A Lonely Melody Beyond Computer Vision

Deep Learning curricula treat augmentation as a core component. Tutorials include sections on data augmentation’s ability to mitigate against common challenges such as class imbalance, error propagation, and scarce data, yet little is published outside of computer vision. You’ll quickly find that resources become scarce once you venture outside cropping and rotating images of cats. The reality is, augmentation strategies are not as well-documented for other types of data, like text or graphs. The journey to implementing custom augmentations here can feel like wandering uncharted territory.

In my case, working with complex machine learning pipelines that chain models together means augmentation can be a daunting but necessary voyage. I did some research on open-source solutions to simulate noise that can be introduced by OCR models and found nlpaug to be initially promising as it was the only one which touted a “pre-defined OCR mapping” that could apply noise conditioned on common OCR mistakes. I was intrigued, so I dug through the source code until I found the highly coveted mapping file. Needless to say, I was a bit disappointed. . . but ChatGPT quickly helped me whip up a much more comprehensive mapping that could be easily substituted. If you’re thinking of using this library I would also caution about this issue.

Harmonizing Graph Augmentation

Unlike images or text, graph data requires specialized augmentation techniques. The connectivity of nodes and edges present unique topology that must be maintained during augmentation, or augmented only with careful intention.

PyTorch provides a powerful and flexible toolkit for data augmentation, primarily through the use of the Transforms class. At its core, a Transform in PyTorch is a function that takes in some data and returns a transformed version of that data. This could be as simple as resizing an image, flipping text characters at random, or moving data to the GPU. Transforms can be chained together and then attached to a PyTorch Dataset class where they are used to modify data each time it is loaded, generally by a PyTorch DataLoader.

PyTorch Geometric extends this paradigm to graph data with its own Dataset, DataLoader, and Transform classes. Just like with pure PyTorch, custom transforms can be defined simply by inheriting from the BaseTransform class and implementing the __call__ and optionally __init__ methods. Transforms can be then be simply attached to your Dataset and will be called as the data is fetched, like so:¹

import torch_geometric.transforms as T
from torch_geometric.datasets import TUDataset
transform = T.Compose([T.ToUndirected(), T.AddSelfLoops()])
dataset = TUDataset(path, name='MUTAG', transform=transform)

While this is the recommended method according to both frameworks, there is a more straightforward approach where you call the transform explicitly on data points or batches:

transform = T.Compose([T.ToUndirected(), T.AddSelfLoops()])
dataset = TUDataset(path, name='MUTAG', transform=transform)
# Call the transform on the first observation in the datset
data = transform(dataset[0])

I caution against attaching transforms directly to the dataset so that you don’t make the same mistakes I did:

# Temporarily remove the transforms
original_transform = None
if dataset.transform is not None:
original_transform = dataset.transform
dataset.transform = None
# Normalize data
...
# Restore the transforms
dataset.transform = original_transform

Composing Augmentation with PyTorch Lightning

Lastly, let’s talk about PyTorch Lightning. While it’s a fantastic tool that simplifies a lot of the boilerplate code in PyTorch, it comes with its own quirks. Certain aspects require a little extra care and handling to ensure that your model training runs smoothly. Lightning abstracts away most of the training loop and requires users simply specify train_dataloader and val_dataloader methods to return some iterator, generally a PyTorch DataLoader.² These methods can be implemented either directly in the LightningModule or in the optional LightningDataModule. After this, mini-batches are sampled and dispatched according to Lightning’s smart “auto” acceleration strategies which generally means they end up where you want them to go, such as on the GPU/TPU.

Attaching transforms directly to your Dataset seems to fit in well with this paradigm and just like most things with PyTorch Lightning, somehow they are magically taken care of! The problem is when they are taken care of. Depending on the operations underpinning the augmentation strategies, it may be more efficient to apply them on the CPU or accelerator. Some augmentations, like those from Kornia are designed to be entirely differentiable and may be faster operating directly on tensors already stored on the accelerator. Most strategies are best done on CPU which can take advantage of PyTorch’s multiprocessing features built into DataLoader workers.³

Lightning has implemented two very useful data hooks which allow users to have more control over what happens when batches are loaded before and after they are sent to accelerators. These can be implemented as methods in either the LightningModule or the LightningDataModule:

# Operates on a mini-batch before it is transferred to the accelerator
on_before_batch_transfer(batch, dataloader_idx):
# Operates on a mini-batch after it is transferred to the accelerator
on_after_batch_transfer(batch, dataloader_idx)

Reading this excellent Lightning tutorial on batched data augmentation finally gave me the tools I needed to take full control of how my data augmentation was being performed. A simple additional hook in my LightningDataModule solved the last of my problems:

 def on_before_batch_transfer(self, batch: Any, dataloader_idx: int) -> Any:
"""
Called before a batch is transferred to the device.
We apply data augmentation here only during training.
"""
if self.trainer.training:
batch = self.transforms(batch)
return batch

This ensured that my augmentation was performed (1) only on specific mini-batches as they are loaded, (2) not on validation or testing data, (3) on the correct hardware, and (4) only within the training loop.

Conducting the Symphony of Randomness

Randomness is not just fundamental to life but also embedded deep within machine learning craft. Respect and wield the power of randomness wisely. Balance it with stability and reproducibility. Navigating through the uncertain terrains of machine learning means not only adapting to randomness but also shaping it to our advantage. In doing so, we turn Poitier’s quote on its head: life may be determined by pure randomness, but we are its agent.

If you’re interested in more data science / python related blogs, please check out https://python-bloggers.com