This project is a Torch implementation which learning to repair multiple errors by optimal alignment learning.
The models are trained using folloing hardware:
- Ubuntu 18.04.5 LTS
- NVIDA TITAN Xp 24GB * 4
- Intel(R) Xeon(R) W-2145 CPU @ 3.70GHz
- 64GB RAM
- Python version is 3.6.7 We use the following version of Pytorch. gpu support (CUDA==10.1)
- torch==1.1.0 gpu support (CUDA>10.1)
- torch==1.5.0 Etc. (Included in "requirements.txt")
- torchtext==0.3.1
- numpy==1.16.1
- tqdm
- matplotlib
- regex
- Use virtualenv
sudo apt-get install build-essential libssl-dev libffi-dev python-dev
sudo apt install python3-pip
sudo pip3 install virtualenv
virtualenv -p python3 venv
. venv/bin/activate
# code your stuff
deactivate
Our dataset is based on the dataset provided by DeepFix. https://www.cse.iitk.ac.in/users/karkare/prutor/prutor-deepfix-09-12-2017.zip
Generate training data based on the DeepFix and DrRepair dataset.
bash data_processing.sh
Train the data with our model.
bash model_training.sh
However, this takes a significant time, so we provide 2 models that were trained.
log/pth
You can check the repair result through the saved model.
bash evaluation.sh
- If the beam size is 100, it takes a significant time.
- We did not fix the seed, so training results may be slightly different. We actually use the average of the three training results.