这里我们注意到,官方提到了对应的环境,这里我们尝试一下新的环境:ubuntu-20.04、CUDA-12.1、cuDNN-8.6.0、TensorRT-8.6、yaml-cpp、Eigen3、libjpeg 这里值得我们留意的就是TensorRT的安装问题,首先不同版本的TensorRT需要不同的算力平台适配程度不一样,我们可以在NVIDIA官网查看:CUDA GPUs - Compute Capability | NVID...
【BEVDet的TensorRT推理实现,使用C++编程】'BEVDet implemented by TensorRT, C++ - BEVDet implemented by TensorRT, C++; Achieving real-time performance on Orin' Chuanhao1999 GitHub: github.com/LCH1238/bevdet-tensorrt-cpp #开源# #机器学习# ...
https://github.com/LCH1238/bevdet-tensorrt-cppgithub.com/LCH1238/bevdet-tensorrt-cpp 本项目实现了: 长时序(long-term)模型的推理。 Depth模型的推理。 在NVIDIA A4000上,BEVDet-r50-lt-depth模型中,TRT FP32模型推理速度比PyTorch FP32模型快2.38倍, TRT FP16模型比PyTorch FP32模型快5.21倍。 在Je...
ubuntu-20.04, cuda-11.3, cudnn-8.6, TensorRT-8.5 yaml-cpp、Eigen3、libjpeg 2. 拉取源码 mkdir -p bev_ws/src cd bev_ws/src git clone https://github.com/linClubs/BEVDet-ROS-TensorRT.git 3. onnx2engine The onnx folder can be downloaded from Baidu Netdisk # 1 创建一个python环境,并...
ubuntu-20.04、CUDA-11.3、cuDNN-8.6.0、TensorRT-8.5 yaml-cpp、Eigen3、libjpeg 2 Build mkdir -p bev_ws/src cd bev_ws/src git clone https://github.com/linClubs/BEVDet-ROS-TensorRT.git cd .. catkin_make source devel/setup.bash 3 Run generate engine Generate the onnx TensorRT engine ref...
BevDet_TensorRT. Contribute to chaomath/BevDet_TensorRT development by creating an account on GitHub.
Convert to TensorRT and test inference speed. 1. install mmdeploy from https://github.com/HuangJunJie2017/mmdeploy 2. convert to TensorRT python tools/convert_bevdet_to_TRT.py $config $checkpoint $work_dir --fuse-conv-bn --fp16 --int8 3. test inference speed python tools/analysis_tools...
1. install mmdeploy from https://github.com/HuangJunJie2017/mmdeploy 2. convert to TensorRT python tools/convert_bevdet_to_TRT.py $config $checkpoint $work_dir --fuse-conv-bn --fp16 --int8 3. test inference speed python tools/analysis_tools/benchmark_trt.py $config $engine...
1. install mmdeploy from https://github.com/HuangJunJie2017/mmdeploy 2. convert to TensorRT python tools/convert_bevdet_to_TRT.py $config $checkpoint $work_dir --fuse-conv-bn --fp16 --int8 3. test inference speed python tools/analysis_tools/benchmark_trt.py $config $engine Acknowledgeme...