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INSTALL.md

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Installation

Requirements

All the codes are tested in the following environment:

  • Linux (tested on Ubuntu 14.04/16.04)
  • Python 3.6+
  • PyTorch 1.1 or higher (tested on PyTorch 1.1)
  • CUDA 9.0 or higher
  • spconv v1.0 (commit 8da6f96)

Install pcdet v0.2

NOTE: Please re-install pcdet v0.2 by running python setup.py develop if you have already installed pcdet v0.1 previously.

a. Clone this repository.

git clone https://github.com/open-mmlab/OpenPCDet.git

b. Install the dependent libraries as follows:

  • Install the dependent python libraries:
pip install -r requirements.txt
  • Install the SparseConv library, we use the non-official implementation from spconv. Note that we use the initial version of spconv, make sure you install the spconv v1.0 (commit 8da6f96) instead of the latest one.

c. Install this pcdet library by running the following command:

python setup.py develop

Dataset Preparation

Currently we provide the dataloader of KITTI dataset, and the supporting of more datasets are on the way.

KITTI Dataset

  • Please download the official KITTI 3D object detection dataset and organize the downloaded files as follows (the road planes could be downloaded from [road plane], which are optional for data augmentation in the training):
  • NOTE: if you already have the data infos from pcdet v0.1, you can choose to use the old infos and set the DATABASE_WITH_FAKELIDAR option in tools/cfgs/dataset_configs/kitti_dataset.yaml as True. The second choice is that you can create the infos and gt database again and leave the config unchanged.
PCDet
├── data
│   ├── kitti
│   │   │──ImageSets
│   │   │──training
│   │   │   ├──calib & velodyne & label_2 & image_2 & (optional: planes)
│   │   │──testing
│   │   │   ├──calib & velodyne & image_2
├── pcdet
├── tools
  • Generate the data infos by running the following command:
python -m pcdet.datasets.kitti.kitti_dataset create_kitti_infos tools/cfgs/dataset_configs/kitti_dataset.yaml