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MMDetection3d Deployment

MMDetection3d aka mmdet3d is an open source object detection toolbox based on PyTorch, towards the next-generation platform for general 3D detection. It is a part of the OpenMMLab project.

Install mmdet3d

These branches are required for mmdet3d deployment

codebase commit
mmdet3d v1.1.0rc1
mmcv v2.0.0rc1
mmdet v3.0.0rc1
mmseg v1.0.0rc0

First checkout and install mmcv/mmdet/mmseg/mmdet3d

python3 -m pip install openmim --user
python3 -m mim install mmcv==2.0.0rc1 mmdet==3.0.0rc1 mmseg==1.0.0rc0 --user

git clone https://github.com/open-mmlab/mmdetection3d --branch v1.1.0rc1
cd mmdetection3d
python3 -m pip install .
cd -

After installation, tools/check_env.py should display mmdet3d version normally

python3 tools/check_env.py
..
11/11 13:56:19 - mmengine - INFO - **********Codebase information**********
11/11 13:56:19 - mmengine - INFO - mmdet:       3.0.0rc1
11/11 13:56:19 - mmengine - INFO - mmseg:       1.0.0rc0
..
11/11 13:56:19 - mmengine - INFO - mmdet3d:     1.1.0rc1

Convert model

For example, use tools/deploy.py to convert centerpoint to onnxruntime format

export MODEL_CONFIG=/path/to/mmdetection3d/configs/centerpoint/centerpoint_pillar02_second_secfpn_head-circlenms_8xb4-cyclic-20e_nus-3d.py

export MODEL_PATH=https://download.openmmlab.com/mmdetection3d/v1.0.0_models/centerpoint/centerpoint_02pillar_second_secfpn_circlenms_4x8_cyclic_20e_nus/centerpoint_02pillar_second_secfpn_circlenms_4x8_cyclic_20e_nus_20210816_064624-0f3299c0.pth

export TEST_DATA=/path/to/mmdetection3d/tests/data/nuscenes/sweeps/LIDAR_TOP/n008-2018-09-18-12-07-26-0400__LIDAR_TOP__1537287083900561.pcd.bin

python3 tools/deploy.py configs/mmdet3d/voxel-detection/voxel-detection_onnxruntime_dynamic.py $MODEL_CONFIG $MODEL_PATH $TEST_DATA --work-dir centerpoint

This step would generate end2end.onnx in work-dir

ls -lah centerpoint
..
-rw-rw-r--  1 rg rg  87M 11月  4 19:48 end2end.onnx

Model inference

At present, the voxelize preprocessing and postprocessing of mmdet3d are not converted into onnx operations; the C++ SDK has not yet implemented the voxelize calculation.

The caller needs to refer to the corresponding python implementation to complete.

Supported models

model dataset onnxruntime openvino tensorrt*
centerpoint nuScenes ✔️ ✔️ ✔️
pointpillars nuScenes ✔️ ✔️ ✔️
pointpillars KITTI ✔️ ✔️ ✔️
  • Make sure trt >= 8.4 for some bug fixed, such as ScatterND, dynamic shape crash and so on.