This is a repository which add prediction module from ViP3D to the StreamPETR model.
- Environment Setup
docker pull a954217436/mmdet_series:v1.0
docker run -it --gpus all -v /home:/home -v /mnt:/mnt --network host --name sean_dev --shm-size 640G a954217436/mmdet_series:v1.0
- Data Preparation
# Please follow the ViP3D processing methods.
1. Download nuscenes(or mini) dataset, organize as follows:
nuscenes_mini
├── maps
│ ├── basemap
│ ├── expansion
│ └── prediction
├── samples
│ ├── CAM_BACK
│ ├── CAM_BACK_LEFT
│ ├── CAM_BACK_RIGHT
│ ├── CAM_FRONT
│ ├── CAM_FRONT_LEFT
│ ├── CAM_FRONT_RIGHT
│ ├── LIDAR_TOP
│ ├── RADAR_BACK_LEFT
│ ├── RADAR_BACK_RIGHT
│ ├── RADAR_FRONT
│ ├── RADAR_FRONT_LEFT
│ └── RADAR_FRONT_RIGHT
└── v1.0-mini
2. Create train/val infos:
python tools/create_data_nusc.py
-
Training and Inference
Please see the config files under projects/configs/vectornet.
python tools/train.py --config projects/configs/vectornet/stream_petr_vov_flash_800_bs16_wk4_seq_24e_mini.py
python tools/test.py --config projects/configs/vectornet/stream_petr_vov_flash_800_bs16_wk4_seq_24e_mini.py
- Layers.
pts_bbox_head.code_weights
pts_bbox_head.match_costs
pts_bbox_head.pc_range
pts_bbox_head.position_range
pts_bbox_head.coords_d
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pts_bbox_head.cls_branches.0.0.bias
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pts_bbox_head.cls_branches.0.3.bias
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pts_bbox_head.cls_branches.0.4.bias
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pts_bbox_head.cls_branches.0.6.bias
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pts_bbox_head.reg_branches.0.0.bias
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pts_bbox_head.reg_branches.0.2.bias
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pts_bbox_head.position_encoder.2.bias
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pts_bbox_head.memory_embed.0.bias
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pts_bbox_head.memory_embed.2.bias
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pts_bbox_head.featurized_pe.conv_reduce.bias
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pts_bbox_head.featurized_pe.conv_expand.bias
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pts_bbox_head.pseudo_reference_points.weight
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