minor changes

MetaAugment/autoaugment_learners/evo_learner.py

@@ -7,12 +7,11 @@ import pygad.torchga as torchga
 import copy
 import torch
 from MetaAugment.controller_networks.evo_controller import evo_controller
-
-from MetaAugment.autoaugment_learners.aa_learner import aa_learner, augmentation_space
 import MetaAugment.child_networks as cn
+from .aa_learner import aa_learner, augmentation_space
 
 
-class evo_learner():
+class evo_learner(aa_learner):
 
     def __init__(self, 
                 sp_num=1,

wapp_util.py

@@ -12,7 +12,9 @@ import torchvision.datasets as datasets
 import MetaAugment.autoaugment_learners as aal
 import MetaAugment.controller_networks as cont_n
 import MetaAugment.child_networks as cn
-print('@@@ import successful')
+from MetaAugment.main import create_toy
+
+import pickle
 
 def parse_users_learner_spec(
             auto_aug_learner, 
@@ -30,6 +32,11 @@ def parse_users_learner_spec(
             learning_rate, 
             max_epochs
             ):
+    """
+    The website receives user inputs on what they want the aa_learner
+    to be. We take those hyperparameters and return an aa_learner
+
+    """
     if auto_aug_learner == 'UCB':
         policies = aal.ucb_learner.generate_policies(num_policies, num_sub_policies)
         q_values, best_q_values = aal.ucb_learner.run_UCB1(