PyTorch is great fun. Seriously! It has only been a few weeks that I started working with it. It already is the least painful thing in the process, which, is kind of the point of having such a library.

The first task that any Machine Learning engineer would struggle with is to load and handle data. PyTorch provides an excellent abstraction in the form of Such dataset classes are handy as they allow treating the dataset as just another iterator (almost) object. This pattern allows us to build a variety of transforms on top a custom base class (e.g. resizing images in an image dataset) staying true to the “separation of concerns” principle. It is always a pleasure to experience such powerful abstractions which are very simple at the core.

This article will first show how dataset classes are used and then illustrate how to build a custom dataset class using some dummy data.


The pytorch/vision repository hosts a handful of common datasets. One of the most popular one being the MNIST dataset.

from torchvision.datasets import MNIST

data_train = MNIST('~/pytorch_data', train=True, download=True)

This one line is all you need to have the data processed and setup for you. Under the hood, it downloads the byte files, decodes and converts them into a readable format. It also handles the case when the dataset has already been downloaded and processed. It cleanly abstracts out all the pestering details. This object can now be accessed using standard indexes.


But that is not all. Most of the time you wouldn’t really be accessing such indices but actually sending in the matrices to your model. PyTorch provides another wrapper interface called the This comes in handy when you need to prepare data batches (and perhaps shuffle them before every run).

from import DataLoader

data_train_loader = DataLoader(data_train, batch_size=64, shuffle=True)

for batch_idx, samples in enumerate(data_train_loader):
    # samples will be a 64 x D dimensional tensor
    # feed it to your neural network model

For those interested in a real working example, I use a similar loader to train LeNet-5. The full source is available at activatedgeek/LeNet-5/

The ones familiar with a standard batch machine learning pipeline should be able to relate to this and understand how terrifically simple this abstraction has made the whole process. For others, I would recommend working on your data manually and see how unassumingly messy this process can become and eat away precious time.

While, most of the times, one will be working on non-standard datasets and would require custom processing to get things done. We will next look at how to design our own data loader. At its core, it is nothing more than utilizing the Python magic methods as we will see. Spending a few hours doing this abstraction now would save many later.

A Custom Dataset Loader

Let us take a look at the actual implementation of the Dataset interface. See here for full context.

class Dataset(object):
    def __getitem__(self, index):
        raise NotImplementedError

    def __len__(self):
        raise NotImplementedError

Told you, this is not at all complex (and why should it be?). This is the necessary and sufficient interace that we must implement to get the nice abstraction (a nice syntactic sugar to say the least) above. Let us call our dataset MyDataset and its purpose is to return squares of values in range [a,b].

class MyDataset(Dataset):
    This dataset contains a list of numbers in the range [a,b] inclusive
    def __init__(self, a=0, b=1):
        super(MyDataset, self).__init__()
        assert a <= b
        self.a = a
        self.b = b
    def __len__(self):
        return self.b - self.a + 1
    def __getitem__(self, index):
        assert self.a <= index <= self.b
        return index, index**2

You could now use this along with a DataLoader class as

data_train = MyDataset(a=1,b=128)
data_train_loader = DataLoader(data_train, batch_size=64, shuffle=True)
print(len(data_train)) # 128

and this would work right away! Notice the use of assertions to ensure some basic sanity checks. But now, let us say I wanted to return the values powered to 8 and also retain all the goodness of MyDataset. All I need is to extend it further and build a derived class.

class MyDatasetDerived(MyDataset):

    def __init__(self, a=0, b=1):
        super(MyDatasetDerived, self).__init__(a, b)

    def __getitem__(self, index):
        index, value = super(MyDatasetDerived, self).__getitem__(index)
        return index, value**4

I know, I know, that dataset means nothing. But in essence, this is all there is to writing data loaders in PyTorch. It just exploits the goodness of Python combined with your own object-oriented programming skills. While a good exercise would be to go through a variety of data loaders for a number of popular datasets like ImageNet and CIFAR-10/100,

A Model Dataset Class

I would like to present the following rules of thumb while designing your next data loader in the form of a following commented template. It consists of some basic rules of thumb I observed to be helpful allowing maximum flexibility with playing with data.

class MyIdealDataset(Dataset):

    def __init__(self, *args):
        super(MyIdealDataset, self).__init__()

        1. Store all meaningful arguments to the constructor here for debugging.
        2. Do most of the heavy-lifting like downloading the dataset, checking for consistency of already existing dataset etc. here
        3. Aspire to store just the sufficient number of variables for usage in other member methods. Keeps the memory footprint low.
        4. For any further derived classes, this is the place to apply any pre-computed transforms over the sufficient variables (e.g. building a paired dataset from a dataset of singleton images)
    def __len__(self):
        This function gets called with len()
        1. The length should be a deterministic function of some instance variables and should be a non-ambiguous representation of the total sample count. This gets tricky especially when certain samples are randomly generated, be careful
        2. This method should be O(1) and contain no heavy-lifting. Ideally, just return a pre-computed variable during the constructor call.
        3. Make sure to override this method in further derived classes to avoid unexpected samplings.

    def __getitem__(self, index):
        1. Make appropriate assertions on the "index" argument. Python allows slices as well, so it is important to be clear of what arguments to support. Just supporting integer indices works well most of the times.
        2. This is the place to load large data on-demand. DONOT ever load all data in the constructor as that unnecessarily bloats memory.
        3. This method should be as fast as possible and should only be using certain pre-computed values. e.g. When loading images, the path directory should be handled during the constructor and this method should only load the file into memory and apply relevant transforms.
        4. Whenever lazy loading is possible, this is the place to be. e.g. Loading images only when called should be here. Keeps the memory footprint low.
        5. Subsequently, this also becomes the place for any input transforms (like resizing, cropping, conversion to tensor and so on)


Writing a DataLoader was so easy that I already submitted a PR to add the Omniglot dataset to the repository of Vision datasets under PyTorch during my first day of working with it. You can check the PR#373 for a more realistic example of writing DataLoaders from scratch.

Overall, the takeaway here is that “separation of concerns” goes a long way. Build the dataset from its unit item and derive custom transformation classes from this base dataset. This rather amazingly helps in fast experimentation with the data.