diff --git a/ganimation/solver.py b/ganimation/solver.py
index 824e7ed..dfffe46 100644
--- a/ganimation/solver.py
+++ b/ganimation/solver.py
@@ -388,6 +388,14 @@ class Solver(Utils):
             black = np.zeros((1,3,128,128))
             black = torch.FloatTensor(black).to(self.device)
 
+            # Initialize Metrics
+            l1_error = 0.0
+            l2_error = 0.0
+            min_dist = 0.0
+            l0_error = 0.0
+            perceptual_error = 0.0
+            n_samples = 0
+
             pgd_attack = attacks.LinfPGDAttack(model=self.G, device=self.device)
 
             images_to_animate_path = sorted(glob.glob(
@@ -400,21 +408,26 @@ class Solver(Utils):
                 all_images = torch.cat([regular_image_transform(Image.open(path)).unsqueeze(0) for path in images_to_animate_path], dim=0).cuda()
 
                 for target_idx in range(targets.size(0)):
-                    if target_idx == 0:
-                        img = regular_image_transform(Image.open(images_to_animate_path[idx])).unsqueeze(0).cuda()
-                        # x_adv, perturb = pgd_attack.perturb(img, black, targets[0, :].unsqueeze(0).cuda())
-                        x_adv, perturb = pgd_attack.perturb_iter_class(image_to_animate, black, targets[:, :].cuda())
-                        # _, perturb = pgd_attack.perturb_iter_data(image_to_animate, all_images, black, targets[68, :].unsqueeze(0).cuda())
+                    # if target_idx == 0:
+                    #     img = regular_image_transform(Image.open(images_to_animate_path[idx])).unsqueeze(0).cuda()
+                    #     # x_adv, perturb = pgd_attack.perturb(img, black, targets[0, :].unsqueeze(0).cuda())
+                    #     x_adv, perturb = pgd_attack.perturb_iter_class(image_to_animate, black, targets[:, :].cuda())
+                    #     # _, perturb = pgd_attack.perturb_iter_data(image_to_animate, all_images, black, targets[68, :].unsqueeze(0).cuda())
                         
                     targets_au = targets[target_idx, :].unsqueeze(0).cuda()
-                    # x_adv, perturb = pgd_attack.perturb(image_to_animate, black, targets_au)
-                    x_adv = image_to_animate
+                    x_adv, perturb = pgd_attack.perturb(image_to_animate, black, targets_au)
+                    # x_adv = image_to_animate
                     # print(image_to_animate.shape, x_adv.shape)
                     resulting_images_att, resulting_images_reg = self.G(
                         x_adv, targets_au)
                     resulting_image = self.imFromAttReg(
                         resulting_images_att, resulting_images_reg, x_adv).cuda()
 
+                    resulting_images_att_noattack, resulting_images_reg_noattack = self.G(
+                        image_to_animate, targets_au)
+                    resulting_image_noattack = self.imFromAttReg(
+                        resulting_images_att_noattack, resulting_images_reg_noattack, image_to_animate).cuda()
+
                     save_image((resulting_image+1)/2, os.path.join(self.animation_results_dir,
                                                                    image_path.split('/')[-1].split('.')[0]
                                                                    + '_' + reference_expression_images[target_idx]))
@@ -423,6 +436,16 @@ class Solver(Utils):
                                                                    image_path.split('/')[-1].split('.')[0]
                                                                    + '_ref.jpg'))
 
+                    l1_error += F.l1_loss(resulting_image, gen_noattack)
+                    l2_error += F.mse_loss(resulting_image, gen_noattack)
+                    l0_error += (resulting_image - gen_noattack).norm(0)
+                    min_dist += (resulting_image - gen_noattack).norm(float('-inf'))
+                    n_samples += 1
+
+            # Print metrics
+            print('{} images. L1 error: {}. L2 error: {}. L0 error: {}. L_-inf error: {}. Perceptual error: {}.'.format(n_samples, 
+            l1_error / n_samples, l2_error / n_samples, l0_error / n_samples, min_dist / n_samples, perceptual_error / n_samples))
+
         # """ Code to modify single Action Units """
 
         # Set data loader.