def show_mask_norms(mask_folder, model_name = 'gtsrb', out_png=False):
options = Options()
options.model_name = model_name
options = justify_options_for_model(options, model_name)
options.data_subset = 'validation'
options.batch_size = 1
options.num_epochs = 1
options.net_mode = 'backdoor_def'
options.load_mode = 'all'
options.fix_level = 'all'
options.build_level = 'mask_only'
options.selected_training_labels = None
options.gen_ori_label = False
ld_paths = dict()
root_folder = mask_folder
print(root_folder)
dirs = os.listdir(root_folder)
for d in dirs:
tt = d.split('_')[0]
if len(tt) == 0:
continue
try:
tgt_id = int(tt)
except:
continue
ld_paths[tgt_id] = get_last_checkpoint_in_folder(os.path.join(root_folder,d))
print(ld_paths)
model, dataset, input_list, feed_list, out_op, aux_out_op = get_output(options, model_name=model_name)
model.add_backbone_saver()
mask_abs = dict()
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
import cv2
init_op = tf.global_variables_initializer()
local_var_init_op = tf.local_variables_initializer()
table_init_ops = tf.tables_initializer() # iterator_initilizor in here
with tf.Session(config=config) as sess:
sess.run(init_op)
sess.run(local_var_init_op)
sess.run(table_init_ops)
for k, v in ld_paths.items():
print(v)
model.load_backbone_model(sess, v)
pattern, mask = sess.run([out_op, aux_out_op])
pattern = (pattern[0]+1)/2
mask = mask[0]
mask_abs[k] = np.sum(np.abs(mask))
if out_png:
show_name = '%d_pattern.png'%k
out_pattern = pattern*255
cv2.imwrite(show_name, out_pattern.astype(np.uint8))
show_name = '%d_mask.png'%k
out_mask = mask*255
cv2.imwrite(show_name, out_mask.astype(np.uint8))
show_name = '%d_color.png'%k
out_color = pattern*mask*255
cv2.imwrite(show_name, out_color.astype(np.uint8))
#cv2.imshow(show_name,out_pattern)
#cv2.waitKey()
#break
out_norms = np.zeros([len(mask_abs),2])
z = 0
for k,v in mask_abs.items():
out_norms[z][0] = k
out_norms[z][1] = v
z = z+1
print('===Results===')
np.save('out_norms.npy', out_norms)
print('write norms to out_norms.npy')
#return
vs = list(mask_abs.values())
import statistics
me = statistics.median(vs)
abvs = abs(vs - me)
mad = statistics.median(abvs)
rvs = abvs / (mad * 1.4826)
print(mask_abs)
print(rvs)
x_arr = [i for i in range(len(mask_abs))]
import matplotlib.pyplot as plt
plt.figure()
plt.boxplot(rvs)
plt.show()
def testtest(params):
print(FLAGS.net_mode)
print(FLAGS.batch_size)
print(FLAGS.num_epochs)
print(params.batch_size)
print(params.num_epochs)
options = Options()
options.data_mode = 'normal'
options.data_subset = 'train'
dataset = CifarDataset(options)
model = Model_Builder('cifar10', dataset.num_classes, options, params)
labels, images = dataset.data
images = np.asarray(images)
data_dict = dict()
data_dict['labels'] = labels
data_dict['images'] = images
save_to_mat('cifar-10.mat', data_dict)
exit(0)
p_class = dataset.get_input_preprocessor()
preprocessor = p_class(options.batch_size,
model.get_input_shapes('train'),
options.batch_size,
model.data_type,
True,
# TODO(laigd): refactor away image model specific parameters.
distortions=params.distortions,
resize_method='bilinear')
ds = preprocessor.create_dataset(batch_size=options.batch_size,
num_splits = 1,
batch_size_per_split = options.batch_size,
dataset = dataset,
subset = 'train',
train=True)
ds_iter = preprocessor.create_iterator(ds)
input_list = ds_iter.get_next()
print(input_list)
# input_list = preprocessor.minibatch(dataset, subset='train', params=params)
# img, lb = input_list
# lb = input_list['img_path']
lb = input_list
print(lb)
b = 0
show = False
local_var_init_op = tf.local_variables_initializer()
table_init_ops = tf.tables_initializer() # iterator_initilizor in here
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
sess.run(local_var_init_op)
sess.run(table_init_ops)
for i in range(330):
print('%d: ' % i)
if b == 0 or b+options.batch_size > dataset.num_examples_per_epoch('train'):
show = True
b = b+options.batch_size
rst = sess.run(lb)
# rst = rst.decode('utf-8')
print(len(rst))
