Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/OuyangJunyuan/RobDet3D
https://github.com/OuyangJunyuan/RobDet3D
Last synced: 18 days ago
JSON representation
- Host: GitHub
- URL: https://github.com/OuyangJunyuan/RobDet3D
- Owner: OuyangJunyuan
- Created: 2023-04-08T10:23:09.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2023-05-17T11:38:04.000Z (over 1 year ago)
- Last Synced: 2024-08-01T03:39:12.639Z (4 months ago)
- Language: Python
- Size: 1.56 MB
- Stars: 13
- Watchers: 2
- Forks: 3
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# RobDet3D
This repository is deeply inspired by [OpenPCDet](https://github.com/open-mmlab/OpenPCDet.git) and [MMDetection3D](https://github.com/open-mmlab/mmdetection3d.git), and incorporates their advantages.
In this repo, we develop `HAVSampler` and `GridBallQuery` to speed up `poin-based` model and its deployment.
# INSTALL
```bash
pip install -r requirements.txt
pip install spconv-cuxxx(e.g.cu113)
python setup.py develop
```
pre-train model can be downloaded from [Google Drive](https://drive.google.com/drive/folders/10TSrJhKvqB3NF0De12DJLBEFCKvcloZC?usp=share_link).
# Dataset
refer to [OpenPCDet](https://github.com/open-mmlab/OpenPCDet.git) to prepare your data:
```shell
mkdir data && cd data
ln -s path/to/your/kitti/dataset kitti
cd ../
python -m rd3d.datasets.kitti.kitti_dataset \
create_kitti_infos configs/base/datasets/kitti_3cls.py
```
# Training
train your model like this:
```shell
python tools/train.py \
--cfg configs/iassd/iassd_hvcsx1_4x8_80e_sparse_kitti_3cls.py \
wandb --group rd3d
```
or train with multi-GPU
```shell
accelerate launch tools/train.py \
--cfg configs/iassd/iassd_hvcsx1_4x8_80e_sparse_kitti_3cls.py \
wandb --group rd3d
```
# Export ONNX Model
```shell
python tools/deploy/export_onnx.py \
--cfg_file configs/iassd/iassd_hvcsx1_4x8_80e_kitti_3cls\(export\).py \
--ckpt tools/models/iassd_hvcsx1_4x8_80e_kitti_3cls\(export\).pth \
--onnx tools/models/trt
```
Then the exported onnx model can be found in fold `tools/models/trt`.
# Export TensorRT Model
```shell
python tools/deploy/export_trt.py \
--onnx ./tools/models/trt/iassd_hvcsx1_4x8_80e_kitti_3cls\(export\).onnx \
--batch 1 \
--type FP32 # FP32 / FP16 / INT8
```
## Test
```shell
python tools/deploy/test_trt_viz.py \
--engine ./tools/models/trt/iassd_hvcsx1_4x8_80e_kitti_3cls\(export\).engine
```
if susses, the following figure can be seen in the visualization window.
## Profile
```shell
python tools/deploy/trt_profile.py \
--engine tools/models/trt/iassd_hvcsx1_4x8_80e_kitti_3cls\(export\).engine \
--batch 1 \
--build_in # or not
```
And the following results will be shown in terminal:
| name | layers | average (ms) | median (ms) | percentage (%) |
|------------:|-------:|-------------:|------------:|---------------:|
| HAVSampling | 1 | 0.106 | 0.102 | 0.9 |
| BallQuery | 8 | 5.197 | 5.054 | 42.4 |
| ForeignNode | 8 | 4.144 | 4.079 | 34.2 |
| NMSBEV | 1 | 0.137 | 0.137 | 1.1 |
| Others | 48 | 2.402 | 2.358 | 19.8 |
| Total | 66 | 12.0 | 11.9 | 100.0 |