@@ -587,57 +587,59 @@ class Solver(object):
        n_samples = 0

        # 11 layers
-        layer_num_orig = 1
-        
-        for i, (x_real, c_org) in enumerate(data_loader):
-            # Black image
-            black = np.zeros((1,3,256,256))
-            black = torch.FloatTensor(black).to(self.device)
+        # layer_num_orig = 1

-            # Prepare input images and target domain labels.
-            x_real = x_real.to(self.device)
-            c_trg_list = self.create_labels(c_org, self.c_dim, self.dataset, self.selected_attrs)
+        for layer_num_orig in range(11):
+            print('Layer ', layer_num_orig)
+            for i, (x_real, c_org) in enumerate(data_loader):
+                # Black image
+                black = np.zeros((1,3,256,256))
+                black = torch.FloatTensor(black).to(self.device)

-            layer_num = (layer_num_orig + 1) * 3 - 1
-            pgd_attack = attacks.LinfPGDAttack(model=self.G, device=self.device, feat=layer_num)
+                # Prepare input images and target domain labels.
+                x_real = x_real.to(self.device)
+                c_trg_list = self.create_labels(c_org, self.c_dim, self.dataset, self.selected_attrs)

-            # Translate images.
-            x_fake_list = [x_real]
+                layer_num = (layer_num_orig + 1) * 3 - 1
+                pgd_attack = attacks.LinfPGDAttack(model=self.G, device=self.device, feat=layer_num)

-            # if i == 0:
-            #     x_adv, perturb = pgd_attack.perturb(x_real, x_real, c_trg_list[0])
+                # Translate images.
+                x_fake_list = [x_real]

-            for c_trg in c_trg_list:
-                # Attack
-                x_adv, perturb = pgd_attack.perturb(x_real, black, c_trg)
-                # x_adv = x_real + perturb
-                # x_adv = self.blur_tensor(x_adv)
+                # if i == 0:
+                #     x_adv, perturb = pgd_attack.perturb(x_real, x_real, c_trg_list[0])

-                # Metrics
-                with torch.no_grad():
-                    # gen, preproc_x = self.G(x_adv, c_trg)
-                    gen, gen_feats = self.G(x_adv, c_trg)
+                for c_trg in c_trg_list:
+                    # Attack
+                    x_adv, perturb = pgd_attack.perturb(x_real, black, c_trg)
+                    # x_adv = x_real + perturb
+                    # x_adv = self.blur_tensor(x_adv)

-                    # Add to lists
-                    # x_fake_list.append(preproc_x)
-                    x_fake_list.append(gen)
+                    # Metrics
+                    with torch.no_grad():
+                        # gen, preproc_x = self.G(x_adv, c_trg)
+                        gen, gen_feats = self.G(x_adv, c_trg)

-                    # No Attack
-                    # gen_noattack, _ = self.G(x_real, c_trg)
-                    gen_noattack, gen_noattack_feats = self.G(x_real, c_trg)
+                        # Add to lists
+                        # x_fake_list.append(preproc_x)
+                        x_fake_list.append(gen)

-                    l1_error += F.l1_loss(gen, gen_noattack)
-                    l2_error += F.mse_loss(gen, gen_noattack)
-                    n_samples += 1
+                        # No Attack
+                        # gen_noattack, _ = self.G(x_real, c_trg)
+                        gen_noattack, gen_noattack_feats = self.G(x_real, c_trg)

-            # Save the translated images.
-            x_concat = torch.cat(x_fake_list, dim=3)
-            result_path = os.path.join(self.result_dir, '{}-images.jpg'.format(i+1))
-            save_image(self.denorm(x_concat.data.cpu()), result_path, nrow=1, padding=0)
-            # print('Saved real and fake images into {}...'.format(result_path))
-            
-            if i == 199:
-                break
+                        l1_error += F.l1_loss(gen, gen_noattack)
+                        l2_error += F.mse_loss(gen, gen_noattack)
+                        n_samples += 1
+
+                # Save the translated images.
+                x_concat = torch.cat(x_fake_list, dim=3)
+                result_path = os.path.join(self.result_dir, '{}-images.jpg'.format(i+1))
+                save_image(self.denorm(x_concat.data.cpu()), result_path, nrow=1, padding=0)
+                # print('Saved real and fake images into {}...'.format(result_path))
+                
+                if i == 199:
+                    break
        
        # Print metrics
        print('{} images. L1 error: {}. L2 error: {}. Perceptual error: {}.'.format(n_samples, l1_error / n_samples, l2_error / n_samples,