computervision-recipes/CONTRIBUTING.md

Contribution Guidelines

Contribution are welcome! Here's a few things to know:

• Microsoft Contributor License Agreement
• Steps to Contributing
• Working with Notebooks
• Coding Guidelines
• Code of Conduct
  • Do not point fingers
  • Provide code feedback based on evidence
  • Ask questions do not give answers

Microsoft Contributor License Agreement

Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.microsoft.com.

When you submit a pull request, a CLA-bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

Steps to Contributing

Here are the basic steps to get started with your first contribution. Please reach out with any questions.

1. Use open issues to discuss the proposed changes. Create an issue describing changes if necessary to collect feedback. Also, please use provided labels to tag issues so everyone can easily sort issues of interest.

2. Fork the repo so you can make and test local changes.

3. Create a new branch for the issue. We suggest prefixing the branch with your username and then a descriptive title: (e.g. gramhagen/update_contributing_docs)

4. Create a test that replicates the issue.

5. Make code changes.

6. Ensure unit tests pass and code style / formatting is consistent, and follows the Zen of Python.

7. We use pre-commit package to run our pre-commit hooks. We use black formatter and flake8 linting on each commit. In order to set up pre-commit on your machine, follow the steps here, please note that you only need to run these steps the first time you use pre-commit for this project.

   • Update your conda environment, pre-commit is part of the yaml file or just do

    $ pip install pre-commit
   

   • Set up pre-commit by running following command, this will put pre-commit under your .git/hooks directory.

   $ pre-commit install
   

   $ git commit -m "message"
   

   • Each time you commit, git will run the pre-commit hooks (black and flake8 for now) on any python files that are getting committed and are part of the git index. If black modifies/formats the file, or if flake8 finds any linting errors, the commit will not succeed. You will need to stage the file again if black changed the file, or fix the issues identified by flake8 and and stage it again.

   • To run pre-commit on all files just run

   $ pre-commit run --all-files
   

8. Create a pull request against staging branch.

Note: We use the staging branch to land all new features, so please remember to create the Pull Request against staging.

Working with Notebooks

It's challenging to do code review for notebooks in GitHub. reviewnb makes it easy to review notebooks in GitHub but only works with public repository. Since we are still in private mode, jupytext is another option that provides conversion of ipython notebooks to multiple formats and also work with pre-commit. However, it falls short of adding the converted files automatically as part of the git commit. An issue has been opened with jupytext for this. In the interim, a more reliable way is to manually convert the notebooks to python script using nbconvert and manually add and commit them to your branch along with the notebook. nbconvert comes pre-installed as part of jupyter installation, run the following command to convert a notebook to python script and save it in python folder under image_classification folder.

$ jupyter nbconvert --output-dir=./image_classification/python --to python ./image_classification/notebooks/mnist.ipynb

As you check these converted files in, we don't want to enforce black formatting and flake8 linting on them since they also contain markdown and metadata that does not play well with these tools and after all that's not the main goal for converting these notebooks to python script, main goal is to make diffing easier. You can commit the python script generated from nbconvert by using following option with git commit command

SKIP=black,flake8 git commit -m "commit message"

Note: We only want to skip black and flake8 hooks for the nbconvert py files, for everything else these hooks should not be skipped.

When you pull updates from remote there might be merge conflicts at times, use nbdime to fix them.

• To install nbdime

pip install ndime

• To do diff between notebooks

nbdiff notebook_1.ipynb notebook_2.ipynb

Coding Guidelines

We strive to maintain high quality code to make the utilities in the repository easy to understand, use, and extend. We also work hard to maintain a friendly and constructive environment. We've found that having clear expectations on the development process and consistent style helps to ensure everyone can contribute and collaborate effectively.

We follow the Google docstring guidlines outlined on this styleguide page. For example:

  def bite(n:int, animal:animal_object) -> bool:
      """
      This function will perform n bites on animal.

      Args:
          n (int): the number of bites to do
          animal (Animal): the animal to bite

      Raises:
          Exception: biting animal has no teeth

      Returns:
          bool: whether or not bite was successful
      """

Since we take a strong dependency on fast.ai, variable naming should follow the standards of fast.ai which are described in this abbreviation guide. For example, in computer vision cases, an image should always be abbreviated with im and not i, img, imag, image, etc. The one exception to this guide is that variable names should be as self-explanatory as possible. For example, the meaning of the variable batch_size is clearer than bs to refer to batch size.

The main variables and abbreviations are given in the table below:

Abbreviation	Description
im	Image
fig	Figure
pt	2D point (column,row)
rect	Rectangle (order: left, top, right, bottom)
width, height, w, h	Image dimensions
scale	Image up/down scaling factor
angle	Rotation angle in degree
table	2D row/column matrix implemented using a list of lists
row, list1D	Single row in a table, i.e. single 1D-list
rowItem	Single item in a row
line, string	Single string
lines, strings	List of strings
list1D	List of items, not necessarily strings
-s	Multiple of something (plural) should be indicated by appending an s to an abbreviation.

Code of Conduct

This project has adopted the Microsoft Open Source Code of Conduct.

For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.

Apart from the official Code of Conduct developed by Microsoft, we adopt the following behaviors, to ensure a great working environment:

Do not point fingers

Let’s be constructive. For example: "This method is missing docstrings" instead of "YOU forgot to put docstrings".

Provide code feedback based on evidence

When making code reviews, try to support your ideas based on evidence (papers, library documentation, stackoverflow, etc) rather than your personal preferences. For example: "When reviewing this code, I saw that the Python implementation the metrics are based on classes, however, scikit-learn and tensorflow use functions. We should follow the standard in the industry."

Ask questions do not give answers

Try to be empathic. For example: "Would it make more sense if ...?" or "Have you considered this ... ?"