TransWeather

This is not an official code for the paper TransWeather: Transformer-based Restoration of Images Degraded by Adverse Weather Conditions

Please check here.

About this repo:

Using the code:

The code is stable while using Python 3.6.13, CUDA >=10.1

• Clone this repository:

git clone https://github.com/jeya-maria-jose/TransWeather
cd TransWeather

To install all the dependencies using conda:

conda env create -f environment.yml
conda activate transweather

If you prefer pip, install following versions:

timm==0.3.2
mmcv-full==1.2.7
torch==1.7.1
torchvision==0.8.2
opencv-python==4.5.1.48

Datasets:

Train Data:

TransWeather is trained on a combination of images sampled from Outdoor-Rain, Snow100K, and Raindrop datasets (similar to All-in-One (CVPR 2020)), dubbed as "All-Weather", containing 18069 images. It can be downloaded from this link.

Dataset format:

Download the datasets and arrange them in the following format.

    TransWeather
    ├── data 
    |   ├── train # Training  
    |   |   ├── <dataset_name>   
    |   |   |   ├── input         # rain images 
    |   |   |   └── gt            # clean images
    |   |   └── dataset_filename.txt
    |   └── test  # Testing         
    |   |   ├── <dataset_name>          
    |   |   |   ├── input         # rain images 
    |   |   |   └── gt            # clean images
    |   |   └── dataset_filename.txt

• format which will be applicated later

  TransWeather
  ├── data 
  |   ├── train # Training  
  |   |   ├── <dataset_name>   
  |   |   |   ├── input         # rain images 
  |   |   |   └── gt            # clean images
  |   |   └── dataset_filename.txt
  |   └── test  # Testing         
  |   |   ├── <dataset_name>          
  |   |   |   ├── input         # rain images 
  |   |   |   └── gt            # clean images
  |   |   └── dataset_filename.txt
  

Text Files:

Link

Should check the path.

Evaluation Code:

To run the evaluation for specific test datasets, run the following commands:

python test_snow100k.py -exp_name TransWeather_weights

python test_test1.py -exp_name TransWeather_weights

python test_raindropa.py -exp_name TransWeather_weights

These scripts will calculate the performance metrics as well as save the predictions in the results folder.

Training the network:

To train the network on All-weather dataset, run the following command:

python train.py  -train_batch_size 32 -exp_name Transweather -epoch_start 0 -num_epochs 250

Extensions:

Note that Transweather is built to solve all adverse weather problem with a single model. We observe that, additionally TransWeather can be easilty modified (removing the transformer decoder) to just focus on an individual restoration task. To train just the Transweather-encoder on other datasets (like Rain-800), organize the dataset similar to all-weather and run the following command:

python train-individual.py  -train_batch_size 32 -exp_name Transweather-finetune -epoch_start 0 -num_epochs 250

Change train-individual.py with the necesarry details of the data to be trained on. Note that the network used there is a sub-section of our original Transweather architecture without the weather queries.

Acknowledgements:

This code-base uses certain code-blocks and helper functions from Syn2Real, Segformer, and ViT.

Citation:

@misc{valanarasu2021transweather,
      title={TransWeather: Transformer-based Restoration of Images Degraded by Adverse Weather Conditions}, 
      author={Jeya Maria Jose Valanarasu and Rajeev Yasarla and Vishal M. Patel},
      year={2021},
      eprint={2111.14813},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}