work on randomsearch_learner

.gitignore

@@ -152,3 +152,4 @@ cython_debug/
 #  and can be added to the global gitignore or merged into this file.  For a more nuclear
 #  option (not recommended) you can uncomment the following to ignore the entire idea folder.
 #.idea/
+MetaAugment/__pycache__/main.cpython-38.pyc

MetaAugment/autoaugment_learners/autoaugment.py

@@ -456,7 +456,7 @@ if __name__=='__main__':
     def test_autoaugment_policy(subpolicies, train_dataset, test_dataset):
 
         aa_transform = AutoAugment()
-        aa_transform.subpolicies = subpolicies1
+        aa_transform.subpolicies = subpolicies
         train_transform = transforms.Compose([
                                                 aa_transform,
                                                 transforms.ToTensor()

MetaAugment/autoaugment_learners/randomsearch_learner.py

@@ -6,7 +6,24 @@ from MetaAugment.autoaugment_learners.autoaugment import *
 import torchvision.transforms.autoaugment as torchaa
 from torchvision.transforms import functional as F, InterpolationMode
 
-    
+policies1 = [
+            (("Invert", 0.8, None), ("Contrast", 0.2, 6)),
+            (("Rotate", 0.7, 2), ("Invert", 0.8, None)),
+            (("Sharpness", 0.8, 1), ("Sharpness", 0.9, 3)),
+            (("ShearY", 0.5, 8), ("Invert", 0.7, None)),
+            (("AutoContrast", 0.5, None), ("Equalize", 0.9, None))
+            ]
+
+# The one that i hand crafted. You'll see that this one usually reaches a much
+# higher poerformance
+policies2 = [
+        (("ShearY", 0.8, 4), ("Rotate", 0.5, 6)),
+        (("TranslateY", 0.7, 4), ("TranslateX", 0.8, 6)),
+        (("Rotate", 0.5, 3), ("ShearY", 0.8, 5)),
+        (("ShearX", 0.5, 6), ("TranslateY", 0.7, 3)),
+        (("Rotate", 0.5, 3), ("TranslateX", 0.5, 5))
+        ]
+
 class randomsearch_learner:
     def __init__(self):
         pass
@@ -32,59 +49,33 @@ class randomsearch_learner:
         
         return good_policy
 
-    def test_autoaugment_policy(policies):
-        aa_transform = AutoAugment()
-        aa_transform.policies = policies
 
+    def test_autoaugment_policy(policy):
+        aa_transform = AutoAugment()
+        aa_transform.policies = policy
         train_transform = transforms.Compose([
                                                 aa_transform,
                                                 transforms.ToTensor()
                                             ])
 
 
-        train_dataset = datasets.MNIST(root='./datasets/mnist/train', train=True, download=False, 
-                                    transform=train_transform)
-        test_dataset = datasets.MNIST(root='./datasets/mnist/test', train=False, download=False,
-                                    transform=torchvision.transforms.ToTensor())
-
-        # create toy dataset from above uploaded data
-        train_loader, test_loader = create_toy(train_dataset, test_dataset, batch_size, 0.01)
-
-        child_network = cn.lenet()
-        sgd = optim.SGD(child_network.parameters(), lr=1e-1)
-
-        best_acc = train_child_network(child_network, train_loader, test_loader, sgd, cost, max_epochs=100)
-
-        train_dataset
-
+if __name__=='__main__':
 
+    train_dataset = datasets.MNIST(root='./datasets/mnist/train', train=True, download=False, 
+                                transform=train_transform)
+    test_dataset = datasets.MNIST(root='./datasets/mnist/test', train=False, download=False,
+                                transform=torchvision.transforms.ToTensor())
+    train_loader, test_loader = create_toy(train_dataset, test_dataset, batch_size=32, n_samples=0.01)
 
+    child_network = cn.lenet()
+    sgd = optim.SGD(child_network.parameters(), lr=1e-1)
 
-if __name__=='__main__':
 
-
-    batch_size = 32
-    n_samples = 0.005
     cost = nn.CrossEntropyLoss()
+    best_acc, acc_log = train_child_network(child_network, train_loader, test_loader,
+                                                sgd, cost, max_epochs=100, logging=True)
 
-    policies1 = [
-            (("Invert", 0.8, None), ("Contrast", 0.2, 6)),
-            (("Rotate", 0.7, 2), ("Invert", 0.8, None)),
-            (("Sharpness", 0.8, 1), ("Sharpness", 0.9, 3)),
-            (("ShearY", 0.5, 8), ("Invert", 0.7, None)),
-            (("AutoContrast", 0.5, None), ("Equalize", 0.9, None))
-            ]
-
-    # The one that i hand crafted. You'll see that this one usually reaches a much
-    # higher poerformance
-    policies2 = [
-            (("ShearY", 0.8, 4), ("Rotate", 0.5, 6)),
-            (("TranslateY", 0.7, 4), ("TranslateX", 0.8, 6)),
-            (("Rotate", 0.5, 3), ("ShearY", 0.8, 5)),
-            (("ShearX", 0.5, 6), ("TranslateY", 0.7, 3)),
-            (("Rotate", 0.5, 3), ("TranslateX", 0.5, 5))
-            ]
 
 
-    learner = RandomSearch_Learner()
+    learner = randomsearch_learner()