link to confirm page && request data from flask

MetaAugment/README.md

 # MetaRL
 
+Documentation:
+https://metaaugment.readthedocs.io/en/latest/
\ No newline at end of file

MetaAugment/autoaugment_learners/baseline.py

-import MetaAugment.child_networks as cn
-from pprint import pprint
-import torchvision.datasets as datasets
-import torchvision
-from MetaAugment.autoaugment_learners.aa_learner import aa_learner
-import pickle
-
-train_dataset = datasets.MNIST(root='./MetaAugment/datasets/mnist/train',
-                                train=True, download=True, transform=None)
-test_dataset = datasets.MNIST(root='./MetaAugment/datasets/mnist/test', 
-                        train=False, download=True, transform=torchvision.transforms.ToTensor())
-child_network = cn.bad_lenet
-
-aalearner = aa_learner(discrete_p_m=True)
-
-# this policy is same as identity function, because probabaility and magnitude are both zero
-null_policy = [(("Contrast", 0.0, 0.0), ("Contrast", 0.0, 0.0))]
-
-
-with open('bad_lenet_baseline.txt', 'w') as file:
-    file.write('')
-
-for _ in range(100):
-    acc = aalearner.test_autoaugment_policy(null_policy, child_network(), train_dataset, test_dataset, 
-                                toy_flag=True, logging=False)
-    with open('bad_lenet_baseline.txt', 'a') as file:
-        file.write(str(acc))
-        file.write('\n')
-
-pprint(aalearner.history)
\ No newline at end of file

README.md

@@ -68,7 +68,4 @@ This section has moved here: [https://facebook.github.io/create-react-app/docs/d
 
 ### `npm run build` fails to minify
 
-This section has moved here: [https://facebook.github.io/create-react-app/docs/troubleshooting#npm-run-build-fails-to-minify](https://facebook.github.io/create-react-app/docs/troubleshooting#npm-run-build-fails-to-minify)
-
+This section has moved here: [https://facebook.github.io/create-react-app/docs/troubleshooting#npm-run-build-fails-to-minify](https://facebook.github.io/create-react-app/docs/troubleshooting#npm-run-build-fails-to-minify)
\ No newline at end of file
-Documentation:
-https://metaaugment.readthedocs.io/en/latest/

backend_react/react_app.py

@@ -179,13 +179,36 @@ def training():
 
     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, batch_size, learning_rate, ds, toy_size, max_epochs, early_stop_num, iterations, IsLeNet, ds_name)     
+        q_values, best_q_values = UCB1_JC.run_UCB1(
+                                                policies,
+                                                batch_size, 
+                                                learning_rate, 
+                                                ds, 
+                                                toy_size, 
+                                                max_epochs, 
+                                                early_stop_num, 
+                                                iterations, 
+                                                IsLeNet, 
+                                                ds_name
+                                                )     
         best_q_values = np.array(best_q_values)
 
     elif auto_aug_learner == 'Evolutionary Learner':
+
         network = cn.evo_controller.evo_controller(fun_num=num_funcs, p_bins=1, m_bins=1, sub_num_pol=1)
         child_network = aal.evo.LeNet()
-        learner = aal.evo.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.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 

benchmark/pickles/04_22_cf_ln_gru.txt

+[(('ShearY', 0.2, 5), ('Rotate', 0.6, 6)),
+ (('TranslateX', 0.8, 3), ('Posterize', 0.1, 3)),
+ (('TranslateY', 0.0, 8), ('Equalize', 0.7, None)),
+ (('Equalize', 0.3, None), ('Contrast', 0.2, 0)),
+ (('ShearX', 0.4, 5), ('Contrast', 0.2, 8)),
+ (('TranslateX', 0.9, 3), ('Solarize', 0.4, 5)),
+ (('Color', 0.2, 4), ('Solarize', 0.6, 8)),
+ (('ShearX', 0.1, 8), ('Equalize', 0.4, None)),
+ (('Posterize', 0.7, 5), ('Solarize', 1.0, 4))][0.6056999564170837, 0.6329999566078186, 0.6171000003814697, 0.62909996509552]original small policys accuracies: [0.6236000061035156, 0.6187999844551086, 0.617900013923645]
\ No newline at end of file

benchmark/pickles/04_22_fm_sn_gru.txt

+[(('Color', 0.9, 3), ('Contrast', 0.8, 3)),
+ (('Sharpness', 0.9, 0), ('Solarize', 0.3, 7)),
+ (('Color', 0.0, 6), ('Solarize', 0.4, 3)),
+ (('Brightness', 0.1, 3), ('Brightness', 0.5, 9)),
+ (('Solarize', 0.9, 6), ('Rotate', 0.6, 1)),
+ (('Contrast', 0.7, 3), ('Posterize', 0.9, 4)),
+ (('Solarize', 0.6, 2), ('Contrast', 0.5, 6)),
+ (('TranslateX', 0.0, 4), ('AutoContrast', 0.3, None)),
+ (('Equalize', 0.0, None), ('Brightness', 0.8, 1))][0.7490999698638916, 0.8359999656677246, 0.8394999504089355]original small policys accuracies: [0.8380999565124512, 0.8376999497413635, 0.8376999497413635]
\ No newline at end of file

