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
• You may need install packages by pip on JUWELS/JURECA, followed the installation instruction from Workflow project

Download data

• Download the ERA5 data (.hkl) from the output of DataPreprocess in the Workflow project

bash data/download_and_preprocess_dataset_era5.sh --data era5 --input_dir /splits --output_dir  data/era5 

Model Training

python scripts/train.py --input_dir data/era5 --dataset era5  --model savp --model_hparams_dict hparams/kth/ours_savp/model_hparams.json --output_dir logs/era5/ours_savp