def main():
batch_img, fns = list_load("./../data_imagenet", images)
for sal in sal_type: # for each gradient type
for init in model: # random or trained
print(sal)
print(init)
tf.reset_default_graph() # erase the graph
sess = tf.Session() # start a new session
vgg16 = prepare_keras_vgg16(sal, init, sess)
graph = tf.get_default_graph()
input = graph.get_tensor_by_name('input_1:0')
logits = graph.get_tensor_by_name('predictions/BiasAdd:0')
num_to_viz = 50
# shape = (num_to_viz, num_input_images, 224, 224, 3)
# TF Graph
saliencies = super_saliency(logits, input, num_to_viz)
# shape = (num_input_images, num_to_viz, 224, 224, 3)
saliencies_val = sess.run(saliencies, feed_dict={input: batch_img})
saliencies_val_trans = np.transpose(saliencies_val, (1, 0, 2, 3, 4))
for idx, name in enumerate(fns):
save_dir = "vgg_keras/{}/{}/{}/".format(name, init, sal)
for index, image in enumerate(saliencies_val_trans[idx]):
simple_plot(image, name + str(index), save_dir)
sess.close()
def main():
for sal in sal_type:
tf.reset_default_graph()
sess = tf.Session()
vgg = prepare_vgg(sal, None, 'trained', sess)
batch_img, fns = list_load("./../data_imagenet", images)
# TF Graph
saliency = tf.gradients(vgg.maxlogit, vgg.imgs)[0]
saliency_vals, prob_vals = sess.run([saliency, vgg.probs],
feed_dict={vgg.images: batch_img})
for idx, sal in enumerate(saliency_vals):
# normalize
min = np.min(sal)
sal -= min
max = np.max(sal)
sal /= max
sal *= 225
print(class_names[np.argmax(prob_vals[idx])])
prob_vals2 = sess.run(vgg.probs, feed_dict={vgg.images: saliency_vals})
for prob in prob_vals2:
print(class_names[np.argmax(prob)])
sess.close()
def main():
# the length of the logits vector
# the only requirement is a positive integer
# can be randomized
output_dim = 100
# reset graph & start session
tf.reset_default_graph()
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
# prepare a randomly initialized shallow CNN with the gradient has been overwritten to "GBP"
# in terms of the GBP reconstruction, this model can be any as long as it's a ConvNet.
model = prepare_GBP_shallow_CNN(sess=sess, output_dim=output_dim)
# tf operation for GBP reconstruction
tfOp_gbp_reconstruction = GBP_Reconstruction(model, output_dim)
# [num_examples, 32, 32, 3]
data_dir = './Plots/SVHN'
names = [
"TEST_1_DeepFool.png",
"TEST_1_FGSM.png",
"TEST_1_IterGS.png",
"TEST_1_IterG.png",
"TEST_0_DeepFool.png",
"TEST_0_FGSM.png",
"TEST_0_IterGS.png",
"TEST_0_IterG.png",
"TEST_2_DeepFool.png",
"TEST_2_FGSM.png",
"TEST_2_IterGS.png",
"TEST_2_IterG.png",
]
X_test_ori, _ = list_load(data_dir, names, size=(32, 32))
# map each training example to its corresponding GBP reconstruction
# X_train_gbp = Map(tfOp_gbp_reconstruction, model.layers_dic['images'], X_train_ori, sess)
X_test_gbp = Map(tfOp_gbp_reconstruction, model.layers_dic['images'],
X_test_ori, sess)
# save to pickle
f = open('./{}.pkl'.format('SVHN-Plot'), 'wb')
# pkl.dump((X_train_gbp, y_train), f, -1)
pkl.dump(X_test_gbp, f, -1)
f.close()
# # visualization
# grid_plot([3, 6], X_train_ori[:18], 'Original_CIFAR10', './Visualization', 'Examples_Ori_CIFAR10')
# grid_plot([3, 6], X_train_gbp[:18], 'GBP_CIFAR10', './Visualization', 'Examples_GBP_CIFAR10')
for index, image in enumerate(X_test_gbp):
simple_plot(image, names[index] + 'GBP', './Plots/SVHN/')
def main():
for sal in sal_type:
for init in model_type:
tf.reset_default_graph()
sess = tf.Session()
vgg = prepare_vgg(sal, None, init, sess)
batch_img, fns = list_load("./../data_imagenet", images)
job(vgg, sal, sess, init, batch_img, fns)
sess.close()
def main():
for sal in sal_type:
for init in model_type:
tf.reset_default_graph()
sess = tf.Session()
vgg = prepare_vgg(sal, None, init, sess)
batch_img, fns = list_load("./../data_imagenet", images)
for idx, image in enumerate(batch_img):
job(vgg, sal, sess, init, np.expand_dims(image, axis=0), [fns[idx]])
sess.close()
def main():
for sal in sal_type:
for n in N:
for init in model_type:
tf.reset_default_graph()
sess = tf.Session()
resnet = prepare_resnet(sal, init, sess, n)
batch_img, fns = list_load("./../data_imagenet", images)
job(resnet, sal, sess, init, batch_img, fns, n)
sess.close()
def main():
for sal in sal_type:
for init in model_type:
tf.reset_default_graph()
sess = tf.Session()
vgg = prepare_vgg(sal, None, init, sess)
