Python库 | torch-vectorized-0.1.10.tar.gz

# Torch Vectorized > Batched and vectorized operations on volume of 3x3 symmetric matrices with Pytorch. The current Pytorch's implementation of batch eigen-decomposition is very slow when dealing with huge number of small matrices (e.g. 500k x 3x3). **This library offers some basic functions like vSymEig, vExpm and vLogm for fast computation (>250x faster) of huge number of small matrices with Pytorch using an analytical solution.** #### Read the documentaton [HERE](https://torch-vectorized.readthedocs.io/en/latest/) ## vSymEig > A quick closed-form solution for volumetric 3x3 matrices Eigen-Decomposition with Pytorch. Solves Eigen-Decomposition of data with shape Bx9xDxHxW, where B is the batch size, 9 is the flattened 3x3 symmetric matrices, D is the depth, H is the Height, W is the width. The goal is to accelerate the Eigen-Decomposition of multiple (>500k) small matrices (3x3) on GPU with Pytorch using an analytical solution. <img src="https://raw.githubusercontent.com/banctilrobitaille/torch-vectorized/master/icons/vsymeig.png" width="100%" vertical-align="bottom"> ## vExpm > Based on vSymEig, computes the matrix exponential for batch of volumetric 3x3 matrices. <img src="https://raw.githubusercontent.com/banctilrobitaille/torch-vectorized/master/icons/vexpm.png" width="100%" vertical-align="bottom"> ## vLogm > Based on vSymEig, computes the matrix logarithm for batch of volumetric 3x3 matrices. <img src="https://raw.githubusercontent.com/banctilrobitaille/torch-vectorized/master/icons/vlogm.png" width="100%" vertical-align="bottom"> ## Install me > pip install torch-vectorized ## How to use ```python from torchvectorized.vlinalg import vSymeig # Random batch of volumetric 3x3 symmetric matrices of size 16x9x32x32x32 input = sym(torch.rand(16, 9, 32, 32, 32)) # Output eig_vals with size: 16x3x32x32x32 and eig_vecs with size 16,3,3,32,32,32 eig_vals, eig_vecs = vSymeig(input, eigen_vectors=True) def sym(self, inputs): # Ensure symmetry of randomly generated 3x3 matrix using (X + X.T) / 2.0 return (inputs + inputs[:, [0, 3, 6, 1, 4, 7, 2, 5, 8], :, :, :]) / 2.0 ``` ## Contributing #### How to contribute ? - [X] Create a branch by feature and/or bug fix - [X] Get the code - [X] Commit and push - [X] Create a pull request #### Branch naming ##### Feature branch > feature/ [Short feature description] [Issue number] ##### Bug branch > fix/ [Short fix description] [Issue number] #### Commits syntax: ##### Adding code: > \+ Added [Short Description] [Issue Number] ##### Deleting code: > \- Deleted [Short Description] [Issue Number] ##### Modifying code: > \* Changed [Short Description] [Issue Number] ##### Merging code: > Y Merged [Short Description] [Issue Number] Icons made by <a href="http://www.flaticon.com/authors/freepik" title="Freepik">Freepik</a> from <a href="http://www.flaticon.com" title="Flaticon">www.flaticon.com</a> is licensed by <a href="http://creativecommons.org/licenses/by/3.0/" title="Creative Commons BY 3.0" target="_blank">CC 3.0 BY</a>