Torch load

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前言 DataLoader是 PyTorch 中用于数据加载的工具类，它可以帮助我们有效地读取和处理数据集。 介绍与使用方式 简单来说，dataloader的作用就是将数据集变成可以进行遍历的对象，每次迭代可以从数据集中返回一组数据。在模型训练时，我们能可以用DataLoader批量读取数据。 结合

Now that our sample program has a rotating 3D cube, let's map a texture onto it instead of having its faces be solid colors.

Pytorch中的序列化和反序列化 troch.save 主要参数 obj:对象 f:输出路径 torch.load 主要参数 f:文件路径 map_location:指定存放位置，cpu或者gpu 对于保存有两种方法： 1.保存整个Moucle,torch.save(net,path) 2.保存模

In this guide, we'll take a look at how to use XMLHttpRequest to issue HTTP requests in order to exchange data between the website and a server.

1.作用：用来加载torch.save()保存的模型文件。 torch.load()先在CPU上加载，不会依赖于保存模型的设备。如果加载失败，可能是因为没有包含某些设备，比如你在gpu上训练保存的模型，而在cpu上加载，可能会报错，此时，需要使用map_location来将存储动态重新映射到可选设备

To enable WebAssembly to be read and edited by humans, there is a textual representation of the Wasm binary format. This is an intermediate form designed to be displayed in text editors, browser developer tools, and other similar environments. This article explains how the text format works in terms of its raw syntax, and how it relates to the underlying bytecode it represents and the wrapper objects that represent Wasm in JavaScript.