PyTorch Custom Datasets From Zero to Hero

PyTorch is a popular open-source machine learning library used for applications such as computer vision, natural language processing, and more. One of the key features of PyTorch is its ability to create custom datasets for training machine learning models. In this article, we will explore the process of creating custom datasets from scratch using PyTorch.

Defining a Custom Dataset Class

The first step in creating a custom dataset in PyTorch is to define a custom dataset class. This class will inherit from PyTorch’s Dataset class and implement the methods __len__ and __getitem__.

Here is an example of how a custom dataset class can be defined:

“`python
import torch
from torch.utils.data import Dataset

class CustomDataset(Dataset):
def __init__(self, data):
self.data = data

def __len__(self):
return len(self.data)

def __getitem__(self, idx):
sample = self.data[idx]
return sample
“`

In this example, the CustomDataset class takes a data parameter in its constructor, which is then stored as an attribute of the class. The __len__ method returns the length of the dataset, and the __getitem__ method returns a specific sample from the dataset at the given index.

Loading the Custom Dataset

Once the custom dataset class has been defined, the next step is to load the custom dataset using PyTorch’s DataLoader class. The DataLoader class is used to load the dataset in batches for training and evaluation.

Here is an example of how a custom dataset can be loaded using the DataLoader class:

“`python
from torch.utils.data import DataLoader

# Assuming `data` is a list of samples
custom_dataset = CustomDataset(data)
dataloader = DataLoader(custom_dataset, batch_size=32, shuffle=True)
“`

In this example, the data list is used to create an instance of the CustomDataset class, which is then passed to the DataLoader class along with a batch size and the shuffle parameter to indicate whether the data should be shuffled during loading.

Conclusion

In this article, we have covered the process of creating custom datasets from scratch using PyTorch. By defining a custom dataset class and loading the dataset using the DataLoader class, it is possible to create and use custom datasets for training machine learning models in PyTorch.