lzb863 / scnn_pytorch-1

Pytorch implementation of "Spatial As Deep: Spatial CNN for Traffic Scene Understanding"

SCNN lane detection in Pytorch

SCNN is a segmentation-tasked lane detection algorithm, described in 'Spatial As Deep: Spatial CNN for Traffic Scene Understanding'. The official implementation is in lua torch.

This repository contains a re-implementation in Pytorch.

Updates

• 2019 / 5 / 08: Evaluation is provided.

• 2019 / 4 / 23: Trained model converted from official t7 model is provided.

Data preparation

CULane

The dataset is available in CULane. Please download and unzip the files in one folder, which later is represented as CULane_path. Then modify the path of CULane_path in config.py.

CULane_path
├── driver_100_30frame
├── driver_161_90frame
├── driver_182_30frame
├── driver_193_90frame
├── driver_23_30frame
├── driver_37_30frame
├── laneseg_label_w16
├── laneseg_label_w16_test
└── list

Note: absolute path is encouraged.

Tusimple

The dataset is available in here. Please download and unzip the files in one folder, which later is represented as Tusimple_path. Then modify the path of Tusimple_path in config.py.

Tusimple_path
├── clips
├── label_data_0313.json
├── label_data_0531.json
├── label_data_0601.json
└── test_label.json

Note: seg_label images and gt.txt, as in CULane dataset format, will be generated the first time Tusimple object is instantiated. It may take time.

Trained Model Provided

• Model trained on CULane Dataset can be converted from official implementation， which can be downloaded here. Please put the vgg_SCNN_DULR_w9.t7 file into experiments/vgg_SCNN_DULR_w9.

  python experiments/vgg_SCNN_DULR_w9/t7_to_pt.py
  

  Model will be cached into experiments/vgg_SCNN_DULR_w9/vgg_SCNN_DULR_w9.pth.

  Note:torch.utils.serialization is obsolete in Pytorch 1.0+. You can directly download the converted model here.

Demo Test

For single image demo test:

python demo_test.py   -i demo/demo.jpg 
                      -w experiments/vgg_SCNN_DULR_w9/vgg_SCNN_DULR_w9.pth 
                      [--visualize / -v]

Train

1. Specify an experiment directory, e.g. experiments/exp0. Assign the path to variable exp_dir in train.py.

2. Modify the hyperparameters in experiments/exp0/cfg.json.

3. Start training:

   python train.py [-r]

4. Monitor on tensorboard:

   tensorboard --logdir='experiments/exp0' > experiments/exp0/board.log 2>&1 &
   

Note

• My model is trained with torch.nn.DataParallel. Modify it according to your hardware configuration.
• Currently the backbone is vgg16 from torchvision. Several modifications are done to the torchvision model according to paper, i.e., i). dilation of last three conv layer is changed to 2, ii). last two maxpooling layer is removed.

Evaluation

• Evaluation code is ported from official implementation and a CMakeLists.txt is provided.

  cd utils/lane_evaluation
  mkdir build && cd build
  cmake ..
  make

• Run test script

  python test.py
  

  Modify directory path exp in utils/lane_evaluation/Run.sh and run it.

  cd utils/lane_evaluation
  sh ./Run.sh
  

  The result will be stored in exp directory, e.g. experiments/vgg_SCNN_DULR_w9/evaluate.

Acknowledgement

This repos is build based on official implementation.