Author: Zen and the Art of Computer Programming
1 Introduction
Image segmentation in the field of deep learning is an important research direction in computer vision. Its purpose is to assign the pixels of objects in the image to corresponding categories to achieve automatic segmentation. In recent years, deep learning technology has achieved great success and achieved state-of-the-art results in many task fields. Among them, the training of image segmentation models often takes up time and a large amount of computing resources. Therefore, how to speed up the model training process and improve efficiency has become a very important issue. PyTorch is an open source deep learning framework that provides many excellent tools to solve this problem. This article will introduce two very important libraries, PyTorch Lightning and DataLoader, and use these two libraries to implement the training and evaluation of an image segmentation model.
PyTorch is a deep learning framework with unique functional features. It provides many practical tools, such as dynamic graph mechanism, distributed computing and automatic derivation. Through these features, the construction, debugging, training, and deployment of deep learning models become very convenient and fast. PyTorch also provides a rich API interface, allowing developers to quickly build their own models. However, for the training of image segmentation models, due to the different sizes of each image, the time to load the training data set is also uncertain, so the optimization of the data loader needs to be taken into consideration. The official recommendation of PyTorch is to preprocess the data set before training to extract data that matches the model input. Such a data loader can maximize the use of the diversity of training data. However, manually coding the data loader, although simple, is still time-consuming and labor-intensive. In order to better solve this problem, PyTorch provides a tool class called DataLoader. DataLoader can put image files and labels into memory in batches, and at the same time increase the data reading speed through multiple processes or threads. However, in general, users still need to customize some code to handle data augmentation, scaling, normalization and other operations, which also has a certain impact on efficiency.
Based on the above reasons, based on PyTorch Lightning and DataL