Torchvision, torchaudio and torch are three important components of the PyTorch deep learning framework. They are closely related to each other and each has different functions and uses.
Torch:
Torch is the core library of PyTorch, which provides functions for tensor operation and calculation graph construction. Tensors are the primary data structure used to store and manipulate data in PyTorch.
Torch provides an automatic derivation (Autograd) function, allowing users to easily build and train neural network models.
Torchvision:
Torchvision is an independent sub-library of PyTorch, mainly used for computer vision tasks, including image processing, data loading, data enhancement, pre-training models, etc.
Torchvision provides loaders for various classic computer vision data sets, such as CIFAR-10, ImageNet, and tools for data preprocessing and data enhancement, which can help users more easily perform image classification, target detection, image segmentation, etc. Task.
Torchaudio:
Torchaudio is also an independent sub-library of PyTorch, used to process audio signals and audio data.
It provides tools for loading, processing, and transforming audio data, as well as functions for building sound processing models.
Why install Torchvision and Torchaudio?
Installing Torchvision and Torchaudio mainly depends on your application requirements. These two libraries will be very useful if you need to do computer vision tasks or audio processing tasks.
Torchvision can accelerate the development of image processing tasks and provides a wealth of tools and pre-trained models, making it easier to build image-related deep learning models.
Torchaudio is very useful for sound signal processing and audio data loading. If your project involves tasks such as speech recognition, audio classification, sound generation, etc., it will be a powerful tool.
In summary, installing Torchvision and Torchaudio depends on your specific project needs, and they provide convenience and efficiency for image processing and audio processing tasks. If your project doesn't cover these areas, you probably don't need to install them.