SpConv: PyTorch Spatially Sparse Convolution Library

This is a spatially sparse convolution library like SparseConvNet but faster and easy to read. This library provide sparse convolution/transposed, submanifold convolution, inverse convolution and sparse maxpool.

The GPU Indice Generation algorithm is a unofficial implementation of paper SECOND. That algorithm (don't include GPU SubM indice generation algorithm) may be protected by patent.

This project only support CUDA 9.0+ or CPU only. If you are using cuda 8.0, please update it to 9.0.

This project only support tensors with spatial volume less than std::numeric_limits<int>::max() (~2e9). if someone really need very large space, open an issue.

News:

2019-5-24: spconv v1.1 released, now indice generation will use hash table as default (CPU code only support hash table). you can use use_hash=False to use dense table when using CUDA. In addition, add CPU only build support.

Install on Ubuntu 16.04/18.04

0. Use git clone xxx.git --recursive to clone this repo.

1. Install boost headers to your system include path, you can use either sudo apt-get install libboostall-dev or download compressed files from boost official website and copy headers to include path.

2. Download cmake >= 3.13.2, then add cmake executables to PATH.

3. Ensure you have installed pytorch 1.0+ in your environment, run python setup.py bdist_wheel (don't use python setup.py install).

4. Run cd ./dist, use pip to install generated whl file.

Install on Windows 10 with CUDA 10 and python 3.6 (python 3.7 may have problem, see this)

Since install newest driver and CUDA is very simple on windows, please use CUDA 10 on windows.

0. Install Visual Studio 2017. Use git clone xxx.git --recursive to clone this repo.

1. Download compressed files from boost official website and copy headers (i.e. boost_1_69/boost) to spconv/include.

2. Download and install cmake >= 3.13.2, select add cmake to User or System PATH.

3. Ensure you have installed pytorch 1.0 in your environment, run python setup.py bdist_wheel (don't use python setup.py install).

4. Run cd ./dist, use pip to install generated whl file.

Compare with SparseConvNet

Features

• SparseConvNet's Sparse Convolution don't support padding and dilation, spconv support this.

• spconv only contains sparse convolutions, the batchnorm and activations can directly use layers from torch.nn, SparseConvNet contains lots of their own implementation of layers such as batchnorm and activations.

Speed

• spconv is faster than SparseConvNet due to gpu indice generation and gather-gemm-scatter algorithm. SparseConvNet use hand-written gemm which is slow.

Usage

SparseConvTensor

features = # your features with shape [N, numPlanes]
indices = # your indices/coordinates with shape [N, ndim + 1], batch index must be put in indices[:, 0]
spatial_shape = # spatial shape of your sparse tensor.
batch_size = # batch size of your sparse tensor.
x = spconv.SparseConvTensor(features, indices, spatial_shape, batch_size)
x_dense_NCHW = x.dense() # convert sparse tensor to dense NCHW tensor.
print(x.sparity) # helper function to check sparity. 

Sparse Convolution

import spconv
from torch import nn
class ExampleNet(nn.Module):
    def __init__(self, shape):
        super().__init__()
        self.net = spconv.SparseSequential(
            spconv.SparseConv3d(32, 64, 3), # just like nn.Conv3d but don't support group and all([d > 1, s > 1])
            nn.BatchNorm1d(64), # non-spatial layers can be used directly in SparseSequential.
            nn.ReLU(),
            spconv.SubMConv3d(64, 64, 3, indice_key="subm0"),
            nn.BatchNorm1d(64),
            nn.ReLU(),
            # when use submanifold convolutions, their indices can be shared to save indices generation time.
            spconv.SubMConv3d(64, 64, 3, indice_key="subm0"),
            nn.BatchNorm1d(64),
            nn.ReLU(),
            spconv.SparseConvTranspose3d(64, 64, 3, 2),
            nn.BatchNorm1d(64),
            nn.ReLU(),
            spconv.ToDense(), # convert spconv tensor to dense and convert it to NCHW format.
            nn.Conv3d(64, 64, 3),
            nn.BatchNorm1d(64),
            nn.ReLU(),
        )
        self.shape = shape

    def forward(self, features, coors, batch_size):
        coors = coors.int() # unlike torch, this library only accept int coordinates.
        x = spconv.SparseConvTensor(features, coors, self.shape, batch_size)
        return self.net(x)# .dense()

Inverse Convolution

Inverse sparse convolution means "inv" of sparse convolution. the output of inverse convolution contains same indices as input of sparse convolution.

Inverse convolution usually used in semantic segmentation.

class ExampleNet(nn.Module):
    def __init__(self, shape):
        super().__init__()
        self.net = spconv.SparseSequential(
            spconv.SparseConv3d(32, 64, 3, 2, indice_key="cp0"),
            spconv.SparseInverseConv3d(64, 32, 3, indice_key="cp0"), # need provide kernel size to create weight
        )
        self.shape = shape

    def forward(self, features, coors, batch_size):
        coors = coors.int()
        x = spconv.SparseConvTensor(features, coors, self.shape, batch_size)
        return self.net(x)

Utility functions

• convert point cloud to voxel

voxel_generator = spconv.utils.VoxelGenerator(
    voxel_size=[0.1, 0.1, 0.1], 
    point_cloud_range=[-50, -50, -3, 50, 50, 1],
    max_num_points=30,
    max_voxels=40000
)

points = # [N, 3+] tensor.
voxels, coords, num_points_per_voxel = voxel_generator.generate(points)

Implementation Details

This implementation use gather-gemm-scatter framework to do sparse convolution.

Projects using spconv:

• second.pytorch: Point Cloud Object Detection in KITTI Dataset.

Authors

• Yan Yan - Initial work - traveller59

• Bo Li - gpu indice generation idea, owner of patent of the sparse conv gpu indice generation algorithm (don't include subm) - prclibo

Third party libraries

• CUDPP: A cuda library. contains a cuda hash implementation.

• robin-map: A fast c++ hash library. almost 2x faster than std::unordered_map in this project.

• pybind11: A head-only python c++ binding library.

• prettyprint: A head-only library for container print.

License

This project is licensed under the Apache license 2.0 License - see the LICENSE.md file for details

The CUDPP hash code is licensed under BSD License.

The robin-map code is licensed under MIT license.