https://github.com/froukje/super-resolution

This repo contains a deployment of a super-resolution network in pytorch-lightning.
https://github.com/froukje/super-resolution

deep-learning deployment pytorch pytorch-lightning super-resolution torchserve web-app

Last synced: about 1 month ago
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This repo contains a deployment of a super-resolution network in pytorch-lightning.

• Host: GitHub
• URL: https://github.com/froukje/super-resolution
• Owner: froukje
• Created: 2022-05-30T11:52:56.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2022-10-23T18:31:31.000Z (about 2 years ago)
• Last Synced: 2023-07-02T20:20:52.988Z (over 1 year ago)
• Topics: deep-learning, deployment, pytorch, pytorch-lightning, super-resolution, torchserve, web-app
• Language: Jupyter Notebook
• Homepage:
• Size: 21.4 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# 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:

![webapp](screenshot_webapp.jpeg)

## Data

* data from [kaggle.com](https://www.kaggle.com/datasets/akhileshdkapse/super-image-resolution)

* raw data saved in data/HR (high resolution) and data/LR (low resolution)

* 100 images

* data exploration: notebooks/data-exploration.ipynb 

## Preprocessing

* preprocessing.py

    * splits the data in validation and training

    * saves splits in h5 files to data/train.h5 and data/valid.h5

## Model

* 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

## Training 

### SRResNet

* 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```

## Evaluation 

* example plots are in notebooks/plot-predictions.ipynb

![example prediction](notebooks/example_prediction.png)

## Deployment

* 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

## Web App

* simple web app is stored in folder ```app```

* run ```docker-compose up``` to start the services

* navigate to ```localhost:9696``` in browser