feat(deployment): centerpoint deployment integration

[vividf]

Summary

Integrates CenterPoint into the unified deployment framework, enabling deployment and evaluation of ONNX and TensorRT models.

Note, this PR include changes in #180

Changes

• Integrated CenterPoint with deployment framework:
  • Moved deployment code from projects/CenterPoint to deployment/projects/centerpoint
  • Implemented component-based export pipeline for ONNX and TensorRT
  • Added runtime inference support with PyTorch, ONNX Runtime, and TensorRT backends
• Deployment capabilities:
  • Export CenterPoint models to ONNX format
  • Export CenterPoint models to TensorRT engines
  • Component-based architecture (voxel encoder, backbone+head) for flexible deployment
• Evaluation capabilities:
  • Evaluate ONNX models using ONNX Runtime
  • Evaluate TensorRT engines
  • Integrated metrics evaluation with deployment pipeline
• Updated CLI: Replaced old deploy.py script with new unified CLI (deployment.cli.main)
• Added Docker support: Created Dockerfile for deployment environment with TensorRT dependencies
• Updated documentation: Added deployment and evaluation instructions in README

Migration Notes

• Old deployment script (projects/CenterPoint/scripts/deploy.py) is removed
• Use new CLI: python -m deployment.cli.main centerpoint <deploy_config> <model_config>
• ONNX model variants are now registered via deployment.projects.centerpoint.onnx_models

How to run

python -m deployment.cli.main centerpoint   deployment/projects/centerpoint/config/deploy_config.py   projects/CenterPoint/configs/t4dataset/Centerpoint/second_secfpn_4xb16_121m_j6gen2_base_t4metric_v2.py   --rot-y-axis-reference

Exported ONNX (Same)

Voxel Encoder

Backbone Head

Test with Deployment pipeline

 python -m deployment.cli.main centerpoint   deployment/projects/centerpoint/config/deploy_config.py   projects/CenterPoint/configs/t4dataset/Centerpoint/second_secfpn_4xb16_121m_j6gen2_base_amp_t4metric_v2.py   --rot-y-axis-reference

Example: TENSORRT

Frame:
Total Num: 1
Skipped Frames: []
Skipped Frames Count: 0

Ground Truth Num: 37

mAP: 0.7897, mAPH: 0.7422 (Center Distance BEV)

Label: car

Threshold	Predict_num	Groundtruth_num	AP	APH	max_f1	optimal_recall	optimal_precision	optimal_conf
0.50	32	18	0.7749	0.7634	0.8125	0.9286	0.7222	0.307514
1.00	32	18	0.8136	0.8020	0.8125	0.9286	0.7222	0.307514
2.00	32	18	0.8342	0.8228	0.8750	1.0000	0.7778	0.307514
4.00	32	18	0.8342	0.8228	0.8750	1.0000	0.7778	0.307514

Label: truck

Threshold	Predict_num	Groundtruth_num	AP	APH	max_f1	optimal_recall	optimal_precision	optimal_conf
0.50	16	8	0.5778	0.5778	0.7692	1.0000	0.6250	0.597714
1.00	16	8	0.7145	0.7111	0.8571	1.0000	0.7500	0.372688
2.00	16	8	0.7145	0.7111	0.8571	1.0000	0.7500	0.372688
4.00	16	8	0.7653	0.7607	0.8571	1.0000	0.7500	0.372688

Label: bus

Threshold	Predict_num	Groundtruth_num	AP	APH	max_f1	optimal_recall	optimal_precision	optimal_conf
0.50	3	1	0.9918	0.9889	1.0000	1.0000	1.0000	0.898974
1.00	3	1	0.9918	0.9889	1.0000	1.0000	1.0000	0.898974
2.00	3	1	0.9918	0.9889	1.0000	1.0000	1.0000	0.898974
4.00	3	1	0.9918	0.9889	1.0000	1.0000	1.0000	0.898974

Label: bicycle

Threshold	Predict_num	Groundtruth_num	AP	APH	max_f1	optimal_recall	optimal_precision	optimal_conf
0.50	2	1	0.9938	0.9444	1.0000	1.0000	1.0000	0.710362
1.00	2	1	0.9938	0.9444	1.0000	1.0000	1.0000	0.710362
2.00	2	1	0.9938	0.9444	1.0000	1.0000	1.0000	0.710362
4.00	2	1	0.9938	0.9444	1.0000	1.0000	1.0000	0.710362

