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Ref papers

Install

Here we applied the third-party implementation of BEVDet4d from BEVDet repo as our baseline.

  • clone bevdet repo and install it.
  • link the projects folder to BEVDet folder.
    • cd /path/to/bevdet/
    • ln -s /path/to/Sparsebit/examples/quantization_aware_training/nuscenes/bevdet4d/projects/ .
  • replace the tools folder in BEVDet with our tools
    • cd /path/to/bevdet
    • mv ./tools tools.bak
    • ln -s /path/to/Sparsebit/examples/quantization_aware_training/nuscenes/bevdet4d/tools/ .

Training

./tools/dist_qat_train.sh /path/to/config /path/to/qconfig /path/to/pretrained-float-model #gpu
  • For example:
./tools/dist_qat_train.sh ./projects/configs/bevdet4d-r50-g7b4-e12.py ./projects/configs/qconfigs/qconfig_r50_lsq_4w4f.yaml ./checkpoints/bevdet4d-r50.pth 7

Evaluation

python3 ./tools/qat_test.py /path/to/config /path/to/qconfig /path/to/checkpoints --eval=bbox
  • For example
python3 ./tools/qat_test.py ./projects/configs/bevdet4d-r50-g7b4-e12.py   ./projects/configs/qconfigs/qconfig_r50_lsq_4w4f.yaml ./checkpoints/bevdet4d-r50-4w4f.pth --eval=bbox

Results

  • experiments setting:
    • in 4w4f, we set the wbit & abit of first conv and head-conv to 8
experiment bits mAP NDS Log
bevdet4d-r50 float 32.2 45.7 -
bevdet4d-r50 4w4f 31.7 45.8 google