Я пытаюсь запустить проект Github 'cleverhans', скрипт находится в папке cleverhans/example/face_adversarial_faces/facenet_fgsm.py, все зависимости установлены, но когда я запускаю python facenet_fgsm.py в виртуальной среде, терминал выдает эту ошибку 'IndexError: index3 выходит за пределы оси 0 с размером 1 ', как избавиться от этой проблемы?
Сценарий facenet_fgsm.py выглядит следующим образом:
import tensorflow.python.util.deprecation as deprecation
deprecation._PRINT_DEPRECATION_WARNINGS = False
import cv2
from skimage.transform import resize
import matplotlib.pyplot as plt
import tensorflow as tf
import numpy as np
from cleverhans.cleverhans.model import Model
from cleverhans.cleverhans.attacks.fast_gradient_method import FastGradientMethod
from cleverhans.examples.facenet_adversarial_faces.set_loader import load_testset
from facenet.src.facenet import load_model
class InceptionResnetV1Model(Model):
model_path = "~/Documents/fnatk/facenet/src/models/20190702-103304"
def __init__(self):
super(InceptionResnetV1Model, self).__init__(scope='model')
# Load Facenet CNN
load_model(self.model_path)
# Save input and output tensors references
graph = tf.get_default_graph()
self.face_input = graph.get_tensor_by_name("input:0")
self.embedding_output = graph.get_tensor_by_name("embeddings:0")
def convert_to_classifier(self):
# Create victim_embedding placeholder
self.victim_embedding_input = tf.placeholder(
tf.float32,
shape=(None, 128))
# Squared Euclidean Distance between embeddings
distance = tf.reduce_sum(
tf.square(self.embedding_output - self.victim_embedding_input),
axis=1)
# Convert distance to a softmax vector
# 0.99 out of 4 is the distance threshold for the Facenet CNN
threshold = 0.99
score = tf.where(
distance > threshold,
0.5 + ((distance - threshold) * 0.5) / (4.0 - threshold),
0.5 * distance / threshold)
reverse_score = 1.0 - score
self.softmax_output = tf.transpose(tf.stack([reverse_score, score]))
# Save softmax layer
self.layer_names = []
self.layers = []
self.layers.append(self.softmax_output)
self.layer_names.append('logits')
# define logits
def calc(self, image):
model_def = "models.inception_resnet_v1"
network = importlib.import_module(model_def)
prelogits, _ = network.inference(images_placeholder, 1.0,
phase_train=False, weight_decay=0.0, reuse=False)
batch_norm_params = {
# Decay for the moving averages
'decay': 0.995,
# epsilon to prevent 0s in variance
'epsilon': 0.001,
# force in-place updates of mean and variance estimates
'updates_collections': None,
# Moving averages ends up in the trainable variables collection
'variables_collections': [tf.GraphKeys.TRAINABLE_VARIABLES],
# Only update statistics during training mode
'is_training': False
}
bottleneck = slim.fully_connected(prelogits, 128, activation_fn=None,
weights_initializer=tf.truncated_normal_initializer(stddev=0.1),
weights_regularizer=slim.l2_regularizer(0.0),
normalizer_fn=slim.batch_norm,
normalizer_params=batch_norm_params,
scope='Bottleneck', reuse=False)
logits = slim.fully_connected(bottleneck, 2, activation_fn=None,
weights_initializer=tf.truncated_normal_initializer(stddev=0.1),
weights_regularizer=slim.l2_regularizer(0.),
scope='Logits', reuse=False)
return logits
def fprop(self, x, set_ref=False):
return dict(zip(self.layer_names, self.layers))
with tf.Graph().as_default():
with tf.Session() as sess:
# Load model
model = InceptionResnetV1Model()
