Graph Attention NeTwork with Residual connections
This is the official repository for the paper: "Graph Neural Networks for Pressure Estimation in Water Distribution Systems".
Some changes in default parameters have propagated you to a minimal example on C-town.
First, you can create a dataset based on the default config using the below command:
python scenegenv7.py --executors 2 --batch_size 50 --config "configs/v7.1/ctown_7v1__EPYNET_config.ini
The created dataset ctown.zip should be located in the datasets folder.
Next, we can train GATRes using the command:
python train.py --model gatres_small --dataset_paths "datasets/ctown.zip" --input_paths "inputs/ctown.inp" --batch_size 32
You can follow the instructions to run the data generation tool and GATRes models.
-
Clone or download this repository.
-
Activate venv environment with available Python (>=3.9), PyTorch (>=2.0), PyTorch Geometric (>=2.3), and/or CUDA (11.8). In case of a new environment, consider installing the required libraries via this command:
pip install -r requirement.txt
|-- checkpoints - dir for storing model checkpoints #created in dataset generation process
|-- configs - config storage for data generation tool
|-- experiment_logs - dir for tracking experiment logs #created for loading model weights
|-- generator - code for data generation tool
|-- inputs - water distribution network topologies
|-- utils - auxiliary functions for running models
|-- ConfigModels.py - default model configurations for running models
|-- GraphModels.py - model definition
|-- evaluation.py - code for testing model
|-- train.py - code for training model
This section will give you an instruction to generate an example dataset. A dataset is a set of arbitrary snapshots, an instantaneous water network state at a particular time. A state can consist of one (some) measurement, such as pressures, heads, demands, etc... To create snapshots, run the following steps:
-
Add a preferred input file (.INP) that contains a topology into the
inputsfolder. Note: Skip this step if you use default public water networks in the folder (under public licenses). -
Add a data generation config (.INI) file. User can create a dummy one using this command:
python generator\EPYNET\ConfigCreator.py -r inputs\<your_inp_path>
You can find the created config file (.INI) in the configs folder.
- Run this command to create a dataset:
python generator\EPYNET\scenegenv7.py --config <your_ini_path>
--executors <num_of_workers>
--batch_size <batch_size_per_worker>
There are many flags and options introduced in scenegenv7.py.
The new dataset will be a zip file (.ZIP).
User can train GATRes model using this default command:
python train.py --model gatres_small
--epochs 500
--batch_size 8
--device 'cuda'
--mask_rate 0.95
--datasets_paths <dataset_path1> <optional_dataset_path2> ...
--input_paths <inp_path1> <optional_inp_path2> ...
--test_data_path <test_dataset_path>
--test_input_path <test_dataset_path>
--save_path <where_to_save_checkpoints>
When you run the above command, the trained model will be tested immediately after training. Otherwise, you can make a manual test using this command:
python evaluation.py --model gatres_small
--model_path <where_to_load_trained_weights>
--batch_size 8
--num_test_trials 10
--test_type 'clean'
--device 'cuda'
--mask_rate 0.95
--datasets_paths <train_dataset_path>
--input_paths <train_inp_path>
--test_data_path <test_dataset_path>
--test_input_path <test_dataset_path>
MIT license. See the LICENSE file for more details.
Please cite the following paper if you use the GATRes model or the Data Generation Tool in your research:
@misc{truong2023graph,
title={Graph Neural Networks for Pressure Estimation in Water Distribution Systems},
author={Huy Truong and Andr\'{e}s Tello and Alexander Lazovik and Victoria Degeler},
year={2023},
eprint={2311.10579},
archivePrefix={arXiv},
primaryClass={cs.LG},
note = {submitted to Water Resources Research}
}
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