What are the best practices for using PyTorch in computer vision?

1 Use torchvision

torchvision is a PyTorch library that provides a variety of utilities, datasets, models, and transforms for computer vision. It can save you a lot of time and effort by simplifying common tasks such as loading and preprocessing images, applying augmentations, downloading and using pre-trained models, and implementing state-of-the-art algorithms. You can use torchvision to access popular datasets such as MNIST, CIFAR-10, ImageNet, COCO, and more, as well as models such as ResNet, VGG, AlexNet, Faster R-CNN, and more. You can also use torchvision to apply standard or custom transformations to your images, such as resizing, cropping, flipping, rotating, normalizing, and more.

2 Use DataLoader

DataLoader is a PyTorch class that allows you to load and iterate over your data in batches, with optional features such as shuffling, sampling, multiprocessing, and collating. DataLoader can handle both in-memory and out-of-memory data sources, such as tensors, lists, files, or custom objects. DataLoader can also work with torchvision datasets and transforms, making it easy to integrate them with your PyTorch code. Using DataLoader can improve your data loading efficiency, memory usage, and performance, as well as enable you to use different batch sizes, sampling strategies, and data augmentation techniques.

3 Use nn.Module

nn.Module is a PyTorch class that represents a neural network module or layer. It can contain other modules, parameters, buffers, and methods, and it can be subclassed to define custom modules. Using nn.Module can help you organize your code, manage your parameters, and implement your forward and backward passes. You can use nn.Module to create simple or complex modules, such as linear layers, convolutional layers, activation functions, dropout layers, batch normalization layers, and more. You can also use nn.Module to build and use pre-defined or custom models, such as sequential models, container models, recurrent models, and more.

4 Use nn.functional

nn.functional is a PyTorch module that provides a variety of functions that operate on tensors and modules. It can be used to perform common operations such as matrix multiplication, convolution, pooling, activation, loss calculation, and more. Using nn.functional can help you avoid creating unnecessary modules or parameters, and it can also offer more flexibility and control over your computations. You can use nn.functional to implement different parts of your model, such as the forward pass, the backward pass, the optimization step, and the evaluation step.

5 Use torch.optim

torch.optim is a PyTorch module that provides a range of optimization algorithms for gradient-based learning. It can be used to update the parameters of your model based on the gradients computed by the backward pass. Using torch.optim can help you simplify your optimization code, choose the best algorithm for your problem, and tune the hyperparameters such as learning rate, momentum, weight decay, and more. You can use torch.optim to implement and use popular optimizers such as SGD, Adam, RMSprop, Adagrad, and more.

6 Use torch.device

torch.device is a PyTorch class that represents a device where tensors and modules can be allocated and operated on. It can be used to specify whether to use a CPU or a GPU, and which GPU to use if there are multiple available. Using torch.device can help you leverage the power of GPUs, accelerate your training and inference, and manage your device memory. You can use torch.device to create and move tensors and modules between devices, as well as to perform device-specific operations such as synchronization and communication.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?