• KNN_CUDA
• • • • Modifications
      • Performance
      • Install
      • Usage

KNN_CUDA

• ref: kNN-CUDA
• ref: pytorch knn cuda
• author: sli@mail.bnu.edu.cn

Modifications

• Aten support
• pytorch v1.0+ support
• pytorch c++ extention

Performance

• dim = 5
• k = 100
• ref = 224
• query = 224
• Intel(R) Core(TM) i7-7700HQ CPU @ 2.80GHz
• NVIDIA GeForce 940MX

Loop	sklearn	CUDA	Memory
100	2.34 ms	0.06 ms	652/1024
1000	2.30 ms	1.40 ms	652/1024

Install

• from source

git clone https://github.com/unlimblue/KNN_CUDA.git
cd KNN_CUDA
make && make install

• from wheel

pip install --upgrade https://github.com/unlimblue/KNN_CUDA/releases/download/0.2/KNN_CUDA-0.2-py3-none-any.whl

And then, make sure ninja has been installed:

1. see https://pytorch.org/tutorials/advanced/cpp_extension.html
2. or just:

wget -P /usr/bin https://github.com/unlimblue/KNN_CUDA/raw/master/ninja

• for windows

You should use branch windows:

git clone --branch windows https://github.com/unlimblue/KNN_CUDA.git
cd C:\\PATH_TO_KNN_CUDA
make
make install

Usage

import torch

# Make sure your CUDA is available.
assert torch.cuda.is_available()

from knn_cuda import KNN
"""
if transpose_mode is True, 
    ref   is Tensor [bs x nr x dim]
    query is Tensor [bs x nq x dim]
    
    return 
        dist is Tensor [bs x nq x k]
        indx is Tensor [bs x nq x k]
else
    ref   is Tensor [bs x dim x nr]
    query is Tensor [bs x dim x nq]
    
    return 
        dist is Tensor [bs x k x nq]
        indx is Tensor [bs x k x nq]
"""

knn = KNN(k=10, transpose_mode=True)

ref = torch.rand(32, 1000, 5).cuda()
query = torch.rand(32, 50, 5).cuda()

dist, indx = knn(ref, query)  # 32 x 50 x 10