feat(ptv3): add a lidar segmentation model with onnx support

[knzo25]

Summary

this PR ports Pointcept's PTv3 with the following features:

• t4dataset support
• onnx deployment support
• most of the original codebase removed since we only want ptv3

Change point

Same as the summary

Note

Since the onnx compatible spconv had to be modified, BEVFusion and other spconv dependent modules should be trained with spconv from now instead of mmcv's implementation

Test performed

Logs [TIER IV INTERNAL LINK]

[2025-04-25 02:10:16,386 INFO test.py line 339 2191] Val result: mIoU/mAcc/allAcc 0.7411/0.8754/0.9103
[2025-04-25 02:10:16,386 INFO test.py line 345 2191] Class_0 - vehicle Result: iou/accuracy 0.9688/0.9838
[2025-04-25 02:10:16,386 INFO test.py line 345 2191] Class_1 - bicycle Result: iou/accuracy 0.3464/0.8544
[2025-04-25 02:10:16,386 INFO test.py line 345 2191] Class_2 - pedestrian Result: iou/accuracy 0.6848/0.7068
[2025-04-25 02:10:16,386 INFO test.py line 345 2191] Class_3 - road Result: iou/accuracy 0.9278/0.9616
[2025-04-25 02:10:16,386 INFO test.py line 345 2191] Class_4 - vegetation Result: iou/accuracy 0.7076/0.8744
[2025-04-25 02:10:16,386 INFO test.py line 345 2191] Class_5 - obstacle Result: iou/accuracy 0.8111/0.8714

[amadeuszsz]

@knzo25
Sorry for late response!
Are you still able to run environment and deploy ONNX for latest model (link)? I followed your instruction in Readme file, but seems the deployment script doesn't work due to missing ConcatDataset (I guess the true issue lies somewhere else).

[scepter914]

Memo
As whole design of AWML, changes of this PR looks great to me.
I asked to review code-level for @amadeuszsz 🙏

[knzo25]

@amadeuszsz
Can you look for a model compatible with the one I submitted in autowarefoundation/autoware_universe#10600?

The one you provided is 5cm per voxel, but for "real time" I recommend the 10cm one

[amadeuszsz]

@amadeuszsz Can you look for a model compatible with the one I submitted in autowarefoundation/autoware_universe#10600?

The one you provided is 5cm per voxel, but for "real time" I recommend the 10cm one

@knzo25
I confirm that the two available models use a grid size of 5 cm. Apart from these models, I can't find anything else in provided documentation