算法部署-使用TensorRT部署FastBEV算法-优质算法部署项目实战.zip

# CUDA-FastBEV This repository contains sources and model for [Fast-BEV](https://github.com/Sense-GVT/Fast-BEV) inference using CUDA & TensorRT. And provide PTQ and QAT int8 quantization code. ## 3D Object Detection(on nuScenes validation set) - For all models, I used the configs/fastbev_m0_r18_s256x704_v200x200x4_c192_d2_f1.py configuration, delete time sequence. - The camera resolution is 256x704 - Backbone is ResNet18, without DCN | **Model** | **Framework** | **Precision** | **mAP** | **FPS** | |:------------------------:|:-------------:|:-------------:|:-------:|:-------:| | ResNet18 | TensorRT | FP16 | 24.3 | 113.6(on RTX2080Ti) | | ResNet18-PTQ | TensorRT | FP16+INT8 | 23.89 | 143.8 | | ResNet18-head-PTQ | TensorRT | FP16 + INT8 | 23.83 | 144.9 | ## Model and Data - For quick practice, I provide an example data of nuScenes.You can download from [Google Drive](https://drive.google.com/file/d/14-fqrtbC5ZOWgkNA0m7jA7bQAySrzLrm/view?usp=sharing) It contains the following: 1. Camera images in 6 directions. 2. Transformation matrix of camera/lidar and vtrans need data. - All models (model.zip) can be downloaded from [Google Drive](https://drive.google.com/file/d/1wwwckM0vux5ub3U4R_zS9pm01QFmMPru/view?usp=sharing) .It contains the following: 1. fastbev onnx models and pytorch models. 2. resnet18 int8 onnx and PTQ models. ## Prerequisites To build fastbev, we need to depend on the following libraries: - CUDA >= 11.0 - CUDNN >= 8.2 - TensorRT >= 8.5.0 - libprotobuf-dev == 3.6.1 - [Compute Capability](https://developer.nvidia.com/cuda-gpus#compute) >= sm_80 - Python >= 3.6 The data in the performance table was obtained by us on the Nvidia Orin platform, using TensorRT-8.6, cuda-11.4 and cudnn8.6 statistics. ## Quick Start for Inference ### 1. Download models and datas to CUDA-FastBEV directory - download model.zip - download nuScenes-example-data.zip ```bash # download models and datas to CUDA-FastBEV cd CUDA-FastBEV # unzip models and datas unzip model.zip unzip nuScenes-example-data.zip # here is the directory structure after unzipping CUDA-FastBEV |-- example-data |-- 0-FRONT.jpg |-- 1-FRONT_RIGHT.jpg |-- ... |-- example-data.pth |-- x.tensor |-- y.tensor `-- valid_c_idx.tensor |-- src |-- ptq |-- model |-- resnet18int8 | |-- fastbev_pre_trt.onnx | |-- fastbev_post_trt_decode.onnx | |-- fastbev_ptq.pth |-- resnet18 `-- resnet18int8head `-- tool ``` ### 2. Configure the environment.sh - Install python dependency libraries ```bash apt install libprotobuf-dev pip install onnx ``` - Modify the TensorRT/CUDA/CUDNN/fastbev variable values in the tool/environment.sh file. ```bash # change the path to the directory you are currently using export TensorRT_Lib=/path/to/TensorRT/lib export TensorRT_Inc=/path/to/TensorRT/include export TensorRT_Bin=/path/to/TensorRT/bin export CUDA_Lib=/path/to/cuda/lib64 export CUDA_Inc=/path/to/cuda/include export CUDA_Bin=/path/to/cuda/bin export CUDA_HOME=/path/to/cuda export CUDNN_Lib=/path/to/cudnn/lib # resnet18/resnet18int8/resnet18int8head export DEBUG_MODEL=resnet18int8 # fp16/int8 export DEBUG_PRECISION=int8 export DEBUG_DATA=example-data export USE_Python=OFF ``` - Apply the environment to the current terminal. ```bash . tool/environment.sh ``` ### 3. Compile and run 1. Building the models for tensorRT ```bash bash tool/build_trt_engine.sh ``` 2. Compile and run the program ```bash bash tool/run.sh ``` ## PTQ and export ONNX - PTQ ```bash python ptq_bev.py ``` - QAT ```bash python qat_bev.py ``` - export ONNX ```bash python export_onnx.py ``` ## DEMO show