The effect is very poor when converted to trt

Accelerated Computing Intelligent Video Analytics TAO Toolkit
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For fp16 poorer than FP32, please use TRT8.6.1. Then the issue is gone in fp16.
Step to use TRT8.6.1 under 5.0.0 pyt docker:

 $ wget https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/secure/8.6.1/tars/TensorRT-8.6.1.6.Linux.x86_64-gnu.cuda-11.8.tar.gz
 $ tar zxvf TensorRT-8.6.1.6.Linux.x86_64-gnu.cuda-11.8.tar.gz
 $ pip install TensorRT-8.6.1.6/python/tensorrt-8.6.1-cp38-none-linux_x86_64.whl
 $ export LD_LIBRARY_PATH=/home/morganh/demo_3.0/public_data/notebook/pointpillars_20230816/TensorRT-8.6.1.6/lib:$LD_LIBRARY_PATH
 $ pointpillars export -e pointpillars.yaml -k nvidia_tlt --save_engine fp16_trt8.6.engine -t fp16
 $ pointpillars inference -e pointpillars.yaml -r result_infer_export_engine_8.6.1 -k nvidia_tlt --trt_engine fp16_trt8.6.engine