Description
I executed dpkg - I libnvinfer7_ 7.2.0-1+cuda10.2_ Arm64.deb and
dpkg -i libnvinfer-dev_ 7.2.0-1+cuda10.2_ Arm64.deb installs tensorrt for my NVIDIA Xavier NX, but later found that tensorrt 7.1.3 already exists, so I use Ctrl + C to terminate the installation. But when I run dpkg - l | grep tensorrt to check tensorrt, I find that libnvinfer is 7.2.0. How can I restore to 7.1.3?
The following is the output after running dpkg - l | grep TensorRT：
ii graphsurgeon-tf 7.1.3-1+cuda10.2 arm64 GraphSurgeon for TensorRT package
ii libnvinfer-bin 7.1.3-1+cuda10.2 arm64 TensorRT binaries
iHR libnvinfer-dev 7.1.3-1+cuda10.2 arm64 TensorRT development libraries and headers
ii libnvinfer-doc 7.1.3-1+cuda10.2 all TensorRT documentation
ii libnvinfer-plugin-dev 7.1.3-1+cuda10.2 arm64 TensorRT plugin libraries
ii libnvinfer-plugin7 7.1.3-1+cuda10.2 arm64 TensorRT plugin libraries
ii libnvinfer-samples 7.1.3-1+cuda10.2 all TensorRT samples
ii libnvinfer7 7.2.0-1+cuda10.2 arm64 TensorRT runtime libraries
ii libnvonnxparsers-dev 7.1.3-1+cuda10.2 arm64 TensorRT ONNX libraries
ii libnvonnxparsers7 7.1.3-1+cuda10.2 arm64 TensorRT ONNX libraries
ii libnvparsers-dev 7.1.3-1+cuda10.2 arm64 TensorRT parsers libraries
ii libnvparsers7 7.1.3-1+cuda10.2 arm64 TensorRT parsers libraries
ii nvidia-container-csv-tensorrt 7.1.3.0-1+cuda10.2 arm64 Jetpack TensorRT CSV file
ii python-libnvinfer 7.1.3-1+cuda10.2 arm64 Python bindings for TensorRT
ii python-libnvinfer-dev 7.1.3-1+cuda10.2 arm64 Python development package for TensorRT
ii python3-libnvinfer 7.1.3-1+cuda10.2 arm64 Python 3 bindings for TensorRT
ii python3-libnvinfer-dev 7.1.3-1+cuda10.2 arm64 Python 3 development package for TensorRT
ii tensorrt 7.1.3.0-1+cuda10.2 arm64 Meta package of TensorRT
ii uff-converter-tf 7.1.3-1+cuda10.2 arm64 UFF converter for TensorRT package
Environment
TensorRT Version: 7.1.3
GPU Type:
Nvidia Driver Version:
CUDA Version: 10.2
CUDNN Version: 8.0.0
Operating System + Version:
Python Version (if applicable): 3.6
TensorFlow Version (if applicable):
PyTorch Version (if applicable):
Baremetal or Container (if container which image + tag):