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

Data Preparation
1 # Builds tensors from numpy arrays BEFORE split
2 # Modifies the scale of pixel values from [0, 255] to [0, 1]
3 x_tensor = torch.as_tensor(images / 255).float()
4 y_tensor = torch.as_tensor(labels.reshape(-1, 1)).float()
5 # Uses index_splitter to generate indices
6 train_idx, val_idx = index_splitter(len(x_tensor), [80, 20])
7 # Uses indices to perform the split
8 x_train_tensor = x_tensor[train_idx]
9 y_train_tensor = y_tensor[train_idx]
10 x_val_tensor = x_tensor[val_idx]
11 y_val_tensor = y_tensor[val_idx]
12 # Builds different composers because of data augmentation on
training set
13 train_composer = Compose([RandomHorizontalFlip(p=.5),
14 Normalize(mean=(.5,), std=(.5,))])
15 val_composer = Compose([Normalize(mean=(.5,), std=(.5,))])
16 # Uses custom dataset to apply composed transforms to each set
17 train_dataset = TransformedTensorDataset(
18 x_train_tensor, y_train_tensor, transform=train_composer
19 )
20 val_dataset = TransformedTensorDataset(
21 x_val_tensor, y_val_tensor, transform=val_composer
22 )
23 # Builds a weighted random sampler to handle imbalanced classes
24 sampler = make_balanced_sampler(y_train_tensor)
25 # Uses sampler in the training set to get a balanced data loader
26 train_loader = DataLoader(
27 dataset=train_dataset, batch_size=16, sampler=sampler
28 )
29 val_loader = DataLoader(dataset=val_dataset, batch_size=16)
We’re almost finished with the data preparation section! There is one last thing to
discuss…
Pixels as Features
So far, we’ve been handling our data as either PIL images or three-dimensional
tensors (CHW) with shape (1, 5, 5). It is also possible to consider each pixel and
channel as an individual feature by flattening the pixels with a nn.Flatten layer.
296 | Chapter 4: Classifying Images