upper bound and lower bound are always equal

Hi,

Recently I am using your auto_LiRPA to compute the perturbation bound of hidden features. However, I found that the ub and lb are always exactly the same.

eps = 0.012
actions = actions.cuda() 
states = states.to(torch.float)/255.0
actions = actions.to(torch.float)
eps_v = eps
state_max = 1.0
state_min = 0.0
state_ub = torch.clamp(states + eps_v, max=state_max)
state_lb = torch.clamp(states - eps_v, min=state_min)
ptb = PerturbationLpNorm(norm = np.inf, eps=eps, x_L=state_lb, x_U=state_ub)
bnd_states = BoundedTensor(states, ptb)
bnd_states = bnd_states.cuda()
#####################################
features = featurenet(bnd_states)
q_values = qnet(features)  # input x and predict based on x
labels = torch.argmax(actions, dim=1)
loss = loss_func(q_values, labels) 
####################################
lb, ub = featurenet.compute_bounds(IBP=False)  
print(torch.mean(lb).item(), torch.mean(ub).item(), torch.max(lb).item(), torch.max(ub).item())

The print results show that the ub and lb are always same.

0.450094610452652 0.450094610452652 2.707549571990967 2.707549571990967
0.48474934697151184 0.48474934697151184 2.94636869430542 2.94636869430542
0.46419665217399597 0.46419665217399597 2.889254093170166 2.889254093170166
0.481861412525177 0.481861412525177 3.1563448905944824 3.1563448905944824
0.48333269357681274 0.48333269357681274 3.0463905334472656 3.0463905334472656
0.4830339848995209 0.4830339848995209 3.1504933834075928 3.1504933834075928
0.49715957045555115 0.49715957045555115 3.2522659301757812 3.2522659301757812
0.478254109621048 0.478254109621048 3.142927408218384 3.142927408218384
0.5154712200164795 0.5154712200164795 3.2538249492645264 3.2538249492645264
0.49771052598953247 0.49771052598953247 3.1791763305664062 3.1791763305664062
0.4898771643638611 0.4898771643638611 3.212333917617798 3.212333917617798
0.5144965648651123 0.5144965648651123 3.347181558609009 3.347181558609009

[xinghua-qu]

@huanzhang12 @shizhouxing Thanks. Hope you guys are safe and healthy during this tough time in the US. Happy lunar new year ahead.

[huanzhang12]

@QuXinghuaNTU You should switch the two lines:

bnd_states = BoundedTensor(states, ptb)
bnd_states = bnd_states.cuda()

The correct way is to do this:

states = states.cuda()
bnd_states = BoundedTensor(states, ptb)

After calling .cuda(), the BoundedTensor becomes a normal torch.Tensor and the bound property gets lost. @shizhouxing maybe you can take a look on how to avoid this.

BTW @QuXinghuaNTU we have a project on using auto_LiRPA with Atari DQN, which might be helpful for you: https://github.com/chenhongge/SA_DQN You can find most relevant code here and here