Video Prediction by GAN

This project aims to adopt the GAN-based architectures, which original proposed by Project Page(https://alexlee-gk.github.io/video_prediction/) Paper(https://arxiv.org/abs/1804.01523), to predict temperature based on ERA5 data

Getting Started

Prerequisites

• Linux or macOS
• Python 3
• CPU or NVIDIA GPU + CUDA CuDNN

Installation

• Clone this repo:

git clone -b master https://gitlab.version.fz-juelich.de/gong1/video_prediction_savp.git
cd video_prediction_savp

• Install TensorFlow >= 1.9 and dependencies from http://tensorflow.org/
• Install other dependencies

pip install -r requirements.txt

Miscellaneous installation considerations

• In python >= 3.6, make sure to add the root directory to the PYTHONPATH, e.g. export PYTHONPATH=path/to/video_prediction_savp`.
• For the best speed and experimental results, we recommend using cudnn version 7.3.0.29 and any tensorflow version >= 1.9 and <= 1.12. The final training loss is worse when using cudnn versions 7.3.1.20 or 7.4.1.5, compared to when using versions 7.3.0.29 and below.
• Add the directories lpips-tensorflow and hickle (get from Workflow project to the PATHONPATH , e.g export PYTHONPATH=path/to/lpips-tensorflow

Set-up on JUWELS

• Set up env and install packages

cd env_setup
./create_env.sh <USER_FOLDER>

Workflow by steps

Data Extraction

Workflow project

cd ../workflow_video_prediction/DataExtraction 
python mpi_stager_v2.py  --source_dir <input_dir1> --destination_dir <output_dir1>

Data Preprocessing

cd  ../workflow_video_prediction/DataPreprocess
python mpi_stager_v2_process_netCDF.py --source_dir <output_dir1> --destination_dir <output_dir2> 

video_prediction/datasets/era5_dataset_v2.py <output_dir2/splits>  <output_dir3>

python scripts/train_v2.py --input_dir <output_dir3> --dataset era5  --model <savp> --model_hparams_dict hparams/kth/ours_savp/model_hparams.json --output_dir <logs/era5/ours_savp>

Postprocessing

Model Evaluation

Groud Truth

End-to-End run the entire workflow

cd bash
./workflow_era5.sh