/PINetTensorrt

This repository is TensorRT implement of PINet

Primary LanguageC++

key points estimation and point instance segmentation approach for lane detection

  • Paper : key points estimation and point instance segmentation approach for lane detection

  • Paper Link : https://arxiv.org/abs/2002.06604

  • Author : Yeongmin Ko, Jiwon Jun, Donghwuy Ko, Moongu Jeon (Gwanju Institute of Science and Technology)

  • This repository is TensorRT implement of PINet

Dependency

  • TensorRT 6.0
  • OpenCV

Convert

  • clone PINet source code
    git clone https://github.com/koyeongmin/PINet.git

you can convert Pytorch weights file to onnx file, follow as:

  • insert this code at end of agent.py :
    def export_onnx(self, input_image, filename):
        torch_out = torch.onnx.export(self.lane_detection_network, input_image, filename, verbose=True)
  • run this code to convert weights file to onnx, please use pytorch 1.0.1
    import torch
    import agent

    batch_size = 1
    input_shape = (3, 256, 512)
    dummy_input = torch.randn(batch_size, *input_shape, device='cuda')
    lane_agent = agent.Agent()
    lane_agent.load_weights(640, "tensor(0.2298)")
    lane_agent.cuda()
    lane_agent.evaluate_mode()
    lane_agent.export_onnx(dummy_input, "pinet.onnx")

Run

  • run this program with image directory
    ./PINetTensorrt --datadir=<path of your test images>
  • or run this program with default images
    ./PINetTensorrt

Test

Object

  • Pytorch implement of PINet
  • Tensorrt C++ implement of PINet

Purpose

  • Tensorrt performance under X86 architecture
  • Tensorrt performance under Xavier

Dataset

data source：tusimple dataset 0531 directory

image format：jpg

image size：1280 x 720

image channels：RGB

count of images：14300

disk space size： 3GB

X86 Computer

OS：ubuntu 18.04

CPU：AMD Ryzen 7 3700X 8-Core Processor

CPU Frequency: 3600 mhz

ram：32GB 3200mhz

video card：Nvidia Titan

vram： 6G

disk：Seagate cool fish 7200rpm

Xavier

OS：ubuntu 18.04

CPU：ARMv8 Processor rev 0 (v8l)

CPU Frequency: 2036 mhz

ram：16GB

Test

Explain
  • end to end：elapsed time of read image， inference，post processing，draw lane line result to image
  • execute： elapsed time of copy host ram to device vram，inference exectute， copy device vram to host ram
  • totally end to end : elapsed time of dataset test, sum of end to end
  • totally execute： elapsed time of dataset test, sum of execute
end to end = totally end to end / count of image in dataset 
execute    = totally execute    / count of image in dataset
X86 && Pytorch Implement
NO. totally end to end end to end(ms) totally execute(s) execute(ms)
1 39m54.79s 167.46 229.92 16.07
2 38m28.37s 161.42 222.29 15.54
3 38m04.18s 159.73 224.73 15.71
4 37m40.54s 158.08 218.91 15.30
5 38m05.84s 159.84 223.84 15.65
average 38m26.74s 161.31 233.94 15.65
X86 && Tensorrt C++ Implement
NO. totally end to end(s) end to end(ms) totally execute(s) execute(ms)
1 335.970 23.494 152.362 10.654
2 346.983 24.264 154.873 10.83
3 338.014 23.637 153.296 10.72
4 342.812 23.972 154.606 10.811
5 343.489 24.02 154.693 10.817
average 341.45 23.88 153.966 10.77
Xavier && Tensorrt C++ Implement
NO. totally end to end(s) end to end(ms) totally execute(s) execute(ms)
1 709.816 49.637 289.398 20.237
2 652.201 45.608 287.493 20.104
3 651.780 45.578 290.308 20.301
4 650.099 45.461 287.789 20.125
5 657.376 45.97 287.569 20.109
average 664.254 46.45 288.51 20.175
Result
  • elapsed time of inference execute under x86 architecture, Tensorrt C++ implement faster 1.5 times than Pytorch implement
  • elapsed time of end to end under x86 architecture, Tensorrt C++ implement faster 10 times than Pytorch implement
  • elapsed time of inference execute under Xavier, x86 architecture faster 2 times，takes 20 ms on average