Label: pedestrian

Threshold	Predict_num	Groundtruth_num	AP	APH	max_f1	optimal_recall	optimal_precision	optimal_conf
0.50	19	9	0.4206	0.2632	0.5882	0.6250	0.5556	0.265326
1.00	19	9	0.4206	0.2632	0.5882	0.6250	0.5556	0.265326
2.00	19	9	0.4206	0.2632	0.5882	0.6250	0.5556	0.265326
4.00	19	9	0.5611	0.3503	0.7059	0.7500	0.6667	0.265326

Summary:

Label	Predict_num	GT_nums	Thresholds	mean AP	APs	Mean APH	APHs
car	32	18	0.50/1.00/2.00/4.00	0.8143	0.7749 / 0.8136 / 0.8342 / 0.8342	0.803	0.7634 / 0.8020 / 0.8228 / 0.8228
truck	16	8	0.50/1.00/2.00/4.00	0.6930	0.5778 / 0.7145 / 0.7145 / 0.7653	0.690	0.5778 / 0.7111 / 0.7111 / 0.7607
bus	3	1	0.50/1.00/2.00/4.00	0.9918	0.9918 / 0.9918 / 0.9918 / 0.9918	0.989	0.9889 / 0.9889 / 0.9889 / 0.9889
bicycle	2	1	0.50/1.00/2.00/4.00	0.9938	0.9938 / 0.9938 / 0.9938 / 0.9938	0.944	0.9444 / 0.9444 / 0.9444 / 0.9444
pedestrian	19	9	0.50/1.00/2.00/4.00	0.4557	0.4206 / 0.4206 / 0.4206 / 0.5611	0.285	0.2632 / 0.2632 / 0.2632 / 0.3503

mAP: 0.8050, mAPH: 0.7548 (Plane Distance)

Label: car

Threshold	Predict_num	Groundtruth_num	AP	APH	max_f1	optimal_recall	optimal_precision	optimal_conf
2.00	32	18	0.8342	0.8228	0.8750	1.0000	0.7778	0.307514
4.00	32	18	0.8342	0.8228	0.8750	1.0000	0.7778	0.307514

Label: truck

Threshold	Predict_num	Groundtruth_num	AP	APH	max_f1	optimal_recall	optimal_precision	optimal_conf
2.00	16	8	0.7145	0.7111	0.8571	1.0000	0.7500	0.372688
4.00	16	8	0.7145	0.7111	0.8571	1.0000	0.7500	0.372688

Label: bus

Threshold	Predict_num	Groundtruth_num	AP	APH	max_f1	optimal_recall	optimal_precision	optimal_conf
2.00	3	1	0.9918	0.9889	1.0000	1.0000	1.0000	0.898974
4.00	3	1	0.9918	0.9889	1.0000	1.0000	1.0000	0.898974

Label: bicycle

Threshold	Predict_num	Groundtruth_num	AP	APH	max_f1	optimal_recall	optimal_precision	optimal_conf
2.00	2	1	0.9938	0.9444	1.0000	1.0000	1.0000	0.710362
4.00	2	1	0.9938	0.9444	1.0000	1.0000	1.0000	0.710362

Label: pedestrian

Threshold	Predict_num	Groundtruth_num	AP	APH	max_f1	optimal_recall	optimal_precision	optimal_conf
2.00	19	9	0.4206	0.2632	0.5882	0.6250	0.5556	0.265326
4.00	19	9	0.5611	0.3503	0.7059	0.7500	0.6667	0.265326

Summary:

Label	Predict_num	GT_nums	Thresholds	mean AP	APs	Mean APH	APHs
car	32	18	2.00/4.00	0.8342	0.8342 / 0.8342	0.823	0.8228 / 0.8228
truck	16	8	2.00/4.00	0.7145	0.7145 / 0.7145	0.711	0.7111 / 0.7111
bus	3	1	2.00/4.00	0.9918	0.9918 / 0.9918	0.989	0.9889 / 0.9889
bicycle	2	1	2.00/4.00	0.9938	0.9938 / 0.9938	0.944	0.9444 / 0.9444
pedestrian	19	9	2.00/4.00	0.4908	0.4206 / 0.5611	0.307	0.2632 / 0.3503

Latency Statistics:
Mean: 841.05 ms
Std: 0.00 ms
Min: 841.05 ms
Max: 841.05 ms
Median: 841.05 ms

Stage-wise Latency Breakdown:
Preprocessing : 515.14 ± 0.00 ms
Model : 324.29 ± 0.00 ms
Voxel Encoder : 3.14 ± 0.00 ms
Middle Encoder : 1.05 ± 0.00 ms
Backbone Head : 133.01 ± 0.00 ms
Postprocessing : 1.60 ± 0.00 ms