invertransforms 0.1.2

A library which turns torchvision transformations invertible and replayable.

invertransforms

A library which turns torchvision transformations invertible and replayable.

Installation

pip install invertransforms

Usage

Simply replace previous torchvision import statements and enjoy the new features.

# from torchvision import transforms as T
import invertransforms as T

transform = T.Compose([
  T.RandomCrop(size=256),
  T.ToTensor(),
])

img_tensor = transform(img)

# invert
img_again = transform.invert(img_tensor)

# replay
img_tensor2 = transform.replay(img2)

All transformations have an inverse transformation attached to it.

inv_transform = transform.inverse()
img_inv = inv_transform(img)

Notes:

If a transformation is random, it is necessary to apply it once before calling invert or inverse(). Otherwise it will raise InvertibleError. On the otherhand, replay can be called before, it will simply set the randomness on its first call.

Documentation

The library's documentation contains the full list of transformations which includes all the ones from torchvision and more.

Use Case

This library can be particularly useful in following situations:

• Reverting your model outputs in order to stack predictions
• Applying the same transformations on two different images
• blah-blah

Features

• Invert any transformations even random ones
• Replay any transformations even random ones
• All classes extends its torchvision transformation equivalent class
• Extensive unit testing
• blah-blah

Contribute

You found a bug, think a feature is missing or just want to help ?

Please feel free to open an issue, pull request or contact me [e-mail]