STDAN-CVPRW-2022 (Keep Update)
This is the official Pytorch implementation of Space-Time Video Super-Resolution Using Deformable Attention Network.
Dependencies
- Python 3.8.0 (Recommend to use Anaconda)
- PyTorch == 1.8.0
- CUDA == 10.1
- Deformable Convolution v2, we use Detectron2's implementation in the network.
Installation
-
Install the required packages:
pip install -r requirements.txt -
Compile the deformable attention and convolution:
cd YOUR_PATH/STDAN/codes/models/modules/DCNv2_latest bash make.sh
Training
Dataset preparation
You require to prepare datasets for training the model, the detailed information is elaborated in Data Preparation.
Train the STDAN model
cd YOUR_PATH/codes
python train.py -opt options/train/train_stdan.yml
Testing
Pretrained Models
Our pretrained model can be downloaded via Google Drive or Baidu Netdisk(access code: gnhk). After you obtain the pretrained model, please put them into the YOUR_PATH/experiments/pretrained_models folder.
Testing on Vid4/SPMC datasets
cd YOUR_PATH/codes
python test.py
Testing on Vimeo-Slow/Medium/Fast datasets
cd YOUR_PATH/codes
python test_vimeo.py
Acknowledgments
We thank Zooming Slow-Mo and Detectron2. They provide many useful codes which facilitate our work.