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add Ultra-Fast-Lane-Detection implementation in TensorRT6

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hwh-hit 2020-09-16 15:11:03 +08:00 committed by wang-xinyu
parent be336a561d
commit d01cf6ff3a
1 changed files with 10 additions and 10 deletions
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lane_det/README.md
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@ -3,7 +3,7 @@
The Pytorch implementation is [Ultra-Fast-Lane-Detection](https://github.com/cfzd/Ultra-Fast-Lane-Detection).
## How to Run
```
1. generate lane.wts and lane.onnx from pytorch with tusimple_18.pth
git clone https://github.com/wang-xinyu/tensorrtx.git
@ -14,20 +14,20 @@ git clone https://github.com/cfzd/Ultra-Fast-Lane-Detection.git
// go to Ultra-Fast-Lane-Detection
python gen_wts.py
// a file 'lane.wts' will be generated.
// then
// then ( not necessary )
python pth2onnx.py
//a file 'lane.onnx' will be generated.
2. build tensorrtx/lane_det and run
```
mkdir build
cd build
cmake ..
make
sudo ./lane_det -s // serialize model to plan file i.e. 'DBNet.engine'
sudo ./lane_det -d ../data // deserialize plan file and run inference, the images in data will be processed.
```
mkdir build
cd build
cmake ..
make
sudo ./lane_det -s // serialize model to plan file i.e. 'lane.engine'
sudo ./lane_det -d ../data // deserialize plan file and run inference, the images in data will be processed.
```
## More Information
1. Changed the preprocess and postprocess in tensorrtx, give a different way to convert NHWC to NCHW in preprocess and just show the reslut using opencv rather than saving the result in postprocess.