move /autoaugmentation stuff into /flask_mvp/auto_autoaugmentation

auto_augmentation/training.py → flask_mvp/auto_augmentation/training.py

@@ -18,7 +18,6 @@ from tqdm import trange
 torch.manual_seed(0)
 # import agents and its functions
 
-from MetaAugment.autoaugment_learners import ucb_learner as UCB1_JC
 import MetaAugment.autoaugment_learners as aal
 import MetaAugment.controller_networks as cont_n
 import MetaAugment.child_networks as cn
@@ -58,8 +57,8 @@ def response():
 
 
     if auto_aug_learner == 'UCB':
-        policies = UCB1_JC.generate_policies(num_policies, num_sub_policies)
-        q_values, best_q_values = UCB1_JC.run_UCB1(
+        policies = aal.ucb_learner.generate_policies(num_policies, num_sub_policies)
+        q_values, best_q_values = aal.ucb_learner.run_UCB1(
                                                 policies, 
                                                 batch_size, 
                                                 learning_rate, 
@@ -76,7 +75,17 @@ def response():
     elif auto_aug_learner == 'Evolutionary Learner':
         network = cont_n.evo_controller(fun_num=num_funcs, p_bins=1, m_bins=1, sub_num_pol=1)
         child_network = cn.LeNet()
-        learner = aal.evo_learner(network=network, fun_num=num_funcs, p_bins=1, mag_bins=1, sub_num_pol=1, ds = ds, ds_name=ds_name, exclude_method=exclude_method, child_network=child_network)
+        learner = aal.evo_learner(
+                                network=network, 
+                                fun_num=num_funcs, 
+                                p_bins=1, 
+                                mag_bins=1, 
+                                sub_num_pol=1, 
+                                ds = ds, 
+                                ds_name=ds_name, 
+                                exclude_method=exclude_method, 
+                                child_network=child_network
+                                )
         learner.run_instance()
     elif auto_aug_learner == 'Random Searcher':
         pass