RTMPose-DeployExamples是基于RTMPose的C语言部署示例,它使用ONNXRuntime和TensorRT库。这些示例展示了如何使用ONNXRuntime将RTMPose模型转换为ONNX格式,然后使用TensorRT进行推理处理。 在RTMPose-DeployExamples中,首先需要安装ONNXRuntime和TensorRT库。然后,编写一个C文件(例如:rtmpose_deploy.c),在其中实现以下功能:...
通过研究多人姿态估计算法的五个方面:范式、骨干网络、定位算法、训练策略和部署推理,我们的 RTMPose-m 模型在 COCO 上达到75.8%AP的同时,能在 Intel i7-11700 CPU 上用 ONNXRuntime 达到90+FPS,在 NVIDIA GTX 1660 Ti GPU 上用 TensorRT 达到430+FPS。RTMPose-s 以72.2%AP的性能,在手机端 Snapdragon865 ...
如图1所示,使用各种推理框架(PyTorch、ONNX Runtime、TensorRT、ncnn)和硬件(Intel i7-11700、GTX 1660Ti、Snapdragon 865)评估RTMPose的效率。RTMPose-m在COCO valset上实现了75.8%的AP,Intel i7-11700 CPU上的帧速率为90+,NVIDIA GeForce GTX 1660 Ti GPU上的帧率为430+,Snapdragon 865芯片上的帧频率为35+。
As the engine files generated by TensorRT are related to hardware, it is necessary to regenerate the engine files on the computer where the code needs to be run.** ### II. Run At first, you should fill in the model locations for RTMDet and RTMPose as follows: ```c++ // set ...
//github.com/open-mmlab/mmdeploy/releases/download/v1.0.0/mmdeploy-1.0.0-linux-x86_64.tar.gz # 解压并将 third_party 中第三方推理库的动态库添加到 PATH # onnxruntime-gpu / tensorrt # for ubuntu wget -c https://github.com/open-mmlab/mmdeploy/releases/download/v1.0.0/mmdeploy-1.0.0...
Optionally, you can use other common backends like opencv, onnxruntime, openvino, tensorrt to accelerate the inference process. For openvino users, please add the path<your python path>\envs\<your env name>\Lib\site-packages\openvino\libsinto your environment path. ...
//github.com/open-mmlab/mmdeploy/releases/download/v1.0.0/mmdeploy-1.0.0-linux-x86_64.tar.gz # unzip then add third party runtime libraries to the PATH # onnxruntime-gpu / tensorrt # for ubuntu wget -c https://github.com/open-mmlab/mmdeploy/releases/download/v1.0.0/mmdeploy-1.0....