Getting Started

This repo contains 3D version of original Pixel Shuffle idea from: Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network, implemented in PyTorch.

Demonstration

Visual intuition of how 3D (un)-shuffle operator works

Installation

1. Clone the repo

   git clone [email protected]:scalyvladimir/pixel_shuffle3d.git

2. Install all the demanded packages with:

   pip3 install torch numpy

Usage

1. PixelUnshuffle3d

   from pixel_shuffle3d import PixelUnshuffle3d
   import torch
   
   pixel_unshuffle = PixelUnshuffle3d(3)
   input = torch.randn(1, 1, 12, 12, 12)
   output = pixel_unshuffle(input)
   print(output.size())
   # torch.Size([1, 27, 4, 4, 4])

2. PixelShuffle3d

   from pixel_shuffle3d import PixelShuffle3d
   import torch
   
   pixel_shuffle = PixelShuffle3d(3)
   input = torch.randn(1, 27, 4, 4, 4)
   output = pixel_shuffle(input)
   print(output.size())
   # torch.Size([1, 1, 12, 12, 12])

Contributing

Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement". Don't forget to give the project a star! Thanks again!

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request