This repository is a reference implementation for "Linearized Multi-Sampling for Differentiable Image Transformation", ICCV 2019. If you use this code in your research, please cite the paper.
This implementation is based on Python3 and PyTorch.
You can install the environment by: conda env create -f environment.yml
Activate the env by: conda activate linearized
A tutorial is in linearized sampler tutorial.ipynb . We built the method to have the same function prototype as torch.nn.functional.grid_sample, so you can replace bilinear sampling with linearized multi-sampling with minimum modification.
Copy ./warp/linearized.py to your project folder, and replace torch.nn.functional.grid_sample in your code with linearized.grid_sample.
We made linearize.py to have minimum dependencies(PyTorch only), so we put some extra utils methods in that file. You can move those utils methods to another place to make it cleaner.
If you find linearized multi-sampling useful in you project, please feel free to let us know by leaving an issue on this git repository or sending an email to [email protected].