def main():
md = GtsrbModel(source_dir='GTSRB')
# _model0 = (0, "models/gtsrbreplace_worst_of_10_model_False.hdf5")
_model0 = (0, "models/gtsrb.oxford.original_model.hdf5")
# _model0 = (0, "models/gtsrbga_loss_model_False.hdf5")
x_original_test, y_original_test = md.load_original_test_data()
model = md.load_model(_model0[0], _model0[1])
# index = 50, 10, 20, 30
index = 10578
save_img(x_original_test[index], "cur_image")
img = copy.copy(x_original_test[index])
label = y_original_test[index]
x, y = generate_image(img, label)
temp_x = copy.copy(x)
value = generate_value(model, md.preprocess_original_imgs(temp_x), y)
# print value
xig, yig, zig = prepare_data(value)
plot(xig, yig, zig, "Loss")
# value = generate_cov2(model, md.preprocess_original_imgs(x_original_test), md.preprocess_original_imgs(x),
# y, y_original_test)
# org_imgs = []
# for i in range(len(y)):
# temp_img = copy.copy(img)
# org_imgs.append(temp_img)
# value = generate_cov(model, md.preprocess_original_imgs(org_imgs), md.preprocess_original_imgs(x), y)
#
# xig, yig, zig = prepare_data(value)
# plot(xig, yig, zig, "Cov")
plt.show()
def main():
print cross_entropy(np.array([[0.4, 0.6], [0.6, 0.4]]),
np.array([[1, 0], [0, 1]]))
for i in range(1):
md = GtsrbModel(source_dir='GTSRB')
if i == 0:
_model0 = (0, "models/gtsrbga_loss_model_False.hdf5")
else:
_model0 = (0, "models/gtsrbga_loss_model_False_q2.hdf5")
#_model0 = (0, "models/gtsrbreplace_worst_of_10_model_False.hdf5")
x_original_test, y_original_test = md.load_original_test_data()
model = md.load_model(_model0[0], _model0[1])
#if i==0:
for i in range(10):
x_part = x_original_test[0:64]
y_part = y_original_test[0:64]
#else:
# x_part = x_original_test[1:128]
# y_part = y_original_test[1:128]
performance(model, md, x_part, y_part)
x_part = x_original_test[640:656]
y_part = y_original_test[640:656]
performance(model, md, x_part, y_part)
x_part = x_original_test[640:2440]
y_part = y_original_test[640:2440]
performance(model, md, x_part, y_part)
def main():
md = GtsrbModel(source_dir='GTSRB')
# _model0 = (0, "models/gtsrbga_loss_model_False.hdf5")
_model0 = (0, "models/gtsrbga_loss_model_False_q2.hdf5")
# md = Cifar10Model()
# _model0 = (0, "models/cifar10replace_worst_of_10_model_False.hdf5")
x_original_test, y_original_test = md.load_original_test_data()
model = md.load_model(_model0[0], _model0[1])
x_part = x_original_test[1:100]
y_part = y_original_test[1:100]
generate_misclassified_perturbations(model, md, x_part, y_part)
def main2():
md = GtsrbModel(source_dir='GTSRB')
_model0 = (0, "models/gtsrbga_loss_model_False.hdf5")
x_original_test, y_original_test = md.load_original_test_data()
model0 = md.load_model(_model0[0], _model0[1])
x_part = x_original_test[0:1000]
y_part = y_original_test[0:1000]
au = Augmenter()
temp_x_original_train = copy.deepcopy(x_part)
x_10, y_train = au.worst_of_10(temp_x_original_train, y_part)
select_worst_cov(md, model0, md.preprocess_original_imgs(temp_x_original_train), x_10, y_train)
def main():
md = GtsrbModel(source_dir='GTSRB')
# _model0 = (0, "models/gtsrb.oxford.original_model.hdf5")
_model0 = (0, "models/gtsrbreplace_worst_of_10_model_False.hdf5")
# md = Cifar10Model()
# _model0 = (0, "models/cifar10replace_worst_of_10_model_False.hdf5")
x_original_test, y_original_test = md.load_original_test_data()
model = md.load_model(_model0[0], _model0[1])
x_part = x_original_test
y_part = y_original_test
# x_part, y_part = rule_out_misclassified_images(model, md, x_part, y_part)
generate_misclassified_perturbations(model, md, x_part, y_part)
ground_truth(x_part, y_part)
def main3():
md = GtsrbModel(source_dir='GTSRB')
_model0 = (0, "models/gtsrbreplace_worst_of_10_cov_model_False.hdf5")
x_original_test, y_original_test = md.load_original_test_data()
model0 = md.load_model(_model0[0], _model0[1])
