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
This project need to work with Workflow_parallel_frame_prediction project
- Clone this repo:
git clone master https://gitlab.version.fz-juelich.de/gong1/video_prediction_savp.git
git clone master https://gitlab.version.fz-juelich.de/gong1/workflow_parallel_frame_prediction.gitSet-up env on JUWELS
- Set up env and install packages
cd video_prediction_savp
source env_setup/create_env.sh <dir_name> <env_name>Workflow by steps
Data Extraction
python3 ../workflow_video_prediction/DataExtraction/mpi_stager_v2.py --source_dir <input_dir1> --destination_dir <output_dir1>e.g.
python3 ../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/extractedDataData Preprocessing
python3 ../workflow_video_prediction/DataPreprocess/mpi_stager_v2_process_netCDF.py --source_dir <output_dir1> --destination_dir <output_dir2>
python3 video_prediction/datasets/era5_dataset_v2.py --source_dir <output_dir2> --destination_dir <.data/exp_name>Example
python3 ../workflow_video_prediction/DataPreprocess/mpi_stager_v2_process_netCDF.py --source_dir /p/scratch/deepacf/bing/extractedData --destination_dir /p/scratch/deepacf/bing/preprocessedData
python3 video_prediction/datasets/era5_dataset_v2.py /p/scratch/deepacf/bing/preprocessedData ./data/era5_64_64_3_3t_normTrarining
python3 scripts/train_v2.py --input_dir <./data/exp_name> --dataset era5 --model <savp> --model_hparams_dict hparams/kth/ours_savp/model_hparams.json --output_dir <./logs/{exp_name}/{mode}/>Example
python3 scripts/train_v2.py --input_dir ./data/era5_size_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 You can trained your own model from the training step , or you can copy the Bing's trained model
python3 scripts/generate_transfer_learning_finetune.py --input_dir <./data/exp_name> --dataset_hparams sequence_length=20 --checkpoint <./logs/{exp_name}/{mode}/{model}> --mode test --results_dir <./results/{exp_name}/{mode}> --batch_size <batch_size> --dataset era5- example: use end_to_end training model from bing for exp_name:era5_size_64_64_3_3t_norm
python3 scripts/generate_transfer_learning_finetune.py --input_dir data/era5_size_64_64_3_3t_norm --dataset_hparams sequence_length=20 --checkpoint /p/project/deepacf/deeprain/bing/video_prediction_savp/logs/era5_size_64_64_3_3t_norm/end_to_end/ours_savp --mode test --results_dir results_test_samples/era5_size_64_64_3_3t_norm/end_to_end --batch_size 4 --dataset era5End-to-End run the entire workflow
./bash/workflow_era5.sh <model> <train_mode> <exp_name>example:
./bash/workflow_era5.sh savp end_to_end era5_size_64_64_3_3t_normRecomendation for output folder structure and name convention
The details can be found name_convention
├── ExtractedData
│ ├── [Year]
│ │ ├── [Month]
│ │ │ ├── **/*.netCDF
├── PreprocessedData
│ ├── [Data_name_convention]
│ │ ├── hickle
│ │ │ ├── train
│ │ │ ├── val
│ │ │ ├── test
│ │ ├── tfrecords
│ │ │ ├── train
│ │ │ ├── val
│ │ │ ├── test
├── Models
│ ├── [Data_name_convention]
│ │ ├── [model_name]
│ │ ├── [model_name]
├── Results
│ ├── [Data_name_convention]
│ │ ├── [training_mode]
│ │ │ ├── [source_data_name_convention]
│ │ │ │ ├── [model_name]