Rpfanet_Training

This project is used to train the model of the object detection network with point clouds only. It is based on OpenPCDet framework which is a a clear, simple, self-contained open source project for LiDAR-based 3D object detection. Till now, the Astyx dataset has been successfully handled and trained.

1.Notice

After downloading the dataset, put them into the proper directory.

In this project, eg. the Astyx dataset is put into /home/ez/project/rpfanet/data/Astyx/. In the directory /Astyx, there are 3 folders named training, testing, ImageSets.

2.Environment

conda env create -f environment_openpcdet.yml

conda activate OpenPCDet

or

conda env create -f environment_vod.yml

conda activate view-of-delft-env

Forget which environment is suitable, just try. QAQ

!Attention!

• Install the SparseConv library, we use the implementation from [spconv].
  • If you use PyTorch 1.1, then make sure you install the spconv v1.0 with (commit 8da6f96) instead of the latest one.
  • If you use PyTorch 1.3+, then you need to install the spconv v1.2. As mentioned by the author of spconv, you need to use their docker if you use PyTorch 1.4+.

3.Generate dataloader

python -m pcdet.datasets.astyx.astyx_dataset create_astyx_infos tools/cfgs/dataset_configs/astyx_dataset_radar.yaml

python3 -m pcdet.datasets.astyx.ttttt create_astyx_infos tools/cfgs/dataset_configs/astyx_dataset_radar.yaml

4.Training

cd tools/

python train.py --cfg_file cfgs/astyx_models/pointpillar.yaml --tcp_port 25851 --extra_tag yourmodelname

or

cd tools/

CUDA_VISIBLE_DEVICES=1 python train.py --cfg_file cfgs/astyx_models/pointpillar.yaml --tcp_port 25851 --extra_tag yourmodelname

5.Testing

cd tools/

python test.py --cfg_file cfgs/astyx_models/pointpillar.yaml --batch_size 4 --ckpt ../output/astyx_models/pointpillar/rpfanet_313/ckpt/checkpoint_epoch_80.pth

or

cd tools/

python test.py --cfg_file cfgs/astyx_models/pointpillar.yaml --batch_size 4 --ckpt ##astyx_models/pointpillar/debug/ckpt/checkpoint_epoch_80.pth

6.Visualization

python3 demo.py --ckpt ../output/astyx_models/pointpillar/rpfanet_313/ckpt/checkpoint_epoch_80.pth

7.Main directory struction

├─data
│  └─Astyx
│      ├─ImageSets
│      ├─training
│      └─testing
├─docker
├─docs
├─pcdet
│  ├─datasets
│  │  ├─astyx
│  │  ├─augmentor
│  │  ├─kitti
│  │  │  └─kitti_object_eval_python
│  │  ├─nuscenes
│  │  └─processor
│  ├─models
│  │  ├─backbones_2d
│  │  │  └─map_to_bev
│  │  ├─backbones_3d
│  │  │  ├─pfe
│  │  │  └─vfe
│  │  ├─dense_heads
│  │  │  └─target_assigner
│  │  ├─detectors
│  │  ├─model_utils
│  │  └─roi_heads
│  │      └─target_assigner
│  ├─ops
│  │  ├─iou3d_nms
│  │  │  └─src
│  │  ├─pointnet2
│  │  │  ├─pointnet2_batch
│  │  │  │  └─src
│  │  │  └─pointnet2_stack
│  │  │      └─src
│  │  ├─roiaware_pool3d
│  │  │  └─src
│  │  └─roipoint_pool3d
│  │      └─src
│  └─utils
└─tools
    ├─cfgs
    │  ├─astyx_models
    │  ├─dataset_configs
    │  ├─kitti_models
    │  └─nuscenes_models
    ├─eval_utils
    ├─scripts
    ├─train_utils
    │  └─optimization
    └─visual_utils

8.More

This project was accomplished on June 1, 2024 and was first upload to the github on March 11, 2025.

Contact Email: [email protected]