x_part = x_original_test[0:32]
y_part = y_original_test[0:32]
au = Augmenter()
start_time = time.time()
temp_x_original_train = copy.deepcopy(x_part)
x_aug, y_train = au.worst_of_10(temp_x_original_train, y_part)
print("augmentation done")
print("--- %s seconds ---" % (time.time() - start_time))
memory_test(md, model0, x_part, x_aug)
print("--- %s seconds ---" % (time.time() - start_time))
gc.collect()
def main():
md = GtsrbModel(source_dir='GTSRB')
_model0 = (0, "models/gtsrbga_loss_model_False.hdf5")
x_original_test, y_original_test = md.load_original_test_data()
model0 = md.load_model(_model0[0], _model0[1])
x_part = x_original_test[0:2000]
y_part = y_original_test[0:2000]
au = Augmenter()
temp_x_original_train = copy.deepcopy(x_part)
x_part2, y_part = au.random_replace(temp_x_original_train, y_part)
x_part = md.preprocess_original_imgs(x_part)
x_part2 = md.preprocess_original_imgs(x_part2)
function_test(model0, x_part, x_part2, y_part)
# target dataset
dataset = args.dataset
if dataset not in DATASET.list():
print("unsupported dataset, please use --help to find supported ones")
exit(1)
config = ExperimentalConfig.gen_config()
config.enable_filters = args.enable_filter
config.enable_optimize = args.enable_optimize
model_index = int(args.model)
# initialize dataset
dat = DATASET.get_name(dataset)
if dat.value == DATASET.gtsrb.value:
target0 = GtsrbModel(source_dir='GTSRB')
elif dat.value == DATASET.cifar10.value:
target0 = Cifar10Model()
elif dat.value == DATASET.fashionmnist.value:
target0 = FashionMnist()
elif dat.value == DATASET.svhn.value:
target0 = SVHN()
config.brightness_range = [0]
config.contrast_range = [1]
elif dat.value == DATASET.imdb.value:
target0 = IMDBModel("dataset")
elif dat.value == DATASET.utk.value:
target0 = UTKModel("dataset")
elif dat.value == DATASET.kvasir.value:
target0 = KvasirModel()
return coverage_diff
def generate_layer_output(model, input_data):
layer_names = [layer.name for layer in model.layers if
'flatten' not in layer.name and 'input' not in layer.name]
intermediate_layer_model = Model(inputs=model.input,
outputs=[model.get_layer(layer_name).output
for layer_name in layer_names])
intermediate_layer_outputs = intermediate_layer_model.predict(input_data)
return intermediate_layer_outputs
"""
if __name__ == '__main__':
md = GtsrbModel(source_dir='GTSRB')
# Load data
x_original_test, y_original_test = md.load_original_test_data()
# x_original_test = x_original_test[0:10]
# y_original_test = y_original_test[0:10]
x_original_train, y_original_train = md.load_original_data('train')
# perturb data
temp_x_test = copy.deepcopy(x_original_test)
pt = Perturbator()
x_perturbed_test, y_perturbed_test = pt.random_perturb(
temp_x_test, y_original_test)
# Load model
config.queue_len = int(0.4 * args.queue)
if config.queue_len < 2:
config.queue_len = 2
config.popsize = args.queue - config.queue_len
config.enable_filters = args.enable_filter
config.enable_optimize = args.enable_optimize
start_point = args.start_point
epoch = args.epoch
model_index = int(args.model)
config.robust_threshold = 0.1**int(args.threshold)
# initialize dataset
dat = DATASET.get_name(dataset)
if dat.value == DATASET.gtsrb.value:
target0 = GtsrbModel('GTSRB', start_point, epoch)
elif dat.value == DATASET.cifar10.value:
target0 = Cifar10Model(start_point, epoch)
elif dat.value == DATASET.fashionmnist.value:
target0 = FashionMnist(start_point, epoch)
elif dat.value == DATASET.svhn.value:
target0 = SVHN("data", start_point, epoch)
config.brightness_range = [0]
config.contrast_range = [1]
elif dat.value == DATASET.imdb.value:
target0 = IMDBModel("dataset", start_point, epoch)
elif dat.value == DATASET.utk.value:
target0 = UTKModel("dataset", start_point, epoch)
elif dat.value == DATASET.kvasir.value:
target0 = KvasirModel("kvasir-dataset", start_point, epoch)
else: