Daniel Voigt Godoy - Deep Learning with PyTorch Step-by-Step A Beginner’s Guide-leanpub

The data preparation step is exactly the same one we used in Chapter 5:
Data Preparation
1 class TransformedTensorDataset(Dataset):
2 def __init__(self, x, y, transform=None):
3 self.x = x
4 self.y = y
5 self.transform = transform
6
7 def __getitem__(self, index):
8 x = self.x[index]
9 if self.transform:
10 x = self.transform(x)
11
12 return x, self.y[index]
13
14 def __len__(self):
15 return len(self.x)
16
17 # Builds tensors from numpy arrays BEFORE split
18 # Modifies the scale of pixel values from [0, 255] to [0, 1]
19 x_tensor = torch.as_tensor(images / 255).float()
20 y_tensor = torch.as_tensor(labels).long()
21
22 # Uses index_splitter to generate indices for training and
23 # validation sets
24 train_idx, val_idx = index_splitter(len(x_tensor), [80, 20])
25 # Uses indices to perform the split
26 x_train_tensor = x_tensor[train_idx]
27 y_train_tensor = y_tensor[train_idx]
28 x_val_tensor = x_tensor[val_idx]
29 y_val_tensor = y_tensor[val_idx]
30
31 # We're not doing any data augmentation now
32 train_composer = Compose([Normalize(mean=(.5,), std=(.5,))])
33 val_composer = Compose([Normalize(mean=(.5,), std=(.5,))])
34
35 # Uses custom dataset to apply composed transforms to each set
36 train_dataset = TransformedTensorDataset(
37 x_train_tensor, y_train_tensor, transform=train_composer)
38 val_dataset = TransformedTensorDataset(
39 x_val_tensor, y_val_tensor, transform=val_composer)
848 | Chapter 10: Transform and Roll Out