spconv | Spatial Sparse Convolution Library | Machine Learning library

by traveller59 Python Version: 2.3.6 License: Apache-2.0

X-Ray Key Features Code Snippets(3)Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | spconv Summary

spconv is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch applications. spconv has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. You can install using 'pip install spconv' or download it from GitHub, PyPI.

spconv is a project that provide heavily-optimized sparse convolution implementation with tensor core support. check benchmark to see how fast spconv 2.x runs. Spconv 1.x code. We won't provide any support for spconv 1.x since it's deprecated. use spconv 2.x if possible.

Support

Quality

Security

License

Reuse

Support

spconv has a medium active ecosystem.

It has 1402 star(s) with 323 fork(s). There are 24 watchers for this library.

It had no major release in the last 12 months.

There are 62 open issues and 523 have been closed. On average issues are closed in 131 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of spconv is 2.3.6

Quality

spconv has 0 bugs and 0 code smells.

Security

spconv has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

spconv code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

spconv is licensed under the Apache-2.0 License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

spconv releases are not available. You will need to build from source code and install.

Deployable package is available in PyPI.

Build file is available. You can build the component from source.

Installation instructions are available. Examples and code snippets are not available.

Top functions reviewed by kandi - BETA

kandi has reviewed spconv and discovered the below as its top functions. This is intended to give you an instant insight into spconv implemented functionality, and help decide if they suit your requirements.

Get implicit gemm pairs .
Devices v2 .
Inverse convolutional backward .
Perform indice convolution of features .
Performs implicit GEMM backend .
Get indices from indices .
Perform implicit Gramm algorithm .
Convert to voxel template .
Tune the model and cache it .
Rotate the non - blocking region .

Get all kandi verified functions for this library.

spconv Key Features

No Key Features are available at this moment for spconv.

spconv Examples and Code Snippets

Install

Python

Lines of Code : 13

License : Non-SPDX (NOASSERTION)

Copy

git clone https://github.com/aim-uofa/DyCo3D.git
cd DyCo3D

pip install -r requirements.txt
conda install -c bioconda google-sparsehash 

conda install libboost
conda install -c daleydeng gcc-5 # need gcc-5.4 for sparseconv

include_directories($INCL

Installation

Python

Lines of Code : 11

License : Permissive (Apache-2.0)

Copy

git clone https://github.com/blakechen97/SASA.git
cd SASA

git clone https://github.com/traveller59/spconv.git
cd spconv
git checkout v1.2.1
git submodule update --init --recursive
python setup.py bdist_wheel
pip install ./dist/spconv-1.2.1-cp36-cp36

Lidar-detection,Install,2. Install dependence python packages

Python

Lines of Code : 9

License : No License

Copy

pip3 install numba pyntcloud pyyaml rospkg pyquaternion protobuf

git clone https://github.com/eric-wieser/ros_numpy
cd ros_numpy && python setup.py install

sudo apt-get install llvm-8
export LLVM_CONFIG=/usr/bin/llvm-config-8
pip3 install n

Community Discussions

Trending Discussions on spconv

C++ template metaprogramming for argument dispatch

QUESTION

C++ template metaprogramming for argument dispatch

Asked 2020-Sep-04 at 09:33

I'm reading the source code of spconv, a sparse convolution library wrote by c++ (and cuda), in the source code I found a complicate template usage, and I summarize it as a minimum working example below:

...

ANSWER

Answered 2020-Sep-04 at 09:33

So the first part, (minus useless part)

Source https://stackoverflow.com/questions/63737086

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install spconv

You need to install python >= 3.6 (>=3.7 for windows) first to use spconv 2.x. You need to install CUDA toolkit first before using prebuilt binaries or build from source. You need at least CUDA 10.2 to build and run spconv 2.x. We won't offer any support for CUDA < 10.2.
The c++ code will be built automatically when you change c++ code in project. For NVIDIA Embedded Platforms, you need to specify cuda arch before build: export CUMM_CUDA_ARCH_LIST="7.2" for xavier, export CUMM_CUDA_ARCH_LIST="6.2" for TX2, export CUMM_CUDA_ARCH_LIST="8.7" for orin. You need to remove cumm in requires section in pyproject.toml after install editable cumm and before install spconv due to pyproject limit (can't find editable installed cumm). You need to ensure pip list | grep spconv and pip list | grep cumm show nothing before install editable spconv/cumm.
uninstall spconv and cumm installed by pip
install build-essential, install CUDA
git clone https://github.com/FindDefinition/cumm, cd ./cumm, pip install -e .
git clone https://github.com/traveller59/spconv, cd ./spconv, pip install -e .
in python, import spconv and wait for build finish.
uninstall spconv and cumm installed by pip
install visual studio 2019 or newer. make sure C++ development component is installed. install CUDA
set powershell script execution policy
start a new powershell, run tools/msvc_setup.ps1
git clone https://github.com/FindDefinition/cumm, cd ./cumm, pip install -e .
git clone https://github.com/traveller59/spconv, cd ./spconv, pip install -e .
in python, import spconv and wait for build finish.
You need to rebuild cumm first if you are build along a CUDA version that not provided in prebuilts.
install build-essential, install CUDA
run export SPCONV_DISABLE_JIT="1"
run pip install pccm cumm wheel
run python setup.py bdist_wheel+pip install dists/xxx.whl
install visual studio 2019 or newer. make sure C++ development component is installed. install CUDA
set powershell script execution policy
start a new powershell, run tools/msvc_setup.ps1
run $Env:SPCONV_DISABLE_JIT = "1"
run pip install pccm cumm wheel
run python setup.py bdist_wheel+pip install dists/xxx.whl

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

Find more information at: