next

stargan/solver.py

@@ -587,6 +587,8 @@ class Solver(object):
             # 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
 
@@ -633,6 +635,8 @@ class Solver(object):
 
                         l1_error += F.l1_loss(gen, gen_noattack)
                         l2_error += F.mse_loss(gen, gen_noattack)
+                        l0_error += np.linalg.norm((gen - gen_noattack).data.cpu(), ord=0)
+                        min_dist += attacks.min_dist((gen - gen_noattack).data.cpu(), ord='-inf')
                         n_samples += 1
 
                 # Save the translated images.
@@ -645,8 +649,8 @@ class Solver(object):
                     break
             
             # Print metrics
-            print('{} images. L1 error: {}. L2 error: {}. Perceptual error: {}.'.format(n_samples, l1_error / n_samples, l2_error / n_samples, 
-            perceptual_error / n_samples))
+            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))
 
     def test_multi(self):
         """Translate images using StarGAN trained on multiple datasets."""