This post is about a small functionality that is found useful in TensorFlow / JAX / PyTorch.

Low-level components of these systems often use a plain list of values/tensors as inputs & outputs. However, end-users that develop models often want to work with more complicated data structures: Dict[str, Any], List[Any], custom classes, and their nested combinations. Therefore, we need bidirectional conversion between nested structures and a plain list of tensors. I found that different libraries invent similar approaches to solve this problem, and it's interesting to list them here.

PyTorch provides two methods to turn an nn.Module into a graph represented in TorchScript format: tracing and scripting. This article will:

1. Compare their pros and cons, with a focus on useful tips for tracing.
2. Try to convince you that torch.jit.trace should be preferred over torch.jit.script for deployment of non-trivial models.

这篇文章说说用户怎么提出好的 feature request / pull request, 以及维护者如何对待它们.

我听过不少人凭借爱好开源了自己的项目后, 却对 issue 太乱感到困扰, 甚至想干脆直接禁用 issue. 其实, 任何项目达到一定规模后, 如果不对 issue 进行适当管理, 都会使 issue 信噪比过低, 失去原本的功能.

这篇文章主要从 maintainer 的角度说说, 在具备规模的项目中管理 issue 的一些方法和原则.

报告错误 / 报 bug 是用户与开发者间最常见的一类交流, 也是常见的 github issue. 但是很多用户并不会科学的报 bug, maintainer 对此也缺乏引导. 因此这篇文章讨论如何科学的报 bug.

相比传统的邮件列表 / bugzilla/sourceforge 等开源平台, github 把开源社区交流的成本 / 门槛降的很低, 因此交流的质量也常常随之下降.

我计划写几篇文章, 从 用户 (User) 和 维护者 (Maintainer) 两者的角度写写开源社区中如何使用 issue/PR 进行沟通, 希望能够:

• 让普通用户学会提问, 学会以 maintainer 更容易接受的方式使用 issue/PR 做贡献.
• 给 maintainer 提供一些管理 issue/PR 的经验, 不要再为 issue 头疼.

In large systems, logs can be terrifying: they are huge in volume, and hard to understand. This note lists some suggestions and common misuse of Python's logging module, with the aim of:

• Reduce redundant logs & spams from libraries.
• Allow more control of logging behaviors.
• Make logs more informative to users.

延续 上一篇文章, 再说一说怎么科学的在 paper 里做 ablations.

Technically, an image is a function that maps a continuous domain, e.g. a box , to intensities such as (R, G, B). To store it on computer memory, an image is discretized to an array array[H][W], where each element array[i][j] is a pixel.

How does discretization work? How does a discrete pixel relate to the abstract notion of the underlying continuous image? These basic questions play an important role in computer graphics & computer vision algorithms.

This article discusses these low-level details, and how they affect our CNN models and deep learning libraries. If you ever wonder which resize function to use or whether you should add/subtract 0.5 or 1 to some pixel coordinates, you may find answers here. Interestingly, these details have contributed to many accuracy improvements in Detectron and Detectron2.

这几年来, 从 FAIR 的几位大佬身边学习到的最多的是对待 research 的态度. 因此说说写 paper 和做实验的体会.

实验与 claims

实验是为了证明或强化文章里给出的 claim/hypothesis 的.

Ross ICCV 2019 tutorial 最后谈了谈怎么写 paper. 第 126 页说, 文章中所有的 claim, 理想情况下都应该 要么是参考文献中已有的, 要么是被实验证明的.