GASDA
This is the PyTorch implementation for our CVPR'19 paper:
S. Zhao, H. Fu, M. Gong and D. Tao. Geometry-Aware Symmetric Domain Adaptation for Monocular Depth Estimation. PAPER POSTER
Environment
- Python 3.6
- PyTorch 0.4.1
- CUDA 9.0
- Ubuntu 16.04
Datasets
Prepare the two datasets according to the datalists (*.txt in datasets)
datasets
|----kitti
|----2011_09_26
|----2011_09_28
|----.........
|----vkitti
|----rgb
|----0006
|-----.......
|----depth
|----0006
|----.......
Training (Tesla V100, 16GB)
-
Train CycleGAN using the official experimental settings, or download our pretrained models.
-
Train F_t
python train.py --model ft --gpu_ids 0 --batchSize 8 --loadSize 256 1024 --g_tgt_premodel ./cyclegan/G_Tgt.pth
- Train F_s
python train.py --model fs --gpu_ids 0 --batchSize 8 --loadSize 256 1024 --g_src_premodel ./cyclegan/G_Src.pth
- Train GASDA using the pretrained F_s, F_t and CycleGAN.
python train.py --freeze_bn --freeze_in --model gasda --gpu_ids 0 --batchSize 3 --loadSize 192 640 --g_src_premodel ./cyclegan/G_Src.pth --g_tgt_premodel ./cyclegan/G_Tgt.pth --d_src_premodel ./cyclegan/D_Src.pth --d_tgt_premodel ./cyclegan/D_Tgt.pth --t_depth_premodel ./checkpoints/vkitti2kitti_ft_bn/**_net_G_Depth_T.pth --s_depth_premodel ./checkpoints/vkitti2kitti_fs_bn/**_net_G_Depth_S.pth
Note: this training strategy is different from that in our paper.
Test
Copy the provided models to GASDA/checkpoints/vkitti2kitti_gasda/, and rename the models 1_* (e.g., 1_net_D_Src.pth), and then
python test.py --test_datafile 'test.txt' --which_epoch 1 --model gasda --gpu_ids 0 --batchSize 1 --loadSize 192 640
Citation
If you use this code for your research, please cite our paper.
@inproceedings{zhao2019geometry,
title={Geometry-Aware Symmetric Domain Adaptation for Monocular Depth Estimation},
author={Zhao, Shanshan and Fu, Huan and Gong, Mingming and Tao, Dacheng},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
pages={9788--9798},
year={2019}
}
Acknowledgments
Code is inspired by T^2Net and CycleGAN.
Contact
Shanshan Zhao: [email protected] or [email protected]
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