```spconv``` is a project that provide heavily-optimized sparse convolution implementation with tensor core support. check [benchmark](docs/BENCHMARK.md) to see how fast spconv 2.x runs.
See [dev plan](docs/SPCONV_DEVELOP_PLAN.md). A complete guide of spconv development will be released soon.
## Usage
Firstly you need to use ```import spconv.pytorch as spconv``` in spconv 2.x.
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## Install
You need to install python >= 3.7 first to use spconv 2.x.
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.
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### Prebuilt
We offer python 3.7-3.10 and cuda 10.2/11.1/11.3/11.4 prebuilt binaries for linux (manylinux) and windows 10/11.
We offer python 3.6-3.10 and cuda 10.2/11.1/11.3/11.4 prebuilt binaries for linux (manylinux).
We offer python 3.7-3.10 and cuda 10.2/11.1/11.4 prebuilt binaries for windows 10/11.
We will provide prebuilts for CUDA versions supported by latest pytorch release. For example, pytorch 1.10 provide cuda 10.2 and 11.3 prebuilts, so we provide them too.
*```x.features = F.relu(x.features)``` now raise error. use ```x = x.replace_feature(F.relu(x.features))``` instead.
* weight layout has been changed to RSKC (native algorithm) or KRSC (implicit gemm), no longer RSCK (spconv 1.x). RS is kernel size, C is input channel, K is output channel.
* all util ops are removed (pillar scatter/nms/rbbox_iou...)
* VoxelGenerator has been replaced by Point2VoxelGPU[1-4]d/Point2VoxelCPU[1-4]d.
* VoxelGenerator has been replaced by ```spconv.pytorch.utils.PointToVoxel``` (torch API) or Point2VoxelGPU[1-4]d/Point2VoxelCPU[1-4]d (tv.Tensor API).
* spconv < 2.1 don't support CPU. spconv 2.1+ support cpu for debug usage.
* test spconv 1.x model in spconv 2.x: set environment variable before run program. Linux: ```export SPCONV_FILTER_HWIO="1"```, Windows powershell: ```$Env:SPCONV_FILTER_HWIO = "1"```. **WARNING** test spconv 1.x model don't support implicit gemm algorithm, you need to train from scratch with spconv 2.x and select ConvAlgo.MaskSplitImplicitGemm.
* test spconv 1.x model in spconv 2.x: Firstly set environment variable before run program, Then set all ```algo``` in conv/pool to ```ConvAlgo.Native```. Linux: ```export SPCONV_FILTER_HWIO="1"```, Windows powershell: ```$Env:SPCONV_FILTER_HWIO = "1"```. **WARNING** test spconv 1.x model don't support implicit gemm algorithm.
