Rangement

Gradient-Descent-The-Ultimate-Optimizer/dataset_aug.py

@@ -1,52 +0,0 @@
-import torch
-from torch.utils.data import Dataset, DataLoader
-from torchvision import transforms
-import torchvision.transforms.functional as TF
-
-class MNIST_aug(Dataset):
-
-    training_file = 'training.pt'
-    test_file = 'test.pt'
-    classes = ['0 - zero', '1 - one', '2 - two', '3 - three', '4 - four',
-               '5 - five', '6 - six', '7 - seven', '8 - eight', '9 - nine']
-               
-    def __init__(self):
-        self.images = [TF.to_pil_image(x) for x in torch.ByteTensor(10, 3, 48, 48)]
-        self.set_stage(0) # initial stage
-        
-    def __getitem__(self, index):
-        image = self.images[index]
-        
-        # Just apply your transformations here
-        image = self.crop(image)
-        x = TF.to_tensor(image)
-        return x
-        
-    def set_stage(self, stage):
-        if stage == 0:
-            print('Using (32, 32) crops')
-            self.crop = transforms.RandomCrop((32, 32))
-        elif stage == 1:
-            print('Using (28, 28) crops')
-            self.crop = transforms.RandomCrop((28, 28))
-        
-    def __len__(self):
-        return len(self.images)
-
-
-dataset = MyData()
-loader = DataLoader(dataset,
-                    batch_size=2,
-                    num_workers=2,
-                    shuffle=True)
-
-for batch_idx, data in enumerate(loader):
-    print('Batch idx {}, data shape {}'.format(
-        batch_idx, data.shape))
-    
-loader.dataset.set_stage(1)
-
-for batch_idx, data in enumerate(loader):
-    print('Batch idx {}, data shape {}'.format(
-        batch_idx, data.shape))
-