PyTorch data processing toolbox such as torch.utils.data and torvision

Data Processing Toolbox Overview

Pytorch involves data processing (data loading, data preprocessing, data enhancement, etc.). The main toolkits and their interrelationships are shown in the

figure.

1. Dataset: is an abstract class, other data sets must inherit this class, and rewrite two of the methods ( getitem , len ).
2. DataLoader: Define a new iterator to implement batch read, shuffle data, and provide parallel acceleration and other functions.
3. random_split: randomly split the data set into a new non-overlapping data set of a given length
4. Sampler: multiple sampling functions

The torchvision in the middle of the figure is the Pytorch visualization processing tool, including four categories:

1. datasets: Provides commonly used dataset loading, inherited from torch.utils.data.Dataset in design
2. models: Provide various classic network structures and trained models in deep learning (if pretrained = True is selected)
3. transforms: Common data preprocessing operations, mainly including operations on Tensor and PIL Image objects
4. utils: contains two functions, one is make_grid, which can stitch multiple pictures in a grid; the other is save_img, which can save tensor as a picture