batch_img, fns = list_load("./../data_imagenet", images)
for idx, image in enumerate(batch_img):
noise_image = image + np.random.normal(
loc=0.0, scale=10.0, size=[224, 224, 3])
job(vgg, sal, sess, init, np.expand_dims(noise_image, axis=0),
[fns[idx] + '_noisy'])
sess.close()
def main():
for sal in sal_type:
for pool in [True, False]: # with and without pooling
tf.reset_default_graph()
sess = tf.Session()
cnn3 = prepare_cnn3(sal, sess, pool=pool)
batch_img, fns = list_load("./../data_imagenet", images)
for idx, image in enumerate(batch_img):
job(cnn3, sal, sess, pool, np.expand_dims(image, axis=0), [fns[idx]])
sess.close()
def main():
for sal in sal_type:
for idx, layer in enumerate(layers):
tf.reset_default_graph()
sess = tf.Session()
vgg = prepare_vgg(sal, idx, 'only', sess)
batch_img, fns = list_load("./../data_imagenet", images)
# TF Graph
saliency = tf.gradients(vgg.maxlogit, vgg.imgs)[0]
saliency_vals = sess.run(saliency,
feed_dict={vgg.images: batch_img})
for index, name in enumerate(fns):
save_dir = 'value_only/{}/{}'.format(name, layer)
simple_plot(saliency_vals[index], name + '_only_' + layer,
save_dir)
sess.close()
def sparse_ratio(vgg, sess, layer_name, image_name, h_idx=None, v_idx=None):
"""
Notice that the sparse ratio will be calculated w.r.t the entire batch!
"""
# get the target layer tensor
if h_idx == None and v_idx == None:
target_tensor = vgg.layers_dic[layer_name]
# get the target "depth row" from the layer tensor
# corresponding to one image patch filtered by all the filters
elif h_idx != None and v_idx != None:
target_tensor = vgg.layers_dic[layer_name][:, h_idx, v_idx]
else:
raise Exception("Error in sparse_ratio !")
batch_img, fns = list_load("./../data_imagenet", [image_name])
target_tensor_val = sess.run(target_tensor,
feed_dict={vgg.images: batch_img})
target_tensor_val[target_tensor_val > 0] = 1.0
return np.sum(target_tensor_val) / np.size(target_tensor_val)
def main():
num_iterations = 100
step_size = 1e-1
image_name = 'Dog_1.JPEG'
# how we would like the "saliency map" be different
diff_type = 'plain' # 'centermass', 'plain'
# define the special gradient for the "saliency map" calculation if necessary
gradient_type = 'PlainSaliency' # 'PlainSaliency', 'GuidedBackprop'
# how we would like to visualize the result gradient
viz_type = 'gradcam' # 'abs', 'plain', 'gradcam'
# for gradcam only
target_layer = 'pool5'
# load the image
batch_img, fns = list_load("./../data_imagenet", [image_name])
sess = tf.Session()
# prepare the networks
vgg_attack = prepare_vgg(gradient_type, 'softplus', 'maxpool', None,
'trained', sess) # used for attack
vgg = prepare_vgg(gradient_type, 'relu', 'maxpool', None, 'trained',
sess) # used for probing
print('Two Networks Prepared ... ')
sal = sal_maxlogit(vgg_attack, viz_type, target_layer)
D = sal_diff(diff_type, vgg_attack, batch_img, sal, sess)
# gradient
Dx = tf.gradients(D, vgg_attack.images)[0]
# the signed gradient
Dx_sign = tf.sign(Dx)
# record the results for each iteration
dict_step_to_image = {}
dict_step_to_dissimilarity = {}
dict_step_to_salmap = {}
dict_step_to_prediction = {}
dict_step_to_perturbation = {}
for step in range(num_iterations):
print('Step {}'.format(step))
if step == 0:
dict_step_to_image[0] = batch_img
dict_step_to_dissimilarity[0] = 0
dict_step_to_salmap[0] = sess.run(
sal_maxlogit(vgg, viz_type, target_layer),
feed_dict={vgg.images: batch_img})
dict_step_to_prediction[0] = np.argmax(
sess.run(vgg.probs, feed_dict={vgg.images: batch_img}))
dict_step_to_perturbation[0] = np.zeros(batch_img.shape)
continue
Dx_sign_val, D_val = sess.run(
[Dx_sign, D],
feed_dict={vgg_attack.images: dict_step_to_image[step - 1]})
sal_map_val, probs_val = sess.run(
[sal_maxlogit(vgg, viz_type, target_layer), vgg.probs],
feed_dict={vgg.images: dict_step_to_image[step - 1]})
dict_step_to_image[step] = dict_step_to_image[
step - 1] + step_size * Dx_sign_val
dict_step_to_perturbation[step] = step_size * Dx_sign_val
dict_step_to_salmap[step] = sal_map_val
dict_step_to_dissimilarity[step] = D_val
dict_step_to_prediction[step] = np.argmax(probs_val)
evaluate(image_name, diff_type, viz_type, dict_step_to_image,
dict_step_to_dissimilarity, dict_step_to_salmap,
dict_step_to_prediction, dict_step_to_perturbation,
num_iterations)
sess.close()