benchmark/pickles/04_22_fm_sn_rs.txt

+[(('ShearX', 1.0, 0), ('Color', 0.3, 2)),
+ (('AutoContrast', 0.0, None), ('Brightness', 0.7, 2)),
+ (('Invert', 0.1, None), ('Contrast', 0.1, 6)),
+ (('Solarize', 0.4, 2), ('Contrast', 0.9, 2)),
+ (('Equalize', 0.0, None), ('Contrast', 0.0, 2)),
+ (('Rotate', 0.4, 0), ('Posterize', 0.5, 9)),
+ (('Posterize', 0.7, 3), ('Invert', 0.1, None)),
+ (('Solarize', 0.6, 1), ('Contrast', 0.0, 0)),
+ (('Color', 0.2, 6), ('Posterize', 0.4, 7))][0.6222999691963196, 0.6868000030517578, 0.8374999761581421, 0.8370999693870544, 0.6934999823570251]original small policys accuracies: [0.8431999683380127, 0.8393999934196472, 0.8377999663352966]
\ No newline at end of file

benchmark/scripts/04_22_ci_gru.py

@@ -53,5 +53,6 @@ rerun_best_policy(
     train_dataset=train_dataset,
     test_dataset=test_dataset,
     child_network_architecture=child_network_architecture,
+    config=config,
     repeat_num=5
     )
\ No newline at end of file

benchmark/scripts/04_22_ci_rs.py

@@ -52,5 +52,6 @@ rerun_best_policy(
     train_dataset=train_dataset,
     test_dataset=test_dataset,
     child_network_architecture=child_network_architecture,
+    config=config,
     repeat_num=5
     )
\ No newline at end of file

benchmark/scripts/04_22_fm_gru.py

@@ -40,6 +40,7 @@ run_benchmark(
     child_network_architecture=child_network_architecture,
     agent_arch=aal.gru_learner,
     config=config,
+    total_iter=144
     )
 
 rerun_best_policy(
@@ -48,5 +49,6 @@ rerun_best_policy(
     train_dataset=train_dataset,
     test_dataset=test_dataset,
     child_network_architecture=child_network_architecture,
+    config=config,
     repeat_num=5
     )
\ No newline at end of file

benchmark/scripts/04_22_fm_rs.py

@@ -48,5 +48,6 @@ rerun_best_policy(
     train_dataset=train_dataset,
     test_dataset=test_dataset,
     child_network_architecture=child_network_architecture,
+    config=config,
     repeat_num=5
     )
\ No newline at end of file

benchmark/scripts/util_04_22.py

@@ -6,7 +6,7 @@ import torch
 import MetaAugment.child_networks as cn
 import MetaAugment.autoaugment_learners as aal
 
-from pprint import pprint
+import pprint
 
 """
 testing gru_learner and randomsearch_learner on
@@ -75,16 +75,21 @@ def get_mega_policy(history, n):
         assert len(history) >= n
 
         # agent.history is a list of (policy(list), val_accuracy(float)) tuples 
-        sorted_history = sorted(history, key=lambda x:x[1]) # sort wrt acc
+        sorted_history = sorted(history, key=lambda x:x[1], reverse=True) # sort wrt acc
 
         best_history = sorted_history[:n]
 
         megapolicy = []
+        # we also want to keep track of how good the best policies were
+        # maybe if we add them all up, they'll become worse! Hopefully better tho
+        orig_accs = []
+
         for policy,acc in best_history:
             for subpolicy in policy:
                 megapolicy.append(subpolicy)
+            orig_accs.append(acc)
         
-        return megapolicy
+        return megapolicy, orig_accs
 
 
 def rerun_best_policy(
@@ -93,25 +98,30 @@ def rerun_best_policy(
     train_dataset,
     test_dataset,
     child_network_architecture,
+    config,
     repeat_num
     ):
 
     with open(agent_pickle, 'rb') as f:
-        agent = torch.load(f, map_location=device)
+        agent = torch.load(f)
     
-    megapol = get_mega_policy(agent.history)
+    megapol, orig_accs = get_mega_policy(agent.history,3)
     print('mega policy to be tested:')
-    pprint(megapol)
-    
+    pprint.pprint(megapol)
+    print(orig_accs)
+
     accs=[]
     for _ in range(repeat_num):
         print(f'{_}/{repeat_num}')
+        temp_agent = aal.aa_learner(**config)
         accs.append(
-                agent.test_autoaugment_policy(megapol,
+                temp_agent.test_autoaugment_policy(megapol,
                                     child_network_architecture,
                                     train_dataset,
                                     test_dataset,
                                     logging=False)
                     )
         with open(accs_txt, 'w') as f:
+            f.write(pprint.pformat(megapol))
             f.write(str(accs))
+            f.write(f'original small policys accuracies: {orig_accs}')