# cheetahinfer

**Repository Path**: AI52CV/cheetahinfer

## Basic Information

- **Project Name**: cheetahinfer
- **Description**: cheetahinfer：一个基于TensorRT的纯C++推断SDK

代码原地址：https://github.com/Media-Smart/cheetahinfer
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 1
- **Forks**: 0
- **Created**: 2021-04-05
- **Last Updated**: 2021-04-05

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

## Introduction
CheetahInfer is a pure C++ inference SDK based on TensorRT, which supports fast inference of CNNs based computer vision model.

## Features

- Efficient
  
  With the help of TensorRT's optimization to CNNs and the pure C++ implmentation of preprocessing and postprocessing, CheetahInfer is really efficent. If you are interested in flexibleness, you can refer to [FlexInfer](https://github.com/Media-Smart/flexinfer).

## Prerequisites
CheetahInfer has several dependencies:
- OpenCV
  - tested on version 4.3.0
- CUDA
  - tested on version 10.2
- TensorRT
  - tested on version 7.1.3.4
- cuDNN
  - optional
  - tested on version 8.0.0
- GCC
  - tested on version 5.4.0

After the installation of above dependencies, we need modify the `TENSORRT_INSTALL_DIR` and `OPENCV_INSTALL_DIR` in file `Makefile.config` and the environment variable `LD_LIBRARY_PATH` and `PATH` in `.bashrc` file accordingly like the following.
```
export LD_LIBRARY_PATH="${LD_LIBRARY_PATH}:/home/yichaoxiong/opt/lib/tensorrt:/home/yichaoxiong/opt/lib/opencv"
export PATH="${PATH}:/usr/local/cuda-10.2/bin"
```

## Preparation for model and data
- Prepare the ONNX file
  - If your model has a PyTorch format, you can use [vedadep](https://github.com/Media-Smart/volksdep) to convert PyTorch model to ONNX model.
- Modify the ONNX file path
  - Some related configurations in `main.cpp` in `classifier` folder also need be corrected accordingly.
- Get some images for testing

## Compilation and running
```
cd classifier
make -j4
./build/main --imgfp /path/to/image
```
If you want speficy which GPU to use, you can make it by setting the environment variable `CUDA_VISIBLE_DEVICES`.

## Contact
This repository is currently maintained by Hongxiang Cai ([@hxcai](http://github.com/hxcai)), Yichao Xiong ([@mileistone](https://github.com/mileistone)).

## Credits
We got some code from [TensorRT](https://github.com/NVIDIA/TensorRT) and [retinanet-examples](https://github.com/NVIDIA/retinanet-examples).