Maocheng Hu 46b2185aee second commit 3 yıl önce
..
model 46b2185aee second commit 3 yıl önce
README.md 46b2185aee second commit 3 yıl önce
basic_ops.py 46b2185aee second commit 3 yıl önce
benchmark.py 46b2185aee second commit 3 yıl önce
config.yml 46b2185aee second commit 3 yıl önce
dataloader.py 46b2185aee second commit 3 yıl önce
forward.py 46b2185aee second commit 3 yıl önce
logger.py 46b2185aee second commit 3 yıl önce
utils.py 46b2185aee second commit 3 yıl önce

README.md

Deep Hough Transform for Semantic Line Detection

High speed performance Jittor implementation Code accompanying the paper "Deep Hough Transform for Semantic Line Detection" (ECCV 2020, PAMI 2021). arXiv2003.04676 | Online Demo | Project page | New dataset | Line Annotator

  • Jittor inference code is open available now.
  • Training code will come soon.

High speed of Jittor framework

Network inference FPS and speedup ratio (without post processing):

  <td rowspan="2"></td>
  <td colspan="3">TITAN XP</td>
  <td colspan="3">Tesla P100</td>
  <td colspan="3">RTX 2080Ti</td>

  <td>bs=1</td>
  <td>bs=4</td>
  <td>bs=8</td>
 <td>bs=1</td>
  <td>bs=4</td>
  <td>bs=8</td>
 <td>bs=1</td>
  <td>bs=4</td>
  <td>bs=8</td>

  <td>Jittor</td>
  <td>44</td>
  <td>54</td>
 <td>56</td>
 <td>42</td>
  <td>49</td>
 <td>52</td>
 <td>82</td>
  <td>98</td>
 <td>100</td>

  <td>Pytorch</td>
  <td>39</td>
  <td>48</td>
 <td>49</td>
 <td>35</td>
  <td>44</td>
 <td>44</td>
 <td>64</td>
  <td>71</td>
 <td>71</td>

  <td>Speedup</td>
  <td>1.13</td>
  <td>1.13</td>
 <td>1.14</td>
 <td>1.20</td>
  <td>1.11</td>
 <td>1.18</td>
 <td>1.28</td>
  <td>1.38</td>
 <td>1.41</td>

Tesla V100 (16G PCI-E) Tesla V100 RTX TITAN
bs=1 bs=4 bs=8 bs=1 bs=4 bs=8 bs=1 bs=4 bs=8
Jittor 89 115 120 88 108 113 27 74 106
Pytorch 38 75 82 10 34 53 9 15 34
Speedup 2.34 1.53 1.46 8.80 3.18 2.13 3.00 4.93 3.12

Requirements

jittor
numpy
scipy
opencv-python
scikit-image
pytorch 1.0<=1.3
tqdm
yml

Pretrain model (based on ResNet50-FPN): http://data.kaizhao.net/projects/deep-hough-transform/dht_r50_fpn_sel-c9a29d40.pth (SEL dataset) and http://data.kaizhao.net/projects/deep-hough-transform/dht_r50_nkl_d97b97138.pth (NKL dataset / used in online demo)

Prepare training data

Download original SEL dataset from here and extract to data/ directory. After that, the directory structure should be like:

data
├── ICCV2017_JTLEE_gtlines_all
├── ICCV2017_JTLEE_gt_pri_lines_for_test
├── ICCV2017_JTLEE_images
├── prepare_data_JTLEE.py
├── Readme.txt
├── test_idx_1716.txt
└── train_idx_1716.txt

Then run python script to generate parametric space label.

cd deep-hough-transfrom
python data/prepare_data_JTLEE.py --root './data/ICCV2017_JTLEE_images/' --label './data/ICCV2017_JTLEE_gtlines_all' --save-dir './data/training/JTLEE_resize_100_100/' --list './data/training/JTLEE.lst' --prefix 'JTLEE_resize_100_100' --fixsize 400 --numangle 100 --numrho 100

For NKL dataset, you can download the dataset and put it to data dir. Then run python script to generate parametric space label.

cd deep-hough-transform
python data/prepare_data_NKL.py --root './data/NKL' --label './data/NKL' --save-dir './data/training/NKL_resize_100_100' --fixsize 400

Forward

Generate visualization results and save coordinates to _.npy file.

CUDA_VISIBLE_DEVICES=0 python forward.py --model (your_best_model.pth) --tmp (your_result_save_dir)

Citation

If our method/dataset are useful to your research, please consider to cite us:

@article{hu2020jittor,
  title={Jittor: a novel deep learning framework with meta-operators and unified graph execution},
  author={Hu, Shi-Min and Liang, Dun and Yang, Guo-Ye and Yang, Guo-Wei and Zhou, Wen-Yang},
  journal={Information Sciences},
  volume={63},
  number={222103},
  pages={1--21},
  year={2020}
}
@article{zhao2021deep,
  author    = {Kai Zhao and Qi Han and Chang-bin Zhang and Jun Xu and Ming-ming Cheng},
  title     = {Deep Hough Transform for Semantic Line Detection},
  journal   = {IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)},
  year      = {2021},
  doi       = {10.1109/TPAMI.2021.3077129}
}
@inproceedings{eccv2020line,
  title={Deep Hough Transform for Semantic Line Detection},
  author={Qi Han and Kai Zhao and Jun Xu and Ming-Ming Cheng},
  booktitle={ECCV},
  pages={750--766},
  year={2020}
}

License

This project is licensed under the Creative Commons NonCommercial (CC BY-NC 3.0) license where only non-commercial usage is allowed. For commercial usage, please contact us.