web app updates

.gitignore copy

-# Byte-compiled / optimized / DLL files
-__pycache__/
-**/__pycache__/
-**/datasets/
-*.py[cod]
-*$py.class
-*.pyc
-*.pyo
-
-# C extensions
-*.so
-
-# Distribution / packaging
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-*.egg-info/
-.installed.cfg
-*.egg
-MANIFEST
-
-# PyInstaller
-#  Usually these files are written by a python script from a template
-#  before PyInstaller builds the exe, so as to inject date/other infos into it.
-*.manifest
-*.spec
-
-# Installer logs
-pip-log.txt
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-.hypothesis/
-.pytest_cache/
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-# Translations
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-# Django stuff:
-*.log
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-# Flask stuff:
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-# Sphinx documentation
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-#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
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-MetaAugment/__pycache__/main.cpython-38.pyc

MetaAugment/UCB1_JC.py

@@ -230,20 +230,20 @@ def run_UCB1(policies, batch_size, learning_rate, ds, toy_size, max_epochs, earl
 
         # open data and apply these transformations
         if ds == "MNIST":
-            train_dataset = datasets.MNIST(root='./MetaAugment/train', train=True, download=True, transform=transform)
-            test_dataset = datasets.MNIST(root='./MetaAugment/test', train=False, download=True, transform=transform)
+            train_dataset = datasets.MNIST(root='./MetaAugment/datasets/mnist/train', train=True, download=True, transform=transform)
+            test_dataset = datasets.MNIST(root='./MetaAugment/datasets/mnist/test', train=False, download=True, transform=transform)
         elif ds == "KMNIST":
-            train_dataset = datasets.KMNIST(root='./MetaAugment/train', train=True, download=True, transform=transform)
-            test_dataset = datasets.KMNIST(root='./MetaAugment/test', train=False, download=True, transform=transform)
+            train_dataset = datasets.KMNIST(root='./MetaAugment/datasets/kmnist/train', train=True, download=True, transform=transform)
+            test_dataset = datasets.KMNIST(root='./MetaAugment/datasets/kmnist/test', train=False, download=True, transform=transform)
         elif ds == "FashionMNIST":
-            train_dataset = datasets.FashionMNIST(root='./MetaAugment/train', train=True, download=True, transform=transform)
-            test_dataset = datasets.FashionMNIST(root='./MetaAugment/test', train=False, download=True, transform=transform)
+            train_dataset = datasets.FashionMNIST(root='./MetaAugment/datasets/fashionmnist/train', train=True, download=True, transform=transform)
+            test_dataset = datasets.FashionMNIST(root='./MetaAugment/datasets/fashionmnist/test', train=False, download=True, transform=transform)
         elif ds == "CIFAR10":
-            train_dataset = datasets.CIFAR10(root='./MetaAugment/train', train=True, download=True, transform=transform)
-            test_dataset = datasets.CIFAR10(root='./MetaAugment/test', train=False, download=True, transform=transform)
+            train_dataset = datasets.CIFAR10(root='./MetaAugment/datasets/fashionmnist/train', train=True, download=True, transform=transform)
+            test_dataset = datasets.CIFAR10(root='./MetaAugment/datasets/fashionmnist/test', train=False, download=True, transform=transform)
         elif ds == "CIFAR100":
-            train_dataset = datasets.CIFAR100(root='./MetaAugment/train', train=True, download=True, transform=transform)
-            test_dataset = datasets.CIFAR100(root='./MetaAugment/test', train=False, download=True, transform=transform)
+            train_dataset = datasets.CIFAR100(root='./MetaAugment/datasets/fashionmnist/train', train=True, download=True, transform=transform)
+            test_dataset = datasets.CIFAR100(root='./MetaAugment/datasets/fashionmnist/test', train=False, download=True, transform=transform)
 
         # check sizes of images
         img_height = len(train_dataset[0][0][0])

auto_augmentation/progress.py

@@ -17,7 +17,7 @@ from tqdm import trange
 torch.manual_seed(0)
 # import agents and its functions
 
-from MetaAugment import UCB1_JC_py as UCB1_JC
+from MetaAugment import UCB1_JC as UCB1_JC
 
 
 

auto_augmentation/templates/home.html

@@ -6,8 +6,9 @@
 <h3>Choose your dataset</h3>
 <form action="/user_input">
   <!-- upload dataset -->
-  <label for="dataset_upload">You can upload your dataset here:</label>
-  <input type="file" name="dataset_upload" class="upload"><br><br>
+  <label for="dataset_upload">You can upload your dataset folder here:</label>
+  <!-- <input type="file" name="dataset_upload" class="upload"><br><br> -->
+  <input type="file" webkitdirectory mozdirectory /><br><br>
 
   <!-- dataset radio button -->
   Or you can select a dataset from our database: <br>
@@ -34,7 +35,7 @@
 <!-- --------------------------------------------------------------- -->
 
 
-  <h3>Choose the network which the dataset is trained on</h3>
+  <h3>Choose the network the dataset is trained on</h3>
   <!-- upload network -->
   <label for="network_upload">Please upload your network here:</label>
   <input type="file" name="network_upload" class="upload"><br><br>