- Aten support
- pytorch v1.0+ support
- pytorch c++ extention
- 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 |
git clone https://github.com/unlimblue/KNN_CUDA.git
cd KNN_CUDA
make && make install
pip install --upgrade https://github.com/unlimblue/KNN_CUDA/releases/download/0.2/KNN_CUDA-0.2-py3-none-any.whl
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