SDFA-Net_pytorch

This is the official repo for our work 'Self-distilled Feature Aggregation for Self-supervised Monocular Depth Estimation' (ECCV 2022).
Paper
Citation information:

@inproceedings{zhou2022self,
  title={Self-distilled feature aggregation for self-supervised monocular depth estimation},
  author={Zhou, Zhengming and Dong, Qiulei},
  booktitle={European Conference on Computer Vision},
  pages={709--726},
  year={2022},
  organization={Springer}
}

Setup

We built and ran the repo with CUDA 10.2, Python 3.7.11, and Pytorch 1.7.0. For using this repo, we recommend creating a virtual environment by Anaconda. Please open a terminal in the root of the repo folder for running the following commands and scripts.

conda env create -f environment.yml
conda activate pytorch170cu10

Pre-trained models

Model Name	Dataset(s)	Abs Rel.	Sq Rel.	RMSE	RMSElog	A1
SDFA-Net-SwinTM_stage1_384 Baidu/Google	K	0.100	0.631	4.090	0.183	0.890
SDFA-Net-SwinTM_384 Baidu/Google	K	0.090	0.538	3.896	0.169	0.906
SDFA-Net-SwinTM_CS+K_384 Baidu/Google	CS+K	0.085	0.531	3.888	0.167	0.911

• code for all the download links of Baidu is sdfa

Prediction

To predict depth maps for your images, please firstly download the pretrained model from the column named Model Name in the above table. After unzipping the downloaded model, you could predict the depth maps for your images by

python predict.py\
 --image_path <path to your image or folder name for your images>\
 --exp_opts options/_base/networks/sdfa_net.yaml\
 --model_path <path to the downloaded or trained model (.pth)>

You also could set --input_size to decide the size that the images are reshaped before they are input to the model. If you want to predict on CPU, please set --cpu. The depth results <image name>_pred.npy and the visualization results <image name>_visual.png will be saved in the same folder as the input images.

Data preparation

Set Data Path

We give an example path_example.py for setting the path in the repository. Please create a python file named path_my.py and copy the code in path_example.py to the path_my.py. Then you can replace the used paths to your folder in the path_my.py. the folder for each dataset should be organized like:

<root of kitti>
|---2011_09_26
|   |---2011_09_26_drive_0001_sync
|   |   |---image_02
|   |   |---image_03
|   |   |---velodyne_points
|   |   |---...
|   |---2011_09_26_drive_0002_sync
|   |   |---image_02
|   |   |---image_03
|   |   |---velodyne_points
|   |   |---...
|   '''
|---2011_09_28
|   |--- ...
|---gt_depths_raw.npz (for raw Eigen test set)
|---gt_depths_improved.npz (for improved Eigen test set)

<root of cityscapes>
|---leftImg8bit
|   |---train
|   |   |---aachen
|   |   |   |---aachen_000000_000019_leftImg8bit.png
|   |   |   |---aachen_000001_000019_leftImg8bit.png
|   |   |   |---...
|   |   |---bochum
|   |   |---...
|   |---train_extra
|   |   |---augsburg
|   |   |---...
|   |---test
|   |   |---...
|   |---val
|   |   |---...
|---rightImg8bit
|   |--- ...
|---camera
|   |--- ...
|---disparity
|   |--- ...
|---gt_depths (for evaluation)
|   |---000_depth.npy
|   |---001_depth.npy
|   |--- ...

KITTI

For training the methods on the KITTI dataset (the Eigen split), you should download the entire KITTI dataset (about 175GB) by:

wget -i ./datasets/kitti_archives_to_download.txt -P <save path>

And you could unzip them with:

cd <save path>
unzip "*.zip"

For evaluating the methods on the KITTI (Eigen raw test set), you should further generate the ground-truth depth file by (as done in the Monodepth2):

python datasets/utils/export_kitti_gt_depth.py --data_path <root of KITTI> --split raw

If you want to evaluate the method on the KITTI improved test set, you should download the annotated depth maps (about 15GB) at Here and unzip it in the root of the KITTI dataset. Then you could generate the imporved ground-truth depth file by:

python datasets/utils/export_kitti_gt_depth.py --data_path <root of KITTI> --split improved

As an alternative, we provide the Eigen test subset and the generated gt_depth files Here (about 2GB) for the people who just want to do the evaluation.

Cityscapes (Optional)

Cityscapes could be used to jointly train the model with KITTI, which is helpful to improve the performance of the model. If you want to use the Cityscapes, please download the following parts of the dataset at Here and unzip them to your <root of cityscapes> (Note: For some files, you should apply for download permission by email.):

leftImg8bit_trainvaltest.zip (11GB)
leftImg8bit_trainextra.zip (44GB)
rightImg8bit_trainvaltest.zip (11GB)
rightImg8bit_trainextra.zip (44GB)
camera_trainvaltest.zip (2MB)
camera_trainextra.zip (8MB)

Then, please generate the camera parameter matrices by:

python datasets/utils/export_cityscapes_matrix.py

Evaluation

To evaluate the methods on the prepared dataset, you could simply use

python evaluate.py\
 --exp_opts <path to the method EVALUATION option>\
 --model_path <path to the downloaded or trained model>

We provide the EVALUATION option files in options/SDFA-Net/eval/*. Here we introduce some important arguments.

Argument	Information
--visual_list	The samples which you want to save the output (path to a .txt file)
--save_pred	Save the predicted depths of the samples which are in --visual_list
--save_visual	Save the visualization results of the samples which are in --visual_list
-gpp	Adopt different post-processing steps.

The output files are saved in eval_res\ by default. Please check evaluate.py for more information about arguments.

Training

Plese firstly download the pretrained Swin-trainsformer (Tiny Size) in their official repo and don't forget to set the path in path_my.py to the downloaded model. Then, you could train SDFA-Net simply use the commands provided in options/SDFA-Net/train/train_scripts.sh. For example, you could use the following commands for training the SDFA-Net on KITTI. As mentioned in our paper, we disabled the self-distilled forward propagation and corresponding losses in early training stage. The training is devideded into two stages:

# train SDFA-Net at stage1
CUDA_VISIBLE_DEVICES=0 python\
 train_dist.py\
 --name SDFA-Net-SwinT-M_192Crop_KITTI_S_St1_B12\
 --exp_opts options/SDFA-Net/train/sdfa_net-swint-m_192crop_kitti_stereo_stage1.yaml\
 --batch_size 12\
 --epoch 25\
 --visual_freq 2000\
 --save_freq 5

After finishing the stage1, please copy the path to the last saved model <exp_log>/model/last_model.pth into <path to .pth>, and use the following command to continue the training:

# train SDFA-Net at stage2
CUDA_VISIBLE_DEVICES=0 python\
 train_dist.py\
 --name SDFA-Net-SwinT-M_192Crop_KITTI_S_St2_B12\
 --exp_opts options/SDFA-Net/train/sdfa_net-swint-m_192crop_kitti_stereo_stage2.yaml\
 --batch_size 12\
 --visual_freq 2000\
 --save_freq 5\
 --pretrained_path <path to .pth>

Acknowledgment

Some of this repo come from Monodepth2, AlignSeg and Swin-transformer.