update /test

pytest.ini

+[pytest]
+filterwarnings =
+    error
+    ignore::UserWarning
+    ignore:function ham\(\) is deprecated:DeprecationWarning
\ No newline at end of file

test/MetaAugment/test_gru_learner.py

@@ -22,7 +22,8 @@ def test_generate_new_policy():
             sp_num=sp_num,
             fun_num=fun_num,
             p_bins=p_bins,
-            m_bins=m_bins
+            m_bins=m_bins,
+            cont_mb_size=2
             )
         for _ in range(4):
             new_policy = agent.generate_new_policy()

test/MetaAugment/test_main.py

+import torch
+import torchvision
+import torchvision.datasets as datasets
+
+import MetaAugment.autoaugment_learners as aal
+import MetaAugment.child_networks as cn
+import MetaAugment.main as main
+
+def test_create_toy():
+    train_dataset = datasets.FashionMNIST(root='./datasets/fashionmnist/train',
+                            train=True, download=True, transform=None)
+    test_dataset = datasets.FashionMNIST(root='./datasets/fashionmnist/test', 
+                            train=False, download=True,
+                            transform=torchvision.transforms.ToTensor())
+    for _ in range(20):
+        train_loader, test_loader = main.create_toy(train_dataset, test_dataset,
+                            batch_size=32, n_samples=1)
+        p = torch.rand_like(torch.tensor([.0]))
+        train_loader, test_loader = main.create_toy(train_dataset, test_dataset,
+                            batch_size=32, n_samples=p)
+    
+    
+    train_dataset = datasets.CIFAR10(root='./datasets/cifar10/train',
+                            train=True, download=True, transform=None)
+    test_dataset = datasets.CIFAR10(root='./datasets/cifar10/train',
+                            train=False, download=True, 
+                            transform=torchvision.transforms.ToTensor())
+    for _ in range(20):
+        train_loader, test_loader = main.create_toy(train_dataset, test_dataset,
+                            batch_size=32, n_samples=1)
+        p = torch.rand_like(torch.tensor([.0]))
+        train_loader, test_loader = main.create_toy(train_dataset, test_dataset,
+                            batch_size=32, n_samples=p)
+
+
+def test_train_cn():
+    train_dataset = datasets.FashionMNIST(root='./datasets/fashionmnist/train',
+                            train=True, download=True,
+                            transform=torchvision.transforms.ToTensor())
+    test_dataset = datasets.FashionMNIST(root='./datasets/fashionmnist/test', 
+                            train=False, download=True,
+                            transform=torchvision.transforms.ToTensor())
+    cn_architecture = cn.Bad_LeNet
+    model = cn_architecture()
+
+    train_loader, test_loader = main.create_toy(train_dataset, test_dataset, 
+                            batch_size=32, n_samples=0.01)
+
+    main.train_child_network(
+                            model, 
+                            train_loader, 
+                            test_loader,
+                            sgd=torch.optim.SGD(model.parameters(),lr=0.1),
+                            cost=torch.nn.CrossEntropyLoss(),
+                            early_stop_flag=True
+                            )
+    
+    main.train_child_network(
+                            model, 
+                            train_loader, 
+                            test_loader,
+                            sgd=torch.optim.SGD(model.parameters(),lr=0.1),
+                            cost=torch.nn.CrossEntropyLoss(),
+                            early_stop_flag=False
+                            )
\ No newline at end of file