L_infinite implementation problem

I try to put the L_infinite attack into cleverhans, but here is some problem I don't understand.
The adversarial image I get always have some nan value. When I look into the code, I find:
In L_infinite implementation, there is a function doit, in which it tries to convert the original image to the tanh-space.

def doit(oimgs, labs, starts, tt, CONST):
# convert to tanh-space
imgs = np.arctanh(np.array(oimgs)*1.999999)
starts = np.arctanh(np.array(starts)*1.999999)

Here I don't understand why it times 1.999999 instead of 0.999999.
Because the range of the original images is [0,1], then it changes into [0, 1.999999]. For the part [1,1.999999], the np.arctanh() value becomes inf or nan.

By the way, I read the original paper for the L2, L0, L infinite attacks, in the paper, the L infinite results are remarkable, but I run the code in this repository, I find the maximum value of the adversarial image is 0.5, the minimum value is -0.5. It is weird, is it the issue of parameters or something goes wrong?

tanh-space has range (-1,1). Image space has range (-0.5, 0.5) See

nn_robust_attacks/setup_mnist.py

Line 26 in d2067d5

data = (data / 255) - 0.5

That implies times 1.999999

Ah! Yes, it is true. I never found it before you told me. @gehuangyi20

Thank you very much! @gehuangyi20