diff --git a/sota/cnn/init_projection.py b/sota/cnn/init_projection.py
index e1d6a6a..9393be3 100644
--- a/sota/cnn/init_projection.py
+++ b/sota/cnn/init_projection.py
@@ -96,12 +96,11 @@ def project_op(model, input, target, args, cell_type, proj_queue=None, selected_
 
             model.candidate_flags[cell_type][selected_eid] = False
             # print(model.get_projected_weights())
-            else:
-                measures = predictive.find_measures(model,
-                                                    proj_queue,
-                                                    ('random', 1, n_classes),
-                                                    torch.device("cuda"),
-                                                    measure_names=[proj_crit])
+            measures = predictive.find_measures(model,
+                                                proj_queue,
+                                                ('random', 1, n_classes),
+                                                torch.device("cuda"),
+                                                measure_names=[proj_crit])
 
             # print(measures)
             for idx in range(num_ops):
diff --git a/zerocostnas/post_validate.py b/zerocostnas/post_validate.py
index a5b6a4d..7617494 100644
--- a/zerocostnas/post_validate.py
+++ b/zerocostnas/post_validate.py
@@ -223,12 +223,11 @@ def main():
                 else:
                     #score =  score_loop(network, None, train_queue, args.gpu, None, args.proj_crit)
                     network.requires_feature = False
-                    else:
-                        measures = predictive.find_measures(network,
-                                                            train_queue,
-                                                            ('random', 1, n_classes),
-                                                            torch.device("cuda"),
-                                                            measure_names=[args.proj_crit])
+                    measures = predictive.find_measures(network,
+                                                        train_queue,
+                                                        ('random', 1, n_classes),
+                                                        torch.device("cuda"),
+                                                        measure_names=[args.proj_crit])