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.
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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.
Based on vSymEig, computes the matrix exponential for batch of volumetric 3x3 matrices.
Based on vSymEig, computes the matrix logarithm for batch of volumetric 3x3 matrices.
pip install torch-vectorized
import torch
from torchvectorized.utils import sym
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, eigenvectors=True)- Create a branch by feature and/or bug fix
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