pytorch/vision

Unit tests for Vision

Open

#110 opened on 2017年3月17日

GitHub で見る
 (8 comments) (3 reactions) (0 assignees)Python (15,050 stars) (6,858 forks)batch import
help wanted

説明

This issue fleshes out the full details and scope of the unit tests needed for torchvision.

There are very limited unit tests under test/ which dont cover the transform outputs themselves, but are limited to dimension and shape checks.

First, let's start with quantitative tests on known results.

We need to have a set of 10 test images, and then do each transformation of vision.transforms on these 10 images, and compare them pixel-wise with known results. We then compare the known results with the computed result from the transforms, and if they are within some threshold, we pass the test.

Some of the transforms such as Horizontal / Vertical flip can also have exact numerical unit-tests.

The test images:

  • 2 monochrome images
  • 2 3-channel images
  • 2 4-channel PNG images with an Alpha component

Can find some on Wikipedia that are freely licensed.

The tests need to cover all transforms under: https://github.com/pytorch/vision#transforms

For similar testing, you can have a look at:

https://github.com/torch/image/blob/master/test/test.lua#L258-L646

コントリビューターガイド

技術スタック
python
領域
testing
Issue 種別
test
難度新しい貢献者にとっての実装難度です。1 は非常に小さな変更、5 は専門的な作業です。
3
推定時間経験ある貢献者が調査、実装、テスト、pull request 準備にかけるおおよその時間範囲です。
3-5 days
活動状況Issue が今どれだけ取り組みやすいかを示します。新しい、活発、古い、ブロック中、またはメンテナー入力待ちなどです。
stale
明確さ期待される変更、受け入れ条件、次の手順がどれだけ明確かを示します。
clear
前提条件
PythonPyTorchtorchvisionunit testing
初心者向け度初回貢献者にどれだけ取り組みやすいかを 1-100 で推定したスコアです。
60
調査方針
Familiarize with current torchvision transforms at https://github.com/pytorch/vision#transforms. Create 10 test images (2 monochrome, 2 3 channel, 2 4 channel) from freely licensed sources. Implement unit tests for each transform comparing output to known results within a threshold, using the Lua test at https://github.com/torch/image/blob/master/test/test.lua#L258 L646 as reference.