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.txtMiscellaneous 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
python ../workflow_video_prediction/DataExtraction/mpi_stager_v2.py --source_dir <input_dir1> --destination_dir <output_dir1>e.g.
python ../workflow_video_prediction/DataExtraction/mpi_stager_v2.py --source_dir "/p/fastdata/slmet/slmet111/met_data/ecmwf/era5/nc/2017/" --destination_dir "/p/scratch/deepacf/bing/extractedData"Data Preprocessing
python ../workflow_video_prediction/DataPreprocess/mpi_stager_v2_process_netCDF.py --source_dir <output_dir1> --destination_dir <output_dir2>
video_prediction/datasets/era5_dataset_v2.py --source_dir <output_dir2> --destination_dir <output_dir3>Example
python ../workflow_video_prediction/DataPreprocess/mpi_stager_v2_process_netCDF.py --source_dir /p/scratch/deepacf/bing/extractedData --destination_dir /p/scratch/deepacf/bing/preprocessedData
video_prediction/datasets/era5_dataset_v2.py /p/scratch/deepacf/bing/preprocessedData ./data/era5_64_64_3_3t_normTrarining
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_directory>Example
python scripts/train_v2.py --input_dir ./data/era5_64_64_3_3t_norm --dataset era5 --model savp --model_hparams_dict hparams/kth/ours_savp/model_hparams.json --output_dir logs/era5_64_64_3_3t_norm/end_to_endPostprocessing
Generating prediction frames, model evaluation, and visulization
python scripts/generate_transfer_learning_finetune.py --mode test --results_dir <results_directory> --batch_size <batch_size> --dataset era5End-to-End run the entire workflow
cd bash
./workflow_era5.sh