Super Resolution Model and Web App

This repository contains a super resolution network upscaling images of a factor of 4 and a web app. The final web app looks like th is:

Data

preprocessing.py
- splits the data in validation and training
- saves splits in h5 files to data/train.h5 and data/valid.h5

train 2 models:
- SRResNet
- SRGAN (ToDo)
based on: Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network, Ledig et al. (2017), https://doi.org/10.48550/arXiv.1609.04802
model code based on https://github.com/sgrvinod/a-PyTorch-Tutorial-to-Super-Resolution
converted to pytorch lightning
added NNI for hyperparamtertuning
added mlflow

run script with desired parameters, e.g. python3 srresnet.py --n-blocks 8 --batch-size 12 --n-channels 22 --gpus 1 --n-epochs 200
use batch script saved in jobs/start_job.sh or start_job_nni.sh to include hyperparameter tuning using nni
results are not reported to nni, but to mlflow
example files for hyperparamter tuning are in nni/config/config.py and nni/search_space/search_space.json

model is served using torchserve
the handler script is based on the BaseHandler class and can be found in deployment/srnet_handler.py
create the .mar file using the create-mar.sh script
run docker build -t srnet-mar:v1 . to create the docker image
docker run -p 8080:8080 -p 8081:8081 srnet-mar:v1
check registeres models: curl http://127.0.0.1:8081/models
make predictions: curl http://127.0.0.1:8080/predictions/srnet -T ../data/LR/0.png
The config.properties file is needed to change the maximun output size