full_pol to sub_pol

MetaAugment/autoaugment_learners/evo_learner.py

@@ -221,22 +221,21 @@ class evo_learner(aa_learner):
             self.train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=self.batch_size)
 
             for idx, (test_x, label_x) in enumerate(self.train_loader):
-                full_policy = self._get_single_policy_cov(test_x)
+                sub_pol = self._get_single_policy_cov(test_x)
 
 
-                while self._in_pol_dict(full_policy):
-                    full_policy = self._get_single_policy_cov(test_x)[0]
+                while self._in_pol_dict(sub_pol):
+                    sub_pol = self._get_single_policy_cov(test_x)[0]
 
 
-            fit_val = self._test_autoaugment_policy(full_policy,child_network_architecture,train_dataset,test_dataset)
+            fit_val = self._test_autoaugment_policy(sub_pol,child_network_architecture,train_dataset,test_dataset)
 
-            self.history.append((full_policy, fit_val))
-            self.running_policy.append((full_policy, fit_val))
+            self.history.append((sub_pol, fit_val))
+            self.running_policy.append((sub_pol, fit_val))
 
             if len(self.running_policy) > self.sp_num:
                 self.running_policy = sorted(self.running_policy, key=lambda x: x[1], reverse=True)
                 self.running_policy = self.running_policy[:self.sp_num]
-                print("appended policy: ", self.running_policy)
 
 
             if len(self.history_best) == 0: