Spconv-OpenPCDet

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Spconv-OpenPCDet

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OpenPCDet with spconv package already included for one-step installation. Uses spconv & voxel CUDA ops from mmdetection3d repository.

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# Spconv-OpenPCDet
OpenPCDet with spconv package **already included** for **one-step** installation. Uses spconv & voxel CUDA ops from mmdetection3d repository.

## Purpose
I noticed that [mmdetection3d](https://github.com/open-mmlab/mmdetection3d) had adopted parts of the [spconv](https://github.com/traveller59/spconv) repository into their cuda operations, allowing simple installation via `python setup.py develop`. In the past, I've had some trouble building spconv from the original repository on machines with scattered CUDA installations (especially when I did not have sudo privilages), so I wanted to try incorporating spconv into [OpenPCDet](https://github.com/open-mmlab/OpenPCDet) like mmdetection3d did.

## Changes
`pcdet/ops/spconv` and `pcdet/ops/voxel` are from the mmdetection3d repository, and `setup.py` was modified to compile the new cuda operations. To adapt calls to spconv & voxelization, `import spconv` was generally changed to `from pcdet.ops import spconv as spconv`, and voxelization usage in `pcdet/datasets/processor/data_processor.py` was slightly adjusted (only the `transform_points_to_voxels` function).

I have tested this repository on a fairly new pytorch version: pytorch 1.8, torchaudio 0.8.0, torchvision 0.9.0, cudatoolkit 11.1.1, which warranted a slight change in distributed training initialization in `common_utils.py`.

**Nov. 2 2021:** Now able to train all models in OpenPCDet on 12 GB GPUs (2080 Ti) due to 4096 -> 27 change in `pcdet/ops/spconv/src/indice_cuda.cu`
However, 3D sparse convolution kernel sizes larger than 3x3x3 do not work with a 27 limit. This does not affect OpenPCDet models, but please change 27 to something higher if you need to implement larger kenels.

## Installation
Here is how I setup for my environment:
```
conda create -n spconv-openpcdet python=3.7
conda activate spconv-openpcdet
conda install pytorch==1.8.0 torchvision==0.9.0 torchaudio==0.8.0 cudatoolkit=11.1 -c pytorch -c conda-forge
pip install -r requirements.txt
python setup.py develop
```
I imagine that earlier versions of pytorch would work as well (since OpenPCDet supports 1.1, 1.3, 1.5 and mmdetection3d works for earlier versions).

Note that mmcv, and the mm* packages do not need to be installed.

## Numbers
I have tested SECOND, training with 4 GPUs, 2x batch size on each GPU (to reach parity with total batch size used in OpenPCDet).
The results are the 3D detection performance of moderate difficulty on the *val* set of KITTI dataset.

|                                             | training time | Car@R11 | Pedestrian@R11 | Cyclist@R11 | 
|---------------------------------------------|----------:|:-------:|:-------:|:-------:|
| SECOND (OpenPCDet)       |  1.7 hours (8x1080Ti) | 78.62 | 52.98 | 67.15 |
| SECOND (Spconv-OpenPCDet) | 3.0 hours (4x2080Ti) | 78.61 | 52.03 | 64.92 |
| PV-RCNN (OpenPCDet)       |  5.0 hours (8x1080Ti) | 83.61 | 57.90 | 70.47 |
| PV-RCNN (Spconv-OpenPCDet) | 7.0 hours (4x2080Ti) | 82.89 | 59.44 | 70.43 |

I believe this is sufficiently reproduced as I only trained once. Multiple training runs were done to optimize for Car in the original repository.

## Acknowledgement
This repository is basically a copy of OpenPCDet, with some elements of mmdetection3d's usage of spconv within it.

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