla_x = tf.gradients(cross_entropy_avg, x_adv)[0]
# EXPERIMENT: add some random noise to gradient (avoid "overfitting"?)
#if False:
# print('[WARNING]: using experimental noisy gradient!')
# nabla_x = nabla_x + tf.random_normal(tf.shape(nabla_x), mean=0, stddev=1e-2)
# EXPERIMENT: avoid moving in directions corresponding to small values of gradient
#if False:
# print('[WARNING]: using experimental gradient clipping!')
# # 1e-2 too large of a threshold
# nabla_x = tf.where(tf.abs(nabla_x) < 1e-3, tf.zeros(tf.shape(nabla_x)), nabla_x)
x_next = x_adv - alpha * tf.sign(nabla_x)
# Always clip at the end
x_next = tf.clip_by_value(x_next, x_min, x_max)
x_adv = x_next
#--------------------------------------------------
# set up model weight loading
#--------------------------------------------------
savers = []
if 'InceptionV3' in ensemble:
savers.append(
(tf.train.Saver(slim.get_model_variables(scope='InceptionV3')),
'./Weights/inception_v3.ckpt'))
if 'resnet_v2_101' in ensemble:
savers.append(
(tf.train.Saver(slim.get_model_variables(scope='resnet_v2_101')),
'./Weights/resnet_v2_101.ckpt'))
if 'InceptionResnetV2' in ensemble:
savers.append((tf.train.Saver(
slim.get_model_variables(scope='InceptionResnetV2')),
'./Weights/ens_adv_inception_resnet_v2.ckpt'))
if 'Adv-InceptionV3' in ensemble:
savers.append(
(tf.train.Saver(slim.get_model_variables(scope='Adv-InceptionV3')),
'./Weights/InceptionV3-adv.ckpt'))
print('[INFO]: FG(S)M setup complete; took %0.2f seconds\n' %
(time.time() - tic))
return x_adv, [x_input, target_class_input], savers
image = imresize(image, (target_width, target_height))
return image.astype(np.float32) / 255
from tensorflow.contrib.slim.nets import inception
import tensorflow.contrib.slim as slim
tf.reset_default_graph()
X = tf.placeholder(tf.float32, shape = [None, height, width, channels], name = 'X')
training = tf.placeholder_with_default(False, shape = [])
with slim.arg_scope(inception.inception_v3_arg_scope()):
logits, end_points = inception.inception_v3(X, num_classes = 1001, is_training = training)
saver = tf.train.Saver()
prelogits = tf.squeeze(end_points['PreLogits'], axis = [1,2])
n_outputs = len(bear_classes)
with tf.name_scope('new_output'):
bear_logits = tf.layers.dense(prelogits, n_outputs, name = 'bear_logits')
y_proba = tf.nn.softmax(bear_logits, name = 'Y_proba')
y = tf.placeholder(tf.int32, shape = [None])
training_rate = .01
with tf.name_scope('train'):
def _inception_v3(x):
with tf.name_scope("inception_v3"):
x_ = tf.image.resize_images(x, (299, 299))
y, _ = inception.inception_v3(x_, num_classes=10, is_training=True)
return y