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TensorRT Ultra-Fast-Lane-Detection

Description

This sample contains code that performs TensorRT inference on Jetson.

  1. Download ONNX Ultra-Fast-Lane-Detection Model from PINTO_model_zoo.
  2. Convert ONNX Model to Serialize engine and inference on Jetson.

Reference

Howto

Install dependency

pip3 install -U scipy

Download ONNX Model

Clone PINTO_model_zoo repository and download Ultra-Fast-Lane-Detection model.

git clone https://github.com/PINTO0309/PINTO_model_zoo.git
cd PINTO_model_zoo/140_Ultra-Fast-Lane-Detection/
./download.sh

Check trtexec

/usr/src/tensorrt/bin/trtexec --onnx=./saved_model_culane/ultra_falst_lane_detection_culane_288x800.onnx

or

/usr/src/tensorrt/bin/trtexec --onnx=./saved_model_tusimple/ultra_falst_lane_detection_tusimple_288x800.onnx

Convert ONNX Model to TensorRT Serialize engine file.

Copy ultra_falst_lane_detection_culane_288x800.onnx or ultra_falst_lane_detection_tusimple_288x800.onnx to tensorrt-examples/models.
In the following, culane is taken as an example.

cp ~/PINTO_model_zoo/140_Ultra-Fast-Lane-Detection/saved_model_culane/ultra_falst_lane_detection_culane_288x800.onnx ~/tensorrt-examples/models/

Convert to Serialize engine file. If you want to convert to FP16 model, add --fp16 to the argument of convert_onnxgs2trt.py.

cd ~/tensorrt-examples/python/utils
python3 convert_onnxgs2trt.py \
    --model /home/jetson/tensorrt-examples/models/ultra_falst_lane_detection_culane_288x800.onnx \
    --output /home/jetson/tensorrt-examples/models/ultra_falst_lane_detection_culane_288x800.trt \

Finally you can run the demo.

python3 trt_ultra_fast_lane_detection_capture.py \
    --model ../../models/ultra_falst_lane_detection_culane_288x800.trt
    --model_config culane

or 

python3 trt_ultra_fast_lane_detection_image.py \
    --model ../../models/ultra_falst_lane_detection_culane_288x800.trt
    --model_config culane
    --input input_image.png
    --output output_image.png