class inception:
im_size = inception_v4.default_image_size
names = imagenet.create_readable_names_for_imagenet_labels()
def __init__(self, imgs, sess=None, weights=None):
self.imgs = imgs
self.sess = sess
self.model()
@classmethod
def preprocess(cls, imgs):
images = images[:, :, :, 3].astype(np.float32)
images = np.divide(images, 255.)
images = np.subtract(images, 0.5)
images = np.multiply(images, 2.0)
images = tf.convert_to_tensor(images)
return images
def model(self):
with slim.arg_scope(inception_v4_arg_scope()):
logits, _ = inception_v4(processed_images,
num_classes=1001,
is_training=False)
self.probs = tf.nn.softmax(logits)
init_fn = slim.assign_from_checkpoint_fn(
'inception_v4.ckpt', slim.get_model_variables('InceptionV4'))
init_fn(self.sess)
class inception:
im_size = inception_v4.default_image_size
names = imagenet.create_readable_names_for_imagenet_labels()
def __init__(self, imgs, sess=None, weights=None, reuse=None):
self.imgs = imgs
self.sess = sess
self.model(reuse)
print('\nCONSTRUCTING\n')
@classmethod
def preprocess(cls, images):
images = images[:, :, :, :3].astype(np.float32)
images = np.divide(images, 255.)
images = np.subtract(images, 0.5)
images = np.multiply(images, 2.0)
return images
def model(self, reuse):
with slim.arg_scope(inception_v4_arg_scope()):
self.logits, _ = inception_v4(self.imgs,
num_classes=1001,
reuse=reuse,
is_training=False)
self.probs = tf.nn.softmax(self.logits)[:, 1:]
init_fn = slim.assign_from_checkpoint_fn(WEIGHTS_FILE,
slim.get_model_variables())
init_fn(self.sess)
def predict(image, version='V3'):
tf.reset_default_graph()
# Process the image
raw_image, processed_image = process_image(image)
print(raw_image.shape)
class_names = imagenet.create_readable_names_for_imagenet_labels()
# Create a placeholder for the images
X = tf.placeholder(tf.float32, [None, 299, 299, 3], name="X")
'''
inception_v3 function returns logits and end_points dictionary
logits are output of the network before applying softmax activation
'''
if version.upper() == 'V3':
print("V3!!")
model_ckpt_path = INCEPTION_V3_CKPT_PATH
with tf.contrib.slim.arg_scope(inception_v3.inception_v3_arg_scope()):
# Set the number of classes and is_training parameter
logits, end_points = inception_v3.inception_v3(X, num_classes=1001, is_training=False)
elif version.upper() == 'V4':
model_ckpt_path = INCEPTION_V4_CKPT_PATH
with tf.contrib.slim.arg_scope(inception_v4.inception_v4_arg_scope()):
# Set the number of classes and is_training parameter
# Logits
logits, end_points = inception_v4.inception_v4(X, num_classes=1001, is_training=False)
predictions = end_points.get('Predictions', 'No key named predictions')
saver = tf.train.Saver()
with tf.Session() as sess:
print("model_ckpt_path", model_ckpt_path)
saver.restore(sess, model_ckpt_path)
prediction_values = predictions.eval({X: processed_image})
try:
# Add an index to predictions and then sort by probability
prediction_values = [(i, prediction) for i, prediction in enumerate(prediction_values[0,:])]
prediction_values = sorted(prediction_values, key=lambda x: x[1], reverse=True)
# Plot the image
#plot_color_image(raw_image)
#plt.show()
print("Using Inception_{} CNN\nPrediction: Probability\n".format(version))
# Display the image and predictions
for i in range(10):
predicted_class = class_names[prediction_values[i][0]]
probability = prediction_values[i][1]
print("{}: {:.2f}%".format(predicted_class, probability*100))
# If the predictions do not come out right
except:
print(predictions)
def showImage(np_image_raw, np_probabilities):
names = imagenet.create_readable_names_for_imagenet_labels()
np_probability = np_probabilities[0, :]
sorted_inds = [j[0] for j in sorted(enumerate(-np_probability), key=lambda x:x[1])]
plt.figure()
plt.imshow(np_image_raw.astype(np.uint8))
plt.axis('off')
plt.show()
for k in range(5):
index = sorted_inds[k]
# Shift the index of a class name by one.
print('Probability %0.2f%% => [%s]' % (np_probability[index] * 100, names[index+1]))
'../../poggio-urop-data/ILSVRC2-12_img_val/ILSVRC2012_val_00000002.JPEG',
mode='RGB')
image = imresize(image, (image_size, image_size))
image2 = imresize(image2, (image_size, image_size))
imgs = tf.placeholder(tf.float32, [None, image_size, image_size, 3])
images = np.array([image, image2])
sess = tf.Session()
network = model(imgs, sess)
processed_images = inception.preprocess(images)
probabilities = np.array(
sess.run(network.probs, feed_dict={network.imgs: processed_images}))
# with slim.arg_scope(inception_v4_arg_scope()):
# logits, _ = inception_v4(processed_images, num_classes=1001, is_training=False)
# probabilities = tf.nn.softmax(logits)
#
# init_fn = slim.assign_from_checkpoint_fn('inception_v4.ckpt', slim.get_model_variables('InceptionV4'))
#
# with tf.Session() as sess:
# init_fn(sess)
# probabilities = sess.run(probabilities, feed_dict={inception.imgs: images})
names = imagenet.create_readable_names_for_imagenet_labels()
print(probabilities.shape)
for prob in probabilities:
inds = np.argsort(prob)[::-1]
for j in range(5):
print(names[inds[j]])
eps = 2.0 * max_epsilon / 255.0
batch_shape = (batch_size, image_height, image_width, 3)
num_classes = 1001
categories = pd.read_csv(
"../preReq/nips-2017-adversarial-learning-development-set/categories.csv")
image_classes = pd.read_csv(
"../preReq/nips-2017-adversarial-learning-development-set/images.csv")
filterCutOff = 0.9
#power = 10
samples = 100
e = math.e
nameV3 = create_readable_names_for_imagenet_labels()
def best_fit_distribution(data, bins=1000, ax=None):
"""Model data by finding best fit distribution to data"""
# Get histogram of original data
y, x = np.histogram(data, bins=bins, density=True)
x = (x + np.roll(x, -1))[:-1] / 2.0
# Distributions to check
DISTRIBUTIONS = [
st.cauchy, st.dweibull, st.gennorm
#st.alpha,st.anglit,st.arcsine,st.beta,st.betaprime,st.bradford,st.burr,st.cauchy,st.chi,st.chi2,st.cosine, #cauchy
#st.dgamma,st.dweibull,st.erlang,st.expon,st.exponnorm,st.exponweib,st.exponpow,st.f,st.fatiguelife,st.fisk, #dweibull
#st.foldcauchy,st.foldnorm,st.frechet_r,st.frechet_l,st.genlogistic,st.genpareto,st.gennorm,st.genexpon, #gennorm
#st.genextreme,st.gausshyper,st.gamma,st.gengamma,st.genhalflogistic,st.gilbrat,st.gompertz,st.gumbel_r,
