diff --git a/stargan/solver.py b/stargan/solver.py
index 610e547..eda5505 100644
--- a/stargan/solver.py
+++ b/stargan/solver.py
@@ -574,75 +574,76 @@ class Solver(object):
 
         layer_dict = {0: 2, 1: 5, 2: 8, 3: 9, 4: 10, 5: 11, 6: 12, 7: 13, 8: 14, 9: 17, 10: 20}
 
-        for layer_num_orig in range(11):
-            # Load the trained generator.
-            self.restore_model(self.test_iters)
+        # for layer_num_orig in range(11):
+        # Load the trained generator.
+        self.restore_model(self.test_iters)
+        
+        # Set data loader.
+        if self.dataset == 'CelebA':
+            data_loader = self.celeba_loader
+        elif self.dataset == 'RaFD':
+            data_loader = self.rafd_loader
+
+        # Initialize Metrics
+        l1_error = 0.0
+        l2_error = 0.0
+        perceptual_error = 0.0
+        n_samples = 0
+
+        # 11 layers
+        # layer_num_orig = 1
+
+        # 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)
+
+            # 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)
+
+            # layer_num = layer_dict[layer_num_orig]
+            layer_num = None
+            pgd_attack = attacks.LinfPGDAttack(model=self.G, device=self.device, feat=layer_num)
+
+            # Translate images.
+            x_fake_list = [x_real]
+
+            # if i == 0:
+            #     x_adv, perturb = pgd_attack.perturb(x_real, x_real, c_trg_list[0])
+
+            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)
+
+                # Metrics
+                with torch.no_grad():
+                    # gen, preproc_x = self.G(x_adv, c_trg)
+                    gen, gen_feats = self.G(x_adv, c_trg)
+
+                    # Add to lists
+                    # x_fake_list.append(preproc_x)
+                    x_fake_list.append(gen)
+
+                    # No Attack
+                    # gen_noattack, _ = self.G(x_real, c_trg)
+                    gen_noattack, gen_noattack_feats = self.G(x_real, c_trg)
+
+                    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))
             
-            # Set data loader.
-            if self.dataset == 'CelebA':
-                data_loader = self.celeba_loader
-            elif self.dataset == 'RaFD':
-                data_loader = self.rafd_loader
-
-            # Initialize Metrics
-            l1_error = 0.0
-            l2_error = 0.0
-            perceptual_error = 0.0
-            n_samples = 0
-
-            # 11 layers
-            # layer_num_orig = 1
-
-            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)
-
-                # 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)
-
-                layer_num = layer_dict[layer_num_orig]
-                pgd_attack = attacks.LinfPGDAttack(model=self.G, device=self.device, feat=layer_num)
-
-                # Translate images.
-                x_fake_list = [x_real]
-
-                # if i == 0:
-                #     x_adv, perturb = pgd_attack.perturb(x_real, x_real, c_trg_list[0])
-
-                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)
-
-                    # Metrics
-                    with torch.no_grad():
-                        # gen, preproc_x = self.G(x_adv, c_trg)
-                        gen, gen_feats = self.G(x_adv, c_trg)
-
-                        # Add to lists
-                        # x_fake_list.append(preproc_x)
-                        x_fake_list.append(gen)
-
-                        # No Attack
-                        # gen_noattack, _ = self.G(x_real, c_trg)
-                        gen_noattack, gen_noattack_feats = self.G(x_real, c_trg)
-
-                        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
+            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,