@@ -91,32 +91,6 @@ class TestModel(BaseModel):
        d = (generated - generated_noattack).norm(float('-inf'))
        return l1, l2, l0, d
    def attack(self, label, inst, image=None):
        # Encode Inputs        
        image = Variable(image) if image is not None else None
        input_label, inst_map, real_image, _ = self.encode_input(Variable(label), Variable(inst), image, infer=True)
        # Fake Generation
        if self.use_features:
            if self.opt.use_encoded_image:
                # encode the real image to get feature map
                feat_map = self.netE.forward(real_image, inst_map)
            else:
                # sample clusters from precomputed features             
                feat_map = self.sample_features(inst_map)
            input_concat = torch.cat((input_label, feat_map), dim=1)                        
        else:
            input_concat = input_label  
        # Attack
        pgd_attack = attacks.LinfPGDAttack(model=self.netG)
        black = np.zeros((1, 3, input_concat.size(2), input_concat.size(3)))
        black = torch.FloatTensor(black).cuda()
        # print(input_concat.size())
        input_adv, perturb = pgd_attack.perturb(input_concat, black)      
        return input_adv, perturb
    def optimize_parameters(self):
        """No optimization for test model."""
        pass