fast-reid/projects/FastRT/tools/How_to_Generate.md

Fastreid Model Deployment

The gen_wts.py script convert a fastreid model to .wts format file, then it will be used in FastRT directly.

Convert Environment

• Same as fastreid.

How to Generate

This is a general example for converting fastreid to TensorRT model. We use FastRT to build the model with nvidia TensorRT APIs.

In this part you need to convert the pytorch model to '.wts' file using gen_wts.py follow instructions below.

1. Run command line below to generate the '.wts' file from pytorch model

   It's similar to how you use fastreid.

   python projects/FastRT/tools/gen_wts.py --config-file='config/you/use/in/fastreid/xxx.yml' \
   --verify --show_model --wts_path='outputs/trt_model_file/xxx.wts' \
   MODEL.WEIGHTS '/path/to/checkpoint_file/model_best.pth' MODEL.DEVICE "cuda:0"
   

   then you can check the TensorRT model weights outputs/trt_model_file/xxx.wts.

2. Copy the outputs/trt_model_file/xxx.wts to FastRT

More convert examples

• Ex1. sbs_R50-ibn

  • resnet50, ibn, non-local, gempoolp

  python projects/FastRT/tools/gen_wts.py --config-file='configs/DukeMTMC/sbs_R50-ibn.yml' \
  --verify --show_model --wts_path='outputs/trt_model_file/sbs_R50-ibn.wts' \
  MODEL.WEIGHTS '/path/to/checkpoint_file/model_best.pth' MODEL.DEVICE "cuda:0"
  

• Ex2. sbs_R50

  • resnet50, gempoolp

  python projects/FastRT/tools/gen_wts.py --config-file='configs/DukeMTMC/sbs_R50.yml' \
  --verify --show_model --wts_path='outputs/trt_model_file/sbs_R50.wts' \
  MODEL.WEIGHTS '/path/to/checkpoint_file/model_best.pth' MODEL.DEVICE "cuda:0"
  

• Ex3. sbs_r34_distill

  • train-alone distill-r34 (hint: distill-resnet is slightly different from resnet34), gempoolp

  python projects/FastRT/tools/gen_wts.py --config-file='projects/FastDistill/configs/sbs_r34.yml' \
  --verify --show_model --wts_path='outputs/to/trt_model_file/sbs_r34_distill.wts' \
  MODEL.WEIGHTS '/path/to/checkpoint_file/model_best.pth' MODEL.DEVICE "cuda:0"
  

• Ex4.kd-r34-r101_ibn

  • teacher model(r101_ibn), student model(distill-r34). the one for deploying is student model, gempoolp

  python projects/FastRT/tools/gen_wts.py --config-file='projects/FastDistill/configs/kd-sbs_r101ibn-sbs_r34.yml' \
  --verify --show_model --wts_path='outputs/to/trt_model_file/kd_r34_distill.wts' \
  MODEL.WEIGHTS '/path/to/checkpoint_file/model_best.pth' MODEL.DEVICE "cuda:0"
  

Acknowledgements

Thanks to tensorrtx for demonstrating the usage of trt network definition APIs.