def test_violation_example():
""" Demonstrate via a handcoded example. """
dom = DeeppolyDom()
err_eps = 1e-4
in_neurons = 1
fc1_neurons = 3
kernel_size, stride = 2, 1 # stride must be < kernel size, so as to allow overlapping
fc2_neurons = (fc1_neurons -
kernel_size) / stride + 1 # formula: (W - F + 2P) / S + 1
assert int(fc2_neurons) == fc2_neurons
fc2_neurons = int(fc2_neurons)
out_neurons = 1
lb = torch.tensor([[0.1]])
ub = torch.tensor([[0.12]])
pt = torch.tensor([[0.1010]])
fc1 = dom.Linear(in_neurons, fc1_neurons)
fc1.weight.data = torch.tensor([[0.9624], [-0.6785], [0.9087]])
fc1.bias.data = torch.tensor([0.3255, 0.7965, 0.6321])
relu = dom.ReLU()
mp = dom.MaxPool1d(kernel_size=kernel_size, stride=stride)
fc2 = dom.Linear(fc2_neurons, out_neurons, bias=False)
fc2.weight.data = torch.tensor([-0.6859, -0.4253])
def forward(x):
x = fc1(x)
x = relu(x)
x = x.unsqueeze(dim=1)
x = mp(x)
x = x.squeeze(dim=1)
x = fc2(x)
return x
print('in pt:', pt.squeeze().item())
print('in lb:', lb.squeeze().item())
print('in ub:', ub.squeeze().item())
e = dom.Ele.by_intvl(lb, ub)
out = forward(pt)
out_e = forward(e)
out_lb, out_ub = out_e.gamma()
assert not (out_lb <= out + err_eps).all()
print('out pt:', out.squeeze().item())
print('out lb:', out_lb.squeeze().item())
print('out ub:', out_ub.squeeze().item())
return
def test_sample_violation():
""" It suffices to generate such violations by sampling.
Having both MaxPool1d and FC2 is necessary to reproduce the bug. FC1 must have bias to easily reproduce the
bug while FC2 may have no bias. Eps = 1e-4 is maximal magnitude to reproduce the bug because the weights and
input bounds are initialized small.
"""
dom = DeeppolyDom()
err_eps = 1e-4
in_neurons = 1
fc1_neurons = 3
kernel_size, stride = 2, 1
out_neurons = 1
lb = torch.tensor([[0.1]])
ub = torch.tensor([[0.12]])
# fixed
fc2_neurons = (fc1_neurons - kernel_size) / stride + 1
assert int(fc2_neurons) == fc2_neurons
fc2_neurons = int(fc2_neurons) # if using MaxPool1d
# fc2_neurons = fc1_neurons # if not using MaxPool1d
fc1 = dom.Linear(in_neurons, fc1_neurons, bias=True)
relu = dom.ReLU()
mp = dom.MaxPool1d(kernel_size=kernel_size, stride=stride)
fc2 = dom.Linear(fc2_neurons, out_neurons, bias=False)
def forward(x):
x = fc1(x)
x = relu(x)
x = x.unsqueeze(dim=1)
x = mp(x)
x = x.squeeze(dim=1)
x = fc2(x)
return x
def reset_params():
fc1.reset_parameters()
fc2.reset_parameters()
return
k = 0
while True:
k += 1
reset_params()
pts = sample_points(lb, ub, 10000)
e = dom.Ele.by_intvl(lb, ub)
out_conc = forward(pts)
out_lb, out_ub = forward(e).gamma()
if (out_lb <= out_conc + err_eps).all():
continue
print(f'After {k} resets')
print('LB <= conc?', (out_lb <= out_conc + err_eps).all())
print('LB <= conc? detail', out_lb <= out_conc + err_eps)
bits = out_conc + err_eps <= out_lb
bits = bits.any(dim=1) # any dimension violation is sufficient
idxs = bits.nonzero().squeeze(dim=1)
idx = idxs[0] # just pick the 1st one to debug
viol_in = pts[[idx]]
viol_out = out_conc[[idx]]
print('conc in:', viol_in.squeeze().item())
print('out lb:', out_lb.squeeze().item())
print('out ub:', out_ub.squeeze().item())
print('conc out:', viol_out.squeeze().item())
torch.save([fc1, fc2, viol_in], 'error_ctx.pt')
break
return