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():
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)
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)
# 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
_model0 = (0, "models/gtsrb.oxford.model0.hdf5")
model0 = md.load_model(_model0[0], _model0[1])
x_original_test = md.preprocess_original_imgs(x_original_test)
x_perturbed_test = md.preprocess_original_imgs(x_perturbed_test)
loss_set = generate_loss(x_perturbed_test, y_perturbed_test)
print loss_set
nc = NeuralCoverage(model0)
coverage_diff_set = nc.generate_coverage_diff(x_original_test,
x_perturbed_test)
print coverage_diff_set
print np.corrcoef(loss_set, coverage_diff_set)
import matplotlib
import matplotlib.pyplot as plt