# Convert to classifier
model.convert_to_classifier()
# Load pairs of faces and their labels in one-hot encoding
faces1, faces2, labels = load_testset(10)
# Create victims' embeddings using Facenet itself
graph = tf.get_default_graph()
phase_train_placeholder = graph.get_tensor_by_name("phase_train:0")
feed_dict = {model.face_input: faces2,
phase_train_placeholder: False}
victims_embeddings = sess.run(
model.embedding_output, feed_dict=feed_dict)
def fgsm_attack(eps, steps):
# Define FGSM for the model
alpha = eps / steps
fgsm = FastGradientMethod(model)
fgsm_params = {'eps': alpha,
'clip_min': 0.,
'clip_max': 1.}
adv_x = fgsm.generate(model.face_input, **fgsm_params)
# Run FGSM
adv = faces1
for i in range(steps):
print("FGSM step " + str(i + 1))
feed_dict = {model.face_input: adv,
model.victim_embedding_input: victims_embeddings,
phase_train_placeholder: False}
adv = sess.run(adv_x, feed_dict=feed_dict)
# Test accuracy of the model
batch_size = graph.get_tensor_by_name("batch_size:0")
feed_dict = {model.face_input: faces1,
model.victim_embedding_input: victims_embeddings,
phase_train_placeholder: False,
batch_size: 64}
real_labels = sess.run(model.softmax_output, feed_dict=feed_dict)
accuracy = np.mean(
(np.argmax(labels, axis=-1)) == (np.argmax(real_labels, axis=-1))
)
print('Accuracy: ' + str(accuracy * 100) + '%')
# Test accuracy against adversarial examples
feed_dict = {model.face_input: adv,
model.victim_embedding_input: victims_embeddings,
phase_train_placeholder: False,
batch_size: 64}
adversarial_labels = sess.run(
model.softmax_output, feed_dict=feed_dict)
same_faces_index = np.where((np.argmax(labels, axis=-1) == 0))
different_faces_index = np.where((np.argmax(labels, axis=-1) == 1))
dodge_acc = np.mean(
(np.argmax(labels[same_faces_index], axis=-1)) ==
(np.argmax(adversarial_labels[same_faces_index], axis=-1))
)
print('Accuracy against adversarial examples for '
+ 'same person faces (dodging): '
+ str(dodge_acc * 100)
+ '%')
impersonate_acc = np.mean(
(np.argmax(labels[different_faces_index], axis=-1)) == (
np.argmax(adversarial_labels[different_faces_index], axis=-1))
)
print('Accuracy against adversarial examples for '
+ 'different people faces (impersonation): '
+ str(impersonate_acc * 100)
+ '%')
return dodge_acc, impersonate_acc
# Doesn't work atm
def plot_graph(eps, dodge_accs, impersonate_accs):
plt.plot(eps, dodge_accs, label = "dodge")
plt.plot(eps, impersonate_accs, label = "impersonation")
plt.xlabel("Epsilon")
plt.ylabel("Accuracy (%)")
plt.title("Accuracy against Epsilon graph")
plt.legend()
plt.show()
#all_eps = [0.01, 0.05, 0.1, 0.2, 0.3, 0.5]
all_eps = [0.01]
all_steps = [1]
dodge_accs = []
impersonate_accs = []
#steps = 10
# eps = 0.3
for eps in all_eps:
for steps in all_steps:
print("eps: " + str(eps) + " steps: " + str(steps))
dodge_acc, impersonate_acc = fgsm_attack(eps, steps)
dodge_accs.append(dodge_acc)
impersonate_accs.append(impersonate_acc)
#plot_graph(np.array(eps), np.array(dodge_accs), np.array(impersonate_accs))
И терминал броситьпоявляется следующая ошибка:
Traceback (most recent call last):
File "facenet_fgsm.py", line 191, in <module>
dodge_acc, impersonate_acc = fgsm_attack(eps, steps)
File "facenet_fgsm.py", line 151, in fgsm_attack
(np.argmax(adversarial_labels[same_faces_index], axis=-1))
IndexError: index 3 is out of bounds for axis 0 with size 1
Я ожидаю успешного запуска этого скрипта.