Merge branch 'master' into master

.github/workflows/build.yaml

+name: build
+
+on:
+  push:
+    branches:
+      - master
+  schedule:
+    # * is a special character in YAML so you have to quote this string
+    - cron:  '0 0 * * 6' # base builds run every saturday
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    env:
+      DOCKER_IMAGE_NAME: scrin/dev-spconv
+      DOCKER_FILE_PATH: ./Dockerfile
+
+    # TODO: create a action to reuse code. the problem is how to reuse docker-login.
+    steps:
+      - uses: actions/checkout@master
+      - name: Build Docker
+        run: |
+          docker build . --file ${{env.DOCKER_FILE_PATH}} --tag ${{env.DOCKER_IMAGE_NAME}}:latest
+          docker tag ${{env.DOCKER_IMAGE_NAME}}:latest ${{env.DOCKER_IMAGE_NAME}}:${{ github.sha }}
+          
+
+      - name: Login to Registry
+        uses: azure/docker-login@v1
+        with:
+          username: ${{ secrets.DOCKER_USERNAME }}
+          password: ${{ secrets.DOCKER_PASSWORD }}
+
+      - name: Publish to Registry
+        run: |
+          docker push ${{env.DOCKER_IMAGE_NAME}}:latest
+          docker push ${{env.DOCKER_IMAGE_NAME}}:${{ github.sha }}

Dockerfile

+FROM scrin/dev:latest
+
+RUN PROBLEM_FILE=/usr/local/lib/python3.8/dist-packages/torch/share/cmake/Caffe2/Caffe2Targets.cmake && \
+    sed -i 's/-Wall;-Wextra;-Wno-unused-parameter;-Wno-missing-field-initializers;-Wno-write-strings;-Wno-unknown-pragmas;-Wno-missing-braces;-fopenmp//g' $PROBLEM_FILE && \
+    sed -i 's/-Wall;-Wextra;-Wno-unused-parameter;-Wno-missing-field-initializers;-Wno-write-strings;-Wno-unknown-pragmas;-Wno-missing-braces//g' $PROBLEM_FILE && \
+    cd /root && \
+    git clone --depth 1 --recursive https://www.github.com/traveller59/spconv.git && \
+    cd ./spconv && \
+    SPCONV_FORCE_BUILD_CUDA=1 python setup.py install

README.md

 # SpConv: PyTorch Spatially Sparse Convolution Library
 
-This is a variation version of [spconv](https://github.com/traveller59/spconv)
+[![Build Status](https://github.com/traveller59/spconv/workflows/build/badge.svg)](https://github.com/traveller59/spconv/actions?query=workflow%3Abuild)
+
+This is a spatially sparse convolution library like [SparseConvNet](https://github.com/facebookresearch/SparseConvNet) but faster and easy to read. This library provide sparse convolution/transposed, submanifold convolution, inverse convolution and sparse maxpool.
+
 
 2020-5-2, we add ConcatTable, JoinTable, AddTable, and Identity function to build ResNet and Unet in this version of spconv.
 
 
+## Docker:
+
+```docker pull scrin/dev-spconv```, contains python 3.8, cuda 10.1, fish shell, newest pytorch and tensorflow.
+
 ## Install on Ubuntu 16.04/18.04
 
 * if you are using pytorch 1.4+ and encounter "nvcc fatal: unknown -Wall", you need to go to torch package dir and remove flags contains "-Wall" in INTERFACE_COMPILE_OPTIONS in Caffe2Targets.cmake. This problem can't be fixed in this project (to avoid this, I need to remove all torch dependency in cuda sources and drop half support).
 
 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.
+1. Install boost headers to your system include path, you can use either ```sudo apt-get install libboost-all-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.
 

include/spconv/point2voxel.h

@@ -71,7 +71,7 @@ int points_to_voxel_3d_np(py::array_t<DType> points, py::array_t<DType> voxels,
     if (voxelidx == -1) {
       voxelidx = voxel_num;
       if (voxel_num >= max_voxels)
-        break;
+        continue;
       voxel_num += 1;
       coor_to_voxelidx_rw(coor[0], coor[1], coor[2]) = voxelidx;
       for (int k = 0; k < NDim; ++k) {
@@ -139,7 +139,7 @@ int points_to_voxel_3d_np_mean(py::array_t<DType> points,
     if (voxelidx == -1) {
       voxelidx = voxel_num;
       if (voxel_num >= max_voxels)
-        break;
+        continue;
       voxel_num += 1;
       coor_to_voxelidx_rw(coor[0], coor[1], coor[2]) = voxelidx;
       for (int k = 0; k < NDim; ++k) {
@@ -225,7 +225,7 @@ int points_to_voxel_3d_with_filtering(
     if (voxelidx == -1) {
       voxelidx = voxel_num;
       if (voxel_num >= max_voxels)
-        break;
+        continue;
       voxel_num += 1;
       coor_to_voxelidx_rw(coor[0], coor[1], coor[2]) = voxelidx;
       for (int k = 0; k < NDim; ++k) {

setup.py

@@ -15,6 +15,8 @@ from pathlib import Path
 
 LIBTORCH_ROOT = str(Path(torch.__file__).parent)
 
+SPCONV_FORCE_BUILD_CUDA = os.getenv("SPCONV_FORCE_BUILD_CUDA")
+
 PYTHON_VERSION = "{}.{}".format(sys.version_info.major, sys.version_info.minor)
 
 class CMakeExtension(Extension):
@@ -46,7 +48,7 @@ class CMakeBuild(build_ext):
                       '-DPYBIND11_PYTHON_VERSION={}'.format(PYTHON_VERSION),
                       '-DSPCONV_BuildTests=OFF',
                       ] #  -arch=sm_61
-        if not torch.cuda.is_available():
+        if not torch.cuda.is_available() and SPCONV_FORCE_BUILD_CUDA is None:
             cmake_args += ['-DSPCONV_BuildCUDA=OFF']
         else:
             cuda_flags = ["\"--expt-relaxed-constexpr\""]