def isSafe(self, target):
assert len(self.al) > 0
od, _ = torch.min(h.preDomRes(self.al[0], target).lb(), 1)
for a in self.al[1:]:
od1, _ = torch.min(h.preDomRes(a, target).lb(), 1)
od = torch.min(od, od1)
return od.gt(0.0).long()
def loss(self, dom, target, width_weight=0, tot_weight=1, **args):
if not self.width_weight is None:
width_weight = self.width_weight
if not self.tot_weight is None:
tot_weight = self.tot_weight
r = -h.preDomRes(dom, target).lb()
tot = F.softplus(r.max(1)[0]) # kinda works
if self.log_loss:
tot = (tot + 1).log()
if self.pow_loss is not None and self.pow_loss > 0 and self.pow_loss != 1:
tot = tot.pow(self.pow_loss)
ls = tot * tot_weight
if width_weight > 0:
ls += dom.diameter() * width_weight
return ls / (width_weight + tot_weight)
def labels(self):
target = torch.max(self.ub(), 1)[1]
l = list(h.preDomRes(self,target).lb()[0])
return [target.item()] + [ i for i,v in zip(range(len(l)), l) if v <= 0]
def isSafe(self, target):
od,_ = torch.min(h.preDomRes(self,target).lb(), 1)
return od.gt(0.0).long()
def loss(self, target, **args):
r = -h.preDomRes(self, target).lb()
return F.softplus(r.max(1)[0])
for inputs, targets in trainloader:
inputs, targets = inputs.to(device), targets.to(device)
with torch.no_grad():
raw = net(inputs).vanillaTensorPart().detach()
if epoch == 0:
outputs = net(inputs)
loss = criterion(outputs, targets)
else:
abstractinputs = domain.box(inputs,
w=1.0 / 255,
model=net,
target=targets)
abstractoutput = net(abstractinputs.to_dtype())
loss = -helpers.preDomRes(abstractoutput, targets).lb()
loss = loss.max(1)[0]
loss = torch.nn.functional.softplus(loss).mean()
meanloss.append(loss.cpu().data.numpy())
optimizer.zero_grad()
loss.backward()
torch.nn.utils.clip_grad_norm_(net.parameters(), 1)
optimizer.step()
_, predicted = raw.max(1)
total += targets.size(0)
correct += predicted.eq(targets).sum().item()
if random.randint(0, 30) == 0: