How to manage sparseconvtensor randomness?

[jisoo0-0]

Hi, … … I’m having some trouble to control the randomness of the model called Virconv(GitHub - hailanyi/VirConv: Virtual Sparse Convolution for Multimodal 3D Object Detection)

I have followed the custom tensor class for spconv(GitHub - traveller59/spconv: Spatial Sparse Convolution Library), but it seems no problem with how they use torch. I don’t know where the randomness came from…
I checked and found that randomness started on the sparseconvtensor of spconv module.

Is there a reason why randomness is out of control when my environment is as follows?

ccimport 0.3.7
certifi 2023.7.22
charset-normalizer 3.3.2
click 8.1.7
cloudpickle 2.2.1
cumm-cu111 0.2.9
cytoolz 0.12.0
dask 2023.6.0
easydict 1.11
fire 0.5.0
fsspec 2023.9.2
idna 3.4
imagecodecs 2023.1.23
imageio 2.31.4
importlib-metadata 6.0.0
lark 1.1.8
llvmlite 0.36.0
locket 1.0.0
networkx 3.1
ninja 1.11.1.1
numba 0.53.1
numpy 1.23.1
packaging 23.1
partd 1.4.1
pccm 0.3.4
pcdet 0.3.0+ca5ad85 /home/workspace/jisoocv/KITTI/VirConv
Pillow 10.0.1
pip 23.3
portalocker 2.8.2
prefetch-generator 1.0.3
protobuf 4.25.0
pybind11 2.11.1
PyWavelets 1.4.1
PyYAML 6.0.1
requests 2.31.0
scikit-image 0.19.3
scipy 1.11.3
setuptools 68.0.0
six 1.16.0
spconv-cu111 2.1.20
tensorboardX 2.6.2.2
termcolor 2.3.0
tifffile 2023.4.12
toolz 0.12.0
torch 1.8.1+cu111
torch-scatter 2.0.8
torchaudio 0.8.1
torchvision 0.9.1+cu111
tqdm 4.66.1
typing_extensions 4.8.0
urllib3 2.1.0
wheel 0.41.2
zipp 3.11.0

pytorch 1.8.1
cudnn8
Ubuntu 18.04
NVIDIA CUDA 11.1

with two A6000 gpus(I’ve tried to train on multi-GPU mode with distributed learning).