Official PyTorch code for Transformer-Based Attention Networks for Continuous Pixel-Wise Prediction.
Guanglei Yang, Hao Tang, Mingli Ding, Nicu Sebe, Elisa Ricci.
ICCV 2021
Apply Transformer into depth predciton and surface normal estimation.

Citation

@inproceedings{yang2021transformers,
  title={Transformer-Based Attention Networks for Continuous Pixel-Wise Prediction},
  author={Yang, Guanglei and Tang, Hao and Ding, Mingli and Sebe, Nicu and Ricci, Elisa},
  booktitle={ICCV},
  year={2021}
}

Prepare Pretrain Model

We choose R50-ViT-B_16 as our encoder.

wget https://storage.googleapis.com/vit_models/imagenet21k/R50+ViT-B_16.npz 
mkdir ./model/vit_checkpoint/imagenet21k 
mv R50+ViT-B_16.npz ./model/vit_checkpoint/imagenet21k/R50+ViT-B_16.npz

Prepare Dateset

prepare nyu

mkdir -p pytorch/dataset/nyu_depth_v2
python utils/download_from_gdrive.py 1AysroWpfISmm-yRFGBgFTrLy6FjQwvwP pytorch/dataset/nyu_depth_v2/sync.zip
cd pytorch/dataset/nyu_depth_v2
unzip sync.zip

test set

go to utils
wget http://horatio.cs.nyu.edu/mit/silberman/nyu_depth_v2/nyu_depth_v2_labeled.mat
python extract_official_train_test_set_from_mat.py nyu_depth_v2_labeled.mat splits.mat ../pytorch/dataset/nyu_depth_v2/official_splits/

Prepare kitti

cd dataset
mkdir kitti_dataset
cd kitti_dataset
### image move kitti_archives_to_download.txt into kitti_dataset
wget -i kitti_archives_to_download.txt

### label
wget https://s3.eu-central-1.amazonaws.com/avg-kitti/data_depth_annotated.zip
unzip data_depth_annotated.zip
cd train
mv * ../
cd ..  
rm -r train
cd val
mv * ../
cd ..
rm -r val
rm data_depth_annotated.zip

Environment

pip install -r requirements.txt

Run

Before running, please make models fold first!!!

Train

CUDA_VISIBLE_DEVICES=0,1,2,3 python bts_main.py arguments_train_nyu.txt
CUDA_VISIBLE_DEVICES=0,1,2,3 python bts_main.py arguments_train_eigen.txt

Test: Pick up nice result

CUDA_VISIBLE_DEVICES=1 python bts_test.py arguments_test_nyu.txt
python ../utils/eval_with_pngs.py --pred_path vis_att_bts_nyu_v2_pytorch_att/raw/ --gt_path ./dataset/nyu_depth_v2/official_splits/test/ --dataset nyu --min_depth_eval 1e-3 --max_depth_eval 10 --eigen_crop
CUDA_VISIBLE_DEVICES=1 python bts_test.py arguments_test_eigen.txt
python ../utils/eval_with_pngs.py --pred_path vis_att_bts_eigen_v2_pytorch_att/raw/ --gt_path ./dataset/kitti_dataset/ --dataset kitti --min_depth_eval 1e-3 --max_depth_eval 80 --do_kb_crop --garg_crop

Debug

CUDA_VISIBLE_DEVICES=1 python bts_main.py arguments_train_nyu_debug.txt

Download Pretrained Model

sh scripts/download_TransDepth_model.sh kitti_depth

sh scripts/download_TransDepth_model.sh nyu_depth

sh scripts/download_TransDepth_model.sh nyu_surfacenormal

Note: Please try to execute the command line a second time, if it doesn’t work the first time.

Reference

BTS

ViT

TransUNet

Do‘s code

Visualization Result Share

We provide all vis result of all tasks. link

Collaborations

We are always interested in meeting new people and hearing about potential collaborations. If you'd like to work together or get in contact with us, please email bjdxtanghao@gmail.com. Some of our projects are listed here.