Handling images with PyTorch

As deep learning engineers, we frequently work with image data. PyTorch provides powerful tools for loading, displaying, and augmenting images.

In this post, let’s explore the essential techniques for handling images effectively using PyTorch.

If you’re not a paid Medium member, you can read this post here

1] Loading Images with PyTorch

PyTorch offers several ways to load and preprocess images, but the most convenient approach is using torchvision.datasets and DataLoader. Let's look at how to set up an efficient image loading pipeline.

Basic Image Loading Setup

from torch.utils.data import DataLoader
from torchvision import datasets, transforms
from pathlib import Path

class ImageDataManager:
    def __init__(
        self,
        data_dir: str,
        image_size: tuple = (128, 128),
        batch_size: int = 32,
        num_workers: int = 4
    ):
        self.data_dir = Path(data_dir)
        self.image_size = image_size
        self.batch_size = batch_size
        self.num_workers = num_workers
        
        # Basic transformations
        self.base_transforms = transforms.Compose([
            transforms.Resize(image_size),
            transforms.ToTensor(),
            transforms.Normalize(
                mean=[0.485…